From a1af08726d18d015d3dc1a3aea653c689858a456 Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Mon, 16 Dec 2024 09:46:15 -0500
Subject: [PATCH] More datagit status!

---
 pytorch/batch.py                              |    2 +-
 .../altra_16_csr_10_10_10_amazon0312.json     |    1 +
 .../altra_16_csr_10_10_10_amazon0312.output   |   71 +
 .../altra_16_csr_10_10_10_darcy003.json       |    1 +
 .../altra_16_csr_10_10_10_darcy003.output     |   71 +
 .../altra_16_csr_10_10_10_helm2d03.json       |    1 +
 .../altra_16_csr_10_10_10_helm2d03.output     |   74 +
 .../altra_16_csr_10_10_10_language.json       |    1 +
 .../altra_16_csr_10_10_10_language.output     |   71 +
 .../altra_16_csr_10_10_10_marine1.json        |    1 +
 .../altra_16_csr_10_10_10_marine1.output      |   51 +
 .../altra_16_csr_10_10_10_mario002.json       |    1 +
 .../altra_16_csr_10_10_10_mario002.output     |   93 +
 .../altra_16_csr_10_10_10_test1.json          |    1 +
 .../altra_16_csr_10_10_10_test1.output        |   51 +
 ...epyc_7313p_16_csr_10_10_10_amazon0312.json |    1 +
 ...yc_7313p_16_csr_10_10_10_amazon0312.output |   93 +
 .../epyc_7313p_16_csr_10_10_10_darcy003.json  |    1 +
 ...epyc_7313p_16_csr_10_10_10_darcy003.output |   71 +
 .../epyc_7313p_16_csr_10_10_10_helm2d03.json  |    1 +
 ...epyc_7313p_16_csr_10_10_10_helm2d03.output |   97 +
 .../epyc_7313p_16_csr_10_10_10_language.json  |    1 +
 ...epyc_7313p_16_csr_10_10_10_language.output |   93 +
 .../epyc_7313p_16_csr_10_10_10_marine1.json   |    1 +
 .../epyc_7313p_16_csr_10_10_10_marine1.output |   97 +
 .../epyc_7313p_16_csr_10_10_10_mario002.json  |    1 +
 ...epyc_7313p_16_csr_10_10_10_mario002.output |   71 +
 .../epyc_7313p_16_csr_10_10_10_test1.json     |    1 +
 .../epyc_7313p_16_csr_10_10_10_test1.output   |   74 +
 .../xeon_4216_16_csr_10_10_10_amazon0312.json |    1 +
 ...eon_4216_16_csr_10_10_10_amazon0312.output |   71 +
 .../xeon_4216_16_csr_10_10_10_darcy003.json   |    1 +
 .../xeon_4216_16_csr_10_10_10_darcy003.output |   71 +
 .../xeon_4216_16_csr_10_10_10_helm2d03.json   |    1 +
 .../xeon_4216_16_csr_10_10_10_helm2d03.output |   74 +
 .../xeon_4216_16_csr_10_10_10_language.json   |    1 +
 .../xeon_4216_16_csr_10_10_10_language.output |   71 +
 .../xeon_4216_16_csr_10_10_10_marine1.json    |    1 +
 .../xeon_4216_16_csr_10_10_10_marine1.output  |   74 +
 .../xeon_4216_16_csr_10_10_10_mario002.json   |    1 +
 .../xeon_4216_16_csr_10_10_10_mario002.output |   71 +
 .../xeon_4216_16_csr_10_10_10_test1.json      |    1 +
 .../xeon_4216_16_csr_10_10_10_test1.output    |   74 +
 .../altra_1_csr_10_10_10_amazon0312.json      |    1 +
 .../altra_1_csr_10_10_10_amazon0312.output    |   49 +
 .../altra_1_csr_10_10_10_darcy003.json        |    1 +
 .../altra_1_csr_10_10_10_darcy003.output      |   49 +
 .../altra_1_csr_10_10_10_helm2d03.json        |    1 +
 .../altra_1_csr_10_10_10_helm2d03.output      |   51 +
 .../altra_1_csr_10_10_10_language.json        |    1 +
 .../altra_1_csr_10_10_10_language.output      |   49 +
 .../altra_1_csr_10_10_10_marine1.json         |    1 +
 .../altra_1_csr_10_10_10_marine1.output       |   51 +
 .../altra_1_csr_10_10_10_mario002.json        |    1 +
 .../altra_1_csr_10_10_10_mario002.output      |   49 +
 .../altra_1_csr_10_10_10_test1.json           |    1 +
 .../altra_1_csr_10_10_10_test1.output         |   51 +
 .../epyc_7313p_1_csr_10_10_10_amazon0312.json |    1 +
 ...pyc_7313p_1_csr_10_10_10_amazon0312.output |   71 +
 .../epyc_7313p_1_csr_10_10_10_darcy003.json   |    1 +
 .../epyc_7313p_1_csr_10_10_10_darcy003.output |   71 +
 .../epyc_7313p_1_csr_10_10_10_helm2d03.json   |    1 +
 .../epyc_7313p_1_csr_10_10_10_helm2d03.output |   74 +
 .../epyc_7313p_1_csr_10_10_10_language.json   |    1 +
 .../epyc_7313p_1_csr_10_10_10_language.output |   71 +
 .../epyc_7313p_1_csr_10_10_10_marine1.json    |    1 +
 .../epyc_7313p_1_csr_10_10_10_marine1.output  |   74 +
 .../epyc_7313p_1_csr_10_10_10_mario002.json   |    1 +
 .../epyc_7313p_1_csr_10_10_10_mario002.output |   71 +
 .../epyc_7313p_1_csr_10_10_10_test1.json      |    1 +
 .../epyc_7313p_1_csr_10_10_10_test1.output    |   51 +
 .../xeon_4216_1_csr_10_10_10_amazon0312.json  |    1 +
 ...xeon_4216_1_csr_10_10_10_amazon0312.output |   49 +
 .../xeon_4216_1_csr_10_10_10_darcy003.json    |    1 +
 .../xeon_4216_1_csr_10_10_10_darcy003.output  |   71 +
 .../xeon_4216_1_csr_10_10_10_helm2d03.json    |    1 +
 .../xeon_4216_1_csr_10_10_10_helm2d03.output  |   74 +
 .../xeon_4216_1_csr_10_10_10_language.json    |    1 +
 .../xeon_4216_1_csr_10_10_10_language.output  |   71 +
 .../xeon_4216_1_csr_10_10_10_marine1.json     |    1 +
 .../xeon_4216_1_csr_10_10_10_marine1.output   |   51 +
 .../xeon_4216_1_csr_10_10_10_mario002.json    |    1 +
 .../xeon_4216_1_csr_10_10_10_mario002.output  |   71 +
 .../xeon_4216_1_csr_10_10_10_test1.json       |    1 +
 .../xeon_4216_1_csr_10_10_10_test1.output     |   51 +
 .../altra_max_csr_10_10_10_amazon0312.json    |    1 +
 .../altra_max_csr_10_10_10_amazon0312.output  |   49 +
 .../altra_max_csr_10_10_10_darcy003.json      |    1 +
 .../altra_max_csr_10_10_10_darcy003.output    |   49 +
 .../altra_max_csr_10_10_10_helm2d03.json      |    1 +
 .../altra_max_csr_10_10_10_helm2d03.output    |   51 +
 .../altra_max_csr_10_10_10_language.json      |    1 +
 .../altra_max_csr_10_10_10_language.output    |   49 +
 .../altra_max_csr_10_10_10_marine1.json       |    1 +
 .../altra_max_csr_10_10_10_marine1.output     |   51 +
 .../altra_max_csr_10_10_10_mario002.json      |    1 +
 .../altra_max_csr_10_10_10_mario002.output    |   49 +
 .../altra_max_csr_10_10_10_test1.json         |    1 +
 .../altra_max_csr_10_10_10_test1.output       |   51 +
 ...pyc_7313p_max_csr_10_10_10_amazon0312.json |    1 +
 ...c_7313p_max_csr_10_10_10_amazon0312.output |   93 +
 .../epyc_7313p_max_csr_10_10_10_darcy003.json |    1 +
 ...pyc_7313p_max_csr_10_10_10_darcy003.output |   93 +
 .../epyc_7313p_max_csr_10_10_10_helm2d03.json |    1 +
 ...pyc_7313p_max_csr_10_10_10_helm2d03.output |   97 +
 .../epyc_7313p_max_csr_10_10_10_language.json |    1 +
 ...pyc_7313p_max_csr_10_10_10_language.output |   93 +
 .../epyc_7313p_max_csr_10_10_10_marine1.json  |    1 +
 ...epyc_7313p_max_csr_10_10_10_marine1.output |   97 +
 .../epyc_7313p_max_csr_10_10_10_mario002.json |    1 +
 ...pyc_7313p_max_csr_10_10_10_mario002.output |   71 +
 .../epyc_7313p_max_csr_10_10_10_test1.json    |    1 +
 .../epyc_7313p_max_csr_10_10_10_test1.output  |   74 +
 ...xeon_4216_max_csr_10_10_10_amazon0312.json |    1 +
 ...on_4216_max_csr_10_10_10_amazon0312.output |   71 +
 .../xeon_4216_max_csr_10_10_10_darcy003.json  |    1 +
 ...xeon_4216_max_csr_10_10_10_darcy003.output |   71 +
 .../xeon_4216_max_csr_10_10_10_helm2d03.json  |    1 +
 ...xeon_4216_max_csr_10_10_10_helm2d03.output |   74 +
 .../xeon_4216_max_csr_10_10_10_language.json  |    1 +
 ...xeon_4216_max_csr_10_10_10_language.output |   71 +
 .../xeon_4216_max_csr_10_10_10_marine1.json   |    1 +
 .../xeon_4216_max_csr_10_10_10_marine1.output |   74 +
 .../xeon_4216_max_csr_10_10_10_mario002.json  |    1 +
 ...xeon_4216_max_csr_10_10_10_mario002.output |   71 +
 .../xeon_4216_max_csr_10_10_10_test1.json     |    1 +
 .../xeon_4216_max_csr_10_10_10_test1.output   |   74 +
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_100000_0.0001.output |   58 +-
 ...6_csr_10_10_10_synthetic_100000_0.001.json |    1 +
 ...csr_10_10_10_synthetic_100000_0.001.output |   45 +
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_1e-05.output |   72 +-
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_10000_0.0001.output |   66 +-
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_0.001.output |   52 +-
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   42 +-
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   42 +-
 ...a_16_csr_10_10_10_synthetic_10000_0.1.json |    1 +
 ...16_csr_10_10_10_synthetic_10000_0.1.output |   45 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2752 +++++++-------
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 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    2 +-
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 ..._csr_10_10_10_synthetic_5000_0.0001.output |   81 +
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 ...16_csr_10_10_10_synthetic_5000_0.05.output |   45 +
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 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    1 +
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  437 +++
 ...6_csr_20_10_10_synthetic_30000_0.0001.json |    1 -
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 ...16_csr_20_10_10_synthetic_30000_0.001.json |    1 -
 ..._csr_20_10_10_synthetic_30000_0.001.output |   45 -
 ..._16_csr_20_10_10_synthetic_30000_0.01.json |    0
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 ...sr_10_10_10_synthetic_100000_0.0001.output |  120 +-
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 ...csr_10_10_10_synthetic_100000_0.001.output |   65 +
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 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 3210 +++++++----------
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 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2944 +++++++--------
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 ..._csr_10_10_10_synthetic_100000_0.0001.json |    2 +-
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 ...1_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_10000_0.0001.output |   66 +-
 ..._1_csr_10_10_10_synthetic_10000_0.001.json |    2 +-
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 ...p_1_csr_10_10_10_synthetic_10000_0.01.json |    2 +-
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 ..._csr_10_10_10_synthetic_5000_0.0001.output |   81 +
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 ..._1_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
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 ...x_csr_10_10_10_synthetic_5000_1e-05.output |  356 ++
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2833 ++++++++-------
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 ...x_csr_10_10_10_synthetic_5000_1e-05.output |  383 ++
 pytorch/synthetic_sizes                       |    2 +-
 490 files changed, 27153 insertions(+), 17002 deletions(-)
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 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
 create mode 100644 pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output

diff --git a/pytorch/batch.py b/pytorch/batch.py
index f391f3c..0581479 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 < 10000000])
+        if size ** 2 * density <= 10000000])
 
 #for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
 for i, parameter in parameter_list:
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..bf1ce83
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1345, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.307875871658325, "TIME_S_1KI": 7.663848231716227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 549.730076084137, "W": 38.131256134653135, "J_1KI": 408.7212461592096, "W_1KI": 28.350376308292294, "W_D": 16.28825613465314, "J_D": 234.82426732969293, "W_D_1KI": 12.110227609407538, "J_D_1KI": 9.003886698444267}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..7077482
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_amazon0312.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 7.806152820587158}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.5486, 0.8485, 0.8195,  ..., 0.3778, 0.3275, 0.7623])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 7.806152820587158 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 suitesparse csr 1345 -m matrices/389000+_cols/amazon0312.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.307875871658325}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6343, 0.9450, 0.3421,  ..., 0.5967, 0.9759, 0.2168])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.307875871658325 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6343, 0.9450, 0.3421,  ..., 0.5967, 0.9759, 0.2168])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.307875871658325 seconds
+
+[24.12, 24.04, 24.4, 24.52, 24.56, 24.68, 24.68, 24.64, 24.56, 24.28]
+[24.24, 24.0, 24.0, 24.8, 25.52, 28.6, 36.84, 43.04, 50.0, 55.48, 57.44, 57.44, 57.68, 57.56]
+14.416783809661865
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1345, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.307875871658325, 'TIME_S_1KI': 7.663848231716227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 549.730076084137, 'W': 38.131256134653135}
+[24.12, 24.04, 24.4, 24.52, 24.56, 24.68, 24.68, 24.64, 24.56, 24.28, 24.12, 24.24, 24.0, 23.96, 23.96, 24.08, 24.08, 24.08, 24.08, 24.08]
+436.85999999999996
+21.842999999999996
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1345, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.307875871658325, 'TIME_S_1KI': 7.663848231716227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 549.730076084137, 'W': 38.131256134653135, 'J_1KI': 408.7212461592096, 'W_1KI': 28.350376308292294, 'W_D': 16.28825613465314, 'J_D': 234.82426732969293, 'W_D_1KI': 12.110227609407538, 'J_D_1KI': 9.003886698444267}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..629b0ac
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1641, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.180182933807373, "TIME_S_1KI": 6.203645907256169, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 572.6159508895875, "W": 39.25282662523282, "J_1KI": 348.94329731236286, "W_1KI": 23.920064975766497, "W_D": 17.70882662523282, "J_D": 258.33443012809755, "W_D_1KI": 10.791484841701902, "J_D_1KI": 6.576163827971908}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..ee1fc72
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_darcy003.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/darcy003.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 6.3963303565979}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.2730, 0.2238, 0.6515,  ..., 0.6572, 0.5843, 0.9667])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 6.3963303565979 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 suitesparse csr 1641 -m matrices/389000+_cols/darcy003.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.180182933807373}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.4101, 0.8547, 0.0587,  ..., 0.2935, 0.9064, 0.8922])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.180182933807373 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.4101, 0.8547, 0.0587,  ..., 0.2935, 0.9064, 0.8922])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.180182933807373 seconds
+
+[23.68, 23.72, 23.96, 23.96, 24.16, 24.28, 24.28, 24.52, 24.76, 24.52]
+[24.16, 23.84, 23.72, 26.96, 28.84, 34.04, 41.68, 44.88, 51.8, 55.52, 55.8, 56.2, 56.16, 56.16]
+14.587890863418579
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1641, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.180182933807373, 'TIME_S_1KI': 6.203645907256169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 572.6159508895875, 'W': 39.25282662523282}
+[23.68, 23.72, 23.96, 23.96, 24.16, 24.28, 24.28, 24.52, 24.76, 24.52, 24.08, 24.08, 24.12, 24.04, 23.6, 23.44, 23.28, 23.28, 23.4, 23.72]
+430.88
+21.544
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1641, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.180182933807373, 'TIME_S_1KI': 6.203645907256169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 572.6159508895875, 'W': 39.25282662523282, 'J_1KI': 348.94329731236286, 'W_1KI': 23.920064975766497, 'W_D': 17.70882662523282, 'J_D': 258.33443012809755, 'W_D_1KI': 10.791484841701902, 'J_D_1KI': 6.576163827971908}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..4535749
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1801, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.381673336029053, "TIME_S_1KI": 5.764393856762384, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.8333181858063, "W": 39.805332297033694, "J_1KI": 323.6165009360391, "W_1KI": 22.10179472350566, "W_D": 18.366332297033694, "J_D": 268.92151824545857, "W_D_1KI": 10.197852469202495, "J_D_1KI": 5.66232785630344}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..c022936
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_helm2d03.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 5.829137325286865}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.8248, 0.9604, 0.9464,  ..., 0.7437, 0.8759, 0.5369])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 5.829137325286865 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 suitesparse csr 1801 -m matrices/389000+_cols/helm2d03.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.381673336029053}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.8790, 0.0885, 0.6163,  ..., 0.1605, 0.4532, 0.8862])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.381673336029053 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.8790, 0.0885, 0.6163,  ..., 0.1605, 0.4532, 0.8862])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.381673336029053 seconds
+
+[23.72, 23.72, 23.68, 23.68, 23.68, 23.6, 23.68, 23.64, 23.64, 23.72]
+[23.88, 24.0, 24.28, 26.28, 27.2, 34.12, 40.64, 47.44, 53.0, 57.04, 57.04, 56.96, 57.52, 57.24]
+14.642091512680054
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1801, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.381673336029053, 'TIME_S_1KI': 5.764393856762384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8333181858063, 'W': 39.805332297033694}
+[23.72, 23.72, 23.68, 23.68, 23.68, 23.6, 23.68, 23.64, 23.64, 23.72, 23.68, 24.04, 24.0, 24.12, 24.0, 23.92, 24.08, 24.04, 23.76, 23.88]
+428.78000000000003
+21.439
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1801, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.381673336029053, 'TIME_S_1KI': 5.764393856762384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8333181858063, 'W': 39.805332297033694, 'J_1KI': 323.6165009360391, 'W_1KI': 22.10179472350566, 'W_D': 18.366332297033694, 'J_D': 268.92151824545857, 'W_D_1KI': 10.197852469202495, 'J_D_1KI': 5.66232785630344}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json
new file mode 100644
index 0000000..319d17a
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 2142, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.489102602005005, "TIME_S_1KI": 4.896873296921104, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 527.8806602096558, "W": 35.901030267154354, "J_1KI": 246.44288525194017, "W_1KI": 16.760518332004835, "W_D": 14.631030267154355, "J_D": 215.1313725399972, "W_D_1KI": 6.830546343209316, "J_D_1KI": 3.1888638390332944}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output
new file mode 100644
index 0000000..e43ddab
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_language.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 4.9012720584869385}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.0783, 0.8612, 0.3161,  ..., 0.8531, 0.6998, 0.6080])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 4.9012720584869385 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 suitesparse csr 2142 -m matrices/389000+_cols/language.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.489102602005005}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.5677, 0.4069, 0.3735,  ..., 0.4488, 0.2885, 0.1400])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.489102602005005 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.5677, 0.4069, 0.3735,  ..., 0.4488, 0.2885, 0.1400])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.489102602005005 seconds
+
+[23.72, 23.8, 23.88, 23.8, 23.8, 23.52, 23.48, 23.36, 23.36, 23.24]
+[23.28, 23.4, 23.56, 24.68, 26.6, 30.12, 36.56, 41.44, 45.96, 49.92, 50.84, 50.96, 50.76, 51.04]
+14.703774690628052
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.489102602005005, 'TIME_S_1KI': 4.896873296921104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 527.8806602096558, 'W': 35.901030267154354}
+[23.72, 23.8, 23.88, 23.8, 23.8, 23.52, 23.48, 23.36, 23.36, 23.24, 23.6, 23.64, 23.64, 23.68, 23.68, 23.68, 23.76, 23.64, 23.64, 23.52]
+425.4
+21.27
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 2142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.489102602005005, 'TIME_S_1KI': 4.896873296921104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 527.8806602096558, 'W': 35.901030267154354, 'J_1KI': 246.44288525194017, 'W_1KI': 16.760518332004835, 'W_D': 14.631030267154355, 'J_D': 215.1313725399972, 'W_D_1KI': 6.830546343209316, 'J_D_1KI': 3.1888638390332944}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..ca7dd60
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 12.712969779968262, "TIME_S_1KI": 12.712969779968262, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 642.9576612091064, "W": 41.13704135154824, "J_1KI": 642.9576612091064, "W_1KI": 41.13704135154824, "W_D": 19.36904135154824, "J_D": 302.73138558578484, "W_D_1KI": 19.36904135154824, "J_D_1KI": 19.36904135154824}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..30a4718
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_marine1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 12.712969779968262}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      18,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.7918, 0.6380, 0.4821,  ..., 0.8085, 0.1927, 0.4528])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 12.712969779968262 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      18,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.7918, 0.6380, 0.4821,  ..., 0.8085, 0.1927, 0.4528])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 12.712969779968262 seconds
+
+[24.16, 24.24, 24.24, 24.08, 24.2, 24.48, 24.48, 24.6, 24.28, 24.24]
+[23.96, 23.92, 24.76, 26.04, 30.04, 34.84, 41.8, 41.8, 48.8, 54.68, 57.92, 59.36, 59.6, 60.0, 59.68]
+15.629652500152588
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 12.712969779968262, 'TIME_S_1KI': 12.712969779968262, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 642.9576612091064, 'W': 41.13704135154824}
+[24.16, 24.24, 24.24, 24.08, 24.2, 24.48, 24.48, 24.6, 24.28, 24.24, 24.24, 24.16, 24.08, 24.12, 24.12, 24.0, 23.92, 24.0, 24.0, 24.08]
+435.36
+21.768
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 12.712969779968262, 'TIME_S_1KI': 12.712969779968262, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 642.9576612091064, 'W': 41.13704135154824, 'J_1KI': 642.9576612091064, 'W_1KI': 41.13704135154824, 'W_D': 19.36904135154824, 'J_D': 302.73138558578484, 'W_D_1KI': 19.36904135154824, 'J_D_1KI': 19.36904135154824}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..de53ae3
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1694, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.441875219345093, "TIME_S_1KI": 6.164034958291081, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 563.8270891189575, "W": 38.54107447042048, "J_1KI": 332.8377149462559, "W_1KI": 22.751519758217523, "W_D": 16.675074470420483, "J_D": 243.94386582851405, "W_D_1KI": 9.843609486670887, "J_D_1KI": 5.81086746556723}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..4db99ef
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_mario002.output
@@ -0,0 +1,93 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 7.140163421630859}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1948, 0.1869, 0.5638,  ..., 0.6155, 0.6170, 0.7726])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 7.140163421630859 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 suitesparse csr 1470 -m matrices/389000+_cols/mario002.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 9.107451438903809}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1872, 0.8693, 0.3135,  ..., 0.4431, 0.3648, 0.2379])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 9.107451438903809 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 suitesparse csr 1694 -m matrices/389000+_cols/mario002.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.441875219345093}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.6416, 0.1879, 0.5321,  ..., 0.0693, 0.5314, 0.2281])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.441875219345093 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.6416, 0.1879, 0.5321,  ..., 0.0693, 0.5314, 0.2281])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.441875219345093 seconds
+
+[24.04, 23.88, 24.2, 24.4, 24.52, 24.32, 24.64, 24.08, 24.08, 24.2]
+[24.04, 23.88, 24.16, 26.0, 26.68, 32.8, 39.08, 45.28, 50.52, 54.72, 54.84, 55.48, 55.4, 55.76]
+14.629251956939697
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1694, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.441875219345093, 'TIME_S_1KI': 6.164034958291081, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 563.8270891189575, 'W': 38.54107447042048}
+[24.04, 23.88, 24.2, 24.4, 24.52, 24.32, 24.64, 24.08, 24.08, 24.2, 24.0, 23.96, 24.08, 24.08, 24.44, 24.72, 24.6, 24.56, 24.48, 24.32]
+437.32
+21.866
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1694, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.441875219345093, 'TIME_S_1KI': 6.164034958291081, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 563.8270891189575, 'W': 38.54107447042048, 'J_1KI': 332.8377149462559, 'W_1KI': 22.751519758217523, 'W_D': 16.675074470420483, 'J_D': 243.94386582851405, 'W_D_1KI': 9.843609486670887, 'J_D_1KI': 5.81086746556723}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json
new file mode 100644
index 0000000..45ba869
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 33.385778188705444, "TIME_S_1KI": 33.385778188705444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1758.8809212875371, "W": 47.990539203077404, "J_1KI": 1758.8809212875371, "W_1KI": 47.990539203077404, "W_D": 26.191539203077404, "J_D": 959.9350073668963, "W_D_1KI": 26.191539203077404, "J_D_1KI": 26.191539203077404}
diff --git a/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output
new file mode 100644
index 0000000..fec9ae3
--- /dev/null
+++ b/pytorch/output_389000+_16core/altra_16_csr_10_10_10_test1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 33.385778188705444}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3716, 0.7020, 0.0579,  ..., 0.5562, 0.4218, 0.2724])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 33.385778188705444 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3716, 0.7020, 0.0579,  ..., 0.5562, 0.4218, 0.2724])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 33.385778188705444 seconds
+
+[24.12, 24.32, 24.32, 24.28, 24.08, 24.2, 24.0, 24.0, 24.08, 24.08]
+[24.24, 24.16, 24.36, 28.92, 31.16, 35.28, 37.04, 40.44, 46.16, 49.16, 54.2, 56.72, 56.48, 56.72, 56.72, 56.48, 57.24, 56.52, 56.44, 56.56, 56.52, 57.2, 56.84, 56.8, 56.92, 56.92, 58.08, 57.8, 57.88, 57.44, 57.4, 56.88, 56.44, 57.16, 57.12]
+36.65057635307312
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 33.385778188705444, 'TIME_S_1KI': 33.385778188705444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1758.8809212875371, 'W': 47.990539203077404}
+[24.12, 24.32, 24.32, 24.28, 24.08, 24.2, 24.0, 24.0, 24.08, 24.08, 24.24, 24.24, 24.24, 24.28, 24.48, 24.36, 24.4, 24.16, 24.16, 24.32]
+435.98
+21.799
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 33.385778188705444, 'TIME_S_1KI': 33.385778188705444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1758.8809212875371, 'W': 47.990539203077404, 'J_1KI': 1758.8809212875371, 'W_1KI': 47.990539203077404, 'W_D': 26.191539203077404, 'J_D': 959.9350073668963, 'W_D_1KI': 26.191539203077404, 'J_D_1KI': 26.191539203077404}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..6a184df
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20402, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.640971422195435, "TIME_S_1KI": 0.5215651123515065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1986.5667917442322, "W": 146.96, "J_1KI": 97.3711788914926, "W_1KI": 7.203215371042055, "W_D": 111.28150000000001, "J_D": 1504.2741728054286, "W_D_1KI": 5.454440741103813, "J_D_1KI": 0.2673483355114113}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..1947bf8
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_amazon0312.output
@@ -0,0 +1,93 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.5630712509155273}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.4842, 0.5105, 0.4860,  ..., 0.7675, 0.4934, 0.1706])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 0.5630712509155273 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18647', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 9.59645700454712}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6749, 0.4854, 0.2428,  ..., 0.8655, 0.6324, 0.8376])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 9.59645700454712 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '20402', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.640971422195435}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.0724, 0.4329, 0.4595,  ..., 0.8349, 0.8167, 0.6766])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.640971422195435 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.0724, 0.4329, 0.4595,  ..., 0.8349, 0.8167, 0.6766])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.640971422195435 seconds
+
+[41.07, 39.13, 39.76, 39.13, 39.16, 39.59, 39.67, 39.13, 39.06, 38.95]
+[146.96]
+13.517738103866577
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.640971422195435, 'TIME_S_1KI': 0.5215651123515065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.5667917442322, 'W': 146.96}
+[41.07, 39.13, 39.76, 39.13, 39.16, 39.59, 39.67, 39.13, 39.06, 38.95, 39.85, 39.43, 39.2, 39.39, 39.57, 39.13, 39.11, 38.95, 44.59, 39.27]
+713.5699999999999
+35.6785
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20402, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.640971422195435, 'TIME_S_1KI': 0.5215651123515065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.5667917442322, 'W': 146.96, 'J_1KI': 97.3711788914926, 'W_1KI': 7.203215371042055, 'W_D': 111.28150000000001, 'J_D': 1504.2741728054286, 'W_D_1KI': 5.454440741103813, 'J_D_1KI': 0.2673483355114113}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..d5b5a92
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 25477, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.081330299377441, "TIME_S_1KI": 0.39570319501422624, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1725.3062429666518, "W": 138.54, "J_1KI": 67.72014927058333, "W_1KI": 5.4378459002237305, "W_D": 102.46124999999999, "J_D": 1275.9999587640166, "W_D_1KI": 4.021715665109706, "J_D_1KI": 0.1578567203795465}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..66066f0
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_darcy003.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.41213083267211914}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.5658, 0.2599, 0.8647,  ..., 0.0110, 0.8951, 0.0945])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.41213083267211914 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25477', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.081330299377441}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.2219, 0.1747, 0.4109,  ..., 0.8392, 0.1445, 0.6192])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.081330299377441 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.2219, 0.1747, 0.4109,  ..., 0.8392, 0.1445, 0.6192])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.081330299377441 seconds
+
+[40.52, 39.06, 39.46, 40.78, 43.93, 39.17, 39.42, 39.04, 39.35, 39.02]
+[138.54]
+12.453488111495972
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 25477, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.081330299377441, 'TIME_S_1KI': 0.39570319501422624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1725.3062429666518, 'W': 138.54}
+[40.52, 39.06, 39.46, 40.78, 43.93, 39.17, 39.42, 39.04, 39.35, 39.02, 39.79, 46.26, 39.61, 38.94, 39.43, 40.38, 39.06, 38.95, 39.02, 40.1]
+721.575
+36.07875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 25477, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.081330299377441, 'TIME_S_1KI': 0.39570319501422624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1725.3062429666518, 'W': 138.54, 'J_1KI': 67.72014927058333, 'W_1KI': 5.4378459002237305, 'W_D': 102.46124999999999, 'J_D': 1275.9999587640166, 'W_D_1KI': 4.021715665109706, 'J_D_1KI': 0.1578567203795465}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..0316f37
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30984, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.317180871963501, "TIME_S_1KI": 0.33298414897894074, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1916.101525504589, "W": 149.37, "J_1KI": 61.84164489751449, "W_1KI": 4.820875290472502, "W_D": 113.45200000000001, "J_D": 1455.3494695825577, "W_D_1KI": 3.661631809966435, "J_D_1KI": 0.11817815033457382}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..257335e
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_helm2d03.output
@@ -0,0 +1,97 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.39348506927490234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.1720, 0.8662, 0.8556,  ..., 0.0402, 0.8663, 0.3929])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 0.39348506927490234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26684', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 9.042525291442871}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.7375, 0.5933, 0.9050,  ..., 0.8578, 0.6740, 0.2052])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 9.042525291442871 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30984', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.317180871963501}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.0633, 0.8834, 0.2857,  ..., 0.1984, 0.6858, 0.2922])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.317180871963501 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.0633, 0.8834, 0.2857,  ..., 0.1984, 0.6858, 0.2922])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.317180871963501 seconds
+
+[40.3, 39.14, 39.07, 38.97, 39.41, 40.63, 44.46, 38.96, 39.02, 39.22]
+[149.37]
+12.827887296676636
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30984, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.317180871963501, 'TIME_S_1KI': 0.33298414897894074, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.101525504589, 'W': 149.37}
+[40.3, 39.14, 39.07, 38.97, 39.41, 40.63, 44.46, 38.96, 39.02, 39.22, 40.56, 39.23, 39.36, 44.65, 39.73, 38.95, 39.17, 38.94, 39.11, 39.04]
+718.3599999999999
+35.91799999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30984, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.317180871963501, 'TIME_S_1KI': 0.33298414897894074, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.101525504589, 'W': 149.37, 'J_1KI': 61.84164489751449, 'W_1KI': 4.820875290472502, 'W_D': 113.45200000000001, 'J_D': 1455.3494695825577, 'W_D_1KI': 3.661631809966435, 'J_D_1KI': 0.11817815033457382}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json
new file mode 100644
index 0000000..26cda3b
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30366, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.31269907951355, "TIME_S_1KI": 0.33961335307625473, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1775.0306821584702, "W": 137.59, "J_1KI": 58.454543968862225, "W_1KI": 4.531054468813805, "W_D": 102.3395, "J_D": 1320.2685696399212, "W_D_1KI": 3.370200223934664, "J_D_1KI": 0.11098597852646591}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output
new file mode 100644
index 0000000..a5b64fc
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_language.output
@@ -0,0 +1,93 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.3785414695739746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.2846, 0.0684, 0.3415,  ..., 0.3157, 0.0663, 0.9624])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 0.3785414695739746 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '27738', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.591154098510742}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.1194, 0.5235, 0.5697,  ..., 0.7322, 0.5132, 0.4627])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 9.591154098510742 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30366', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.31269907951355}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.4535, 0.4285, 0.5680,  ..., 0.4151, 0.8586, 0.5793])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.31269907951355 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.4535, 0.4285, 0.5680,  ..., 0.4151, 0.8586, 0.5793])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.31269907951355 seconds
+
+[40.67, 39.06, 39.61, 38.65, 39.12, 40.0, 38.74, 39.55, 38.64, 38.57]
+[137.59]
+12.900869846343994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30366, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.31269907951355, 'TIME_S_1KI': 0.33961335307625473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.0306821584702, 'W': 137.59}
+[40.67, 39.06, 39.61, 38.65, 39.12, 40.0, 38.74, 39.55, 38.64, 38.57, 39.44, 38.8, 39.85, 39.35, 38.9, 38.8, 38.74, 39.19, 39.29, 38.76]
+705.01
+35.2505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30366, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.31269907951355, 'TIME_S_1KI': 0.33961335307625473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.0306821584702, 'W': 137.59, 'J_1KI': 58.454543968862225, 'W_1KI': 4.531054468813805, 'W_D': 102.3395, 'J_D': 1320.2685696399212, 'W_D_1KI': 3.370200223934664, 'J_D_1KI': 0.11098597852646591}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..c6e3e05
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19495, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.71416425704956, "TIME_S_1KI": 0.5495852401666869, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2110.003728497028, "W": 158.07, "J_1KI": 108.23307147971418, "W_1KI": 8.108232880225698, "W_D": 122.3625, "J_D": 1633.3607340306044, "W_D_1KI": 6.276609387022313, "J_D_1KI": 0.32195995829814383}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..d7a731b
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_marine1.output
@@ -0,0 +1,97 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 0.6046795845031738}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.7423, 0.4233, 0.1707,  ..., 0.4030, 0.8937, 0.1151])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 0.6046795845031738 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '17364', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 9.352032661437988}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.3036, 0.7996, 0.4739,  ..., 0.0238, 0.6033, 0.9918])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 9.352032661437988 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19495', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.71416425704956}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.1117, 0.6424, 0.8924,  ..., 0.5333, 0.0312, 0.4242])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.71416425704956 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.1117, 0.6424, 0.8924,  ..., 0.5333, 0.0312, 0.4242])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.71416425704956 seconds
+
+[41.06, 39.65, 39.88, 39.81, 39.59, 39.74, 39.82, 39.26, 40.0, 39.37]
+[158.07]
+13.34854006767273
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19495, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.71416425704956, 'TIME_S_1KI': 0.5495852401666869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2110.003728497028, 'W': 158.07}
+[41.06, 39.65, 39.88, 39.81, 39.59, 39.74, 39.82, 39.26, 40.0, 39.37, 41.26, 39.31, 39.83, 39.61, 39.66, 39.59, 39.58, 39.11, 39.31, 39.11]
+714.15
+35.707499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19495, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.71416425704956, 'TIME_S_1KI': 0.5495852401666869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2110.003728497028, 'W': 158.07, 'J_1KI': 108.23307147971418, 'W_1KI': 8.108232880225698, 'W_D': 122.3625, 'J_D': 1633.3607340306044, 'W_D_1KI': 6.276609387022313, 'J_D_1KI': 0.32195995829814383}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..aeda75b
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 23986, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.204793214797974, "TIME_S_1KI": 0.4254478952221285, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1608.246218369007, "W": 138.89, "J_1KI": 67.04937123192725, "W_1KI": 5.79046110230968, "W_D": 103.45274999999998, "J_D": 1197.9083732981082, "W_D_1KI": 4.313047194196614, "J_D_1KI": 0.1798151919534985}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..6816aec
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_mario002.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.43773889541625977}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9846, 0.2787, 0.2893,  ..., 0.3452, 0.1271, 0.5089])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.43773889541625977 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23986', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.204793214797974}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.2099, 0.5726, 0.9552,  ..., 0.7541, 0.8652, 0.1203])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.204793214797974 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.2099, 0.5726, 0.9552,  ..., 0.7541, 0.8652, 0.1203])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.204793214797974 seconds
+
+[40.06, 38.94, 39.06, 39.11, 39.73, 38.92, 39.35, 38.88, 39.43, 39.21]
+[138.89]
+11.579280138015747
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 23986, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.204793214797974, 'TIME_S_1KI': 0.4254478952221285, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1608.246218369007, 'W': 138.89}
+[40.06, 38.94, 39.06, 39.11, 39.73, 38.92, 39.35, 38.88, 39.43, 39.21, 41.54, 39.4, 39.48, 39.09, 39.42, 38.94, 39.43, 39.44, 39.0, 41.44]
+708.7450000000001
+35.437250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 23986, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.204793214797974, 'TIME_S_1KI': 0.4254478952221285, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1608.246218369007, 'W': 138.89, 'J_1KI': 67.04937123192725, 'W_1KI': 5.79046110230968, 'W_D': 103.45274999999998, 'J_D': 1197.9083732981082, 'W_D_1KI': 4.313047194196614, 'J_D_1KI': 0.1798151919534985}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json
new file mode 100644
index 0000000..ce7af8e
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2652, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.818459033966064, "TIME_S_1KI": 4.079358610092784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1727.0440453481674, "W": 123.26, "J_1KI": 651.2232448522501, "W_1KI": 46.47812971342383, "W_D": 87.892, "J_D": 1231.4891711320877, "W_D_1KI": 33.14177978883861, "J_D_1KI": 12.49690037286524}
diff --git a/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output
new file mode 100644
index 0000000..4cb795c
--- /dev/null
+++ b/pytorch/output_389000+_16core/epyc_7313p_16_csr_10_10_10_test1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.9585680961608887}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.7261, 0.8238, 0.5826,  ..., 0.6988, 0.4899, 0.5621])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 3.9585680961608887 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2652', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.818459033966064}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.8039, 0.1348, 0.9933,  ..., 0.2390, 0.5536, 0.8375])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 10.818459033966064 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.8039, 0.1348, 0.9933,  ..., 0.2390, 0.5536, 0.8375])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 10.818459033966064 seconds
+
+[40.14, 39.06, 39.78, 39.45, 39.62, 39.78, 39.17, 39.08, 39.09, 38.93]
+[123.26]
+14.011390924453735
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.818459033966064, 'TIME_S_1KI': 4.079358610092784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1727.0440453481674, 'W': 123.26}
+[40.14, 39.06, 39.78, 39.45, 39.62, 39.78, 39.17, 39.08, 39.09, 38.93, 39.84, 39.42, 39.65, 39.42, 38.97, 39.03, 39.01, 38.92, 39.07, 38.77]
+707.36
+35.368
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.818459033966064, 'TIME_S_1KI': 4.079358610092784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1727.0440453481674, 'W': 123.26, 'J_1KI': 651.2232448522501, 'W_1KI': 46.47812971342383, 'W_D': 87.892, 'J_D': 1231.4891711320877, 'W_D_1KI': 33.14177978883861, 'J_D_1KI': 12.49690037286524}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..a5b23aa
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8118, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.907745361328125, "TIME_S_1KI": 1.3436493423661153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.0154501628876, "W": 88.97, "J_1KI": 160.2630512642138, "W_1KI": 10.959595959595958, "W_D": 72.9665, "J_D": 1066.9949853243827, "W_D_1KI": 8.988236018723823, "J_D_1KI": 1.1071983270169774}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..cece27c
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_amazon0312.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 1.293351411819458}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.2979, 0.5597, 0.8769,  ..., 0.0942, 0.8424, 0.4292])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 1.293351411819458 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', 'suitesparse', 'csr', '8118', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.907745361328125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6448, 0.4532, 0.0841,  ..., 0.5791, 0.6160, 0.9399])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.907745361328125 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6448, 0.4532, 0.0841,  ..., 0.5791, 0.6160, 0.9399])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.907745361328125 seconds
+
+[18.52, 17.54, 17.79, 17.99, 17.73, 17.74, 17.74, 17.7, 17.79, 17.98]
+[88.97]
+14.623080253601074
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.907745361328125, 'TIME_S_1KI': 1.3436493423661153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.0154501628876, 'W': 88.97}
+[18.52, 17.54, 17.79, 17.99, 17.73, 17.74, 17.74, 17.7, 17.79, 17.98, 18.52, 17.6, 17.66, 17.43, 18.0, 17.58, 17.75, 17.82, 17.89, 17.62]
+320.06999999999994
+16.003499999999995
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.907745361328125, 'TIME_S_1KI': 1.3436493423661153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.0154501628876, 'W': 88.97, 'J_1KI': 160.2630512642138, 'W_1KI': 10.959595959595958, 'W_D': 72.9665, 'J_D': 1066.9949853243827, 'W_D_1KI': 8.988236018723823, 'J_D_1KI': 1.1071983270169774}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..a5c7de7
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13615, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.780885696411133, "TIME_S_1KI": 0.7918388319068037, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1298.2887223243713, "W": 89.8, "J_1KI": 95.35723263491526, "W_1KI": 6.595666544252662, "W_D": 73.5375, "J_D": 1063.172682827711, "W_D_1KI": 5.401211898641204, "J_D_1KI": 0.3967103855043117}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..7488c34
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_darcy003.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.7711856365203857}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.3467, 0.9628, 0.5083,  ..., 0.1832, 0.8742, 0.8835])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.7711856365203857 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', 'suitesparse', 'csr', '13615', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.780885696411133}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0947, 0.1039, 0.1767,  ..., 0.1078, 0.6970, 0.7249])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.780885696411133 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0947, 0.1039, 0.1767,  ..., 0.1078, 0.6970, 0.7249])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.780885696411133 seconds
+
+[19.22, 17.93, 18.06, 17.51, 17.76, 17.39, 17.9, 17.91, 17.46, 17.78]
+[89.8]
+14.457558155059814
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13615, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.780885696411133, 'TIME_S_1KI': 0.7918388319068037, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1298.2887223243713, 'W': 89.8}
+[19.22, 17.93, 18.06, 17.51, 17.76, 17.39, 17.9, 17.91, 17.46, 17.78, 18.08, 17.65, 17.61, 17.86, 17.77, 17.72, 21.98, 18.52, 17.82, 17.72]
+325.25
+16.2625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13615, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.780885696411133, 'TIME_S_1KI': 0.7918388319068037, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1298.2887223243713, 'W': 89.8, 'J_1KI': 95.35723263491526, 'W_1KI': 6.595666544252662, 'W_D': 73.5375, 'J_D': 1063.172682827711, 'W_D_1KI': 5.401211898641204, 'J_D_1KI': 0.3967103855043117}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..6b89e52
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12165, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.51817512512207, "TIME_S_1KI": 0.8646259864465327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.3463970065116, "W": 89.63, "J_1KI": 105.98819539716494, "W_1KI": 7.3678586107685975, "W_D": 73.37299999999999, "J_D": 1055.4860335552692, "W_D_1KI": 6.031483764899301, "J_D_1KI": 0.49580631030820393}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..9f3e3b4
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_helm2d03.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.8631081581115723}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.5077, 0.7328, 0.3933,  ..., 0.4074, 0.1030, 0.0500])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 0.8631081581115723 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', 'suitesparse', 'csr', '12165', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.51817512512207}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9100, 0.1506, 0.3829,  ..., 0.6719, 0.7400, 0.8631])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.51817512512207 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9100, 0.1506, 0.3829,  ..., 0.6719, 0.7400, 0.8631])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.51817512512207 seconds
+
+[18.23, 17.69, 18.03, 22.07, 17.84, 17.63, 17.63, 17.56, 17.59, 17.97]
+[89.63]
+14.385210275650024
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12165, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.51817512512207, 'TIME_S_1KI': 0.8646259864465327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.3463970065116, 'W': 89.63}
+[18.23, 17.69, 18.03, 22.07, 17.84, 17.63, 17.63, 17.56, 17.59, 17.97, 18.65, 17.64, 17.75, 18.23, 17.75, 18.05, 17.76, 17.53, 18.1, 17.73]
+325.14
+16.256999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12165, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.51817512512207, 'TIME_S_1KI': 0.8646259864465327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.3463970065116, 'W': 89.63, 'J_1KI': 105.98819539716494, 'W_1KI': 7.3678586107685975, 'W_D': 73.37299999999999, 'J_D': 1055.4860335552692, 'W_D_1KI': 6.031483764899301, 'J_D_1KI': 0.49580631030820393}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json
new file mode 100644
index 0000000..3630ff9
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13328, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.746992826461792, "TIME_S_1KI": 0.8063470007849484, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1285.765242910385, "W": 89.74, "J_1KI": 96.47098161092326, "W_1KI": 6.733193277310924, "W_D": 73.49574999999999, "J_D": 1053.0229646939038, "W_D_1KI": 5.51438700480192, "J_D_1KI": 0.4137445231694118}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output
new file mode 100644
index 0000000..437027f
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_language.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.7877652645111084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.0377, 0.4846, 0.1087,  ..., 0.8181, 0.0416, 0.1571])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 0.7877652645111084 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', 'suitesparse', 'csr', '13328', '-m', 'matrices/389000+_cols/language.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.746992826461792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.5687, 0.0271, 0.0300,  ..., 0.3524, 0.7739, 0.4785])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.746992826461792 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.5687, 0.0271, 0.0300,  ..., 0.3524, 0.7739, 0.4785])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.746992826461792 seconds
+
+[18.42, 17.72, 17.91, 18.41, 17.89, 17.65, 19.38, 18.4, 18.2, 17.87]
+[89.74]
+14.327671527862549
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13328, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.746992826461792, 'TIME_S_1KI': 0.8063470007849484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1285.765242910385, 'W': 89.74}
+[18.42, 17.72, 17.91, 18.41, 17.89, 17.65, 19.38, 18.4, 18.2, 17.87, 19.34, 17.9, 18.16, 17.6, 17.86, 17.8, 17.9, 17.65, 17.71, 17.86]
+324.885
+16.24425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13328, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.746992826461792, 'TIME_S_1KI': 0.8063470007849484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1285.765242910385, 'W': 89.74, 'J_1KI': 96.47098161092326, 'W_1KI': 6.733193277310924, 'W_D': 73.49574999999999, 'J_D': 1053.0229646939038, 'W_D_1KI': 5.51438700480192, 'J_D_1KI': 0.4137445231694118}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..64c9afa
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5897, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.55986738204956, "TIME_S_1KI": 1.790718565719783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.1635441350936, "W": 89.05999999999999, "J_1KI": 220.30923251400603, "W_1KI": 15.102594539596403, "W_D": 72.89299999999999, "J_D": 1063.327287476301, "W_D_1KI": 12.361031032728503, "J_D_1KI": 2.096155847503562}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..17a33fe
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_marine1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.7802655696868896}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.5518, 0.1807, 0.4021,  ..., 0.3397, 0.2107, 0.4589])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 1.7802655696868896 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', 'suitesparse', 'csr', '5897', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.55986738204956}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.5366, 0.8162, 0.5634,  ..., 0.9410, 0.0469, 0.5938])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.55986738204956 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.5366, 0.8162, 0.5634,  ..., 0.9410, 0.0469, 0.5938])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.55986738204956 seconds
+
+[18.26, 17.65, 17.89, 17.69, 17.78, 17.53, 17.59, 17.98, 17.75, 17.71]
+[89.06]
+14.587508916854858
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5897, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.55986738204956, 'TIME_S_1KI': 1.790718565719783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.1635441350936, 'W': 89.05999999999999}
+[18.26, 17.65, 17.89, 17.69, 17.78, 17.53, 17.59, 17.98, 17.75, 17.71, 18.09, 17.83, 17.84, 17.57, 17.77, 17.94, 21.22, 17.67, 17.65, 17.92]
+323.34000000000003
+16.167
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5897, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.55986738204956, 'TIME_S_1KI': 1.790718565719783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.1635441350936, 'W': 89.05999999999999, 'J_1KI': 220.30923251400603, 'W_1KI': 15.102594539596403, 'W_D': 72.89299999999999, 'J_D': 1063.327287476301, 'W_D_1KI': 12.361031032728503, 'J_D_1KI': 2.096155847503562}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..ebecca4
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13697, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.777354717254639, "TIME_S_1KI": 0.7868405283824662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1316.45538356781, "W": 89.82, "J_1KI": 96.11268040941886, "W_1KI": 6.557640359202745, "W_D": 73.69574999999999, "J_D": 1080.128777928829, "W_D_1KI": 5.3804300211725185, "J_D_1KI": 0.3928181369038854}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..4381db9
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_mario002.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.7665784358978271}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.5208, 0.6283, 0.9927,  ..., 0.7747, 0.7207, 0.3302])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.7665784358978271 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', 'suitesparse', 'csr', '13697', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.777354717254639}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9293, 0.9172, 0.4372,  ..., 0.0528, 0.4444, 0.3291])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.777354717254639 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9293, 0.9172, 0.4372,  ..., 0.0528, 0.4444, 0.3291])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.777354717254639 seconds
+
+[18.67, 17.76, 17.77, 17.98, 17.83, 17.63, 17.99, 17.72, 18.01, 17.56]
+[89.82]
+14.656595230102539
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13697, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.777354717254639, 'TIME_S_1KI': 0.7868405283824662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.45538356781, 'W': 89.82}
+[18.67, 17.76, 17.77, 17.98, 17.83, 17.63, 17.99, 17.72, 18.01, 17.56, 18.11, 17.79, 18.09, 17.72, 17.66, 18.49, 17.86, 18.34, 17.71, 17.93]
+322.485
+16.12425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13697, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.777354717254639, 'TIME_S_1KI': 0.7868405283824662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1316.45538356781, 'W': 89.82, 'J_1KI': 96.11268040941886, 'W_1KI': 6.557640359202745, 'W_D': 73.69574999999999, 'J_D': 1080.128777928829, 'W_D_1KI': 5.3804300211725185, 'J_D_1KI': 0.3928181369038854}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json
new file mode 100644
index 0000000..480eeda
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1887, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.573626518249512, "TIME_S_1KI": 5.603405680047436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.276729001999, "W": 85.01, "J_1KI": 764.8525325924743, "W_1KI": 45.050344462109166, "W_D": 68.79925, "J_D": 1168.055011149168, "W_D_1KI": 36.459591944886064, "J_D_1KI": 19.321458370368873}
diff --git a/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output
new file mode 100644
index 0000000..9043b10
--- /dev/null
+++ b/pytorch/output_389000+_16core/xeon_4216_16_csr_10_10_10_test1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 5.56341028213501}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.2549, 0.6086, 0.7138,  ..., 0.3139, 0.6424, 0.7605])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 5.56341028213501 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', 'suitesparse', 'csr', '1887', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.573626518249512}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3061, 0.1365, 0.6683,  ..., 0.9071, 0.4159, 0.8227])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 10.573626518249512 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3061, 0.1365, 0.6683,  ..., 0.9071, 0.4159, 0.8227])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 10.573626518249512 seconds
+
+[18.08, 17.49, 17.66, 17.63, 17.95, 17.53, 17.51, 17.68, 17.93, 17.61]
+[85.01]
+16.977728843688965
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1887, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.573626518249512, 'TIME_S_1KI': 5.603405680047436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.276729001999, 'W': 85.01}
+[18.08, 17.49, 17.66, 17.63, 17.95, 17.53, 17.51, 17.68, 17.93, 17.61, 18.14, 17.76, 17.8, 22.06, 18.45, 17.61, 17.74, 18.01, 17.71, 17.56]
+324.21500000000003
+16.21075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1887, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.573626518249512, 'TIME_S_1KI': 5.603405680047436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.276729001999, 'W': 85.01, 'J_1KI': 764.8525325924743, 'W_1KI': 45.050344462109166, 'W_D': 68.79925, 'J_D': 1168.055011149168, 'W_D_1KI': 36.459591944886064, 'J_D_1KI': 19.321458370368873}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..f65875c
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 88.16403460502625, "TIME_S_1KI": 88.16403460502625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2373.6426183891294, "W": 25.696559171496023, "J_1KI": 2373.6426183891294, "W_1KI": 25.696559171496023, "W_D": 4.897559171496024, "J_D": 452.3973461956977, "W_D_1KI": 4.897559171496024, "J_D_1KI": 4.897559171496024}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..59f928c
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_amazon0312.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 88.16403460502625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.0536, 0.3063, 0.9163,  ..., 0.7704, 0.9774, 0.8233])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 88.16403460502625 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.0536, 0.3063, 0.9163,  ..., 0.7704, 0.9774, 0.8233])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 88.16403460502625 seconds
+
+[22.96, 23.04, 23.08, 23.12, 23.16, 23.24, 23.24, 22.92, 22.8, 22.84]
+[22.88, 23.08, 23.44, 27.52, 29.2, 30.24, 30.68, 28.56, 27.76, 26.76, 26.68, 26.84, 26.68, 26.6, 26.56, 26.56, 26.52, 26.72, 26.88, 26.8, 26.92, 26.88, 26.8, 27.0, 27.0, 26.96, 27.08, 26.92, 27.04, 27.08, 27.0, 26.88, 26.72, 26.84, 26.96, 27.04, 27.16, 27.16, 27.28, 27.44, 27.32, 27.44, 27.6, 27.56, 27.44, 27.44, 27.36, 27.72, 27.84, 28.0, 27.96, 27.92, 27.6, 27.56, 27.68, 27.4, 27.4, 27.12, 27.12, 27.12, 27.28, 27.32, 27.36, 27.24, 27.2, 27.16, 27.24, 27.2, 27.24, 27.32, 27.24, 27.28, 27.2, 26.92, 26.92, 27.2, 27.24, 27.32, 27.24, 27.24, 26.96, 26.96, 27.04, 26.84, 26.88, 26.96, 27.4, 27.48]
+92.37200212478638
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 88.16403460502625, 'TIME_S_1KI': 88.16403460502625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2373.6426183891294, 'W': 25.696559171496023}
+[22.96, 23.04, 23.08, 23.12, 23.16, 23.24, 23.24, 22.92, 22.8, 22.84, 23.16, 22.88, 22.76, 23.04, 23.12, 23.44, 23.56, 23.36, 23.24, 23.0]
+415.98
+20.799
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 88.16403460502625, 'TIME_S_1KI': 88.16403460502625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2373.6426183891294, 'W': 25.696559171496023, 'J_1KI': 2373.6426183891294, 'W_1KI': 25.696559171496023, 'W_D': 4.897559171496024, 'J_D': 452.3973461956977, 'W_D_1KI': 4.897559171496024, 'J_D_1KI': 4.897559171496024}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..e2b106a
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 53.30746507644653, "TIME_S_1KI": 53.30746507644653, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.7467108154294, "W": 25.681407272182962, "J_1KI": 1536.7467108154294, "W_1KI": 25.681407272182962, "W_D": 4.658407272182959, "J_D": 278.7538851470942, "W_D_1KI": 4.658407272182959, "J_D_1KI": 4.658407272182959}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..5b58f1e
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_darcy003.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/darcy003.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 53.30746507644653}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.6125, 0.5278, 0.3887,  ..., 0.3141, 0.8902, 0.1690])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 53.30746507644653 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.6125, 0.5278, 0.3887,  ..., 0.3141, 0.8902, 0.1690])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 53.30746507644653 seconds
+
+[23.16, 23.4, 23.4, 23.32, 23.48, 23.4, 23.36, 23.56, 23.24, 23.28]
+[23.32, 22.84, 26.36, 27.44, 28.48, 28.48, 29.44, 30.12, 27.2, 26.6, 26.92, 27.16, 27.24, 27.2, 27.28, 27.12, 27.12, 26.96, 27.08, 27.04, 26.48, 26.72, 26.84, 26.76, 26.96, 27.12, 26.68, 26.68, 26.84, 26.64, 26.8, 26.96, 26.72, 26.6, 26.64, 26.44, 26.36, 26.68, 26.8, 26.84, 27.16, 27.28, 27.36, 27.0, 27.0, 27.12, 27.04, 26.96, 26.88, 26.88, 26.88, 26.64, 27.32, 27.88, 28.04, 28.0, 27.64]
+59.83888244628906
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 53.30746507644653, 'TIME_S_1KI': 53.30746507644653, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.7467108154294, 'W': 25.681407272182962}
+[23.16, 23.4, 23.4, 23.32, 23.48, 23.4, 23.36, 23.56, 23.24, 23.28, 23.36, 23.32, 23.28, 23.32, 23.28, 23.36, 23.4, 23.48, 23.32, 23.28]
+420.46000000000004
+21.023000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 53.30746507644653, 'TIME_S_1KI': 53.30746507644653, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.7467108154294, 'W': 25.681407272182962, 'J_1KI': 1536.7467108154294, 'W_1KI': 25.681407272182962, 'W_D': 4.658407272182959, 'J_D': 278.7538851470942, 'W_D_1KI': 4.658407272182959, 'J_D_1KI': 4.658407272182959}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..b96c66c
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 62.08789658546448, "TIME_S_1KI": 62.08789658546448, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1791.450892791748, "W": 25.109871656394702, "J_1KI": 1791.450892791748, "W_1KI": 25.109871656394702, "W_D": 4.4798716563947, "J_D": 319.61414173126184, "W_D_1KI": 4.4798716563947, "J_D_1KI": 4.4798716563947}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..13ebb65
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_helm2d03.output
@@ -0,0 +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 suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 62.08789658546448}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.2732, 0.1117, 0.4132,  ..., 0.8859, 0.7833, 0.1406])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 62.08789658546448 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.2732, 0.1117, 0.4132,  ..., 0.8859, 0.7833, 0.1406])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 62.08789658546448 seconds
+
+[23.16, 22.92, 22.92, 22.96, 22.84, 23.04, 23.12, 23.2, 23.36, 23.44]
+[23.36, 23.32, 23.32, 24.2, 25.04, 27.48, 28.36, 28.84, 28.4, 27.24, 26.92, 26.76, 26.72, 26.8, 26.68, 26.6, 26.6, 26.56, 26.52, 26.56, 26.48, 26.48, 26.44, 26.6, 26.6, 26.56, 26.4, 26.48, 26.56, 26.36, 26.28, 26.2, 26.44, 26.6, 26.68, 27.2, 27.2, 27.04, 27.04, 26.84, 26.64, 26.64, 26.44, 26.4, 26.48, 26.48, 26.16, 26.52, 26.72, 26.8, 26.84, 26.84, 26.72, 26.64, 26.6, 26.8, 26.72, 26.8, 26.96, 27.0, 27.2, 27.32, 27.16, 26.88, 26.72, 26.68, 26.68, 26.68]
+71.34448623657227
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 62.08789658546448, 'TIME_S_1KI': 62.08789658546448, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1791.450892791748, 'W': 25.109871656394702}
+[23.16, 22.92, 22.92, 22.96, 22.84, 23.04, 23.12, 23.2, 23.36, 23.44, 23.08, 23.08, 23.04, 22.76, 22.48, 22.48, 22.48, 22.64, 22.96, 22.96]
+412.6
+20.630000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 62.08789658546448, 'TIME_S_1KI': 62.08789658546448, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1791.450892791748, 'W': 25.109871656394702, 'J_1KI': 1791.450892791748, 'W_1KI': 25.109871656394702, 'W_D': 4.4798716563947, 'J_D': 319.61414173126184, 'W_D_1KI': 4.4798716563947, 'J_D_1KI': 4.4798716563947}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json
new file mode 100644
index 0000000..e4470f0
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 32.617069482803345, "TIME_S_1KI": 32.617069482803345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 877.9408810043335, "W": 25.358346526187013, "J_1KI": 877.9408810043335, "W_1KI": 25.358346526187013, "W_D": 4.655346526187017, "J_D": 161.17450821805022, "W_D_1KI": 4.655346526187017, "J_D_1KI": 4.655346526187017}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output
new file mode 100644
index 0000000..8ed6535
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_language.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 32.617069482803345}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.1808, 0.0389, 0.7706,  ..., 0.1715, 0.5157, 0.4224])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 32.617069482803345 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.1808, 0.0389, 0.7706,  ..., 0.1715, 0.5157, 0.4224])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 32.617069482803345 seconds
+
+[23.16, 23.16, 22.96, 23.16, 22.92, 22.92, 23.0, 22.92, 22.84, 23.16]
+[23.24, 23.4, 24.8, 25.6, 27.28, 28.0, 28.0, 28.48, 27.76, 27.96, 26.76, 26.64, 26.76, 26.88, 26.88, 26.96, 26.96, 27.2, 27.0, 27.12, 27.08, 26.68, 27.08, 27.16, 27.08, 27.08, 27.28, 27.0, 27.0, 27.24, 27.16, 27.36, 27.36]
+34.62137722969055
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 32.617069482803345, 'TIME_S_1KI': 32.617069482803345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 877.9408810043335, 'W': 25.358346526187013}
+[23.16, 23.16, 22.96, 23.16, 22.92, 22.92, 23.0, 22.92, 22.84, 23.16, 22.96, 23.28, 23.08, 23.16, 23.04, 22.88, 22.76, 22.88, 22.96, 23.0]
+414.05999999999995
+20.702999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 32.617069482803345, 'TIME_S_1KI': 32.617069482803345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 877.9408810043335, 'W': 25.358346526187013, 'J_1KI': 877.9408810043335, 'W_1KI': 25.358346526187013, 'W_D': 4.655346526187017, 'J_D': 161.17450821805022, 'W_D_1KI': 4.655346526187017, 'J_D_1KI': 4.655346526187017}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..91cb90d
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 136.75263905525208, "TIME_S_1KI": 136.75263905525208, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3809.28440010071, "W": 25.30736544046405, "J_1KI": 3809.28440010071, "W_1KI": 25.30736544046405, "W_D": 4.417365440464049, "J_D": 664.905294132235, "W_D_1KI": 4.417365440464049, "J_D_1KI": 4.417365440464049}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..5e3841d
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_marine1.output
@@ -0,0 +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 suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 136.75263905525208}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      18,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.1033, 0.2543, 0.2854,  ..., 0.8643, 0.3799, 0.0773])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 136.75263905525208 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      18,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.1033, 0.2543, 0.2854,  ..., 0.8643, 0.3799, 0.0773])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 136.75263905525208 seconds
+
+[23.6, 23.2, 23.36, 23.44, 23.4, 23.52, 23.52, 23.4, 23.16, 22.92]
+[23.12, 23.0, 23.0, 25.76, 27.64, 28.96, 29.28, 28.32, 27.36, 27.36, 27.0, 26.64, 26.6, 26.6, 26.76, 26.64, 26.6, 26.6, 26.4, 26.48, 26.56, 26.76, 26.96, 27.0, 27.04, 26.84, 26.92, 26.64, 26.64, 26.8, 26.88, 26.88, 26.88, 27.04, 27.12, 26.96, 26.96, 26.84, 26.6, 26.6, 26.24, 26.04, 26.0, 26.24, 26.48, 26.88, 27.2, 27.36, 27.16, 27.24, 27.16, 27.04, 27.04, 27.04, 26.88, 26.8, 26.48, 26.68, 26.64, 26.72, 26.68, 26.84, 26.6, 26.48, 26.36, 26.4, 26.28, 26.48, 26.6, 26.48, 26.48, 26.8, 26.76, 26.72, 26.76, 26.76, 26.68, 26.52, 26.48, 26.56, 26.6, 26.52, 26.28, 26.32, 26.24, 26.44, 26.52, 26.6, 26.44, 26.52, 26.56, 26.4, 26.56, 26.64, 26.92, 27.04, 26.92, 26.92, 26.96, 26.92, 26.84, 26.88, 27.16, 27.08, 26.96, 26.72, 26.56, 26.44, 26.4, 26.4, 26.64, 26.72, 26.84, 26.96, 27.2, 26.88, 26.8, 26.8, 26.92, 26.92, 27.08, 27.28, 27.2, 27.0, 26.92, 26.8, 26.64, 26.92, 26.72, 26.76, 26.8, 26.8, 26.76, 26.84, 26.88, 26.72, 26.6, 26.64, 26.52, 26.8, 26.96, 26.96, 27.16]
+150.5207805633545
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 136.75263905525208, 'TIME_S_1KI': 136.75263905525208, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3809.28440010071, 'W': 25.30736544046405}
+[23.6, 23.2, 23.36, 23.44, 23.4, 23.52, 23.52, 23.4, 23.16, 22.92, 23.28, 23.28, 23.28, 23.16, 23.08, 22.96, 22.84, 22.8, 22.92, 23.16]
+417.8
+20.89
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 136.75263905525208, 'TIME_S_1KI': 136.75263905525208, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3809.28440010071, 'W': 25.30736544046405, 'J_1KI': 3809.28440010071, 'W_1KI': 25.30736544046405, 'W_D': 4.417365440464049, 'J_D': 664.905294132235, 'W_D_1KI': 4.417365440464049, 'J_D_1KI': 4.417365440464049}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..931b93e
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 51.17483377456665, "TIME_S_1KI": 51.17483377456665, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1410.4799920654298, "W": 25.376735316064863, "J_1KI": 1410.4799920654298, "W_1KI": 25.376735316064863, "W_D": 4.522735316064864, "J_D": 251.3809437370302, "W_D_1KI": 4.522735316064864, "J_D_1KI": 4.522735316064864}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..103b1d5
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_mario002.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 51.17483377456665}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0328, 0.3187, 0.7172,  ..., 0.3931, 0.0888, 0.1198])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 51.17483377456665 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0328, 0.3187, 0.7172,  ..., 0.3931, 0.0888, 0.1198])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 51.17483377456665 seconds
+
+[23.16, 23.16, 23.16, 23.28, 23.28, 23.48, 23.44, 23.4, 23.44, 23.44]
+[23.4, 23.48, 23.92, 24.84, 26.8, 27.6, 28.64, 28.08, 28.08, 26.88, 26.8, 26.8, 26.76, 26.76, 26.8, 26.64, 26.68, 26.48, 26.72, 26.96, 27.12, 27.2, 27.08, 27.12, 27.2, 27.12, 26.88, 27.04, 27.04, 27.2, 27.36, 27.24, 27.2, 27.2, 27.12, 27.12, 27.2, 26.92, 26.88, 26.88, 26.92, 26.84, 26.6, 26.84, 26.68, 26.72, 26.96, 26.72, 26.64, 26.84, 26.76, 27.0, 27.2]
+55.58161735534668
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 51.17483377456665, 'TIME_S_1KI': 51.17483377456665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.4799920654298, 'W': 25.376735316064863}
+[23.16, 23.16, 23.16, 23.28, 23.28, 23.48, 23.44, 23.4, 23.44, 23.44, 23.08, 22.96, 22.96, 23.04, 23.08, 22.96, 23.04, 23.12, 22.88, 23.12]
+417.08
+20.854
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 51.17483377456665, 'TIME_S_1KI': 51.17483377456665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1410.4799920654298, 'W': 25.376735316064863, 'J_1KI': 1410.4799920654298, 'W_1KI': 25.376735316064863, 'W_D': 4.522735316064864, 'J_D': 251.3809437370302, 'W_D_1KI': 4.522735316064864, 'J_D_1KI': 4.522735316064864}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json
new file mode 100644
index 0000000..4bb27e2
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 283.81978273391724, "TIME_S_1KI": 283.81978273391724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7375.577878112791, "W": 25.373727151984518, "J_1KI": 7375.577878112791, "W_1KI": 25.373727151984518, "W_D": 4.492727151984518, "J_D": 1305.9358129017337, "W_D_1KI": 4.492727151984518, "J_D_1KI": 4.492727151984518}
diff --git a/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output
new file mode 100644
index 0000000..c57d8f0
--- /dev/null
+++ b/pytorch/output_389000+_1core/altra_1_csr_10_10_10_test1.output
@@ -0,0 +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 suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 283.81978273391724}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.4069, 0.7437, 0.0499,  ..., 0.6858, 0.0232, 0.7224])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 283.81978273391724 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.4069, 0.7437, 0.0499,  ..., 0.6858, 0.0232, 0.7224])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 283.81978273391724 seconds
+
+[22.72, 22.6, 22.72, 22.52, 22.8, 23.2, 23.32, 23.36, 23.36, 23.36]
+[23.36, 23.0, 23.52, 26.52, 28.0, 29.08, 29.6, 28.4, 27.72, 27.56, 27.4, 26.84, 27.08, 27.28, 27.24, 27.04, 26.76, 26.76, 26.68, 26.48, 26.44, 26.48, 26.36, 26.2, 26.48, 26.48, 26.64, 26.64, 26.68, 26.52, 26.52, 26.68, 26.64, 26.68, 26.68, 26.68, 26.64, 26.64, 26.64, 26.92, 26.84, 26.6, 26.72, 26.68, 26.44, 26.76, 26.96, 26.8, 26.8, 26.96, 27.0, 26.92, 26.96, 26.96, 26.64, 26.52, 26.36, 26.56, 26.6, 26.72, 26.72, 26.72, 26.64, 26.6, 26.52, 26.64, 26.52, 26.48, 26.44, 26.44, 26.48, 26.56, 26.76, 26.64, 26.76, 26.72, 26.76, 26.8, 26.76, 26.64, 26.44, 26.44, 26.44, 26.44, 26.64, 26.56, 26.76, 26.76, 26.68, 26.68, 26.72, 26.72, 26.8, 26.8, 26.64, 26.4, 26.52, 26.56, 26.68, 26.68, 26.96, 26.72, 26.64, 26.76, 26.76, 26.84, 26.76, 26.96, 26.8, 26.76, 26.68, 26.72, 26.8, 27.12, 27.08, 26.92, 26.68, 26.28, 26.2, 26.44, 26.4, 26.68, 26.88, 26.92, 27.08, 26.96, 26.96, 26.88, 26.84, 26.68, 26.64, 26.68, 26.52, 26.48, 26.28, 26.28, 26.24, 26.44, 26.76, 27.04, 26.92, 27.0, 27.0, 27.0, 26.84, 26.92, 26.88, 26.92, 26.92, 26.8, 26.8, 26.84, 26.92, 26.92, 27.08, 26.92, 27.0, 26.92, 26.8, 26.8, 26.96, 26.84, 26.92, 26.84, 26.68, 26.6, 26.68, 26.72, 26.96, 26.76, 26.76, 26.6, 26.4, 26.28, 26.2, 26.44, 26.8, 26.76, 26.92, 27.04, 26.8, 26.6, 26.6, 26.32, 26.24, 26.4, 26.28, 26.36, 26.64, 26.64, 26.6, 26.68, 26.68, 26.44, 26.24, 26.12, 26.36, 26.44, 26.68, 26.8, 26.92, 26.96, 26.92, 26.88, 26.88, 26.76, 26.76, 26.8, 26.68, 26.72, 26.64, 26.52, 26.64, 26.92, 26.92, 26.76, 26.6, 26.6, 26.56, 26.36, 26.36, 26.64, 26.6, 26.72, 26.76, 26.84, 26.72, 27.04, 26.72, 26.6, 26.88, 26.76, 26.72, 26.64, 26.8, 27.04, 27.04, 26.96, 27.04, 27.04, 26.8, 26.68, 26.88, 26.84, 27.0, 26.92, 26.96, 27.0, 26.76, 26.8, 26.88, 26.88, 26.96, 27.0, 27.12, 27.32, 27.32, 27.32, 27.32, 27.24, 26.88, 26.6, 26.68, 26.64, 26.92, 27.08, 27.28, 27.04, 26.92, 27.08, 27.4, 27.56, 27.6, 27.48, 27.2, 26.88]
+290.67774844169617
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 283.81978273391724, 'TIME_S_1KI': 283.81978273391724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7375.577878112791, 'W': 25.373727151984518}
+[22.72, 22.6, 22.72, 22.52, 22.8, 23.2, 23.32, 23.36, 23.36, 23.36, 23.48, 23.28, 23.2, 23.24, 23.24, 23.32, 23.6, 23.8, 23.52, 23.52]
+417.62
+20.881
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 283.81978273391724, 'TIME_S_1KI': 283.81978273391724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7375.577878112791, 'W': 25.373727151984518, 'J_1KI': 7375.577878112791, 'W_1KI': 25.373727151984518, 'W_D': 4.492727151984518, 'J_D': 1305.9358129017337, 'W_D_1KI': 4.492727151984518, 'J_D_1KI': 4.492727151984518}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..4f2ba5e
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1665, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.677687168121338, "TIME_S_1KI": 6.413025326199002, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 984.0334419322014, "W": 72.51, "J_1KI": 591.0110762355564, "W_1KI": 43.54954954954955, "W_D": 37.19775000000001, "J_D": 504.81078423160335, "W_D_1KI": 22.340990990991, "J_D_1KI": 13.418012607201801}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..78545de
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_amazon0312.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 6.305053472518921}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6406, 0.1468, 0.7280,  ..., 0.0181, 0.2043, 0.6040])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 6.305053472518921 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1665', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.677687168121338}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.7736, 0.6337, 0.2989,  ..., 0.0625, 0.4089, 0.3233])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.677687168121338 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.7736, 0.6337, 0.2989,  ..., 0.0625, 0.4089, 0.3233])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.677687168121338 seconds
+
+[40.11, 38.6, 39.5, 38.36, 38.75, 38.98, 39.85, 38.73, 38.93, 38.39]
+[72.51]
+13.571003198623657
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1665, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.677687168121338, 'TIME_S_1KI': 6.413025326199002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 984.0334419322014, 'W': 72.51}
+[40.11, 38.6, 39.5, 38.36, 38.75, 38.98, 39.85, 38.73, 38.93, 38.39, 39.83, 38.29, 40.69, 40.97, 41.34, 39.2, 38.48, 38.73, 38.45, 38.46]
+706.2449999999999
+35.31224999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1665, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.677687168121338, 'TIME_S_1KI': 6.413025326199002, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 984.0334419322014, 'W': 72.51, 'J_1KI': 591.0110762355564, 'W_1KI': 43.54954954954955, 'W_D': 37.19775000000001, 'J_D': 504.81078423160335, 'W_D_1KI': 22.340990990991, 'J_D_1KI': 13.418012607201801}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..0c5532e
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2770, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.539327144622803, "TIME_S_1KI": 3.804811243546138, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.3520177507401, "W": 70.14, "J_1KI": 339.477262725899, "W_1KI": 25.32129963898917, "W_D": 35.342749999999995, "J_D": 473.8327099424004, "W_D_1KI": 12.759115523465702, "J_D_1KI": 4.606178889337799}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..0191954
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_darcy003.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 3.790585994720459}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.8724, 0.5946, 0.8360,  ..., 0.1630, 0.5271, 0.0708])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 3.790585994720459 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2770', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.539327144622803}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.4798, 0.6348, 0.4010,  ..., 0.9410, 0.2128, 0.7861])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.539327144622803 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.4798, 0.6348, 0.4010,  ..., 0.9410, 0.2128, 0.7861])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.539327144622803 seconds
+
+[39.64, 38.41, 38.51, 38.38, 38.48, 38.55, 38.6, 38.35, 39.22, 38.54]
+[70.14]
+13.406786680221558
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2770, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.539327144622803, 'TIME_S_1KI': 3.804811243546138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.3520177507401, 'W': 70.14}
+[39.64, 38.41, 38.51, 38.38, 38.48, 38.55, 38.6, 38.35, 39.22, 38.54, 39.52, 38.45, 38.63, 38.39, 38.55, 38.9, 38.88, 38.72, 38.83, 38.49]
+695.945
+34.797250000000005
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2770, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.539327144622803, 'TIME_S_1KI': 3.804811243546138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.3520177507401, 'W': 70.14, 'J_1KI': 339.477262725899, 'W_1KI': 25.32129963898917, 'W_D': 35.342749999999995, 'J_D': 473.8327099424004, 'W_D_1KI': 12.759115523465702, 'J_D_1KI': 4.606178889337799}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..6c16e41
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2881, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.489487886428833, "TIME_S_1KI": 3.640919085882969, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 982.9177148509026, "W": 72.89, "J_1KI": 341.1724105695601, "W_1KI": 25.30024297119056, "W_D": 38.247499999999995, "J_D": 515.765472612977, "W_D_1KI": 13.275772301284276, "J_D_1KI": 4.6080431451871835}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..3aa1656
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_helm2d03.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 3.64394474029541}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.2874, 0.3706, 0.3465,  ..., 0.0468, 0.1058, 0.2863])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 3.64394474029541 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2881', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.489487886428833}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.4950, 0.0177, 0.5787,  ..., 0.9424, 0.3532, 0.5521])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.489487886428833 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.4950, 0.0177, 0.5787,  ..., 0.9424, 0.3532, 0.5521])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.489487886428833 seconds
+
+[39.75, 38.21, 38.34, 38.21, 38.27, 38.42, 38.28, 38.17, 38.78, 38.77]
+[72.89]
+13.484946012496948
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2881, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.489487886428833, 'TIME_S_1KI': 3.640919085882969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 982.9177148509026, 'W': 72.89}
+[39.75, 38.21, 38.34, 38.21, 38.27, 38.42, 38.28, 38.17, 38.78, 38.77, 39.12, 38.21, 38.45, 38.24, 39.01, 38.22, 38.93, 38.26, 38.73, 38.6]
+692.8500000000001
+34.642500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2881, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.489487886428833, 'TIME_S_1KI': 3.640919085882969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 982.9177148509026, 'W': 72.89, 'J_1KI': 341.1724105695601, 'W_1KI': 25.30024297119056, 'W_D': 38.247499999999995, 'J_D': 515.765472612977, 'W_D_1KI': 13.275772301284276, 'J_D_1KI': 4.6080431451871835}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json
new file mode 100644
index 0000000..d92fabc
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3433, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.349842309951782, "TIME_S_1KI": 3.01480987764398, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.6259407162667, "W": 65.83, "J_1KI": 252.43983126020004, "W_1KI": 19.175648121176813, "W_D": 31.058999999999997, "J_D": 408.8794636595249, "W_D_1KI": 9.047189047480337, "J_D_1KI": 2.635359466204584}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output
new file mode 100644
index 0000000..994626a
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_language.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 3.058311939239502}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.8774, 0.7244, 0.5547,  ..., 0.2046, 0.1297, 0.0114])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 3.058311939239502 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '3433', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.349842309951782}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.8074, 0.3012, 0.9549,  ..., 0.2881, 0.3396, 0.4512])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.349842309951782 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.8074, 0.3012, 0.9549,  ..., 0.2881, 0.3396, 0.4512])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.349842309951782 seconds
+
+[39.97, 38.36, 38.86, 38.59, 38.26, 38.09, 38.21, 38.92, 38.29, 38.47]
+[65.83]
+13.164604902267456
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3433, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.349842309951782, 'TIME_S_1KI': 3.01480987764398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.6259407162667, 'W': 65.83}
+[39.97, 38.36, 38.86, 38.59, 38.26, 38.09, 38.21, 38.92, 38.29, 38.47, 39.36, 38.28, 38.75, 39.51, 38.42, 38.7, 38.49, 38.22, 39.26, 38.62]
+695.4200000000001
+34.771
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3433, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.349842309951782, 'TIME_S_1KI': 3.01480987764398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.6259407162667, 'W': 65.83, 'J_1KI': 252.43983126020004, 'W_1KI': 19.175648121176813, 'W_D': 31.058999999999997, 'J_D': 408.8794636595249, 'W_D_1KI': 9.047189047480337, 'J_D_1KI': 2.635359466204584}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..7b79af7
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1403, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.47270679473877, "TIME_S_1KI": 7.4645094759364, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1076.08722530365, "W": 76.53000000000002, "J_1KI": 766.9901819698147, "W_1KI": 54.54739843193158, "W_D": 40.18550000000002, "J_D": 565.0477354297641, "W_D_1KI": 28.642551674982194, "J_D_1KI": 20.415218585161934}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..f00e48c
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_marine1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 7.480671405792236}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.5771, 0.6006, 0.1014,  ..., 0.3420, 0.9665, 0.9706])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 7.480671405792236 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1403', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.47270679473877}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.9737, 0.1599, 0.8628,  ..., 0.5469, 0.5754, 0.2289])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.47270679473877 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.9737, 0.1599, 0.8628,  ..., 0.5469, 0.5754, 0.2289])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.47270679473877 seconds
+
+[39.8, 38.66, 38.76, 38.93, 38.51, 38.5, 38.48, 53.83, 45.0, 38.92]
+[76.53]
+14.060985565185547
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1403, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.47270679473877, 'TIME_S_1KI': 7.4645094759364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1076.08722530365, 'W': 76.53000000000002}
+[39.8, 38.66, 38.76, 38.93, 38.51, 38.5, 38.48, 53.83, 45.0, 38.92, 39.54, 38.42, 38.88, 38.56, 45.29, 38.95, 39.28, 38.53, 39.92, 38.52]
+726.89
+36.3445
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1403, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.47270679473877, 'TIME_S_1KI': 7.4645094759364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1076.08722530365, 'W': 76.53000000000002, 'J_1KI': 766.9901819698147, 'W_1KI': 54.54739843193158, 'W_D': 40.18550000000002, 'J_D': 565.0477354297641, 'W_D_1KI': 28.642551674982194, 'J_D_1KI': 20.415218585161934}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..8c1f364
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2778, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.518349885940552, "TIME_S_1KI": 3.786303054694223, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 942.0412521362306, "W": 70.4, "J_1KI": 339.1077221512709, "W_1KI": 25.341972642188626, "W_D": 35.57825000000001, "J_D": 476.0820906081797, "W_D_1KI": 12.807145428365734, "J_D_1KI": 4.61020353792863}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..c39c36c
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_mario002.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 3.778977870941162}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.3385, 0.4156, 0.4762,  ..., 0.6246, 0.7256, 0.2909])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 3.778977870941162 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2778', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.518349885940552}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1706, 0.4199, 0.8169,  ..., 0.9237, 0.2859, 0.4340])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.518349885940552 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1706, 0.4199, 0.8169,  ..., 0.9237, 0.2859, 0.4340])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.518349885940552 seconds
+
+[39.45, 38.78, 38.96, 38.79, 38.45, 38.37, 38.35, 39.22, 38.56, 38.56]
+[70.4]
+13.381267786026001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2778, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.518349885940552, 'TIME_S_1KI': 3.786303054694223, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 942.0412521362306, 'W': 70.4}
+[39.45, 38.78, 38.96, 38.79, 38.45, 38.37, 38.35, 39.22, 38.56, 38.56, 40.19, 38.52, 38.44, 38.38, 38.77, 38.32, 38.58, 39.02, 38.41, 38.83]
+696.435
+34.821749999999994
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2778, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.518349885940552, 'TIME_S_1KI': 3.786303054694223, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 942.0412521362306, 'W': 70.4, 'J_1KI': 339.1077221512709, 'W_1KI': 25.341972642188626, 'W_D': 35.57825000000001, 'J_D': 476.0820906081797, 'W_D_1KI': 12.807145428365734, 'J_D_1KI': 4.61020353792863}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json
new file mode 100644
index 0000000..48065b0
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 20.74174404144287, "TIME_S_1KI": 20.74174404144287, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1964.3499762821195, "W": 78.38, "J_1KI": 1964.3499762821195, "W_1KI": 78.38, "W_D": 43.477, "J_D": 1089.6152579588888, "W_D_1KI": 43.477, "J_D_1KI": 43.477}
diff --git a/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output
new file mode 100644
index 0000000..7a3f429
--- /dev/null
+++ b/pytorch/output_389000+_1core/epyc_7313p_1_csr_10_10_10_test1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 20.74174404144287}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3044, 0.7914, 0.5459,  ..., 0.2990, 0.3126, 0.6970])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 20.74174404144287 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3044, 0.7914, 0.5459,  ..., 0.2990, 0.3126, 0.6970])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 20.74174404144287 seconds
+
+[39.18, 39.6, 38.95, 39.49, 38.43, 39.82, 38.88, 38.36, 38.96, 38.49]
+[78.38]
+25.061877727508545
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 20.74174404144287, 'TIME_S_1KI': 20.74174404144287, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.3499762821195, 'W': 78.38}
+[39.18, 39.6, 38.95, 39.49, 38.43, 39.82, 38.88, 38.36, 38.96, 38.49, 39.91, 38.51, 38.34, 38.55, 38.51, 38.42, 38.42, 38.4, 38.39, 38.48]
+698.06
+34.903
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 20.74174404144287, 'TIME_S_1KI': 20.74174404144287, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.3499762821195, 'W': 78.38, 'J_1KI': 1964.3499762821195, 'W_1KI': 78.38, 'W_D': 43.477, 'J_D': 1089.6152579588888, 'W_D_1KI': 43.477, 'J_D_1KI': 43.477}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..d26e6d4
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 12.424208641052246, "TIME_S_1KI": 12.424208641052246, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 879.7217792987824, "W": 52.38, "J_1KI": 879.7217792987824, "W_1KI": 52.38, "W_D": 36.156000000000006, "J_D": 607.2397986316682, "W_D_1KI": 36.156000000000006, "J_D_1KI": 36.156000000000006}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..3f99e16
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_amazon0312.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 12.424208641052246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6069, 0.8095, 0.9925,  ..., 0.5957, 0.3239, 0.4137])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 12.424208641052246 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.6069, 0.8095, 0.9925,  ..., 0.5957, 0.3239, 0.4137])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 12.424208641052246 seconds
+
+[18.89, 17.73, 17.96, 17.88, 17.85, 17.69, 17.97, 17.83, 17.93, 18.01]
+[52.38]
+16.79499387741089
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 12.424208641052246, 'TIME_S_1KI': 12.424208641052246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 879.7217792987824, 'W': 52.38}
+[18.89, 17.73, 17.96, 17.88, 17.85, 17.69, 17.97, 17.83, 17.93, 18.01, 18.12, 18.25, 18.06, 18.22, 18.08, 17.89, 17.8, 17.75, 19.11, 17.94]
+324.48
+16.224
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 12.424208641052246, 'TIME_S_1KI': 12.424208641052246, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 879.7217792987824, 'W': 52.38, 'J_1KI': 879.7217792987824, 'W_1KI': 52.38, 'W_D': 36.156000000000006, 'J_D': 607.2397986316682, 'W_D_1KI': 36.156000000000006, 'J_D_1KI': 36.156000000000006}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..06cf5ab
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1604, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.463838577270508, "TIME_S_1KI": 6.5235901354554295, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.353337585926, "W": 53.79, "J_1KI": 493.98587131292146, "W_1KI": 33.53491271820449, "W_D": 15.963500000000003, "J_D": 235.15026035606866, "W_D_1KI": 9.952306733167084, "J_D_1KI": 6.204680008208905}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..66e352a
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_darcy003.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 6.544304132461548}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9984, 0.0550, 0.4152,  ..., 0.8933, 0.3177, 0.3432])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 6.544304132461548 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', 'suitesparse', 'csr', '1604', '-m', 'matrices/389000+_cols/darcy003.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.463838577270508}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.3553, 0.0914, 0.5617,  ..., 0.2172, 0.2068, 0.5865])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.463838577270508 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.3553, 0.0914, 0.5617,  ..., 0.2172, 0.2068, 0.5865])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.463838577270508 seconds
+
+[51.54, 47.98, 24.21, 22.55, 39.25, 40.93, 45.39, 44.37, 40.99, 42.3]
+[53.79]
+14.73049521446228
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1604, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.463838577270508, 'TIME_S_1KI': 6.5235901354554295, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.353337585926, 'W': 53.79}
+[51.54, 47.98, 24.21, 22.55, 39.25, 40.93, 45.39, 44.37, 40.99, 42.3, 51.25, 53.2, 44.13, 44.41, 42.29, 42.29, 43.1, 44.05, 43.73, 42.23]
+756.53
+37.826499999999996
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1604, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.463838577270508, 'TIME_S_1KI': 6.5235901354554295, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.353337585926, 'W': 53.79, 'J_1KI': 493.98587131292146, 'W_1KI': 33.53491271820449, 'W_D': 15.963500000000003, 'J_D': 235.15026035606866, 'W_D_1KI': 9.952306733167084, 'J_D_1KI': 6.204680008208905}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..2466ba0
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1567, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.482280731201172, "TIME_S_1KI": 6.6893942126363575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 809.832340335846, "W": 53.88, "J_1KI": 516.8043014268321, "W_1KI": 34.38417358008934, "W_D": 37.52475, "J_D": 564.0080941540002, "W_D_1KI": 23.94687300574346, "J_D_1KI": 15.2819866022613}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..a8b8852
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_helm2d03.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 6.698031663894653}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.6880, 0.8256, 0.6674,  ..., 0.8572, 0.2017, 0.9423])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 6.698031663894653 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', 'suitesparse', 'csr', '1567', '-m', 'matrices/389000+_cols/helm2d03.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.482280731201172}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9695, 0.5429, 0.1111,  ..., 0.2474, 0.2323, 0.6789])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.482280731201172 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9695, 0.5429, 0.1111,  ..., 0.2474, 0.2323, 0.6789])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.482280731201172 seconds
+
+[18.37, 18.1, 22.31, 17.76, 18.12, 17.9, 17.88, 17.63, 17.79, 17.9]
+[53.88]
+15.030295848846436
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1567, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.482280731201172, 'TIME_S_1KI': 6.6893942126363575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.832340335846, 'W': 53.88}
+[18.37, 18.1, 22.31, 17.76, 18.12, 17.9, 17.88, 17.63, 17.79, 17.9, 18.29, 18.08, 17.89, 18.0, 17.96, 17.67, 18.29, 17.73, 17.87, 17.69]
+327.105
+16.35525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1567, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.482280731201172, 'TIME_S_1KI': 6.6893942126363575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.832340335846, 'W': 53.88, 'J_1KI': 516.8043014268321, 'W_1KI': 34.38417358008934, 'W_D': 37.52475, 'J_D': 564.0080941540002, 'W_D_1KI': 23.94687300574346, 'J_D_1KI': 15.2819866022613}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json
new file mode 100644
index 0000000..9d30cd5
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1711, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.39021348953247, "TIME_S_1KI": 6.072597013169182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 758.5677815818786, "W": 52.04, "J_1KI": 443.34762219864325, "W_1KI": 30.414962010520163, "W_D": 35.8595, "J_D": 522.710633428812, "W_D_1KI": 20.95821157218001, "J_D_1KI": 12.249100860420812}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output
new file mode 100644
index 0000000..7a78a51
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_language.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 6.135177135467529}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.0547, 0.0947, 0.9321,  ..., 0.9094, 0.0107, 0.8738])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 6.135177135467529 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', 'suitesparse', 'csr', '1711', '-m', 'matrices/389000+_cols/language.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.39021348953247}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.6001, 0.7097, 0.4908,  ..., 0.7271, 0.7976, 0.2970])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.39021348953247 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.6001, 0.7097, 0.4908,  ..., 0.7271, 0.7976, 0.2970])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.39021348953247 seconds
+
+[18.03, 17.94, 17.82, 17.99, 17.74, 17.56, 17.76, 17.68, 17.79, 18.07]
+[52.04]
+14.576629161834717
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1711, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.39021348953247, 'TIME_S_1KI': 6.072597013169182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5677815818786, 'W': 52.04}
+[18.03, 17.94, 17.82, 17.99, 17.74, 17.56, 17.76, 17.68, 17.79, 18.07, 18.72, 17.93, 17.72, 18.0, 18.39, 17.97, 18.72, 17.86, 18.44, 17.78]
+323.60999999999996
+16.1805
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1711, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.39021348953247, 'TIME_S_1KI': 6.072597013169182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.5677815818786, 'W': 52.04, 'J_1KI': 443.34762219864325, 'W_1KI': 30.414962010520163, 'W_D': 35.8595, 'J_D': 522.710633428812, 'W_D_1KI': 20.95821157218001, 'J_D_1KI': 12.249100860420812}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..bc13ead
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 14.3206205368042, "TIME_S_1KI": 14.3206205368042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1077.2933460760116, "W": 53.62, "J_1KI": 1077.2933460760116, "W_1KI": 53.62, "W_D": 37.37875, "J_D": 750.9861741819977, "W_D_1KI": 37.37875, "J_D_1KI": 37.37875}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..ba23280
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_marine1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 14.3206205368042}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.4405, 0.4136, 0.9296,  ..., 0.1477, 0.1453, 0.8762])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 14.3206205368042 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.4405, 0.4136, 0.9296,  ..., 0.1477, 0.1453, 0.8762])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 14.3206205368042 seconds
+
+[18.25, 17.59, 17.7, 17.8, 18.1, 17.82, 18.62, 21.23, 18.03, 17.76]
+[53.62]
+20.091259717941284
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 14.3206205368042, 'TIME_S_1KI': 14.3206205368042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1077.2933460760116, 'W': 53.62}
+[18.25, 17.59, 17.7, 17.8, 18.1, 17.82, 18.62, 21.23, 18.03, 17.76, 18.27, 17.65, 17.53, 17.72, 17.84, 17.65, 17.87, 17.77, 17.96, 17.61]
+324.825
+16.24125
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 14.3206205368042, 'TIME_S_1KI': 14.3206205368042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1077.2933460760116, 'W': 53.62, 'J_1KI': 1077.2933460760116, 'W_1KI': 53.62, 'W_D': 37.37875, 'J_D': 750.9861741819977, 'W_D_1KI': 37.37875, 'J_D_1KI': 37.37875}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..f7e4602
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1598, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.48720407485962, "TIME_S_1KI": 6.562705929198761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 768.8178126811981, "W": 53.0, "J_1KI": 481.1125235802241, "W_1KI": 33.16645807259074, "W_D": 36.777, "J_D": 533.4870320184231, "W_D_1KI": 23.01439299123905, "J_D_1KI": 14.40199811717087}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..323fb21
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_mario002.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 6.568377256393433}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1410, 0.8504, 0.4141,  ..., 0.6370, 0.5152, 0.1646])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 6.568377256393433 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', 'suitesparse', 'csr', '1598', '-m', 'matrices/389000+_cols/mario002.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.48720407485962}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9888, 0.3844, 0.2800,  ..., 0.8268, 0.5179, 0.1169])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.48720407485962 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9888, 0.3844, 0.2800,  ..., 0.8268, 0.5179, 0.1169])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.48720407485962 seconds
+
+[18.78, 17.92, 18.04, 18.03, 17.82, 17.72, 18.29, 17.74, 17.92, 17.72]
+[53.0]
+14.505996465682983
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1598, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.48720407485962, 'TIME_S_1KI': 6.562705929198761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.8178126811981, 'W': 53.0}
+[18.78, 17.92, 18.04, 18.03, 17.82, 17.72, 18.29, 17.74, 17.92, 17.72, 18.42, 18.18, 17.97, 17.99, 17.82, 17.72, 17.94, 18.12, 18.4, 18.76]
+324.4599999999999
+16.222999999999995
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1598, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.48720407485962, 'TIME_S_1KI': 6.562705929198761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.8178126811981, 'W': 53.0, 'J_1KI': 481.1125235802241, 'W_1KI': 33.16645807259074, 'W_D': 36.777, 'J_D': 533.4870320184231, 'W_D_1KI': 23.01439299123905, 'J_D_1KI': 14.40199811717087}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json
new file mode 100644
index 0000000..54da32a
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 37.58896088600159, "TIME_S_1KI": 37.58896088600159, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2465.35528427124, "W": 53.12, "J_1KI": 2465.35528427124, "W_1KI": 53.12, "W_D": 36.98599999999999, "J_D": 1716.5593099408145, "W_D_1KI": 36.98599999999999, "J_D_1KI": 36.98599999999999}
diff --git a/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output
new file mode 100644
index 0000000..7c7841e
--- /dev/null
+++ b/pytorch/output_389000+_1core/xeon_4216_1_csr_10_10_10_test1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 37.58896088600159}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.9581, 0.4048, 0.0262,  ..., 0.9819, 0.7450, 0.5527])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 37.58896088600159 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.9581, 0.4048, 0.0262,  ..., 0.9819, 0.7450, 0.5527])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 37.58896088600159 seconds
+
+[18.09, 17.76, 17.72, 17.96, 17.74, 17.95, 17.93, 17.84, 17.96, 18.58]
+[53.12]
+46.41105580329895
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 37.58896088600159, 'TIME_S_1KI': 37.58896088600159, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2465.35528427124, 'W': 53.12}
+[18.09, 17.76, 17.72, 17.96, 17.74, 17.95, 17.93, 17.84, 17.96, 18.58, 18.32, 17.63, 17.7, 18.87, 17.96, 17.88, 17.96, 17.6, 17.83, 17.79]
+322.68000000000006
+16.134000000000004
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 37.58896088600159, 'TIME_S_1KI': 37.58896088600159, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2465.35528427124, 'W': 53.12, 'J_1KI': 2465.35528427124, 'W_1KI': 53.12, 'W_D': 36.98599999999999, 'J_D': 1716.5593099408145, 'W_D_1KI': 36.98599999999999, 'J_D_1KI': 36.98599999999999}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..5fe67db
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 30.323144912719727, "TIME_S_1KI": 30.323144912719727, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2708.072883968354, "W": 77.40203729318671, "J_1KI": 2708.072883968354, "W_1KI": 77.40203729318671, "W_D": 53.61603729318671, "J_D": 1875.8697033972746, "W_D_1KI": 53.61603729318671, "J_D_1KI": 53.61603729318671}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..c77cfff
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_amazon0312.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/amazon0312.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 30.323144912719727}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.9519, 0.7886, 0.4122,  ..., 0.0191, 0.4041, 0.8787])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 30.323144912719727 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.9519, 0.7886, 0.4122,  ..., 0.0191, 0.4041, 0.8787])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 30.323144912719727 seconds
+
+[26.56, 26.36, 26.4, 26.4, 26.28, 26.16, 26.4, 26.4, 26.6, 26.56]
+[26.56, 26.64, 26.44, 29.44, 30.6, 36.2, 50.64, 63.2, 76.56, 90.36, 96.08, 94.84, 94.28, 93.0, 94.08, 94.08, 94.76, 95.56, 97.52, 96.08, 95.76, 91.48, 89.72, 90.12, 89.92, 89.6, 90.64, 91.72, 90.48, 91.12, 89.6, 89.6, 90.12, 90.48]
+34.98710083961487
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 30.323144912719727, 'TIME_S_1KI': 30.323144912719727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2708.072883968354, 'W': 77.40203729318671}
+[26.56, 26.36, 26.4, 26.4, 26.28, 26.16, 26.4, 26.4, 26.6, 26.56, 26.8, 26.84, 26.76, 26.72, 26.52, 26.44, 26.32, 26.16, 26.0, 26.0]
+475.72
+23.786
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 30.323144912719727, 'TIME_S_1KI': 30.323144912719727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2708.072883968354, 'W': 77.40203729318671, 'J_1KI': 2708.072883968354, 'W_1KI': 77.40203729318671, 'W_D': 53.61603729318671, 'J_D': 1875.8697033972746, 'W_D_1KI': 53.61603729318671, 'J_D_1KI': 53.61603729318671}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..8890b72
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 20.392087936401367, "TIME_S_1KI": 20.392087936401367, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1566.7498482894898, "W": 69.10901709242208, "J_1KI": 1566.7498482894898, "W_1KI": 69.10901709242208, "W_D": 45.48401709242208, "J_D": 1031.1545421612263, "W_D_1KI": 45.48401709242208, "J_D_1KI": 45.48401709242208}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..05b62a7
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_darcy003.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/darcy003.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 20.392087936401367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.5706, 0.0924, 0.8150,  ..., 0.7995, 0.0048, 0.8110])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 20.392087936401367 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.5706, 0.0924, 0.8150,  ..., 0.7995, 0.0048, 0.8110])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 20.392087936401367 seconds
+
+[26.52, 26.44, 26.28, 26.32, 26.32, 26.12, 26.16, 26.12, 26.12, 26.08]
+[26.52, 26.68, 29.52, 30.56, 30.56, 36.56, 50.04, 66.72, 78.32, 93.24, 95.96, 93.96, 92.08, 92.12, 90.56, 91.2, 90.64, 89.92, 88.64, 89.44, 89.44, 91.68]
+22.670700788497925
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 20.392087936401367, 'TIME_S_1KI': 20.392087936401367, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1566.7498482894898, 'W': 69.10901709242208}
+[26.52, 26.44, 26.28, 26.32, 26.32, 26.12, 26.16, 26.12, 26.12, 26.08, 26.48, 26.28, 26.28, 26.4, 26.2, 26.0, 26.28, 26.28, 26.28, 26.16]
+472.5
+23.625
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 20.392087936401367, 'TIME_S_1KI': 20.392087936401367, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1566.7498482894898, 'W': 69.10901709242208, 'J_1KI': 1566.7498482894898, 'W_1KI': 69.10901709242208, 'W_D': 45.48401709242208, 'J_D': 1031.1545421612263, 'W_D_1KI': 45.48401709242208, 'J_D_1KI': 45.48401709242208}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..1f7ee40
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 21.815975189208984, "TIME_S_1KI": 21.815975189208984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1916.1918925571438, "W": 74.64919082171829, "J_1KI": 1916.1918925571438, "W_1KI": 74.64919082171829, "W_D": 51.65619082171829, "J_D": 1325.9778567373746, "W_D_1KI": 51.65619082171829, "J_D_1KI": 51.65619082171829}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..256e384
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_helm2d03.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/helm2d03.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 21.815975189208984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9058, 0.4925, 0.9859,  ..., 0.1438, 0.2004, 0.4986])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 21.815975189208984 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      14,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9058, 0.4925, 0.9859,  ..., 0.1438, 0.2004, 0.4986])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 21.815975189208984 seconds
+
+[25.36, 25.4, 25.56, 25.56, 25.6, 25.52, 25.52, 25.28, 25.36, 25.16]
+[25.0, 24.96, 25.32, 27.72, 29.24, 40.8, 57.48, 70.72, 86.32, 97.72, 97.72, 96.24, 97.0, 95.72, 95.32, 93.32, 95.8, 96.36, 95.36, 95.72, 94.88, 94.16, 93.92, 92.68, 93.4]
+25.669292211532593
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 21.815975189208984, 'TIME_S_1KI': 21.815975189208984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.1918925571438, 'W': 74.64919082171829}
+[25.36, 25.4, 25.56, 25.56, 25.6, 25.52, 25.52, 25.28, 25.36, 25.16, 25.44, 25.44, 25.28, 25.52, 25.88, 25.84, 25.68, 25.88, 25.8, 25.52]
+459.86
+22.993000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 21.815975189208984, 'TIME_S_1KI': 21.815975189208984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1916.1918925571438, 'W': 74.64919082171829, 'J_1KI': 1916.1918925571438, 'W_1KI': 74.64919082171829, 'W_D': 51.65619082171829, 'J_D': 1325.9778567373746, 'W_D_1KI': 51.65619082171829, 'J_D_1KI': 51.65619082171829}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json
new file mode 100644
index 0000000..6674d3c
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 11.040250062942505, "TIME_S_1KI": 11.040250062942505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1365.697071151733, "W": 66.06671490426629, "J_1KI": 1365.697071151733, "W_1KI": 66.06671490426629, "W_D": 44.15271490426629, "J_D": 912.7021604681013, "W_D_1KI": 44.15271490426629, "J_D_1KI": 44.15271490426629}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output
new file mode 100644
index 0000000..8666779
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_language.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/language.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 11.040250062942505}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.4838, 0.0445, 0.9105,  ..., 0.8272, 0.1700, 0.2253])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 11.040250062942505 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.4838, 0.0445, 0.9105,  ..., 0.8272, 0.1700, 0.2253])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 11.040250062942505 seconds
+
+[24.56, 24.52, 24.08, 24.04, 23.92, 23.84, 23.84, 23.84, 24.04, 24.12]
+[24.28, 24.56, 24.56, 28.8, 30.88, 44.64, 59.0, 72.44, 83.72, 90.96, 88.6, 88.6, 85.88, 86.24, 86.16, 87.16, 86.96, 88.8, 89.32, 90.72]
+20.67148447036743
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 11.040250062942505, 'TIME_S_1KI': 11.040250062942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.697071151733, 'W': 66.06671490426629}
+[24.56, 24.52, 24.08, 24.04, 23.92, 23.84, 23.84, 23.84, 24.04, 24.12, 24.64, 24.56, 24.68, 24.52, 24.56, 24.64, 24.8, 24.72, 24.72, 24.6]
+438.28
+21.913999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 11.040250062942505, 'TIME_S_1KI': 11.040250062942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.697071151733, 'W': 66.06671490426629, 'J_1KI': 1365.697071151733, 'W_1KI': 66.06671490426629, 'W_D': 44.15271490426629, 'J_D': 912.7021604681013, 'W_D_1KI': 44.15271490426629, 'J_D_1KI': 44.15271490426629}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..44a4ce9
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 47.31629490852356, "TIME_S_1KI": 47.31629490852356, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3588.3839783859257, "W": 77.04772778103, "J_1KI": 3588.3839783859257, "W_1KI": 77.04772778103, "W_D": 53.199727781030006, "J_D": 2477.698646305085, "W_D_1KI": 53.199727781030006, "J_D_1KI": 53.199727781030006}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..1934d42
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_marine1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/marine1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 47.31629490852356}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      18,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.9557, 0.7622, 0.1453,  ..., 0.2002, 0.7167, 0.8732])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 47.31629490852356 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      18,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.9557, 0.7622, 0.1453,  ..., 0.2002, 0.7167, 0.8732])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 47.31629490852356 seconds
+
+[26.68, 26.68, 26.56, 26.48, 26.24, 26.08, 26.16, 25.8, 26.04, 26.04]
+[26.4, 26.6, 26.72, 27.8, 29.68, 36.68, 47.12, 59.56, 71.44, 82.04, 90.0, 90.2, 89.8, 91.64, 91.36, 89.28, 89.28, 88.72, 90.36, 90.56, 88.08, 89.32, 91.24, 89.92, 91.16, 94.4, 94.0, 92.04, 91.28, 91.0, 89.92, 89.76, 89.76, 89.8, 90.52, 90.56, 92.08, 91.72, 90.16, 89.0, 89.52, 88.96, 88.76, 89.0, 87.92]
+46.57352113723755
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 47.31629490852356, 'TIME_S_1KI': 47.31629490852356, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3588.3839783859257, 'W': 77.04772778103}
+[26.68, 26.68, 26.56, 26.48, 26.24, 26.08, 26.16, 25.8, 26.04, 26.04, 26.76, 26.48, 26.44, 26.48, 26.72, 26.72, 26.92, 26.92, 27.0, 27.0]
+476.9599999999999
+23.847999999999995
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 47.31629490852356, 'TIME_S_1KI': 47.31629490852356, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3588.3839783859257, 'W': 77.04772778103, 'J_1KI': 3588.3839783859257, 'W_1KI': 77.04772778103, 'W_D': 53.199727781030006, 'J_D': 2477.698646305085, 'W_D_1KI': 53.199727781030006, 'J_D_1KI': 53.199727781030006}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..2a1d024
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 26.970608472824097, "TIME_S_1KI": 26.970608472824097, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1533.6805934524534, "W": 70.7905728448904, "J_1KI": 1533.6805934524534, "W_1KI": 70.7905728448904, "W_D": 46.71357284489041, "J_D": 1012.0514249830246, "W_D_1KI": 46.71357284489041, "J_D_1KI": 46.71357284489041}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..34cf597
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_mario002.output
@@ -0,0 +1,49 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/mario002.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 26.970608472824097}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0717, 0.4634, 0.0880,  ..., 0.8346, 0.7497, 0.2295])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 26.970608472824097 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       7,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0717, 0.4634, 0.0880,  ..., 0.8346, 0.7497, 0.2295])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 26.970608472824097 seconds
+
+[27.04, 26.84, 26.96, 26.8, 26.8, 27.0, 26.88, 26.6, 26.44, 26.44]
+[26.12, 26.4, 26.44, 27.4, 29.4, 37.08, 54.84, 69.0, 83.6, 95.56, 98.68, 97.52, 97.96, 97.76, 97.76, 97.8, 98.4, 95.2, 93.56, 91.64, 89.8]
+21.665040016174316
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 26.970608472824097, 'TIME_S_1KI': 26.970608472824097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1533.6805934524534, 'W': 70.7905728448904}
+[27.04, 26.84, 26.96, 26.8, 26.8, 27.0, 26.88, 26.6, 26.44, 26.44, 26.88, 26.64, 26.8, 27.08, 27.0, 26.92, 26.68, 26.44, 26.32, 26.32]
+481.53999999999996
+24.076999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 26.970608472824097, 'TIME_S_1KI': 26.970608472824097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1533.6805934524534, 'W': 70.7905728448904, 'J_1KI': 1533.6805934524534, 'W_1KI': 70.7905728448904, 'W_D': 46.71357284489041, 'J_D': 1012.0514249830246, 'W_D_1KI': 46.71357284489041, 'J_D_1KI': 46.71357284489041}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json
new file mode 100644
index 0000000..a64dfeb
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 92.92496466636658, "TIME_S_1KI": 92.92496466636658, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8599.791397418978, "W": 85.10134779265783, "J_1KI": 8599.791397418978, "W_1KI": 85.10134779265783, "W_D": 60.408347792657835, "J_D": 6104.4766405498995, "W_D_1KI": 60.408347792657835, "J_D_1KI": 60.408347792657835}
diff --git a/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output
new file mode 100644
index 0000000..6caaca3
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/altra_max_csr_10_10_10_test1.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/389000+_cols/test1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 92.92496466636658}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.1407, 0.4697, 0.2203,  ..., 0.3353, 0.2584, 0.9591])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 92.92496466636658 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.1407, 0.4697, 0.2203,  ..., 0.3353, 0.2584, 0.9591])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 92.92496466636658 seconds
+
+[27.6, 27.24, 27.2, 27.24, 27.04, 26.92, 26.76, 26.64, 26.72, 26.64]
+[26.8, 26.88, 30.16, 31.04, 31.04, 35.8, 41.52, 47.96, 57.12, 73.0, 77.8, 92.16, 95.08, 96.56, 94.84, 94.48, 94.88, 92.8, 91.28, 91.28, 91.72, 91.6, 89.08, 89.24, 90.12, 89.76, 89.48, 89.84, 90.24, 88.68, 88.6, 90.24, 88.36, 90.44, 90.44, 93.28, 93.6, 95.92, 95.52, 91.44, 91.84, 89.96, 91.0, 91.04, 90.84, 90.68, 88.92, 93.0, 95.6, 97.32, 97.32, 98.32, 96.56, 92.48, 91.04, 92.0, 91.72, 94.24, 92.8, 92.52, 91.64, 93.04, 93.56, 98.0, 99.0, 97.0, 97.0, 94.36, 93.2, 93.0, 91.36, 92.44, 95.16, 96.28, 96.76, 96.4, 97.92, 92.76, 93.72, 94.64, 95.76, 96.08, 96.08, 99.8, 99.2, 97.52, 97.16, 96.92, 97.2, 99.08, 99.0, 97.56, 97.8, 97.16, 97.4, 98.52, 99.92, 98.48]
+101.05352759361267
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 92.92496466636658, 'TIME_S_1KI': 92.92496466636658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8599.791397418978, 'W': 85.10134779265783}
+[27.6, 27.24, 27.2, 27.24, 27.04, 26.92, 26.76, 26.64, 26.72, 26.64, 27.96, 27.76, 27.8, 27.8, 27.96, 28.08, 28.04, 27.96, 27.76, 27.68]
+493.86
+24.693
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 92.92496466636658, 'TIME_S_1KI': 92.92496466636658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8599.791397418978, 'W': 85.10134779265783, 'J_1KI': 8599.791397418978, 'W_1KI': 85.10134779265783, 'W_D': 60.408347792657835, 'J_D': 6104.4766405498995, 'W_D_1KI': 60.408347792657835, 'J_D_1KI': 60.408347792657835}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..9573140
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19947, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.311090230941772, "TIME_S_1KI": 0.5169243611040143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1933.8912408947945, "W": 148.79, "J_1KI": 96.951483475951, "W_1KI": 7.459267057702912, "W_D": 113.0615, "J_D": 1469.5116911917924, "W_D_1KI": 5.668095452950318, "J_D_1KI": 0.28415779079311765}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..2ce455e
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_amazon0312.output
@@ -0,0 +1,93 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 0.5623607635498047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.4201, 0.2453, 0.6690,  ..., 0.5318, 0.2145, 0.3171])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 0.5623607635498047 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '18671', '-m', 'matrices/389000+_cols/amazon0312.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 9.828076839447021}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.8027, 0.1127, 0.8893,  ..., 0.1900, 0.1655, 0.6380])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 9.828076839447021 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19947', '-m', 'matrices/389000+_cols/amazon0312.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.311090230941772}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.8053, 0.3778, 0.3769,  ..., 0.3826, 0.6227, 0.7489])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.311090230941772 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.8053, 0.3778, 0.3769,  ..., 0.3826, 0.6227, 0.7489])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.311090230941772 seconds
+
+[40.28, 39.83, 40.54, 39.37, 39.82, 39.28, 40.53, 39.29, 39.59, 39.21]
+[148.79]
+12.997454404830933
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19947, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.311090230941772, 'TIME_S_1KI': 0.5169243611040143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1933.8912408947945, 'W': 148.79}
+[40.28, 39.83, 40.54, 39.37, 39.82, 39.28, 40.53, 39.29, 39.59, 39.21, 39.98, 39.27, 40.58, 39.91, 39.77, 39.38, 39.3, 39.5, 39.26, 39.23]
+714.5699999999999
+35.7285
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19947, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.311090230941772, 'TIME_S_1KI': 0.5169243611040143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1933.8912408947945, 'W': 148.79, 'J_1KI': 96.951483475951, 'W_1KI': 7.459267057702912, 'W_D': 113.0615, 'J_D': 1469.5116911917924, 'W_D_1KI': 5.668095452950318, 'J_D_1KI': 0.28415779079311765}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..c65cfe6
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28421, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 11.00617504119873, "TIME_S_1KI": 0.38725502414407414, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1901.812058868408, "W": 140.38, "J_1KI": 66.91573339672806, "W_1KI": 4.9393054431582275, "W_D": 104.9495, "J_D": 1421.813824420929, "W_D_1KI": 3.6926744308785757, "J_D_1KI": 0.12992767428586524}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..6b3958e
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_darcy003.output
@@ -0,0 +1,93 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/darcy003.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.4240763187408447}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1700, 0.2845, 0.1174,  ..., 0.4107, 0.2054, 0.6347])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.4240763187408447 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24759', '-m', 'matrices/389000+_cols/darcy003.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 9.146852016448975}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0166, 0.8822, 0.7788,  ..., 0.7058, 0.7278, 0.9607])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 9.146852016448975 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '28421', '-m', 'matrices/389000+_cols/darcy003.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 11.00617504119873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0083, 0.7190, 0.2772,  ..., 0.4887, 0.1977, 0.6043])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 11.00617504119873 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0083, 0.7190, 0.2772,  ..., 0.4887, 0.1977, 0.6043])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 11.00617504119873 seconds
+
+[40.16, 39.59, 39.19, 39.3, 39.1, 39.19, 39.25, 39.14, 39.05, 39.19]
+[140.38]
+13.547599792480469
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28421, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 11.00617504119873, 'TIME_S_1KI': 0.38725502414407414, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.812058868408, 'W': 140.38}
+[40.16, 39.59, 39.19, 39.3, 39.1, 39.19, 39.25, 39.14, 39.05, 39.19, 41.13, 39.98, 39.37, 39.35, 39.22, 39.22, 39.21, 39.28, 39.09, 39.68]
+708.61
+35.4305
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28421, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 11.00617504119873, 'TIME_S_1KI': 0.38725502414407414, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.812058868408, 'W': 140.38, 'J_1KI': 66.91573339672806, 'W_1KI': 4.9393054431582275, 'W_D': 104.9495, 'J_D': 1421.813824420929, 'W_D_1KI': 3.6926744308785757, 'J_D_1KI': 0.12992767428586524}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..f538d2a
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 30516, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.102073907852173, "TIME_S_1KI": 0.3310418766500253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1905.9418558597565, "W": 149.24, "J_1KI": 62.45713251604917, "W_1KI": 4.89054922008127, "W_D": 113.06250000000001, "J_D": 1443.9195328205826, "W_D_1KI": 3.7050235941801026, "J_D_1KI": 0.12141249161686009}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..e5c9a6c
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_helm2d03.output
@@ -0,0 +1,97 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.3914318084716797}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.2679, 0.5598, 0.6944,  ..., 0.1144, 0.4933, 0.2716])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 0.3914318084716797 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26824', '-m', 'matrices/389000+_cols/helm2d03.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 9.229604244232178}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.3693, 0.3607, 0.3383,  ..., 0.8476, 0.1262, 0.7740])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 9.229604244232178 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30516', '-m', 'matrices/389000+_cols/helm2d03.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.102073907852173}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9962, 0.9854, 0.1629,  ..., 0.5690, 0.4270, 0.4262])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.102073907852173 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.9962, 0.9854, 0.1629,  ..., 0.5690, 0.4270, 0.4262])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.102073907852173 seconds
+
+[40.11, 39.71, 39.79, 39.24, 41.91, 43.96, 39.7, 39.6, 39.18, 40.31]
+[149.24]
+12.77098536491394
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30516, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.102073907852173, 'TIME_S_1KI': 0.3310418766500253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1905.9418558597565, 'W': 149.24}
+[40.11, 39.71, 39.79, 39.24, 41.91, 43.96, 39.7, 39.6, 39.18, 40.31, 39.88, 39.67, 44.75, 39.75, 39.19, 39.36, 39.18, 39.31, 39.57, 39.06]
+723.55
+36.177499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 30516, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.102073907852173, 'TIME_S_1KI': 0.3310418766500253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1905.9418558597565, 'W': 149.24, 'J_1KI': 62.45713251604917, 'W_1KI': 4.89054922008127, 'W_D': 113.06250000000001, 'J_D': 1443.9195328205826, 'W_D_1KI': 3.7050235941801026, 'J_D_1KI': 0.12141249161686009}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json
new file mode 100644
index 0000000..0278cd1
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 31490, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.491079330444336, "TIME_S_1KI": 0.33315590125259875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1840.929758644104, "W": 139.7, "J_1KI": 58.46077353585595, "W_1KI": 4.436328993331216, "W_D": 104.24499999999999, "J_D": 1373.713118753433, "W_D_1KI": 3.310416005080978, "J_D_1KI": 0.10512594490571539}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output
new file mode 100644
index 0000000..47af1b0
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_language.output
@@ -0,0 +1,93 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.366832971572876}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.4489, 0.3341, 0.6321,  ..., 0.9617, 0.3616, 0.1355])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 0.366832971572876 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '28623', '-m', 'matrices/389000+_cols/language.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 9.543859958648682}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.6001, 0.9333, 0.1063,  ..., 0.8124, 0.4889, 0.2196])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 9.543859958648682 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31490', '-m', 'matrices/389000+_cols/language.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.491079330444336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.8719, 0.5932, 0.6007,  ..., 0.1184, 0.6398, 0.5112])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.491079330444336 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.8719, 0.5932, 0.6007,  ..., 0.1184, 0.6398, 0.5112])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.491079330444336 seconds
+
+[40.31, 39.1, 39.47, 38.93, 39.95, 39.02, 39.07, 38.88, 39.52, 38.92]
+[139.7]
+13.177736282348633
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31490, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.491079330444336, 'TIME_S_1KI': 0.33315590125259875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1840.929758644104, 'W': 139.7}
+[40.31, 39.1, 39.47, 38.93, 39.95, 39.02, 39.07, 38.88, 39.52, 38.92, 40.13, 40.06, 39.2, 39.24, 39.63, 39.8, 39.04, 39.77, 39.24, 39.0]
+709.1
+35.455
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 31490, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.491079330444336, 'TIME_S_1KI': 0.33315590125259875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1840.929758644104, 'W': 139.7, 'J_1KI': 58.46077353585595, 'W_1KI': 4.436328993331216, 'W_D': 104.24499999999999, 'J_D': 1373.713118753433, 'W_D_1KI': 3.310416005080978, 'J_D_1KI': 0.10512594490571539}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..d48619d
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19504, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.365571975708008, "TIME_S_1KI": 0.5314587764411407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2136.8143803691864, "W": 158.23, "J_1KI": 109.55775124944557, "W_1KI": 8.112694831829367, "W_D": 121.8915, "J_D": 1646.0817167716025, "W_D_1KI": 6.249564191960623, "J_D_1KI": 0.3204247432301386}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..5ccf432
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_marine1.output
@@ -0,0 +1,97 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 0.6027348041534424}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.5488, 0.1373, 0.1334,  ..., 0.6361, 0.1287, 0.1871])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 0.6027348041534424 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '17420', '-m', 'matrices/389000+_cols/marine1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 9.3778395652771}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.5314, 0.3776, 0.6404,  ..., 0.2799, 0.5486, 0.5791])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 9.3778395652771 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '19504', '-m', 'matrices/389000+_cols/marine1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.365571975708008}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.7619, 0.3106, 0.0542,  ..., 0.9939, 0.8451, 0.6161])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.365571975708008 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.7619, 0.3106, 0.0542,  ..., 0.9939, 0.8451, 0.6161])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.365571975708008 seconds
+
+[40.92, 39.6, 39.57, 39.38, 39.87, 44.96, 39.72, 39.88, 39.49, 39.37]
+[158.23]
+13.504483222961426
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19504, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.365571975708008, 'TIME_S_1KI': 0.5314587764411407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2136.8143803691864, 'W': 158.23}
+[40.92, 39.6, 39.57, 39.38, 39.87, 44.96, 39.72, 39.88, 39.49, 39.37, 40.1, 41.46, 44.99, 39.34, 39.95, 39.5, 40.61, 39.29, 39.33, 39.27]
+726.77
+36.338499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19504, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.365571975708008, 'TIME_S_1KI': 0.5314587764411407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2136.8143803691864, 'W': 158.23, 'J_1KI': 109.55775124944557, 'W_1KI': 8.112694831829367, 'W_D': 121.8915, 'J_D': 1646.0817167716025, 'W_D_1KI': 6.249564191960623, 'J_D_1KI': 0.3204247432301386}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..69c4b0e
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 24972, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.226792573928833, "TIME_S_1KI": 0.4095303769793702, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1665.3517652535438, "W": 140.07, "J_1KI": 66.68876202360819, "W_1KI": 5.609082172032676, "W_D": 104.13924999999999, "J_D": 1238.155806523025, "W_D_1KI": 4.170240669549895, "J_D_1KI": 0.16699666304460578}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..0369c8f
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_mario002.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.4204576015472412}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.4700, 0.6519, 0.6666,  ..., 0.7659, 0.1482, 0.5452])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.4204576015472412 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24972', '-m', 'matrices/389000+_cols/mario002.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.226792573928833}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0699, 0.8434, 0.7786,  ..., 0.4343, 0.2465, 0.4017])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.226792573928833 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.0699, 0.8434, 0.7786,  ..., 0.4343, 0.2465, 0.4017])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.226792573928833 seconds
+
+[40.64, 39.1, 39.09, 39.17, 39.18, 39.36, 39.46, 39.42, 38.91, 38.87]
+[140.07]
+11.889425039291382
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 24972, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.226792573928833, 'TIME_S_1KI': 0.4095303769793702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.3517652535438, 'W': 140.07}
+[40.64, 39.1, 39.09, 39.17, 39.18, 39.36, 39.46, 39.42, 38.91, 38.87, 39.88, 39.14, 39.13, 39.09, 39.22, 39.35, 39.38, 39.37, 40.66, 59.78]
+718.615
+35.93075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 24972, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.226792573928833, 'TIME_S_1KI': 0.4095303769793702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.3517652535438, 'W': 140.07, 'J_1KI': 66.68876202360819, 'W_1KI': 5.609082172032676, 'W_D': 104.13924999999999, 'J_D': 1238.155806523025, 'W_D_1KI': 4.170240669549895, 'J_D_1KI': 0.16699666304460578}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json
new file mode 100644
index 0000000..f7d6df9
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2723, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 11.063719511032104, "TIME_S_1KI": 4.063062618814581, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1680.215021328926, "W": 125.38, "J_1KI": 617.045545842426, "W_1KI": 46.04480352552332, "W_D": 89.84949999999999, "J_D": 1204.071459235072, "W_D_1KI": 32.99651120088138, "J_D_1KI": 12.117705178436056}
diff --git a/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output
new file mode 100644
index 0000000..a60431b
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/epyc_7313p_max_csr_10_10_10_test1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 3.8556222915649414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.7510, 0.1721, 0.0746,  ..., 0.5838, 0.0016, 0.2497])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 3.8556222915649414 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '2723', '-m', 'matrices/389000+_cols/test1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 11.063719511032104}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3646, 0.9778, 0.6678,  ..., 0.8764, 0.3618, 0.1561])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 11.063719511032104 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.3646, 0.9778, 0.6678,  ..., 0.8764, 0.3618, 0.1561])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 11.063719511032104 seconds
+
+[40.25, 40.25, 39.32, 39.94, 39.64, 39.88, 39.53, 39.13, 39.13, 39.17]
+[125.38]
+13.400981187820435
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2723, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 11.063719511032104, 'TIME_S_1KI': 4.063062618814581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1680.215021328926, 'W': 125.38}
+[40.25, 40.25, 39.32, 39.94, 39.64, 39.88, 39.53, 39.13, 39.13, 39.17, 40.93, 39.54, 39.73, 39.16, 39.13, 39.04, 39.13, 39.33, 39.0, 39.11]
+710.61
+35.5305
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2723, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 11.063719511032104, 'TIME_S_1KI': 4.063062618814581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1680.215021328926, 'W': 125.38, 'J_1KI': 617.045545842426, 'W_1KI': 46.04480352552332, 'W_D': 89.84949999999999, 'J_D': 1204.071459235072, 'W_D_1KI': 32.99651120088138, 'J_D_1KI': 12.117705178436056}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json
new file mode 100644
index 0000000..17b55ec
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8225, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.766162395477295, "TIME_S_1KI": 1.3089559143437441, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.989917767048, "W": 89.05, "J_1KI": 158.17506598991466, "W_1KI": 10.826747720364741, "W_D": 72.6925, "J_D": 1062.012460384965, "W_D_1KI": 8.837993920972645, "J_D_1KI": 1.0745281362884673}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output
new file mode 100644
index 0000000..81fbe8a
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_amazon0312.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/amazon0312.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 1.2765758037567139}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.8946, 0.9409, 0.1159,  ..., 0.4084, 0.7142, 0.8849])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 1.2765758037567139 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '8225', '-m', 'matrices/389000+_cols/amazon0312.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "amazon0312", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400727, 400727], "MATRIX_ROWS": 400727, "MATRIX_SIZE": 160582128529, "MATRIX_NNZ": 3200440, "MATRIX_DENSITY": 1.9930237750099465e-05, "TIME_S": 10.766162395477295}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.2056, 0.8237, 0.5206,  ..., 0.1956, 0.5378, 0.6984])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.766162395477295 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 3200428,
+                            3200438, 3200440]),
+       col_indices=tensor([     1,      2,      3,  ..., 400724,   6009,
+                           400707]),
+       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(400727, 400727),
+       nnz=3200440, layout=torch.sparse_csr)
+tensor([0.2056, 0.8237, 0.5206,  ..., 0.1956, 0.5378, 0.6984])
+Matrix Type: SuiteSparse
+Matrix: amazon0312
+Matrix Format: csr
+Shape: torch.Size([400727, 400727])
+Rows: 400727
+Size: 160582128529
+NNZ: 3200440
+Density: 1.9930237750099465e-05
+Time: 10.766162395477295 seconds
+
+[17.94, 17.59, 17.86, 17.65, 17.7, 17.49, 18.22, 21.41, 17.9, 17.57]
+[89.05]
+14.609656572341919
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8225, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.766162395477295, 'TIME_S_1KI': 1.3089559143437441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.989917767048, 'W': 89.05}
+[17.94, 17.59, 17.86, 17.65, 17.7, 17.49, 18.22, 21.41, 17.9, 17.57, 18.25, 17.56, 21.38, 17.98, 17.81, 17.52, 18.04, 17.55, 17.82, 17.58]
+327.15
+16.357499999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8225, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'amazon0312', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400727, 400727], 'MATRIX_ROWS': 400727, 'MATRIX_SIZE': 160582128529, 'MATRIX_NNZ': 3200440, 'MATRIX_DENSITY': 1.9930237750099465e-05, 'TIME_S': 10.766162395477295, 'TIME_S_1KI': 1.3089559143437441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.989917767048, 'W': 89.05, 'J_1KI': 158.17506598991466, 'W_1KI': 10.826747720364741, 'W_D': 72.6925, 'J_D': 1062.012460384965, 'W_D_1KI': 8.837993920972645, 'J_D_1KI': 1.0745281362884673}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.json
new file mode 100644
index 0000000..f6c6715
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13645, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.674118757247925, "TIME_S_1KI": 0.7822732691277335, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.4411267971993, "W": 89.73, "J_1KI": 94.93888800272622, "W_1KI": 6.576035177720778, "W_D": 73.57575, "J_D": 1062.2205782341362, "W_D_1KI": 5.392139978013925, "J_D_1KI": 0.3951733219504525}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.output
new file mode 100644
index 0000000..528c9fd
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_darcy003.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/darcy003.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.7694816589355469}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.3795, 0.4102, 0.1265,  ..., 0.4337, 0.0977, 0.4509])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.7694816589355469 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13645', '-m', 'matrices/389000+_cols/darcy003.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "darcy003", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.674118757247925}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9376, 0.2227, 0.1775,  ..., 0.8669, 0.9587, 0.8096])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.674118757247925 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.9376, 0.2227, 0.1775,  ..., 0.8669, 0.9587, 0.8096])
+Matrix Type: SuiteSparse
+Matrix: darcy003
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.674118757247925 seconds
+
+[18.58, 18.09, 18.09, 17.74, 17.81, 17.92, 17.82, 18.14, 18.2, 17.95]
+[89.73]
+14.437101602554321
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13645, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.674118757247925, 'TIME_S_1KI': 0.7822732691277335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.4411267971993, 'W': 89.73}
+[18.58, 18.09, 18.09, 17.74, 17.81, 17.92, 17.82, 18.14, 18.2, 17.95, 18.29, 17.6, 18.31, 17.77, 18.5, 17.52, 18.0, 17.51, 17.78, 17.75]
+323.085
+16.154249999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13645, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'darcy003', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.674118757247925, 'TIME_S_1KI': 0.7822732691277335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.4411267971993, 'W': 89.73, 'J_1KI': 94.93888800272622, 'W_1KI': 6.576035177720778, 'W_D': 73.57575, 'J_D': 1062.2205782341362, 'W_D_1KI': 5.392139978013925, 'J_D_1KI': 0.3951733219504525}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json
new file mode 100644
index 0000000..d957b86
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 12273, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.708429336547852, "TIME_S_1KI": 0.8725192973639575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.9024416732789, "W": 89.76, "J_1KI": 104.53046864444545, "W_1KI": 7.313615252994378, "W_D": 73.62625, "J_D": 1052.309446259439, "W_D_1KI": 5.999042613867839, "J_D_1KI": 0.48880001742588114}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output
new file mode 100644
index 0000000..61cc75a
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_helm2d03.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/helm2d03.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 0.855471134185791}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.2921, 0.7848, 0.3759,  ..., 0.1614, 0.6217, 0.8908])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 0.855471134185791 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '12273', '-m', 'matrices/389000+_cols/helm2d03.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "helm2d03", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392257, 392257], "MATRIX_ROWS": 392257, "MATRIX_SIZE": 153865554049, "MATRIX_NNZ": 2741935, "MATRIX_DENSITY": 1.7820330332848923e-05, "TIME_S": 10.708429336547852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.0043, 0.4903, 0.3538,  ..., 0.3528, 0.3455, 0.3342])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.708429336547852 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2741921,
+                            2741928, 2741935]),
+       col_indices=tensor([     0,  98273, 133833,  ..., 392252, 392254,
+                           392256]),
+       values=tensor([ 3.4808, -0.6217, -0.5806,  ..., -0.6940, -0.7602,
+                       3.5476]), size=(392257, 392257), nnz=2741935,
+       layout=torch.sparse_csr)
+tensor([0.0043, 0.4903, 0.3538,  ..., 0.3528, 0.3455, 0.3342])
+Matrix Type: SuiteSparse
+Matrix: helm2d03
+Matrix Format: csr
+Shape: torch.Size([392257, 392257])
+Rows: 392257
+Size: 153865554049
+NNZ: 2741935
+Density: 1.7820330332848923e-05
+Time: 10.708429336547852 seconds
+
+[18.04, 18.17, 18.08, 18.71, 18.3, 17.59, 17.7, 17.54, 17.78, 17.75]
+[89.76]
+14.292585134506226
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12273, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.708429336547852, 'TIME_S_1KI': 0.8725192973639575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.9024416732789, 'W': 89.76}
+[18.04, 18.17, 18.08, 18.71, 18.3, 17.59, 17.7, 17.54, 17.78, 17.75, 18.01, 17.48, 17.78, 17.28, 17.51, 17.82, 17.71, 17.91, 19.53, 17.77]
+322.67499999999995
+16.13375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 12273, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'helm2d03', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392257, 392257], 'MATRIX_ROWS': 392257, 'MATRIX_SIZE': 153865554049, 'MATRIX_NNZ': 2741935, 'MATRIX_DENSITY': 1.7820330332848923e-05, 'TIME_S': 10.708429336547852, 'TIME_S_1KI': 0.8725192973639575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.9024416732789, 'W': 89.76, 'J_1KI': 104.53046864444545, 'W_1KI': 7.313615252994378, 'W_D': 73.62625, 'J_D': 1052.309446259439, 'W_D_1KI': 5.999042613867839, 'J_D_1KI': 0.48880001742588114}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json
new file mode 100644
index 0000000..51eb669
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13367, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.647616863250732, "TIME_S_1KI": 0.7965599508678636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.2656374621392, "W": 89.59, "J_1KI": 95.32921653790224, "W_1KI": 6.702326625271191, "W_D": 73.54725, "J_D": 1046.0847572813632, "W_D_1KI": 5.502150819181567, "J_D_1KI": 0.4116219659745318}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output
new file mode 100644
index 0000000..467022c
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_language.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/language.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 0.785470724105835}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.6209, 0.8638, 0.8531,  ..., 0.3261, 0.5283, 0.1956])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 0.785470724105835 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13367', '-m', 'matrices/389000+_cols/language.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "language", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [399130, 399130], "MATRIX_ROWS": 399130, "MATRIX_SIZE": 159304756900, "MATRIX_NNZ": 1216334, "MATRIX_DENSITY": 7.635264782228233e-06, "TIME_S": 10.647616863250732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.6025, 0.9576, 0.6646,  ..., 0.8829, 0.1742, 0.2421])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.647616863250732 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 1216330,
+                            1216332, 1216334]),
+       col_indices=tensor([     0,      0,      1,  ..., 399128, 399125,
+                           399129]),
+       values=tensor([ 1., -1.,  1.,  ...,  1., -1.,  1.]),
+       size=(399130, 399130), nnz=1216334, layout=torch.sparse_csr)
+tensor([0.6025, 0.9576, 0.6646,  ..., 0.8829, 0.1742, 0.2421])
+Matrix Type: SuiteSparse
+Matrix: language
+Matrix Format: csr
+Shape: torch.Size([399130, 399130])
+Rows: 399130
+Size: 159304756900
+NNZ: 1216334
+Density: 7.635264782228233e-06
+Time: 10.647616863250732 seconds
+
+[18.88, 17.79, 17.82, 17.8, 17.88, 17.82, 17.92, 17.54, 17.65, 17.87]
+[89.59]
+14.223302125930786
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.647616863250732, 'TIME_S_1KI': 0.7965599508678636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.2656374621392, 'W': 89.59}
+[18.88, 17.79, 17.82, 17.8, 17.88, 17.82, 17.92, 17.54, 17.65, 17.87, 18.16, 17.58, 17.67, 17.88, 17.53, 17.55, 17.46, 18.42, 17.89, 18.4]
+320.855
+16.04275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'language', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [399130, 399130], 'MATRIX_ROWS': 399130, 'MATRIX_SIZE': 159304756900, 'MATRIX_NNZ': 1216334, 'MATRIX_DENSITY': 7.635264782228233e-06, 'TIME_S': 10.647616863250732, 'TIME_S_1KI': 0.7965599508678636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.2656374621392, 'W': 89.59, 'J_1KI': 95.32921653790224, 'W_1KI': 6.702326625271191, 'W_D': 73.54725, 'J_D': 1046.0847572813632, 'W_D_1KI': 5.502150819181567, 'J_D_1KI': 0.4116219659745318}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json
new file mode 100644
index 0000000..735b097
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5913, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.544729232788086, "TIME_S_1KI": 1.783312909316436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1297.8479092025757, "W": 89.36, "J_1KI": 219.49059854601313, "W_1KI": 15.112464062235752, "W_D": 73.2365, "J_D": 1063.6732139918804, "W_D_1KI": 12.385675629967869, "J_D_1KI": 2.094651721624872}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output
new file mode 100644
index 0000000..41b8f99
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_marine1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/marine1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 1.7754507064819336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.1497, 0.9722, 0.3177,  ..., 0.4783, 0.5290, 0.8575])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 1.7754507064819336 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '5913', '-m', 'matrices/389000+_cols/marine1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "marine1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [400320, 400320], "MATRIX_ROWS": 400320, "MATRIX_SIZE": 160256102400, "MATRIX_NNZ": 6226538, "MATRIX_DENSITY": 3.885367175883594e-05, "TIME_S": 10.544729232788086}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.3921, 0.4534, 0.1657,  ..., 0.0431, 0.7782, 0.5404])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.544729232788086 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 6226522,
+                            6226531, 6226538]),
+       col_indices=tensor([     0,      1,  10383,  ..., 400315, 400318,
+                           400319]),
+       values=tensor([ 6.2373e+03, -1.8964e+00, -5.7529e+00,  ...,
+                      -6.8099e-01, -6.4187e-01,  1.7595e+01]),
+       size=(400320, 400320), nnz=6226538, layout=torch.sparse_csr)
+tensor([0.3921, 0.4534, 0.1657,  ..., 0.0431, 0.7782, 0.5404])
+Matrix Type: SuiteSparse
+Matrix: marine1
+Matrix Format: csr
+Shape: torch.Size([400320, 400320])
+Rows: 400320
+Size: 160256102400
+NNZ: 6226538
+Density: 3.885367175883594e-05
+Time: 10.544729232788086 seconds
+
+[18.11, 17.77, 17.64, 17.87, 17.55, 17.7, 17.81, 17.95, 18.63, 17.7]
+[89.36]
+14.523812770843506
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5913, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.544729232788086, 'TIME_S_1KI': 1.783312909316436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8479092025757, 'W': 89.36}
+[18.11, 17.77, 17.64, 17.87, 17.55, 17.7, 17.81, 17.95, 18.63, 17.7, 18.27, 17.88, 17.72, 17.88, 17.99, 17.97, 17.75, 18.36, 18.01, 17.9]
+322.46999999999997
+16.1235
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5913, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'marine1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [400320, 400320], 'MATRIX_ROWS': 400320, 'MATRIX_SIZE': 160256102400, 'MATRIX_NNZ': 6226538, 'MATRIX_DENSITY': 3.885367175883594e-05, 'TIME_S': 10.544729232788086, 'TIME_S_1KI': 1.783312909316436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8479092025757, 'W': 89.36, 'J_1KI': 219.49059854601313, 'W_1KI': 15.112464062235752, 'W_D': 73.2365, 'J_D': 1063.6732139918804, 'W_D_1KI': 12.385675629967869, 'J_D_1KI': 2.094651721624872}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json
new file mode 100644
index 0000000..6bd73ad
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 13717, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.75633430480957, "TIME_S_1KI": 0.7841608445585456, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.2889261174203, "W": 89.67, "J_1KI": 94.72107065082892, "W_1KI": 6.537143690311293, "W_D": 73.277, "J_D": 1061.7597260968685, "W_D_1KI": 5.342057301159145, "J_D_1KI": 0.3894479333060542}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output
new file mode 100644
index 0000000..c3613ad
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_mario002.output
@@ -0,0 +1,71 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/mario002.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 0.7654633522033691}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.4671, 0.2005, 0.4838,  ..., 0.7591, 0.5753, 0.8077])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 0.7654633522033691 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '13717', '-m', 'matrices/389000+_cols/mario002.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "mario002", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [389874, 389874], "MATRIX_ROWS": 389874, "MATRIX_SIZE": 152001735876, "MATRIX_NNZ": 2101242, "MATRIX_DENSITY": 1.3823802655215408e-05, "TIME_S": 10.75633430480957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1039, 0.6560, 0.4015,  ..., 0.5631, 0.0997, 0.1433])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.75633430480957 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2101236,
+                            2101239, 2101242]),
+       col_indices=tensor([     0,   1027,   1028,  ..., 196606, 233926,
+                           234127]),
+       values=tensor([ 1.,  0.,  0.,  ..., -1., -1., -1.]),
+       size=(389874, 389874), nnz=2101242, layout=torch.sparse_csr)
+tensor([0.1039, 0.6560, 0.4015,  ..., 0.5631, 0.0997, 0.1433])
+Matrix Type: SuiteSparse
+Matrix: mario002
+Matrix Format: csr
+Shape: torch.Size([389874, 389874])
+Rows: 389874
+Size: 152001735876
+NNZ: 2101242
+Density: 1.3823802655215408e-05
+Time: 10.75633430480957 seconds
+
+[17.93, 17.78, 18.16, 17.81, 21.75, 18.47, 18.11, 17.7, 18.15, 18.27]
+[89.67]
+14.489672422409058
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13717, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.75633430480957, 'TIME_S_1KI': 0.7841608445585456, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.2889261174203, 'W': 89.67}
+[17.93, 17.78, 18.16, 17.81, 21.75, 18.47, 18.11, 17.7, 18.15, 18.27, 22.42, 17.61, 17.63, 17.67, 17.98, 17.5, 17.66, 18.19, 17.57, 17.62]
+327.86
+16.393
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 13717, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'mario002', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [389874, 389874], 'MATRIX_ROWS': 389874, 'MATRIX_SIZE': 152001735876, 'MATRIX_NNZ': 2101242, 'MATRIX_DENSITY': 1.3823802655215408e-05, 'TIME_S': 10.75633430480957, 'TIME_S_1KI': 0.7841608445585456, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.2889261174203, 'W': 89.67, 'J_1KI': 94.72107065082892, 'W_1KI': 6.537143690311293, 'W_D': 73.277, 'J_D': 1061.7597260968685, 'W_D_1KI': 5.342057301159145, 'J_D_1KI': 0.3894479333060542}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json
new file mode 100644
index 0000000..54a5eaf
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1798, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.02889084815979, "TIME_S_1KI": 5.577803586295767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.981861114502, "W": 84.07, "J_1KI": 764.1723365486662, "W_1KI": 46.75750834260289, "W_D": 67.9785, "J_D": 1110.9935285568238, "W_D_1KI": 37.80784204671858, "J_D_1KI": 21.027720826873512}
diff --git a/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output
new file mode 100644
index 0000000..1a5d3f5
--- /dev/null
+++ b/pytorch/output_389000+_maxcore/xeon_4216_max_csr_10_10_10_test1.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/389000+_cols/test1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 5.838165521621704}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.7305, 0.5169, 0.5807,  ..., 0.4360, 0.1397, 0.7206])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 5.838165521621704 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1798', '-m', 'matrices/389000+_cols/test1.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "test1", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [392908, 392908], "MATRIX_ROWS": 392908, "MATRIX_SIZE": 154376696464, "MATRIX_NNZ": 12968200, "MATRIX_DENSITY": 8.400361127706946e-05, "TIME_S": 10.02889084815979}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.9760, 0.7722, 0.6451,  ..., 0.4999, 0.6092, 0.1757])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 10.02889084815979 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       48,  ..., 12968181,
+                            12968191, 12968200]),
+       col_indices=tensor([     0,      1,      8,  ..., 392905, 392906,
+                           392907]),
+       values=tensor([1.0000e+00, 0.0000e+00, 0.0000e+00,  ...,
+                      0.0000e+00, 0.0000e+00, 2.1156e-17]),
+       size=(392908, 392908), nnz=12968200, layout=torch.sparse_csr)
+tensor([0.9760, 0.7722, 0.6451,  ..., 0.4999, 0.6092, 0.1757])
+Matrix Type: SuiteSparse
+Matrix: test1
+Matrix Format: csr
+Shape: torch.Size([392908, 392908])
+Rows: 392908
+Size: 154376696464
+NNZ: 12968200
+Density: 8.400361127706946e-05
+Time: 10.02889084815979 seconds
+
+[17.94, 17.76, 17.81, 17.53, 17.58, 17.64, 18.37, 17.65, 18.76, 17.74]
+[84.07]
+16.343307495117188
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1798, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.02889084815979, 'TIME_S_1KI': 5.577803586295767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.981861114502, 'W': 84.07}
+[17.94, 17.76, 17.81, 17.53, 17.58, 17.64, 18.37, 17.65, 18.76, 17.74, 18.31, 17.88, 17.67, 17.64, 18.57, 17.92, 17.84, 17.7, 17.61, 17.81]
+321.83000000000004
+16.091500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1798, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'test1', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [392908, 392908], 'MATRIX_ROWS': 392908, 'MATRIX_SIZE': 154376696464, 'MATRIX_NNZ': 12968200, 'MATRIX_DENSITY': 8.400361127706946e-05, 'TIME_S': 10.02889084815979, 'TIME_S_1KI': 5.577803586295767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.981861114502, 'W': 84.07, 'J_1KI': 764.1723365486662, 'W_1KI': 46.75750834260289, 'W_D': 67.9785, 'J_D': 1110.9935285568238, 'W_D_1KI': 37.80784204671858, 'J_D_1KI': 21.027720826873512}
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 8bab18f..980d684 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": 1770, "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": 10.45119595527649, "TIME_S_1KI": 5.904630483207056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 518.2880183124543, "W": 35.449367856062224, "J_1KI": 292.81808944206455, "W_1KI": 20.027891444102952, "W_D": 16.922367856062227, "J_D": 247.4137349045278, "W_D_1KI": 9.560659805684875, "J_D_1KI": 5.401502715076201}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1755, "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": 10.253487825393677, "TIME_S_1KI": 5.842443205352523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 467.2838370990753, "W": 31.934101368331916, "J_1KI": 266.2585966376497, "W_1KI": 18.196069155744684, "W_D": 16.879101368331916, "J_D": 246.98773149132728, "W_D_1KI": 9.617721577397104, "J_D_1KI": 5.480183234984104}
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 9bd0023..365e810 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 +1,14 @@
 ['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 1000 -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": 5.932083368301392}
+{"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": 5.980836629867554}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  999982,
-                             999993, 1000000]),
-       col_indices=tensor([37897, 46445, 60989,  ..., 76977, 92294, 96477]),
-       values=tensor([0.9469, 0.5853, 0.3833,  ..., 0.6631, 0.6410, 0.8148]),
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999980,
+                             999989, 1000000]),
+       col_indices=tensor([ 6100, 13265, 27848,  ..., 84407, 91090, 94721]),
+       values=tensor([0.4400, 0.3445, 0.5606,  ..., 0.5861, 0.7102, 0.2795]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0925, 0.0591, 0.1895,  ..., 0.1208, 0.2736, 0.9441])
+tensor([0.6757, 0.5029, 0.1898,  ..., 0.2612, 0.6123, 0.0844])
 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: 5.932083368301392 seconds
+Time: 5.980836629867554 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 1770 -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": 10.45119595527649}
+['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 1755 -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": 10.253487825393677}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      15,  ...,  999984,
-                             999991, 1000000]),
-       col_indices=tensor([ 6148, 23043, 28153,  ..., 62723, 86562, 96964]),
-       values=tensor([0.4836, 0.5090, 0.9509,  ..., 0.7452, 0.4499, 0.9407]),
+tensor(crow_indices=tensor([      0,       5,      18,  ...,  999983,
+                             999994, 1000000]),
+       col_indices=tensor([22305, 51740, 53616,  ..., 72974, 76091, 88145]),
+       values=tensor([0.7756, 0.0657, 0.7358,  ..., 0.9841, 0.0331, 0.5251]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2481, 0.7173, 0.6398,  ..., 0.9063, 0.5779, 0.5048])
+tensor([0.4750, 0.6821, 0.2847,  ..., 0.3502, 0.4038, 0.5877])
 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: 10.45119595527649 seconds
+Time: 10.253487825393677 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      15,  ...,  999984,
-                             999991, 1000000]),
-       col_indices=tensor([ 6148, 23043, 28153,  ..., 62723, 86562, 96964]),
-       values=tensor([0.4836, 0.5090, 0.9509,  ..., 0.7452, 0.4499, 0.9407]),
+tensor(crow_indices=tensor([      0,       5,      18,  ...,  999983,
+                             999994, 1000000]),
+       col_indices=tensor([22305, 51740, 53616,  ..., 72974, 76091, 88145]),
+       values=tensor([0.7756, 0.0657, 0.7358,  ..., 0.9841, 0.0331, 0.5251]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2481, 0.7173, 0.6398,  ..., 0.9063, 0.5779, 0.5048])
+tensor([0.4750, 0.6821, 0.2847,  ..., 0.3502, 0.4038, 0.5877])
 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: 10.45119595527649 seconds
+Time: 10.253487825393677 seconds
 
-[20.2, 20.56, 20.52, 20.56, 20.56, 20.6, 20.4, 20.64, 20.8, 20.88]
-[20.88, 21.44, 21.2, 22.44, 23.6, 27.28, 34.76, 40.76, 46.84, 51.32, 53.0, 52.92, 53.08, 52.84]
-14.62051510810852
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1770, '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': 10.45119595527649, 'TIME_S_1KI': 5.904630483207056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 518.2880183124543, 'W': 35.449367856062224}
-[20.2, 20.56, 20.52, 20.56, 20.56, 20.6, 20.4, 20.64, 20.8, 20.88, 20.56, 20.52, 20.64, 20.64, 20.52, 20.52, 20.6, 20.68, 20.6, 20.72]
-370.53999999999996
-18.526999999999997
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1770, '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': 10.45119595527649, 'TIME_S_1KI': 5.904630483207056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 518.2880183124543, 'W': 35.449367856062224, 'J_1KI': 292.81808944206455, 'W_1KI': 20.027891444102952, 'W_D': 16.922367856062227, 'J_D': 247.4137349045278, 'W_D_1KI': 9.560659805684875, 'J_D_1KI': 5.401502715076201}
+[16.48, 16.16, 16.52, 16.48, 16.48, 16.48, 16.72, 16.72, 16.84, 17.12]
+[17.08, 17.16, 17.96, 19.72, 22.2, 27.16, 34.52, 39.08, 43.72, 45.96, 46.32, 46.32, 46.2, 46.28]
+14.632753610610962
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1755, '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': 10.253487825393677, 'TIME_S_1KI': 5.842443205352523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 467.2838370990753, 'W': 31.934101368331916}
+[16.48, 16.16, 16.52, 16.48, 16.48, 16.48, 16.72, 16.72, 16.84, 17.12, 16.72, 16.8, 16.96, 17.0, 17.04, 16.88, 16.8, 16.88, 16.76, 16.84]
+301.1
+15.055000000000001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1755, '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': 10.253487825393677, 'TIME_S_1KI': 5.842443205352523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 467.2838370990753, 'W': 31.934101368331916, 'J_1KI': 266.2585966376497, 'W_1KI': 18.196069155744684, 'W_D': 16.879101368331916, 'J_D': 246.98773149132728, 'W_D_1KI': 9.617721577397104, 'J_D_1KI': 5.480183234984104}
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
new file mode 100644
index 0000000..0a97dfa
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 57.53653693199158, "TIME_S_1KI": 57.53653693199158, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2684.526071929932, "W": 41.311972802980506, "J_1KI": 2684.526071929932, "W_1KI": 41.311972802980506, "W_D": 26.003972802980506, "J_D": 1689.784782156945, "W_D_1KI": 26.003972802980506, "J_D_1KI": 26.003972802980506}
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
new file mode 100644
index 0000000..01b19d4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.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 1000 -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": 57.53653693199158}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      190,  ...,  9999802,
+                             9999900, 10000000]),
+       col_indices=tensor([  766,  3080,  3658,  ..., 98863, 99077, 99078]),
+       values=tensor([0.0329, 0.7493, 0.2063,  ..., 0.1215, 0.3807, 0.7288]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3516, 0.3347, 0.9443,  ..., 0.8917, 0.2195, 0.8723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 57.53653693199158 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      190,  ...,  9999802,
+                             9999900, 10000000]),
+       col_indices=tensor([  766,  3080,  3658,  ..., 98863, 99077, 99078]),
+       values=tensor([0.0329, 0.7493, 0.2063,  ..., 0.1215, 0.3807, 0.7288]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3516, 0.3347, 0.9443,  ..., 0.8917, 0.2195, 0.8723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 57.53653693199158 seconds
+
+[17.2, 17.2, 16.8, 16.8, 17.0, 16.88, 16.88, 17.0, 17.0, 16.6]
+[16.88, 16.84, 17.36, 19.48, 20.16, 21.92, 24.16, 24.88, 27.6, 33.04, 37.56, 43.08, 47.0, 46.96, 47.68, 47.76, 47.76, 47.48, 47.4, 46.92, 47.28, 47.04, 47.56, 48.36, 48.0, 47.68, 47.44, 46.16, 45.68, 46.04, 46.32, 47.44, 47.76, 47.84, 47.64, 47.36, 47.08, 46.96, 46.96, 47.16, 46.68, 46.24, 46.2, 46.44, 46.56, 47.0, 48.08, 48.0, 48.12, 48.44, 48.48, 48.2, 47.64, 47.32, 47.2, 47.2, 47.56, 47.52, 47.68, 47.8, 47.8, 48.0]
+64.98179316520691
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 57.53653693199158, 'TIME_S_1KI': 57.53653693199158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2684.526071929932, 'W': 41.311972802980506}
+[17.2, 17.2, 16.8, 16.8, 17.0, 16.88, 16.88, 17.0, 17.0, 16.6, 17.12, 17.12, 17.04, 17.0, 17.0, 16.96, 16.88, 16.92, 17.32, 17.8]
+306.16
+15.308000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 57.53653693199158, 'TIME_S_1KI': 57.53653693199158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2684.526071929932, 'W': 41.311972802980506, 'J_1KI': 2684.526071929932, 'W_1KI': 41.311972802980506, 'W_D': 26.003972802980506, 'J_D': 1689.784782156945, 'W_D_1KI': 26.003972802980506, 'J_D_1KI': 26.003972802980506}
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 f3aace9..9a23625 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": 11801, "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": 10.278456687927246, "TIME_S_1KI": 0.8709818394989616, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 437.5742044067384, "W": 32.19958635623455, "J_1KI": 37.0794173719802, "W_1KI": 2.728547271945983, "W_D": 13.391586356234548, "J_D": 181.9841000671388, "W_D_1KI": 1.1347840315426274, "J_D_1KI": 0.09615998911470446}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11928, "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": 10.779903888702393, "TIME_S_1KI": 0.9037478109240772, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 428.71311614990236, "W": 29.263266081724595, "J_1KI": 35.94174347333186, "W_1KI": 2.4533254595677896, "W_D": 14.015266081724594, "J_D": 205.326650100708, "W_D_1KI": 1.1749887727803985, "J_D_1KI": 0.09850677169520444}
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 d71dde9..0a26541 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 1000 -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": 1.063995361328125}
+{"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": 0.8802759647369385}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99997,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([67343, 31299, 81155,  ..., 33224, 88457, 24576]),
-       values=tensor([0.5842, 0.8218, 0.6188,  ..., 0.3932, 0.6826, 0.0146]),
+       col_indices=tensor([50190, 32056, 73796,  ..., 55938, 31334, 37461]),
+       values=tensor([0.0722, 0.7116, 0.8310,  ..., 0.7930, 0.8115, 0.4149]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9733, 0.2979, 0.3395,  ..., 0.2786, 0.7488, 0.6423])
+tensor([0.5168, 0.3496, 0.0063,  ..., 0.9888, 0.0960, 0.5324])
 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: 1.063995361328125 seconds
+Time: 0.8802759647369385 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 9868 -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": 8.779469966888428}
+['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 11928 -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": 10.779903888702393}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99997,  99999,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99995,  99996,
                             100000]),
-       col_indices=tensor([14435, 22527, 43950,  ...,  8583,  8872, 18967]),
-       values=tensor([0.6873, 0.0224, 0.4938,  ..., 0.6581, 0.7037, 0.6316]),
+       col_indices=tensor([15079, 22431, 71484,  ..., 38240, 57604, 63673]),
+       values=tensor([0.6856, 0.2309, 0.0261,  ..., 0.6883, 0.7108, 0.1151]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2290, 0.1645, 0.1242,  ..., 0.3445, 0.2954, 0.7059])
+tensor([0.6131, 0.6051, 0.4027,  ..., 0.3545, 0.9505, 0.4978])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 8.779469966888428 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 11801 -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": 10.278456687927246}
+Time: 10.779903888702393 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99995,  99996,
                             100000]),
-       col_indices=tensor([88946, 66534, 50450,  ..., 63020, 21924, 98776]),
-       values=tensor([0.0165, 0.3102, 0.5959,  ..., 0.2885, 0.2555, 0.6064]),
+       col_indices=tensor([15079, 22431, 71484,  ..., 38240, 57604, 63673]),
+       values=tensor([0.6856, 0.2309, 0.0261,  ..., 0.6883, 0.7108, 0.1151]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1483, 0.9193, 0.9702,  ..., 0.6151, 0.3023, 0.2526])
+tensor([0.6131, 0.6051, 0.4027,  ..., 0.3545, 0.9505, 0.4978])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,30 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.278456687927246 seconds
+Time: 10.779903888702393 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 100000, 100000,
-                            100000]),
-       col_indices=tensor([88946, 66534, 50450,  ..., 63020, 21924, 98776]),
-       values=tensor([0.0165, 0.3102, 0.5959,  ..., 0.2885, 0.2555, 0.6064]),
-       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1483, 0.9193, 0.9702,  ..., 0.6151, 0.3023, 0.2526])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 100000
-Density: 1e-05
-Time: 10.278456687927246 seconds
-
-[20.52, 20.48, 20.76, 20.96, 20.96, 21.16, 21.32, 21.28, 21.28, 21.2]
-[21.36, 21.64, 21.64, 23.32, 23.96, 29.24, 34.28, 39.64, 43.16, 45.96, 45.88, 46.84, 47.12]
-13.589435577392578
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11801, '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': 10.278456687927246, 'TIME_S_1KI': 0.8709818394989616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 437.5742044067384, 'W': 32.19958635623455}
-[20.52, 20.48, 20.76, 20.96, 20.96, 21.16, 21.32, 21.28, 21.28, 21.2, 21.04, 20.92, 20.64, 20.52, 20.52, 20.4, 20.72, 20.96, 21.24, 21.32]
-376.16
-18.808
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11801, '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': 10.278456687927246, 'TIME_S_1KI': 0.8709818394989616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 437.5742044067384, 'W': 32.19958635623455, 'J_1KI': 37.0794173719802, 'W_1KI': 2.728547271945983, 'W_D': 13.391586356234548, 'J_D': 181.9841000671388, 'W_D_1KI': 1.1347840315426274, 'J_D_1KI': 0.09615998911470446}
+[17.04, 17.24, 17.0, 17.0, 16.8, 16.64, 16.64, 16.68, 16.92, 16.84]
+[16.64, 16.52, 16.72, 17.8, 20.04, 25.4, 30.88, 34.96, 39.88, 41.96, 42.28, 42.44, 42.56, 42.56]
+14.650214195251465
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11928, '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': 10.779903888702393, 'TIME_S_1KI': 0.9037478109240772, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 428.71311614990236, 'W': 29.263266081724595}
+[17.04, 17.24, 17.0, 17.0, 16.8, 16.64, 16.64, 16.68, 16.92, 16.84, 16.84, 16.84, 17.0, 17.0, 16.92, 17.0, 17.16, 17.0, 17.16, 17.2]
+304.96000000000004
+15.248000000000001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11928, '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': 10.779903888702393, 'TIME_S_1KI': 0.9037478109240772, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 428.71311614990236, 'W': 29.263266081724595, 'J_1KI': 35.94174347333186, 'W_1KI': 2.4533254595677896, 'W_D': 14.015266081724594, 'J_D': 205.326650100708, 'W_D_1KI': 1.1749887727803985, 'J_D_1KI': 0.09850677169520444}
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 ddd2b32..47534f3 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": 33464, "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": 10.751937627792358, "TIME_S_1KI": 0.321298638172136, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.9264199829101, "W": 23.099563679174377, "J_1KI": 9.799379033675296, "W_1KI": 0.6902810088206544, "W_D": 4.345563679174376, "J_D": 61.690565237998875, "W_D_1KI": 0.12985786753449605, "J_D_1KI": 0.0038805243705025113}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32824, "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": 10.612937211990356, "TIME_S_1KI": 0.3233285770165232, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 275.6484677696228, "W": 19.42651909848855, "J_1KI": 8.397771989081855, "W_1KI": 0.5918388709020398, "W_D": 4.498519098488551, "J_D": 63.83078154373167, "W_D_1KI": 0.13704969225227123, "J_D_1KI": 0.004175289186335341}
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 c550e8c..0d66d25 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 1000 -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": 0.358994722366333}
+{"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": 0.3622722625732422}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([4769, 2640, 4731,  ..., 7727, 9096,  344]),
-       values=tensor([0.5549, 0.8764, 0.0270,  ..., 0.0575, 0.5131, 0.9423]),
+tensor(crow_indices=tensor([    0,     2,     4,  ...,  9997, 10000, 10000]),
+       col_indices=tensor([2430, 5032, 1477,  ...,  758, 3153, 4599]),
+       values=tensor([0.8038, 0.4543, 0.3152,  ..., 0.6785, 0.4391, 0.0535]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2724, 0.3491, 0.1026,  ..., 0.4580, 0.8295, 0.5142])
+tensor([0.9594, 0.1900, 0.3074,  ..., 0.8950, 0.9459, 0.6732])
 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: 0.358994722366333 seconds
+Time: 0.3622722625732422 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 29248 -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": 9.177036046981812}
+['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 28983 -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": 9.27123761177063}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9997,  9998, 10000]),
-       col_indices=tensor([8143, 7461, 5162,  ..., 7740, 5053, 9684]),
-       values=tensor([0.7267, 0.3238, 0.0105,  ..., 0.5150, 0.5465, 0.0983]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([1532, 2817,  884,  ..., 2356, 6175, 1948]),
+       values=tensor([0.3809, 0.2852, 0.7235,  ..., 0.6592, 0.2563, 0.7726]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8883, 0.6326, 0.2674,  ..., 0.1564, 0.2088, 0.8392])
+tensor([0.6771, 0.1497, 0.5070,  ..., 0.8092, 0.9643, 0.7887])
 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: 9.177036046981812 seconds
+Time: 9.27123761177063 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 33464 -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": 10.751937627792358}
+['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 32824 -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": 10.612937211990356}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     5,  ...,  9994,  9997, 10000]),
-       col_indices=tensor([1608, 4931, 8613,  ..., 2107, 3637, 7054]),
-       values=tensor([0.4097, 0.1049, 0.8257,  ..., 0.2263, 0.1754, 0.1229]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([3350, 4490, 6839,  ..., 6784, 8596, 8737]),
+       values=tensor([0.3991, 0.5600, 0.2439,  ..., 0.8859, 0.9485, 0.6345]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9092, 0.1064, 0.7261,  ..., 0.1695, 0.8231, 0.3389])
+tensor([0.2861, 0.2741, 0.4038,  ..., 0.8389, 0.9796, 0.7969])
 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: 10.751937627792358 seconds
+Time: 10.612937211990356 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     5,  ...,  9994,  9997, 10000]),
-       col_indices=tensor([1608, 4931, 8613,  ..., 2107, 3637, 7054]),
-       values=tensor([0.4097, 0.1049, 0.8257,  ..., 0.2263, 0.1754, 0.1229]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([3350, 4490, 6839,  ..., 6784, 8596, 8737]),
+       values=tensor([0.3991, 0.5600, 0.2439,  ..., 0.8859, 0.9485, 0.6345]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9092, 0.1064, 0.7261,  ..., 0.1695, 0.8231, 0.3389])
+tensor([0.2861, 0.2741, 0.4038,  ..., 0.8389, 0.9796, 0.7969])
 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: 10.751937627792358 seconds
+Time: 10.612937211990356 seconds
 
-[20.16, 20.16, 20.16, 20.32, 20.36, 20.88, 21.6, 22.28, 22.28, 22.28]
-[21.52, 20.68, 23.48, 24.56, 27.0, 27.0, 27.6, 28.4, 25.44, 25.08, 23.88, 23.84, 23.72, 23.68]
-14.196217060089111
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 33464, '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': 10.751937627792358, 'TIME_S_1KI': 0.321298638172136, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.9264199829101, 'W': 23.099563679174377}
-[20.16, 20.16, 20.16, 20.32, 20.36, 20.88, 21.6, 22.28, 22.28, 22.28, 20.28, 20.68, 20.64, 20.84, 20.84, 20.88, 20.6, 20.6, 20.48, 20.24]
-375.08000000000004
-18.754
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 33464, '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': 10.751937627792358, 'TIME_S_1KI': 0.321298638172136, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.9264199829101, 'W': 23.099563679174377, 'J_1KI': 9.799379033675296, 'W_1KI': 0.6902810088206544, 'W_D': 4.345563679174376, 'J_D': 61.690565237998875, 'W_D_1KI': 0.12985786753449605, 'J_D_1KI': 0.0038805243705025113}
+[16.64, 16.84, 16.96, 16.88, 16.84, 16.72, 17.08, 16.96, 16.92, 16.92]
+[17.08, 16.72, 16.76, 21.08, 22.52, 24.76, 25.6, 23.4, 22.04, 20.32, 20.04, 20.0, 20.0, 20.12]
+14.189287662506104
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32824, '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': 10.612937211990356, 'TIME_S_1KI': 0.3233285770165232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.6484677696228, 'W': 19.42651909848855}
+[16.64, 16.84, 16.96, 16.88, 16.84, 16.72, 17.08, 16.96, 16.92, 16.92, 16.36, 16.04, 15.84, 15.92, 16.12, 16.28, 16.36, 16.68, 16.72, 16.88]
+298.56
+14.928
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32824, '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': 10.612937211990356, 'TIME_S_1KI': 0.3233285770165232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.6484677696228, 'W': 19.42651909848855, 'J_1KI': 8.397771989081855, 'W_1KI': 0.5918388709020398, 'W_D': 4.498519098488551, 'J_D': 63.83078154373167, 'W_D_1KI': 0.13704969225227123, 'J_D_1KI': 0.004175289186335341}
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 91eebfb..650d27e 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": 4693, "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": 10.608984231948853, "TIME_S_1KI": 2.260597535041307, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.61959093093867, "W": 23.443356307834602, "J_1KI": 71.08876857680346, "W_1KI": 4.995388090312082, "W_D": 4.929356307834599, "J_D": 70.14907820272437, "W_D_1KI": 1.0503635857307905, "J_D_1KI": 0.223814955408223}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4599, "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": 10.21649432182312, "TIME_S_1KI": 2.2214599525599303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 276.3690100479126, "W": 19.391688491473598, "J_1KI": 60.09328333287945, "W_1KI": 4.2165010853389, "W_D": 4.4646884914736, "J_D": 63.630433167457575, "W_D_1KI": 0.9707954971675582, "J_D_1KI": 0.21108838816428752}
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 bec4359..7a35653 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 1000 -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": 2.2371175289154053}
+{"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": 2.282747268676758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99981,  99991,
+tensor(crow_indices=tensor([     0,     15,     26,  ...,  99983,  99992,
                             100000]),
-       col_indices=tensor([  11,  880, 2486,  ..., 7621, 8410, 9572]),
-       values=tensor([0.7919, 0.7111, 0.9252,  ..., 0.0051, 0.9566, 0.6694]),
+       col_indices=tensor([ 746, 1254, 2691,  ..., 5665, 9904, 9986]),
+       values=tensor([0.7024, 0.2927, 0.8116,  ..., 0.2675, 0.5863, 0.1724]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8227, 0.5043, 0.0669,  ..., 0.5765, 0.9663, 0.4234])
+tensor([0.2042, 0.3555, 0.3767,  ..., 0.6038, 0.4952, 0.0036])
 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: 2.2371175289154053 seconds
+Time: 2.282747268676758 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 4693 -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": 10.608984231948853}
+['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 4599 -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": 10.21649432182312}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     27,  ...,  99982,  99994,
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99989,
                             100000]),
-       col_indices=tensor([ 135, 2132, 2413,  ..., 7244, 7277, 8789]),
-       values=tensor([0.8089, 0.0016, 0.7063,  ..., 0.2204, 0.7876, 0.4440]),
+       col_indices=tensor([5193, 5456, 6247,  ..., 5100, 5946, 8330]),
+       values=tensor([0.7086, 0.0012, 0.4180,  ..., 0.5448, 0.8405, 0.8114]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2483, 0.7850, 0.0043,  ..., 0.4009, 0.1492, 0.4510])
+tensor([0.0495, 0.0946, 0.7654,  ..., 0.8976, 0.3544, 0.9283])
 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: 10.608984231948853 seconds
+Time: 10.21649432182312 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     27,  ...,  99982,  99994,
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99989,
                             100000]),
-       col_indices=tensor([ 135, 2132, 2413,  ..., 7244, 7277, 8789]),
-       values=tensor([0.8089, 0.0016, 0.7063,  ..., 0.2204, 0.7876, 0.4440]),
+       col_indices=tensor([5193, 5456, 6247,  ..., 5100, 5946, 8330]),
+       values=tensor([0.7086, 0.0012, 0.4180,  ..., 0.5448, 0.8405, 0.8114]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2483, 0.7850, 0.0043,  ..., 0.4009, 0.1492, 0.4510])
+tensor([0.0495, 0.0946, 0.7654,  ..., 0.8976, 0.3544, 0.9283])
 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: 10.608984231948853 seconds
+Time: 10.21649432182312 seconds
 
-[20.32, 20.32, 20.36, 20.6, 20.68, 20.44, 20.64, 20.8, 20.88, 20.84]
-[20.84, 20.52, 23.32, 24.96, 27.48, 27.48, 28.36, 28.96, 25.92, 25.2, 24.36, 24.56, 24.48, 24.08]
-14.23088002204895
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4693, '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': 10.608984231948853, 'TIME_S_1KI': 2.260597535041307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.61959093093867, 'W': 23.443356307834602}
-[20.32, 20.32, 20.36, 20.6, 20.68, 20.44, 20.64, 20.8, 20.88, 20.84, 20.68, 20.8, 20.52, 20.64, 20.64, 20.68, 20.4, 20.48, 20.36, 20.24]
-370.28000000000003
-18.514000000000003
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4693, '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': 10.608984231948853, 'TIME_S_1KI': 2.260597535041307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.61959093093867, 'W': 23.443356307834602, 'J_1KI': 71.08876857680346, 'W_1KI': 4.995388090312082, 'W_D': 4.929356307834599, 'J_D': 70.14907820272437, 'W_D_1KI': 1.0503635857307905, 'J_D_1KI': 0.223814955408223}
+[16.44, 16.28, 16.32, 16.56, 16.56, 16.6, 16.76, 16.76, 16.72, 16.68]
+[16.52, 16.48, 16.6, 20.0, 22.08, 24.8, 25.56, 23.6, 23.04, 20.28, 20.28, 20.04, 20.16, 20.2]
+14.251931190490723
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4599, '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': 10.21649432182312, 'TIME_S_1KI': 2.2214599525599303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 276.3690100479126, 'W': 19.391688491473598}
+[16.44, 16.28, 16.32, 16.56, 16.56, 16.6, 16.76, 16.76, 16.72, 16.68, 16.4, 16.48, 16.68, 16.36, 16.44, 16.64, 16.64, 16.8, 16.8, 16.76]
+298.53999999999996
+14.926999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4599, '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': 10.21649432182312, 'TIME_S_1KI': 2.2214599525599303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 276.3690100479126, 'W': 19.391688491473598, 'J_1KI': 60.09328333287945, 'W_1KI': 4.2165010853389, 'W_D': 4.4646884914736, 'J_D': 63.630433167457575, 'W_D_1KI': 0.9707954971675582, 'J_D_1KI': 0.21108838816428752}
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 dd61d28..d799afd 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": 1000, "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": 21.223905086517334, "TIME_S_1KI": 21.223905086517334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 606.5871645927429, "W": 23.902485146880146, "J_1KI": 606.5871645927429, "W_1KI": 23.902485146880146, "W_D": 5.469485146880146, "J_D": 138.80228213262555, "W_D_1KI": 5.469485146880146, "J_D_1KI": 5.469485146880146}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 21.366477489471436, "TIME_S_1KI": 21.366477489471436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 489.4337509441374, "W": 19.31282985940674, "J_1KI": 489.4337509441374, "W_1KI": 19.31282985940674, "W_D": 4.539829859406739, "J_D": 115.05025275492645, "W_D_1KI": 4.539829859406739, "J_D_1KI": 4.539829859406739}
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 dbf2821..7e1dd33 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 1000 -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": 21.223905086517334}
+{"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": 21.366477489471436}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     193,  ...,  999807,
-                             999898, 1000000]),
-       col_indices=tensor([  45,   67,   78,  ..., 9873, 9905, 9941]),
-       values=tensor([0.2793, 0.5501, 0.9236,  ..., 0.0106, 0.8963, 0.7259]),
+tensor(crow_indices=tensor([      0,      93,     201,  ...,  999801,
+                             999899, 1000000]),
+       col_indices=tensor([ 106,  113,  159,  ..., 9934, 9937, 9966]),
+       values=tensor([0.1214, 0.4144, 0.1866,  ..., 0.5194, 0.7412, 0.0565]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2312, 0.2281, 0.2895,  ..., 0.4123, 0.5947, 0.5960])
+tensor([0.4749, 0.5757, 0.5717,  ..., 0.5026, 0.5396, 0.1085])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.223905086517334 seconds
+Time: 21.366477489471436 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     193,  ...,  999807,
-                             999898, 1000000]),
-       col_indices=tensor([  45,   67,   78,  ..., 9873, 9905, 9941]),
-       values=tensor([0.2793, 0.5501, 0.9236,  ..., 0.0106, 0.8963, 0.7259]),
+tensor(crow_indices=tensor([      0,      93,     201,  ...,  999801,
+                             999899, 1000000]),
+       col_indices=tensor([ 106,  113,  159,  ..., 9934, 9937, 9966]),
+       values=tensor([0.1214, 0.4144, 0.1866,  ..., 0.5194, 0.7412, 0.0565]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2312, 0.2281, 0.2895,  ..., 0.4123, 0.5947, 0.5960])
+tensor([0.4749, 0.5757, 0.5717,  ..., 0.5026, 0.5396, 0.1085])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.223905086517334 seconds
+Time: 21.366477489471436 seconds
 
-[20.16, 20.16, 20.16, 20.04, 20.28, 20.72, 20.6, 20.64, 20.6, 20.44]
-[20.44, 20.64, 23.68, 24.76, 27.96, 27.96, 29.28, 30.08, 27.32, 27.04, 23.96, 23.92, 23.72, 23.6, 23.72, 23.92, 24.08, 24.24, 24.24, 24.36, 24.24, 24.12, 24.4, 23.96, 24.12]
-25.377577304840088
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 21.223905086517334, 'TIME_S_1KI': 21.223905086517334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.5871645927429, 'W': 23.902485146880146}
-[20.16, 20.16, 20.16, 20.04, 20.28, 20.72, 20.6, 20.64, 20.6, 20.44, 20.2, 20.32, 20.32, 20.52, 20.52, 20.8, 20.8, 20.72, 20.68, 20.76]
-368.65999999999997
-18.433
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 21.223905086517334, 'TIME_S_1KI': 21.223905086517334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.5871645927429, 'W': 23.902485146880146, 'J_1KI': 606.5871645927429, 'W_1KI': 23.902485146880146, 'W_D': 5.469485146880146, 'J_D': 138.80228213262555, 'W_D_1KI': 5.469485146880146, 'J_D_1KI': 5.469485146880146}
+[16.76, 16.8, 16.72, 16.6, 16.36, 16.12, 16.0, 16.04, 16.28, 16.36]
+[16.48, 16.48, 16.36, 17.76, 18.4, 22.04, 22.96, 22.96, 22.68, 21.88, 20.28, 20.28, 20.36, 20.0, 20.0, 19.8, 19.72, 19.84, 19.96, 20.12, 20.32, 20.36, 20.56, 20.72, 20.6]
+25.34241509437561
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 21.366477489471436, 'TIME_S_1KI': 21.366477489471436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 489.4337509441374, 'W': 19.31282985940674}
+[16.76, 16.8, 16.72, 16.6, 16.36, 16.12, 16.0, 16.04, 16.28, 16.36, 16.6, 16.56, 16.28, 16.28, 16.28, 16.24, 16.56, 16.68, 16.52, 16.56]
+295.46000000000004
+14.773000000000001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 21.366477489471436, 'TIME_S_1KI': 21.366477489471436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 489.4337509441374, 'W': 19.31282985940674, 'J_1KI': 489.4337509441374, 'W_1KI': 19.31282985940674, 'W_D': 4.539829859406739, 'J_D': 115.05025275492645, 'W_D_1KI': 4.539829859406739, 'J_D_1KI': 4.539829859406739}
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 af8ffc0..fda3205 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": 1000, "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": 112.2527105808258, "TIME_S_1KI": 112.2527105808258, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2847.1031341934195, "W": 24.02975891792854, "J_1KI": 2847.1031341934195, "W_1KI": 24.02975891792854, "W_D": 5.456758917928539, "J_D": 646.5298079283226, "W_D_1KI": 5.456758917928539, "J_D_1KI": 5.456758917928539}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 106.60694670677185, "TIME_S_1KI": 106.60694670677185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2300.291395263673, "W": 20.29267619003776, "J_1KI": 2300.291395263673, "W_1KI": 20.29267619003776, "W_D": 5.365676190037762, "J_D": 608.2302134094255, "W_D_1KI": 5.365676190037762, "J_D_1KI": 5.365676190037762}
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 d49413b..b832c55 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 1000 -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": 112.2527105808258}
+{"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": 106.60694670677185}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     493,     999,  ..., 4999078,
-                            4999538, 5000000]),
-       col_indices=tensor([   9,   32,   79,  ..., 9948, 9954, 9975]),
-       values=tensor([0.7230, 0.3394, 0.4856,  ..., 0.5860, 0.3031, 0.1676]),
+tensor(crow_indices=tensor([      0,     513,     983,  ..., 4998990,
+                            4999536, 5000000]),
+       col_indices=tensor([   3,    6,   54,  ..., 9902, 9976, 9979]),
+       values=tensor([0.3821, 0.3276, 0.4096,  ..., 0.9878, 0.3843, 0.9439]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5227, 0.7065, 0.1059,  ..., 0.0574, 0.9985, 0.1783])
+tensor([0.8065, 0.5635, 0.0733,  ..., 0.7202, 0.3714, 0.0072])
 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: 112.2527105808258 seconds
+Time: 106.60694670677185 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     493,     999,  ..., 4999078,
-                            4999538, 5000000]),
-       col_indices=tensor([   9,   32,   79,  ..., 9948, 9954, 9975]),
-       values=tensor([0.7230, 0.3394, 0.4856,  ..., 0.5860, 0.3031, 0.1676]),
+tensor(crow_indices=tensor([      0,     513,     983,  ..., 4998990,
+                            4999536, 5000000]),
+       col_indices=tensor([   3,    6,   54,  ..., 9902, 9976, 9979]),
+       values=tensor([0.3821, 0.3276, 0.4096,  ..., 0.9878, 0.3843, 0.9439]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5227, 0.7065, 0.1059,  ..., 0.0574, 0.9985, 0.1783])
+tensor([0.8065, 0.5635, 0.0733,  ..., 0.7202, 0.3714, 0.0072])
 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: 112.2527105808258 seconds
+Time: 106.60694670677185 seconds
 
-[20.36, 20.76, 20.76, 20.64, 20.76, 20.64, 20.44, 20.2, 20.64, 20.52]
-[20.84, 20.72, 21.32, 21.96, 24.12, 27.04, 27.04, 28.68, 28.56, 28.24, 25.72, 24.44, 24.36, 24.24, 24.24, 24.56, 24.28, 24.4, 24.4, 24.44, 24.56, 24.2, 24.24, 24.04, 24.28, 24.12, 24.12, 24.28, 24.32, 24.24, 24.56, 24.56, 24.6, 24.44, 24.6, 24.6, 24.44, 24.44, 24.44, 24.4, 24.36, 24.36, 24.28, 24.28, 24.32, 24.24, 24.28, 24.08, 24.04, 24.04, 24.2, 24.24, 24.32, 24.6, 24.68, 24.36, 24.36, 24.28, 24.24, 24.08, 24.24, 24.32, 24.36, 24.6, 24.6, 24.64, 24.68, 24.6, 24.6, 24.4, 24.28, 24.4, 24.4, 24.2, 24.32, 24.36, 24.4, 24.44, 24.56, 24.44, 24.44, 24.4, 24.28, 24.4, 24.56, 24.56, 24.64, 24.76, 24.68, 24.44, 24.44, 24.36, 24.32, 24.32, 24.16, 24.24, 24.2, 24.12, 23.8, 23.88, 23.88, 23.76, 24.08, 24.24, 24.4, 24.4, 24.6, 24.52, 24.4, 24.56, 24.48, 24.4, 24.68, 24.72, 24.68, 24.8, 24.8]
-118.48238444328308
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 112.2527105808258, 'TIME_S_1KI': 112.2527105808258, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2847.1031341934195, 'W': 24.02975891792854}
-[20.36, 20.76, 20.76, 20.64, 20.76, 20.64, 20.44, 20.2, 20.64, 20.52, 20.52, 20.56, 20.56, 20.56, 20.8, 20.88, 20.8, 20.8, 20.68, 20.56]
-371.46000000000004
-18.573
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 112.2527105808258, 'TIME_S_1KI': 112.2527105808258, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2847.1031341934195, 'W': 24.02975891792854, 'J_1KI': 2847.1031341934195, 'W_1KI': 24.02975891792854, 'W_D': 5.456758917928539, 'J_D': 646.5298079283226, 'W_D_1KI': 5.456758917928539, 'J_D_1KI': 5.456758917928539}
+[16.56, 16.36, 16.32, 16.32, 16.44, 16.44, 16.32, 16.32, 16.32, 16.04]
+[16.0, 16.2, 16.52, 20.8, 21.96, 24.64, 26.28, 24.4, 23.76, 23.12, 21.52, 21.52, 20.64, 20.52, 20.48, 20.32, 20.28, 20.28, 20.28, 20.6, 20.68, 20.88, 20.8, 20.8, 20.64, 20.6, 20.6, 20.4, 20.32, 20.48, 20.32, 20.16, 20.32, 20.36, 20.24, 20.4, 20.4, 20.56, 20.48, 20.48, 20.84, 20.92, 20.8, 20.68, 20.48, 20.44, 20.28, 20.68, 20.68, 20.56, 20.52, 20.4, 20.24, 20.28, 20.32, 20.32, 20.56, 20.6, 20.56, 20.76, 21.0, 21.0, 21.0, 21.04, 21.0, 20.8, 20.56, 20.4, 20.32, 20.24, 20.32, 20.72, 20.68, 20.68, 20.84, 20.8, 20.56, 20.56, 20.72, 20.8, 20.72, 20.92, 20.92, 20.88, 20.92, 20.92, 20.88, 20.88, 20.68, 20.32, 20.12, 20.08, 20.12, 20.4, 20.48, 20.56, 20.64, 20.52, 20.52, 20.4, 20.32, 20.28, 20.24, 20.24, 20.36, 20.52, 20.32, 20.32, 20.44, 20.44, 20.44, 20.44]
+113.35574340820312
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 106.60694670677185, 'TIME_S_1KI': 106.60694670677185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.291395263673, 'W': 20.29267619003776}
+[16.56, 16.36, 16.32, 16.32, 16.44, 16.44, 16.32, 16.32, 16.32, 16.04, 16.52, 16.72, 16.8, 16.96, 17.08, 17.12, 17.0, 16.64, 16.52, 16.6]
+298.53999999999996
+14.926999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 106.60694670677185, 'TIME_S_1KI': 106.60694670677185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.291395263673, 'W': 20.29267619003776, 'J_1KI': 2300.291395263673, 'W_1KI': 20.29267619003776, 'W_D': 5.365676190037762, 'J_D': 608.2302134094255, 'W_D_1KI': 5.365676190037762, 'J_D_1KI': 5.365676190037762}
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
new file mode 100644
index 0000000..ce0e17e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 215.95656299591064, "TIME_S_1KI": 215.95656299591064, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4458.621953725812, "W": 20.314604170190528, "J_1KI": 4458.621953725812, "W_1KI": 20.314604170190528, "W_D": 5.2026041701905275, "J_D": 1141.8605538864108, "W_D_1KI": 5.2026041701905275, "J_D_1KI": 5.2026041701905275}
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
new file mode 100644
index 0000000..3241974
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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 1000 -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": 215.95656299591064}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      987,     1996,  ...,  9998019,
+                             9999013, 10000000]),
+       col_indices=tensor([  25,   29,   35,  ..., 9989, 9993, 9996]),
+       values=tensor([0.8438, 0.2270, 0.6737,  ..., 0.5218, 0.6879, 0.5182]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6846, 0.7185, 0.0206,  ..., 0.2576, 0.7966, 0.0945])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 215.95656299591064 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      987,     1996,  ...,  9998019,
+                             9999013, 10000000]),
+       col_indices=tensor([  25,   29,   35,  ..., 9989, 9993, 9996]),
+       values=tensor([0.8438, 0.2270, 0.6737,  ..., 0.5218, 0.6879, 0.5182]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6846, 0.7185, 0.0206,  ..., 0.2576, 0.7966, 0.0945])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 215.95656299591064 seconds
+
+[16.56, 16.44, 16.64, 16.6, 16.56, 16.92, 16.84, 16.92, 16.96, 16.72]
+[16.52, 16.4, 17.44, 17.44, 19.24, 21.04, 24.24, 25.48, 25.32, 24.52, 22.6, 22.16, 20.6, 20.6, 21.08, 21.08, 20.92, 21.04, 20.88, 20.64, 20.6, 20.72, 20.52, 20.76, 20.76, 20.52, 20.44, 20.28, 20.28, 20.28, 20.44, 20.68, 20.72, 20.96, 20.68, 20.72, 20.48, 20.4, 20.2, 20.48, 20.48, 20.24, 20.28, 20.28, 20.2, 20.04, 20.16, 20.28, 20.4, 20.56, 20.6, 20.52, 20.56, 20.56, 20.6, 20.64, 20.72, 20.52, 20.4, 20.28, 20.44, 20.56, 20.52, 20.6, 20.56, 20.56, 20.4, 20.48, 20.28, 20.24, 20.36, 20.44, 20.48, 20.52, 20.36, 20.44, 20.36, 20.36, 20.32, 20.52, 20.52, 20.64, 20.56, 20.52, 20.56, 20.64, 20.36, 20.64, 20.64, 20.72, 20.72, 20.64, 20.8, 20.52, 20.36, 20.32, 20.44, 20.4, 20.56, 20.8, 21.08, 20.84, 20.84, 20.84, 20.76, 20.4, 20.36, 20.48, 20.6, 20.56, 20.76, 20.64, 20.68, 20.72, 20.72, 20.56, 20.56, 20.56, 20.8, 20.6, 20.56, 20.44, 20.44, 20.28, 20.48, 20.56, 20.72, 20.72, 20.56, 20.72, 20.76, 20.68, 20.72, 20.6, 20.64, 20.76, 20.88, 21.08, 20.96, 20.96, 20.72, 20.64, 20.52, 20.44, 20.32, 20.48, 20.6, 20.56, 20.6, 20.84, 20.68, 20.68, 20.64, 20.64, 20.6, 20.44, 20.28, 20.4, 20.16, 20.52, 20.76, 20.92, 20.96, 20.68, 20.68, 20.64, 20.24, 20.16, 20.36, 20.56, 20.6, 20.72, 20.48, 20.48, 20.4, 20.24, 20.24, 20.32, 20.44, 20.28, 20.64, 20.8, 20.88, 21.0, 21.16, 20.76, 20.68, 20.4, 20.4, 20.48, 20.48, 20.52, 20.6, 20.56, 20.32, 20.2, 20.04, 20.04, 20.16, 20.36, 20.4, 20.4, 20.4, 20.4, 20.28, 20.36, 20.4, 20.52, 20.8, 21.04, 21.2, 21.04, 20.72, 20.72, 20.6, 20.6, 20.52]
+219.47865271568298
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 215.95656299591064, 'TIME_S_1KI': 215.95656299591064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4458.621953725812, 'W': 20.314604170190528}
+[16.56, 16.44, 16.64, 16.6, 16.56, 16.92, 16.84, 16.92, 16.96, 16.72, 16.8, 17.08, 16.92, 17.2, 17.12, 16.88, 16.68, 16.52, 16.6, 16.64]
+302.24
+15.112
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 215.95656299591064, 'TIME_S_1KI': 215.95656299591064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4458.621953725812, 'W': 20.314604170190528, 'J_1KI': 4458.621953725812, 'W_1KI': 20.314604170190528, 'W_D': 5.2026041701905275, 'J_D': 1141.8605538864108, 'W_D_1KI': 5.2026041701905275, 'J_D_1KI': 5.2026041701905275}
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 b09b822..7258aa7 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": 141369, "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": 10.808244943618774, "TIME_S_1KI": 0.0764541373541496, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.87872554779057, "W": 22.096174468711904, "J_1KI": 2.2273534194044706, "W_1KI": 0.15630141310125914, "W_D": 3.7551744687119033, "J_D": 53.51263643360139, "W_D_1KI": 0.02656292729461129, "J_D_1KI": 0.00018789782268114857}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 141920, "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": 10.197850227355957, "TIME_S_1KI": 0.0718563291104563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 278.53162281036373, "W": 19.575620643059622, "J_1KI": 1.9625959893627658, "W_1KI": 0.1379341928062262, "W_D": 4.584620643059623, "J_D": 65.2322524514198, "W_D_1KI": 0.032304260449969154, "J_D_1KI": 0.00022762303022808028}
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 ea3d934..5a62a56 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 +1,266 @@
 ['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 1000 -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": 0.09768295288085938}
+{"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": 0.08288788795471191}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([9792, 3011, 8315, 6730, 9843, 4902, 9114,  233,  327,
-                           1077, 5110, 4341, 9618, 1148, 4296, 9363, 2120, 5266,
-                           1510, 7695, 5476, 9179, 3305,   25, 5170, 9334, 9520,
-                             18, 2410, 8122, 6722, 5458, 1189, 9940,  135,  139,
-                           2746, 4302,  817, 8119, 9183, 5557, 7078, 7532, 5204,
-                           9640, 2857, 2903, 8250, 2446, 4645, 4964, 6111, 2787,
-                           4305, 8541, 2087, 6834, 9039, 5610,  449, 6263, 9809,
-                           5478, 8383, 5854, 2328, 3230,  867, 3772, 2544,  739,
-                           5716, 4182, 7270, 9111, 2105, 2273, 7055, 6308, 4091,
-                           9837, 2327, 5713, 7469, 8593, 3004, 1329, 1982, 6739,
-                           2484, 7531,  861,  603, 3312, 9947, 2174, 8338,  554,
-                           5146,  610,  333, 2059, 2323, 8214, 7253,  520, 2440,
-                           6747, 6391, 6453, 8692, 6979, 9688, 8514, 2146, 5042,
-                           9573, 4252, 1574, 2537,  436, 6639, 6623, 2518, 4727,
-                           2296, 1644, 9804, 6277, 7729, 2049, 6367, 7493, 7667,
-                           9462, 9071,  774, 5867, 4293, 6321, 3828, 2008, 1939,
-                           2236, 1977, 7507, 5348, 3725,  219, 3205,  807,   73,
-                           2687, 7978, 8256, 1078, 4313, 7128, 6500, 6252, 2583,
-                           9745, 3139,  421,  464, 4788, 8521, 5551, 7352, 6138,
-                           2196, 1230, 8030, 6107, 9447, 1650, 9498, 3057,  208,
-                           6423,  791, 3773, 2437, 1425, 9123, 9688,  777, 6169,
-                           2978, 5480, 8386, 8675, 3317, 9250, 4808, 2626, 3836,
-                           6049, 7965, 4797,  144, 2153, 1800, 6876, 5900, 3203,
-                           8704, 2454, 5563, 7457, 5321,  988, 3129, 3035, 8288,
-                           7117, 7904, 1711, 8667, 7253, 9565, 9574, 6169, 1173,
-                           8859, 7821, 3367, 2539, 5401, 6150, 5503,   32, 4874,
-                            457, 7102, 6703, 9779, 6403, 6731, 8810, 7361, 3381,
-                           6421, 5956,  129, 1166, 5720, 7654,  159, 4181, 7055,
-                           1967, 8488, 1584, 1842, 8508, 2958, 8715, 3789, 8840,
-                           4377, 9219, 4638, 2619, 5440, 3657, 9925, 2551,  995,
-                           1723, 5389, 1404, 4574, 2739, 6959, 3058, 9080, 2031,
-                           8984, 5389, 1349, 5813, 7136,  480, 5283, 5584, 5761,
-                           5503, 9598, 4112, 9769, 1912,  284, 8557, 4577, 3598,
-                           7400, 6471, 9266, 7423,  181,  547, 1708, 2217, 3427,
-                           1505,  745, 7566, 5833,  867, 3999, 5022, 8690, 4305,
-                           5228, 1769, 3228, 1613, 4985, 3045, 8897, 3696, 4082,
-                           9166, 5733, 4104, 7168, 1873, 5017, 8961, 5996, 4553,
-                           7627, 2820, 2130,  305, 5545, 6465, 4590, 1371,  174,
-                           7873, 1810, 2887, 9649, 8279, 8772, 5892, 9213, 6586,
-                           1163, 3158, 5082, 8524, 1930, 9343, 8281, 3120, 8449,
-                            600, 5485, 4322, 6884, 7048, 7308, 2967, 3418, 7490,
-                           3344, 4257, 4605, 6323, 9225, 3126, 8896, 9886, 5796,
-                           3701, 2719,  576, 7498,  987, 2280, 6310, 8362, 3442,
-                           2965, 9718,  459, 4443, 2991, 8486, 7861, 8908, 1116,
-                           4841, 4004, 9555, 9381, 6796, 6359, 5653, 2399, 2081,
-                           2575, 4124, 3953, 9593, 7589,  862, 2954, 8958,  589,
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1182, 0.0531, 0.2619,  ..., 0.1829, 0.3048, 0.7863])
+tensor([0.4940, 0.1637, 0.9163,  ..., 0.2332, 0.8903, 0.4879])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,757 +268,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.09768295288085938 seconds
+Time: 0.08288788795471191 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 107490 -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": 7.983633518218994}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  999, 1000, 1000]),
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8535, 0.9437, 0.0129,  ..., 0.9247, 0.1049, 0.8510])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 7.983633518218994 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 141369 -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": 10.808244943618774}
+['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 126677 -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": 9.372220277786255}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+                      0.5369, 0.1848, 0.5378, 0.5862, 0.0851, 0.9818]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0651, 0.6329, 0.6141,  ..., 0.3243, 0.1158, 0.5219])
+tensor([0.9110, 0.9462, 0.7927,  ..., 0.0987, 0.6084, 0.0709])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +540,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.808244943618774 seconds
+Time: 9.372220277786255 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 141920 -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": 10.197850227355957}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+                      1.9479e-01, 9.4967e-01, 6.5866e-01, 2.6908e-01,
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+                      6.8241e-01, 2.0082e-01, 6.7192e-01, 6.3818e-01,
+                      8.3533e-01, 1.4580e-01, 6.7572e-01, 3.1304e-02,
+                      5.6257e-01, 6.4916e-01, 4.5939e-01, 1.6735e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0651, 0.6329, 0.6141,  ..., 0.3243, 0.1158, 0.5219])
+tensor([0.0747, 0.3561, 0.1255,  ..., 0.0664, 0.4841, 0.3262])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +919,389 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.808244943618774 seconds
+Time: 10.197850227355957 seconds
 
-[20.16, 20.24, 20.2, 20.4, 20.4, 20.4, 20.44, 20.32, 20.16, 20.08]
-[20.0, 19.96, 20.64, 22.76, 24.36, 26.04, 26.44, 25.96, 25.96, 24.84, 23.4, 23.52, 23.44, 23.4]
-14.250372886657715
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141369, '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': 10.808244943618774, 'TIME_S_1KI': 0.0764541373541496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.87872554779057, 'W': 22.096174468711904}
-[20.16, 20.24, 20.2, 20.4, 20.4, 20.4, 20.44, 20.32, 20.16, 20.08, 20.64, 20.44, 20.4, 20.6, 20.64, 20.48, 20.36, 20.32, 20.32, 20.52]
-366.82000000000005
-18.341
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141369, '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': 10.808244943618774, 'TIME_S_1KI': 0.0764541373541496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.87872554779057, 'W': 22.096174468711904, 'J_1KI': 2.2273534194044706, 'W_1KI': 0.15630141310125914, 'W_D': 3.7551744687119033, 'J_D': 53.51263643360139, 'W_D_1KI': 0.02656292729461129, 'J_D_1KI': 0.00018789782268114857}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+                      5.6257e-01, 6.4916e-01, 4.5939e-01, 1.6735e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0747, 0.3561, 0.1255,  ..., 0.0664, 0.4841, 0.3262])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.197850227355957 seconds
+
+[16.64, 16.64, 16.92, 16.76, 17.12, 17.2, 17.04, 17.0, 16.76, 16.88]
+[16.68, 16.88, 19.88, 22.24, 22.24, 24.24, 24.56, 25.08, 21.56, 20.04, 19.4, 19.68, 19.88, 19.92]
+14.228495121002197
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141920, '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': 10.197850227355957, 'TIME_S_1KI': 0.0718563291104563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.53162281036373, 'W': 19.575620643059622}
+[16.64, 16.64, 16.92, 16.76, 17.12, 17.2, 17.04, 17.0, 16.76, 16.88, 16.16, 16.08, 16.16, 16.12, 16.12, 16.24, 16.56, 16.84, 16.88, 17.08]
+299.82
+14.991
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141920, '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': 10.197850227355957, 'TIME_S_1KI': 0.0718563291104563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.53162281036373, 'W': 19.575620643059622, 'J_1KI': 1.9625959893627658, 'W_1KI': 0.1379341928062262, 'W_D': 4.584620643059623, 'J_D': 65.2322524514198, 'W_D_1KI': 0.032304260449969154, 'J_D_1KI': 0.00022762303022808028}
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 3cf2612..35c105f 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": 1458, "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": 10.73922610282898, "TIME_S_1KI": 7.365724350362812, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 524.544666223526, "W": 35.90986855086994, "J_1KI": 359.77000426853635, "W_1KI": 24.629539472475955, "W_D": 17.579868550869936, "J_D": 256.7936518120765, "W_D_1KI": 12.0575230115706, "J_D_1KI": 8.269906043601233}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1484, "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": 10.88543152809143, "TIME_S_1KI": 7.335196447500964, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 480.2575412559509, "W": 32.95171788766838, "J_1KI": 323.6236800916111, "W_1KI": 22.204661649372223, "W_D": 16.90171788766838, "J_D": 246.33548707246783, "W_D_1KI": 11.389297767970607, "J_D_1KI": 7.67472895415809}
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 ac4d6c8..2875e7e 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 1000 -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": 7.201478004455566}
+{"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": 7.073613166809082}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       6,  ..., 2499994,
-                            2499998, 2500000]),
-       col_indices=tensor([111852, 327751, 365150,  ..., 493517,  11445,
-                           207886]),
-       values=tensor([0.9407, 0.2669, 0.8671,  ..., 0.7942, 0.4760, 0.2816]),
+tensor(crow_indices=tensor([      0,       6,      13,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([  8141,  69274, 149925,  ..., 390687, 407872,
+                           439375]),
+       values=tensor([0.4271, 0.3560, 0.2859,  ..., 0.3294, 0.0849, 0.5690]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4423, 0.4635, 0.1741,  ..., 0.0346, 0.7600, 0.4318])
+tensor([0.1896, 0.3447, 0.8973,  ..., 0.8957, 0.5716, 0.6993])
 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: 7.201478004455566 seconds
+Time: 7.073613166809082 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 1458 -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": 10.73922610282898}
+['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 1484 -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": 10.88543152809143}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       4,  ..., 2499994,
+tensor(crow_indices=tensor([      0,      10,      15,  ..., 2499994,
                             2500000, 2500000]),
-       col_indices=tensor([198857, 399888, 193187,  ..., 179513, 216653,
-                           450880]),
-       values=tensor([0.4554, 0.7901, 0.7135,  ..., 0.0158, 0.9399, 0.2709]),
+       col_indices=tensor([ 19808,  30523,  42041,  ..., 253465, 473291,
+                           475423]),
+       values=tensor([0.2655, 0.1335, 0.5252,  ..., 0.0072, 0.8874, 0.1974]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8645, 0.3649, 0.9819,  ..., 0.4118, 0.2155, 0.1417])
+tensor([0.7673, 0.2797, 0.0430,  ..., 0.8352, 0.7956, 0.1250])
 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: 10.73922610282898 seconds
+Time: 10.88543152809143 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       4,  ..., 2499994,
+tensor(crow_indices=tensor([      0,      10,      15,  ..., 2499994,
                             2500000, 2500000]),
-       col_indices=tensor([198857, 399888, 193187,  ..., 179513, 216653,
-                           450880]),
-       values=tensor([0.4554, 0.7901, 0.7135,  ..., 0.0158, 0.9399, 0.2709]),
+       col_indices=tensor([ 19808,  30523,  42041,  ..., 253465, 473291,
+                           475423]),
+       values=tensor([0.2655, 0.1335, 0.5252,  ..., 0.0072, 0.8874, 0.1974]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8645, 0.3649, 0.9819,  ..., 0.4118, 0.2155, 0.1417])
+tensor([0.7673, 0.2797, 0.0430,  ..., 0.8352, 0.7956, 0.1250])
 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: 10.73922610282898 seconds
+Time: 10.88543152809143 seconds
 
-[20.76, 20.72, 20.4, 20.4, 20.36, 20.36, 20.12, 20.36, 20.28, 20.32]
-[20.56, 20.48, 21.52, 22.84, 24.72, 30.76, 37.24, 43.6, 43.6, 49.32, 53.6, 53.68, 53.6, 53.56]
-14.607256650924683
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1458, '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': 10.73922610282898, 'TIME_S_1KI': 7.365724350362812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 524.544666223526, 'W': 35.90986855086994}
-[20.76, 20.72, 20.4, 20.4, 20.36, 20.36, 20.12, 20.36, 20.28, 20.32, 20.48, 20.4, 20.32, 20.04, 20.2, 20.4, 20.36, 20.36, 20.48, 20.52]
-366.6
-18.330000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1458, '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': 10.73922610282898, 'TIME_S_1KI': 7.365724350362812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 524.544666223526, 'W': 35.90986855086994, 'J_1KI': 359.77000426853635, 'W_1KI': 24.629539472475955, 'W_D': 17.579868550869936, 'J_D': 256.7936518120765, 'W_D_1KI': 12.0575230115706, 'J_D_1KI': 8.269906043601233}
+[20.16, 19.76, 19.4, 19.48, 19.12, 18.68, 18.6, 18.12, 18.12, 17.68]
+[17.44, 17.16, 17.36, 21.36, 23.28, 27.36, 35.28, 38.6, 44.16, 47.92, 48.84, 48.56, 48.96, 49.04]
+14.574582815170288
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1484, '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': 10.88543152809143, 'TIME_S_1KI': 7.335196447500964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 480.2575412559509, 'W': 32.95171788766838}
+[20.16, 19.76, 19.4, 19.48, 19.12, 18.68, 18.6, 18.12, 18.12, 17.68, 16.68, 16.56, 16.56, 16.56, 16.72, 16.64, 16.76, 17.08, 17.04, 17.08]
+321.0
+16.05
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1484, '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': 10.88543152809143, 'TIME_S_1KI': 7.335196447500964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 480.2575412559509, 'W': 32.95171788766838, 'J_1KI': 323.6236800916111, 'W_1KI': 22.204661649372223, 'W_D': 16.90171788766838, 'J_D': 246.33548707246783, 'W_D_1KI': 11.389297767970607, 'J_D_1KI': 7.67472895415809}
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 36f1dbe..0acd95a 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": 3515, "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": 10.52223539352417, "TIME_S_1KI": 2.9935235827949276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 470.9832169723511, "W": 32.170385708153674, "J_1KI": 133.99238036197755, "W_1KI": 9.15231456846477, "W_D": 13.629385708153674, "J_D": 199.53792237424858, "W_D_1KI": 3.8774923778531076, "J_D_1KI": 1.1031272767718656}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3392, "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": 10.727018594741821, "TIME_S_1KI": 3.1624465196762443, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 423.8805270671845, "W": 29.00744018011372, "J_1KI": 124.96477802688223, "W_1KI": 8.55172175121277, "W_D": 13.914440180113719, "J_D": 203.32922177100187, "W_D_1KI": 4.102134487061827, "J_D_1KI": 1.2093556860441705}
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 60bafaf..f123524 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 1000 -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": 2.9865975379943848}
+{"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": 3.0953831672668457}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 249989, 249995,
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249991,
                             250000]),
-       col_indices=tensor([12071, 16957, 24871,  ..., 32088, 41674, 47752]),
-       values=tensor([0.0278, 0.4403, 0.7542,  ..., 0.8727, 0.3256, 0.0294]),
+       col_indices=tensor([  782, 10679, 21591,  ..., 21721, 25862, 26402]),
+       values=tensor([0.1080, 0.2599, 0.9753,  ..., 0.8598, 0.0309, 0.7621]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9906, 0.0790, 0.7013,  ..., 0.2118, 0.2385, 0.3873])
+tensor([0.0624, 0.3415, 0.4601,  ..., 0.0482, 0.7737, 0.1465])
 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: 2.9865975379943848 seconds
+Time: 3.0953831672668457 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 3515 -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": 10.52223539352417}
+['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 3392 -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": 10.727018594741821}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     10,  ..., 249992, 249995,
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 249992, 249997,
                             250000]),
-       col_indices=tensor([ 9701, 11138, 26862,  ..., 20273, 37187, 48197]),
-       values=tensor([0.8537, 0.5403, 0.1220,  ..., 0.0155, 0.7712, 0.8752]),
+       col_indices=tensor([29888, 37512, 45145,  ..., 10362, 27481, 28096]),
+       values=tensor([0.5987, 0.4413, 0.1210,  ..., 0.9023, 0.1888, 0.1246]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5658, 0.7328, 0.9479,  ..., 0.1014, 0.1582, 0.5663])
+tensor([0.0260, 0.0462, 0.3716,  ..., 0.4992, 0.3586, 0.2225])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.52223539352417 seconds
+Time: 10.727018594741821 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     10,  ..., 249992, 249995,
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 249992, 249997,
                             250000]),
-       col_indices=tensor([ 9701, 11138, 26862,  ..., 20273, 37187, 48197]),
-       values=tensor([0.8537, 0.5403, 0.1220,  ..., 0.0155, 0.7712, 0.8752]),
+       col_indices=tensor([29888, 37512, 45145,  ..., 10362, 27481, 28096]),
+       values=tensor([0.5987, 0.4413, 0.1210,  ..., 0.9023, 0.1888, 0.1246]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5658, 0.7328, 0.9479,  ..., 0.1014, 0.1582, 0.5663])
+tensor([0.0260, 0.0462, 0.3716,  ..., 0.4992, 0.3586, 0.2225])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.52223539352417 seconds
+Time: 10.727018594741821 seconds
 
-[20.72, 20.68, 20.52, 20.64, 20.64, 20.68, 20.68, 20.72, 20.72, 20.72]
-[20.64, 20.72, 21.28, 22.68, 24.8, 29.24, 34.6, 38.2, 42.72, 43.84, 43.84, 44.32, 44.16, 44.08]
-14.640272617340088
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3515, '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': 10.52223539352417, 'TIME_S_1KI': 2.9935235827949276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 470.9832169723511, 'W': 32.170385708153674}
-[20.72, 20.68, 20.52, 20.64, 20.64, 20.68, 20.68, 20.72, 20.72, 20.72, 20.32, 20.24, 20.44, 20.32, 20.48, 20.64, 20.52, 20.64, 20.96, 20.84]
-370.82
-18.541
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3515, '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': 10.52223539352417, 'TIME_S_1KI': 2.9935235827949276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 470.9832169723511, 'W': 32.170385708153674, 'J_1KI': 133.99238036197755, 'W_1KI': 9.15231456846477, 'W_D': 13.629385708153674, 'J_D': 199.53792237424858, 'W_D_1KI': 3.8774923778531076, 'J_D_1KI': 1.1031272767718656}
+[16.52, 16.28, 16.24, 16.16, 16.44, 16.44, 16.6, 16.52, 16.56, 16.68]
+[16.44, 16.64, 17.44, 19.52, 22.44, 27.08, 32.32, 35.8, 38.92, 39.84, 40.12, 40.12, 40.24, 40.6]
+14.612820863723755
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3392, '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': 10.727018594741821, 'TIME_S_1KI': 3.1624465196762443, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 423.8805270671845, 'W': 29.00744018011372}
+[16.52, 16.28, 16.24, 16.16, 16.44, 16.44, 16.6, 16.52, 16.56, 16.68, 16.84, 16.68, 16.84, 17.24, 17.32, 17.48, 17.4, 17.24, 16.96, 16.88]
+301.86
+15.093
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3392, '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': 10.727018594741821, 'TIME_S_1KI': 3.1624465196762443, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 423.8805270671845, 'W': 29.00744018011372, 'J_1KI': 124.96477802688223, 'W_1KI': 8.55172175121277, 'W_D': 13.914440180113719, 'J_D': 203.32922177100187, 'W_D_1KI': 4.102134487061827, 'J_D_1KI': 1.2093556860441705}
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 091be62..3f36d4c 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": 1000, "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": 27.268765687942505, "TIME_S_1KI": 27.268765687942505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1234.21608165741, "W": 36.82999610504039, "J_1KI": 1234.21608165741, "W_1KI": 36.82999610504039, "W_D": 18.278996105040388, "J_D": 612.5504571070677, "W_D_1KI": 18.278996105040388, "J_D_1KI": 18.278996105040388}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 29.441463470458984, "TIME_S_1KI": 29.441463470458984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.5749865341186, "W": 32.87033016720936, "J_1KI": 1067.5749865341186, "W_1KI": 32.87033016720936, "W_D": 17.51733016720936, "J_D": 568.9344591989515, "W_D_1KI": 17.51733016720936, "J_D_1KI": 17.51733016720936}
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 9980b68..c095d19 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 1000 -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": 27.268765687942505}
+{"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": 29.441463470458984}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     102,  ..., 2499892,
-                            2499945, 2500000]),
-       col_indices=tensor([  987,  1836,  5791,  ..., 47187, 47558, 49789]),
-       values=tensor([0.1085, 0.8855, 0.3536,  ..., 0.4174, 0.4340, 0.6085]),
+tensor(crow_indices=tensor([      0,      37,      79,  ..., 2499907,
+                            2499951, 2500000]),
+       col_indices=tensor([ 2466,  3763,  4276,  ..., 47502, 48879, 49149]),
+       values=tensor([0.0148, 0.9908, 0.2997,  ..., 0.9281, 0.7443, 0.4383]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6397, 0.2759, 0.9232,  ..., 0.5725, 0.2810, 0.6127])
+tensor([0.6700, 0.5614, 0.5608,  ..., 0.8928, 0.8615, 0.5607])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 27.268765687942505 seconds
+Time: 29.441463470458984 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     102,  ..., 2499892,
-                            2499945, 2500000]),
-       col_indices=tensor([  987,  1836,  5791,  ..., 47187, 47558, 49789]),
-       values=tensor([0.1085, 0.8855, 0.3536,  ..., 0.4174, 0.4340, 0.6085]),
+tensor(crow_indices=tensor([      0,      37,      79,  ..., 2499907,
+                            2499951, 2500000]),
+       col_indices=tensor([ 2466,  3763,  4276,  ..., 47502, 48879, 49149]),
+       values=tensor([0.0148, 0.9908, 0.2997,  ..., 0.9281, 0.7443, 0.4383]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6397, 0.2759, 0.9232,  ..., 0.5725, 0.2810, 0.6127])
+tensor([0.6700, 0.5614, 0.5608,  ..., 0.8928, 0.8615, 0.5607])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 27.268765687942505 seconds
+Time: 29.441463470458984 seconds
 
-[20.68, 20.56, 20.48, 20.12, 20.08, 20.28, 20.44, 20.68, 20.96, 20.96]
-[20.84, 20.76, 20.6, 24.92, 26.28, 30.48, 35.16, 37.64, 40.0, 42.72, 43.28, 43.52, 43.36, 43.36, 43.52, 42.92, 43.08, 42.76, 42.76, 42.52, 42.68, 42.8, 42.88, 43.04, 43.16, 42.96, 42.88, 42.76, 42.52, 42.72, 42.64, 42.64]
-33.511165142059326
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 27.268765687942505, 'TIME_S_1KI': 27.268765687942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.21608165741, 'W': 36.82999610504039}
-[20.68, 20.56, 20.48, 20.12, 20.08, 20.28, 20.44, 20.68, 20.96, 20.96, 20.44, 20.76, 20.8, 20.8, 20.72, 20.84, 20.84, 20.56, 20.68, 20.76]
-371.02
-18.551
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 27.268765687942505, 'TIME_S_1KI': 27.268765687942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.21608165741, 'W': 36.82999610504039, 'J_1KI': 1234.21608165741, 'W_1KI': 36.82999610504039, 'W_D': 18.278996105040388, 'J_D': 612.5504571070677, 'W_D_1KI': 18.278996105040388, 'J_D_1KI': 18.278996105040388}
+[16.92, 17.36, 17.08, 16.84, 17.2, 17.28, 17.4, 17.44, 17.36, 17.36]
+[17.08, 17.24, 17.08, 21.52, 22.36, 25.24, 29.48, 32.12, 34.64, 38.4, 38.76, 38.64, 38.8, 39.0, 39.0, 39.44, 39.4, 39.28, 39.32, 39.32, 39.24, 39.12, 39.0, 39.08, 39.32, 39.36, 39.28, 39.28, 39.16, 38.92, 39.08]
+32.47837734222412
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 29.441463470458984, 'TIME_S_1KI': 29.441463470458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.5749865341186, 'W': 32.87033016720936}
+[16.92, 17.36, 17.08, 16.84, 17.2, 17.28, 17.4, 17.44, 17.36, 17.36, 16.68, 16.88, 16.76, 16.92, 16.84, 17.04, 17.0, 17.0, 16.68, 17.0]
+307.06000000000006
+15.353000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 29.441463470458984, 'TIME_S_1KI': 29.441463470458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.5749865341186, 'W': 32.87033016720936, 'J_1KI': 1067.5749865341186, 'W_1KI': 32.87033016720936, 'W_D': 17.51733016720936, 'J_D': 568.9344591989515, 'W_D_1KI': 17.51733016720936, 'J_D_1KI': 17.51733016720936}
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 02be475..719c8cd 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": 19539, "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": 10.163734436035156, "TIME_S_1KI": 0.5201767969719615, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 495.2794138240814, "W": 33.936722825817846, "J_1KI": 25.348247803064712, "W_1KI": 1.7368710182618274, "W_D": 13.302722825817849, "J_D": 194.14263413858416, "W_D_1KI": 0.6808292556332386, "J_D_1KI": 0.03484463153862729}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 20098, "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": 10.650188207626343, "TIME_S_1KI": 0.5299128374776765, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 402.8303679275513, "W": 27.52967890413959, "J_1KI": 20.04330619601708, "W_1KI": 1.3697720621026763, "W_D": 12.350678904139592, "J_D": 180.72235947370535, "W_D_1KI": 0.6145227835674988, "J_D_1KI": 0.030576315233729664}
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 4c99396..b0c7cd1 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 1000 -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": 0.5373842716217041}
+{"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": 0.648245096206665}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 25000, 25000]),
-       col_indices=tensor([13933,   723, 18387,  ..., 22194, 38514,  2158]),
-       values=tensor([0.9124, 0.6353, 0.3193,  ..., 0.0372, 0.2371, 0.8076]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24999, 25000]),
+       col_indices=tensor([  889, 16856, 49649,  ..., 20622, 24354, 47394]),
+       values=tensor([0.8512, 0.0995, 0.9072,  ..., 0.9114, 0.3857, 0.4483]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6534, 0.7497, 0.2436,  ..., 0.0965, 0.5741, 0.5754])
+tensor([0.8531, 0.5584, 0.8209,  ..., 0.8853, 0.7506, 0.6837])
 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: 0.5373842716217041 seconds
+Time: 0.648245096206665 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 19539 -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": 10.163734436035156}
+['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 16197 -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": 8.461615800857544}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24996, 24998, 25000]),
-       col_indices=tensor([44723, 48345, 32100,  ..., 22467, 28064, 29572]),
-       values=tensor([0.7283, 0.2640, 0.9583,  ..., 0.2460, 0.4237, 0.5300]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([37259, 33129, 13575,  ..., 31298, 24333,  9136]),
+       values=tensor([0.0302, 0.8728, 0.1875,  ..., 0.5590, 0.6136, 0.6206]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7754, 0.9311, 0.4703,  ..., 0.3816, 0.8788, 0.3934])
+tensor([0.6191, 0.3887, 0.4199,  ..., 0.2754, 0.8424, 0.8817])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,15 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.163734436035156 seconds
+Time: 8.461615800857544 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 20098 -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": 10.650188207626343}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24996, 24998, 25000]),
-       col_indices=tensor([44723, 48345, 32100,  ..., 22467, 28064, 29572]),
-       values=tensor([0.7283, 0.2640, 0.9583,  ..., 0.2460, 0.4237, 0.5300]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([   35,  8013, 35741,  ..., 26171, 43365,  6398]),
+       values=tensor([0.7135, 0.5997, 0.1893,  ..., 0.8752, 0.2236, 0.9882]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7754, 0.9311, 0.4703,  ..., 0.3816, 0.8788, 0.3934])
+tensor([0.7523, 0.4685, 0.7648,  ..., 0.0829, 0.9708, 0.7467])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -50,13 +53,29 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.163734436035156 seconds
+Time: 10.650188207626343 seconds
 
-[25.08, 25.2, 25.2, 25.4, 25.08, 25.4, 25.12, 25.0, 25.28, 25.36]
-[25.56, 25.6, 26.0, 26.32, 28.76, 32.48, 32.48, 37.24, 41.24, 45.24, 45.88, 45.72, 45.6, 45.64]
-14.594202756881714
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19539, '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': 10.163734436035156, 'TIME_S_1KI': 0.5201767969719615, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 495.2794138240814, 'W': 33.936722825817846}
-[25.08, 25.2, 25.2, 25.4, 25.08, 25.4, 25.12, 25.0, 25.28, 25.36, 20.68, 20.68, 20.6, 20.6, 20.52, 20.72, 20.52, 20.72, 20.72, 20.72]
-412.67999999999995
-20.633999999999997
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19539, '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': 10.163734436035156, 'TIME_S_1KI': 0.5201767969719615, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 495.2794138240814, 'W': 33.936722825817846, 'J_1KI': 25.348247803064712, 'W_1KI': 1.7368710182618274, 'W_D': 13.302722825817849, 'J_D': 194.14263413858416, 'W_D_1KI': 0.6808292556332386, 'J_D_1KI': 0.03484463153862729}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([   35,  8013, 35741,  ..., 26171, 43365,  6398]),
+       values=tensor([0.7135, 0.5997, 0.1893,  ..., 0.8752, 0.2236, 0.9882]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7523, 0.4685, 0.7648,  ..., 0.0829, 0.9708, 0.7467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.650188207626343 seconds
+
+[16.64, 16.56, 16.56, 16.96, 17.08, 17.16, 16.88, 16.96, 16.48, 16.36]
+[16.44, 16.44, 16.68, 18.12, 19.04, 22.72, 28.64, 33.16, 36.92, 39.76, 39.32, 39.28, 39.68, 39.6]
+14.632585048675537
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 20098, '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': 10.650188207626343, 'TIME_S_1KI': 0.5299128374776765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 402.8303679275513, 'W': 27.52967890413959}
+[16.64, 16.56, 16.56, 16.96, 17.08, 17.16, 16.88, 16.96, 16.48, 16.36, 17.12, 16.92, 16.68, 16.88, 16.76, 16.8, 16.8, 17.12, 17.32, 17.2]
+303.58
+15.178999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 20098, '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': 10.650188207626343, 'TIME_S_1KI': 0.5299128374776765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 402.8303679275513, 'W': 27.52967890413959, 'J_1KI': 20.04330619601708, 'W_1KI': 1.3697720621026763, 'W_D': 12.350678904139592, 'J_D': 180.72235947370535, 'W_D_1KI': 0.6145227835674988, 'J_D_1KI': 0.030576315233729664}
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
new file mode 100644
index 0000000..c3c7f57
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 96690, "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": 10.663233041763306, "TIME_S_1KI": 0.11028268736956569, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 262.53495136260995, "W": 18.48739374467239, "J_1KI": 2.7152234084456506, "W_1KI": 0.19120274841940624, "W_D": 3.6973937446723912, "J_D": 52.505783147812, "W_D_1KI": 0.038239670541652615, "J_D_1KI": 0.0003954873362462779}
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
new file mode 100644
index 0000000..61cae4f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_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-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -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": 0.11520600318908691}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([4712, 1560, 1507,  ..., 2651,  244, 3781]),
+       values=tensor([0.1646, 0.3564, 0.3355,  ..., 0.5785, 0.6935, 0.4198]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6842, 0.2217, 0.0992,  ..., 0.1824, 0.3701, 0.4149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.11520600318908691 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 91141 -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": 9.897401094436646}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1451, 2006, 3586,  ..., 3975, 4446, 2086]),
+       values=tensor([0.6609, 0.8356, 0.1353,  ..., 0.7408, 0.3224, 0.8471]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2892, 0.1223, 0.3419,  ..., 0.7884, 0.7802, 0.0113])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 9.897401094436646 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 96690 -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": 10.663233041763306}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 2499, 2500]),
+       col_indices=tensor([2205, 2444, 2425,  ..., 3761, 2544, 2990]),
+       values=tensor([0.2656, 0.9114, 0.3983,  ..., 0.8675, 0.7517, 0.7885]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1659, 0.6941, 0.7553,  ..., 0.7483, 0.8019, 0.7277])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.663233041763306 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 2499, 2500]),
+       col_indices=tensor([2205, 2444, 2425,  ..., 3761, 2544, 2990]),
+       values=tensor([0.2656, 0.9114, 0.3983,  ..., 0.8675, 0.7517, 0.7885]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1659, 0.6941, 0.7553,  ..., 0.7483, 0.8019, 0.7277])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.663233041763306 seconds
+
+[16.32, 16.04, 16.32, 16.56, 16.56, 16.64, 16.96, 16.96, 16.76, 16.76]
+[16.76, 16.88, 17.08, 18.96, 20.48, 21.96, 22.64, 22.36, 21.0, 19.8, 19.8, 19.6, 19.72, 19.8]
+14.20075511932373
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 96690, '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': 10.663233041763306, 'TIME_S_1KI': 0.11028268736956569, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 262.53495136260995, 'W': 18.48739374467239}
+[16.32, 16.04, 16.32, 16.56, 16.56, 16.64, 16.96, 16.96, 16.76, 16.76, 16.48, 16.4, 16.2, 16.28, 16.28, 16.12, 16.12, 16.28, 16.36, 16.36]
+295.79999999999995
+14.789999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 96690, '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': 10.663233041763306, 'TIME_S_1KI': 0.11028268736956569, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 262.53495136260995, 'W': 18.48739374467239, 'J_1KI': 2.7152234084456506, 'W_1KI': 0.19120274841940624, 'W_D': 3.6973937446723912, 'J_D': 52.505783147812, 'W_D_1KI': 0.038239670541652615, 'J_D_1KI': 0.0003954873362462779}
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
new file mode 100644
index 0000000..810fc32
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 17852, "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": 10.54783010482788, "TIME_S_1KI": 0.5908486502816425, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 261.2027643966674, "W": 18.408959018952206, "J_1KI": 14.6315686979984, "W_1KI": 1.0311986902841253, "W_D": 3.4579590189522076, "J_D": 49.0646132674217, "W_D_1KI": 0.19370149109075777, "J_D_1KI": 0.010850408418707023}
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
new file mode 100644
index 0000000..5d5c1f0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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 1000 -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": 0.6220724582672119}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24988, 24993, 25000]),
+       col_indices=tensor([2208, 3192, 3630,  ..., 2657, 2751, 4682]),
+       values=tensor([0.3516, 0.9043, 0.4344,  ..., 0.9354, 0.2858, 0.8708]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1847, 0.5253, 0.6086,  ..., 0.9552, 0.0514, 0.1920])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.6220724582672119 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 16879 -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": 9.927400827407837}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24988, 24992, 25000]),
+       col_indices=tensor([1765, 1880, 2380,  ..., 3402, 4335, 4928]),
+       values=tensor([0.8113, 0.6065, 0.0419,  ..., 0.8515, 0.2786, 0.9879]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6729, 0.2847, 0.7618,  ..., 0.5837, 0.8359, 0.7138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.927400827407837 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 17852 -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": 10.54783010482788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    10,  ..., 24988, 24991, 25000]),
+       col_indices=tensor([ 732, 2237, 2424,  ..., 1459, 1662, 4133]),
+       values=tensor([0.5131, 0.9715, 0.7721,  ..., 0.9714, 0.5730, 0.7149]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7581, 0.5458, 0.9932,  ..., 0.3205, 0.5744, 0.9847])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.54783010482788 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    10,  ..., 24988, 24991, 25000]),
+       col_indices=tensor([ 732, 2237, 2424,  ..., 1459, 1662, 4133]),
+       values=tensor([0.5131, 0.9715, 0.7721,  ..., 0.9714, 0.5730, 0.7149]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7581, 0.5458, 0.9932,  ..., 0.3205, 0.5744, 0.9847])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.54783010482788 seconds
+
+[16.36, 16.36, 16.32, 16.6, 16.8, 17.04, 17.2, 17.08, 16.8, 16.52]
+[16.48, 16.4, 17.36, 19.52, 19.52, 21.32, 21.84, 22.56, 21.0, 20.28, 19.68, 19.84, 19.92, 19.92]
+14.188893795013428
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17852, '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': 10.54783010482788, 'TIME_S_1KI': 0.5908486502816425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 261.2027643966674, 'W': 18.408959018952206}
+[16.36, 16.36, 16.32, 16.6, 16.8, 17.04, 17.2, 17.08, 16.8, 16.52, 16.4, 16.36, 16.32, 16.32, 16.76, 16.96, 16.72, 16.44, 16.2, 16.2]
+299.02
+14.950999999999999
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17852, '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': 10.54783010482788, 'TIME_S_1KI': 0.5908486502816425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 261.2027643966674, 'W': 18.408959018952206, 'J_1KI': 14.6315686979984, 'W_1KI': 1.0311986902841253, 'W_D': 3.4579590189522076, 'J_D': 49.0646132674217, 'W_D_1KI': 0.19370149109075777, 'J_D_1KI': 0.010850408418707023}
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
new file mode 100644
index 0000000..cca3281
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1933, "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": 10.477078676223755, "TIME_S_1KI": 5.420113127896407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 268.2967868804932, "W": 18.818848497168876, "J_1KI": 138.79813082281075, "W_1KI": 9.7355656995183, "W_D": 3.947848497168877, "J_D": 56.283734206199696, "W_D_1KI": 2.0423427300408057, "J_D_1KI": 1.0565663373206444}
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
new file mode 100644
index 0000000..3224ea8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_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 1000 -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": 5.431562900543213}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     79,  ..., 249900, 249941,
+                            250000]),
+       col_indices=tensor([  80,  388,  404,  ..., 4737, 4807, 4857]),
+       values=tensor([0.4885, 0.5213, 0.1721,  ..., 0.5810, 0.1625, 0.7107]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1545, 0.4718, 0.9539,  ..., 0.2261, 0.6017, 0.7355])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 5.431562900543213 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 1933 -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": 10.477078676223755}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     40,     89,  ..., 249917, 249964,
+                            250000]),
+       col_indices=tensor([ 165,  177,  195,  ..., 4656, 4719, 4927]),
+       values=tensor([0.2100, 0.9405, 0.2582,  ..., 0.7931, 0.5258, 0.8197]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9173, 0.2185, 0.4076,  ..., 0.3362, 0.1795, 0.2923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.477078676223755 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     40,     89,  ..., 249917, 249964,
+                            250000]),
+       col_indices=tensor([ 165,  177,  195,  ..., 4656, 4719, 4927]),
+       values=tensor([0.2100, 0.9405, 0.2582,  ..., 0.7931, 0.5258, 0.8197]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9173, 0.2185, 0.4076,  ..., 0.3362, 0.1795, 0.2923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.477078676223755 seconds
+
+[17.04, 17.04, 16.96, 17.04, 16.64, 16.4, 16.44, 16.32, 16.24, 16.36]
+[16.44, 16.52, 17.76, 19.52, 21.52, 21.52, 22.36, 22.76, 21.68, 21.36, 19.84, 20.04, 20.0, 20.08]
+14.25681209564209
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1933, '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': 10.477078676223755, 'TIME_S_1KI': 5.420113127896407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 268.2967868804932, 'W': 18.818848497168876}
+[17.04, 17.04, 16.96, 17.04, 16.64, 16.4, 16.44, 16.32, 16.24, 16.36, 16.4, 16.24, 16.32, 16.36, 16.36, 16.48, 16.68, 16.56, 16.4, 16.08]
+297.41999999999996
+14.870999999999999
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1933, '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': 10.477078676223755, 'TIME_S_1KI': 5.420113127896407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 268.2967868804932, 'W': 18.818848497168876, 'J_1KI': 138.79813082281075, 'W_1KI': 9.7355656995183, 'W_D': 3.947848497168877, 'J_D': 56.283734206199696, 'W_D_1KI': 2.0423427300408057, 'J_D_1KI': 1.0565663373206444}
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
new file mode 100644
index 0000000..04d5dbf
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 26.625333547592163, "TIME_S_1KI": 26.625333547592163, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 615.7839365768435, "W": 20.26185984115326, "J_1KI": 615.7839365768435, "W_1KI": 20.26185984115326, "W_D": 5.26185984115326, "J_D": 159.91467674255395, "W_D_1KI": 5.26185984115326, "J_D_1KI": 5.26185984115326}
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
new file mode 100644
index 0000000..b962b23
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_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 1000 -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": 26.625333547592163}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     251,     521,  ..., 1249514,
+                            1249753, 1250000]),
+       col_indices=tensor([  14,   21,   29,  ..., 4968, 4983, 4999]),
+       values=tensor([0.5630, 0.8243, 0.2167,  ..., 0.8539, 0.0380, 0.9608]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8150, 0.5277, 0.3367,  ..., 0.0434, 0.1834, 0.0206])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.625333547592163 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     251,     521,  ..., 1249514,
+                            1249753, 1250000]),
+       col_indices=tensor([  14,   21,   29,  ..., 4968, 4983, 4999]),
+       values=tensor([0.5630, 0.8243, 0.2167,  ..., 0.8539, 0.0380, 0.9608]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8150, 0.5277, 0.3367,  ..., 0.0434, 0.1834, 0.0206])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.625333547592163 seconds
+
+[16.8, 16.84, 16.76, 16.6, 16.6, 16.68, 17.2, 17.2, 17.04, 17.0]
+[16.84, 16.4, 16.52, 21.4, 23.24, 25.84, 26.88, 26.88, 24.16, 22.8, 20.48, 20.64, 20.68, 20.72, 20.68, 20.44, 20.28, 20.32, 20.28, 20.28, 20.12, 20.12, 20.08, 20.12, 19.96, 20.12, 20.04, 19.92, 19.96, 20.2]
+30.391283988952637
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 26.625333547592163, 'TIME_S_1KI': 26.625333547592163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 615.7839365768435, 'W': 20.26185984115326}
+[16.8, 16.84, 16.76, 16.6, 16.6, 16.68, 17.2, 17.2, 17.04, 17.0, 16.4, 16.32, 16.36, 16.36, 16.56, 16.52, 16.56, 16.68, 16.44, 16.36]
+300.0
+15.0
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 26.625333547592163, 'TIME_S_1KI': 26.625333547592163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 615.7839365768435, 'W': 20.26185984115326, 'J_1KI': 615.7839365768435, 'W_1KI': 20.26185984115326, 'W_D': 5.26185984115326, 'J_D': 159.91467674255395, 'W_D_1KI': 5.26185984115326, 'J_D_1KI': 5.26185984115326}
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
new file mode 100644
index 0000000..3a570df
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "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": 53.914990186691284, "TIME_S_1KI": 53.914990186691284, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1144.4719800853732, "W": 20.188888585576834, "J_1KI": 1144.4719800853732, "W_1KI": 20.188888585576834, "W_D": 5.141888585576837, "J_D": 291.48446611952824, "W_D_1KI": 5.141888585576837, "J_D_1KI": 5.141888585576837}
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
new file mode 100644
index 0000000..aab9423
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_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 1000 -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": 53.914990186691284}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     982,  ..., 2498978,
+                            2499487, 2500000]),
+       col_indices=tensor([   3,   18,   24,  ..., 4984, 4986, 4997]),
+       values=tensor([0.0150, 0.0039, 0.1247,  ..., 0.8538, 0.3013, 0.1357]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4451, 0.4302, 0.3190,  ..., 0.9031, 0.3775, 0.0047])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.914990186691284 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     982,  ..., 2498978,
+                            2499487, 2500000]),
+       col_indices=tensor([   3,   18,   24,  ..., 4984, 4986, 4997]),
+       values=tensor([0.0150, 0.0039, 0.1247,  ..., 0.8538, 0.3013, 0.1357]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4451, 0.4302, 0.3190,  ..., 0.9031, 0.3775, 0.0047])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.914990186691284 seconds
+
+[16.52, 16.4, 16.4, 16.56, 16.36, 16.52, 16.8, 16.56, 16.56, 16.76]
+[16.48, 16.28, 19.4, 20.36, 22.04, 22.04, 24.88, 25.48, 22.92, 22.96, 20.08, 20.12, 20.12, 20.04, 19.92, 20.0, 20.24, 20.32, 20.32, 20.4, 20.4, 20.04, 20.08, 20.4, 20.48, 20.68, 20.96, 20.6, 20.48, 20.44, 20.44, 20.12, 20.08, 20.2, 19.96, 20.0, 20.32, 20.44, 20.44, 20.48, 20.64, 20.44, 20.44, 20.56, 20.76, 20.64, 20.8, 20.6, 20.64, 20.44, 20.4, 20.08, 20.08, 20.2, 20.36, 20.36]
+56.68821120262146
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 53.914990186691284, 'TIME_S_1KI': 53.914990186691284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1144.4719800853732, 'W': 20.188888585576834}
+[16.52, 16.4, 16.4, 16.56, 16.36, 16.52, 16.8, 16.56, 16.56, 16.76, 16.72, 16.68, 16.8, 16.84, 16.84, 16.84, 17.0, 17.2, 17.0, 17.16]
+300.93999999999994
+15.046999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, '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': 53.914990186691284, 'TIME_S_1KI': 53.914990186691284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1144.4719800853732, 'W': 20.188888585576834, 'J_1KI': 1144.4719800853732, 'W_1KI': 20.188888585576834, 'W_D': 5.141888585576837, 'J_D': 291.48446611952824, 'W_D_1KI': 5.141888585576837, 'J_D_1KI': 5.141888585576837}
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
new file mode 100644
index 0000000..aba0d5e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 293134, "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": 10.89356017112732, "TIME_S_1KI": 0.03716239048055606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 278.4485914421082, "W": 19.57952781791354, "J_1KI": 0.9499020633638819, "W_1KI": 0.06679377969772711, "W_D": 4.613527817913537, "J_D": 65.610893910408, "W_D_1KI": 0.01573863085794735, "J_D_1KI": 5.3690908792386244e-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
new file mode 100644
index 0000000..ebedd90
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,437 @@
+['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 1000 -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": 0.04628562927246094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([ 683, 1119, 1321, 2450, 3482, 3631, 1761, 3022,  756,
+                           3517,   37, 3468, 4655, 1287,  913, 1692, 3561, 1823,
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+                           2513, 4919, 1689, 1039, 4346, 4963,  184, 2438, 3794,
+                            631, 3050, 4745, 3174, 1910, 3181, 4415]),
+       values=tensor([0.5133, 0.9500, 0.6089, 0.1299, 0.0389, 0.7021, 0.0545,
+                      0.1504, 0.2775, 0.3654, 0.5414, 0.7066, 0.5062, 0.9276,
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+                      0.0263, 0.0960, 0.2106, 0.8204, 0.3496, 0.0650, 0.0530,
+                      0.2300, 0.7920, 0.0833, 0.8839, 0.6947, 0.7490, 0.6930,
+                      0.4034, 0.9770, 0.5568, 0.5813, 0.4457, 0.4409, 0.3165,
+                      0.4290, 0.8018, 0.4890, 0.7248, 0.5066, 0.4197, 0.9251,
+                      0.4526, 0.8257, 0.6029, 0.9210, 0.8099, 0.1966, 0.6605,
+                      0.5583, 0.0851, 0.2553, 0.8703, 0.6237, 0.8267, 0.9769,
+                      0.6623, 0.5390, 0.0172, 0.1684, 0.4788, 0.5289, 0.0477,
+                      0.8018, 0.0914, 0.3275, 0.7127, 0.1031, 0.8096, 0.1163,
+                      0.3143, 0.8185, 0.2797, 0.8908, 0.1307, 0.5822, 0.2044,
+                      0.6227, 0.4853, 0.6034, 0.6732, 0.0321]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7002, 0.3467, 0.9676,  ..., 0.8135, 0.6463, 0.9360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04628562927246094 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 226852 -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": 8.58342981338501}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 250, 250, 250]),
+       col_indices=tensor([2568, 3647,  442,  965,  263, 2383,  651, 4423, 3036,
+                           1922, 4223, 1539, 1097, 1063, 4112, 2393, 2048,  391,
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+                           2835, 3630, 2085,  603, 3829, 4234,  710,  378, 2071,
+                           1558, 4206, 4361, 1063, 3780,  352,  168]),
+       values=tensor([6.8562e-01, 9.9314e-01, 3.4074e-01, 1.7233e-01,
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+                      3.4373e-01, 9.9556e-01, 1.4700e-01, 1.2820e-01,
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+                      1.8548e-01, 9.7019e-01, 2.2577e-01, 3.2056e-01,
+                      4.1451e-02, 1.3423e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.0595, 0.8939, 0.2592,  ..., 0.5348, 0.8468, 0.6804])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 8.58342981338501 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 277505 -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": 9.940149784088135}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([3014, 4957, 1583, 2867, 2783,  475, 2139, 2382, 3400,
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+       values=tensor([0.8419, 0.4424, 0.5698, 0.2999, 0.9295, 0.4679, 0.3442,
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+                      0.1236, 0.2320, 0.4008, 0.3127, 0.0194, 0.2149, 0.2742,
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+                      0.5274, 0.2875, 0.2494, 0.8088, 0.9923, 0.5445, 0.3175,
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+                      0.6261, 0.6426, 0.9521, 0.0053, 0.5755, 0.0743, 0.7500,
+                      0.3281, 0.4225, 0.6900, 0.0916, 0.8990, 0.2711, 0.5755,
+                      0.5712, 0.7556, 0.0051, 0.6971, 0.0437, 0.5565, 0.4256,
+                      0.2960, 0.6043, 0.6836, 0.9303, 0.4472, 0.6016, 0.6132,
+                      0.9503, 0.8339, 0.2697, 0.0658, 0.7983, 0.4874, 0.1771,
+                      0.9875, 0.2001, 0.2752, 0.5608, 0.4997, 0.6797, 0.1612,
+                      0.9007, 0.9904, 0.7264, 0.3981, 0.6661]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5019, 0.1367, 0.6742,  ..., 0.0249, 0.2703, 0.5698])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 9.940149784088135 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 293134 -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": 10.89356017112732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([3878, 1793,  196, 4523, 2590, 2367,  223, 4753,  811,
+                           1344, 3831, 3891, 1471,  342,   75, 2706, 3424, 2402,
+                           4777, 1498,  772, 4383, 2094, 2326, 2946, 1126, 2886,
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+                           1937, 3764,   94, 3879, 1282, 4972, 2002, 1481, 4954,
+                           2173, 3425, 2770, 4498,  897,  827,  829, 4189, 4991,
+                           1934, 1179, 1128, 2114, 2395, 2017, 3561, 4691, 2694,
+                           1416, 4554,  620, 4382, 4973, 3582,  230, 4891, 3107,
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+                           2660, 1731,  777, 3782, 2858, 3541, 2112, 2412, 4004,
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+                            377, 2695, 2905,  534, 1022, 2504, 1436, 3486, 3980,
+                           4913,  361, 4684, 2741,  722, 1718, 3274,  513, 1785,
+                           4555,  575,  662, 3842, 1584, 2198,  215]),
+       values=tensor([0.8076, 0.3370, 0.6780, 0.9299, 0.5410, 0.0897, 0.3343,
+                      0.8017, 0.5673, 0.5602, 0.9800, 0.8553, 0.3447, 0.6119,
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+                      0.3218, 0.1649, 0.9155, 0.2697, 0.4415, 0.6177, 0.1457,
+                      0.9256, 0.6524, 0.8106, 0.1943, 0.2636, 0.7375, 0.5837,
+                      0.4529, 0.4107, 0.4337, 0.4074, 0.1673, 0.9988, 0.7338,
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+                      0.8068, 0.8325, 0.0916, 0.8219, 0.9882, 0.3617, 0.8114,
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+                      0.0251, 0.3317, 0.4666, 0.5981, 0.5539, 0.1688, 0.7416,
+                      0.8841, 0.4123, 0.1102, 0.1371, 0.7232, 0.6598, 0.7427,
+                      0.8150, 0.1180, 0.3866, 0.1447, 0.4442, 0.5099, 0.1417,
+                      0.2917, 0.8599, 0.3553, 0.2307, 0.1388, 0.1482, 0.8529,
+                      0.3988, 0.9926, 0.3184, 0.2404, 0.4847, 0.5288, 0.0738,
+                      0.0517, 0.1797, 0.1796, 0.7215, 0.5955, 0.6432, 0.0017,
+                      0.6486, 0.6664, 0.4487, 0.7630, 0.7774, 0.9276, 0.9518,
+                      0.4507, 0.3399, 0.7495, 0.4581, 0.6140, 0.0659, 0.8137,
+                      0.4343, 0.4836, 0.5681, 0.7947, 0.1417, 0.8229, 0.0824,
+                      0.2070, 0.8783, 0.3511, 0.9580, 0.1053, 0.3375, 0.2396,
+                      0.0513, 0.5334, 0.3977, 0.6765, 0.1035, 0.8974, 0.8093,
+                      0.7238, 0.8002, 0.6243, 0.9654, 0.2803]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4617, 0.6014, 0.4133,  ..., 0.3579, 0.3877, 0.5185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.89356017112732 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([3878, 1793,  196, 4523, 2590, 2367,  223, 4753,  811,
+                           1344, 3831, 3891, 1471,  342,   75, 2706, 3424, 2402,
+                           4777, 1498,  772, 4383, 2094, 2326, 2946, 1126, 2886,
+                           1923,  932, 4969, 3541,  896,  863, 2668, 4869, 4410,
+                           1937, 3764,   94, 3879, 1282, 4972, 2002, 1481, 4954,
+                           2173, 3425, 2770, 4498,  897,  827,  829, 4189, 4991,
+                           1934, 1179, 1128, 2114, 2395, 2017, 3561, 4691, 2694,
+                           1416, 4554,  620, 4382, 4973, 3582,  230, 4891, 3107,
+                           1979,  140,  933, 4466,  308, 3556, 3173, 4882, 4797,
+                           1238, 3925, 3354,  291, 1290,  414, 2445, 3042, 2325,
+                           2660, 1731,  777, 3782, 2858, 3541, 2112, 2412, 4004,
+                            942, 1750, 1450,   45,  960, 1184, 3190, 4107, 4960,
+                           4877, 4158, 1493, 3953, 2210, 1987, 3321, 3595, 1029,
+                           1281, 3168, 3718, 1747, 2899, 1694, 1765, 2658, 2297,
+                           2811, 4794, 2166, 3385, 3144,  590,  756, 1857, 2472,
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+                             92, 3486,  728,  546, 1233, 1206, 3098,  284, 3217,
+                           1908, 3538,  556, 1018, 3457, 4789, 3509,  986, 1353,
+                           3714,  674, 3739, 3917,  378, 4295, 1240,  678,  634,
+                            156,  182, 2959,  787, 2431,  894, 4155, 1278, 4710,
+                            115, 3800, 3528, 4651, 4055, 3457, 4797, 3790, 2898,
+                           2898, 1677, 2106, 3532, 1869, 3926, 1788, 4954, 1802,
+                           3662, 3116, 1672, 1899, 2743, 4402,  201, 2790, 4915,
+                           3309, 2448, 4340,   71, 1083, 3547, 1833, 4517, 4811,
+                           4522, 4837, 4905, 1773, 2748, 2712, 2488, 4842, 2297,
+                            377, 2695, 2905,  534, 1022, 2504, 1436, 3486, 3980,
+                           4913,  361, 4684, 2741,  722, 1718, 3274,  513, 1785,
+                           4555,  575,  662, 3842, 1584, 2198,  215]),
+       values=tensor([0.8076, 0.3370, 0.6780, 0.9299, 0.5410, 0.0897, 0.3343,
+                      0.8017, 0.5673, 0.5602, 0.9800, 0.8553, 0.3447, 0.6119,
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+                      0.0517, 0.1797, 0.1796, 0.7215, 0.5955, 0.6432, 0.0017,
+                      0.6486, 0.6664, 0.4487, 0.7630, 0.7774, 0.9276, 0.9518,
+                      0.4507, 0.3399, 0.7495, 0.4581, 0.6140, 0.0659, 0.8137,
+                      0.4343, 0.4836, 0.5681, 0.7947, 0.1417, 0.8229, 0.0824,
+                      0.2070, 0.8783, 0.3511, 0.9580, 0.1053, 0.3375, 0.2396,
+                      0.0513, 0.5334, 0.3977, 0.6765, 0.1035, 0.8974, 0.8093,
+                      0.7238, 0.8002, 0.6243, 0.9654, 0.2803]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4617, 0.6014, 0.4133,  ..., 0.3579, 0.3877, 0.5185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.89356017112732 seconds
+
+[16.36, 16.36, 16.4, 16.6, 16.6, 16.96, 16.96, 16.88, 16.88, 16.48]
+[16.32, 16.28, 19.04, 20.36, 23.52, 24.24, 24.96, 24.96, 22.16, 21.36, 19.68, 19.76, 19.8, 19.64]
+14.221415042877197
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 293134, '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': 10.89356017112732, 'TIME_S_1KI': 0.03716239048055606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.4485914421082, 'W': 19.57952781791354}
+[16.36, 16.36, 16.4, 16.6, 16.6, 16.96, 16.96, 16.88, 16.88, 16.48, 16.6, 16.64, 16.68, 16.76, 16.68, 16.56, 16.4, 16.4, 16.52, 16.64]
+299.32000000000005
+14.966000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 293134, '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': 10.89356017112732, 'TIME_S_1KI': 0.03716239048055606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.4485914421082, 'W': 19.57952781791354, 'J_1KI': 0.9499020633638819, 'W_1KI': 0.06679377969772711, 'W_D': 4.613527817913537, 'J_D': 65.610893910408, 'W_D_1KI': 0.01573863085794735, 'J_D_1KI': 5.3690908792386244e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json
deleted file mode 100644
index d8def16..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 9519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.79372525215149, "TIME_S_1KI": 2.2894973476364626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 607.9340667724609, "W": 24.009013571692563, "J_1KI": 63.86532900225454, "W_1KI": 2.5222201462015508, "W_D": 5.522013571692561, "J_D": 139.82332749271384, "W_D_1KI": 0.5801043777384768, "J_D_1KI": 0.06094173523883567}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output
deleted file mode 100644
index 91dd9e3..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['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 1000 -ss 30000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.2060210704803467}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     6,  ..., 89996, 89998, 90000]),
-       col_indices=tensor([ 2876,  4713,  6957,  ..., 29701, 15647, 23288]),
-       values=tensor([0.6297, 0.3832, 0.4268,  ..., 0.4020, 0.1713, 0.6526]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.2297, 0.3740, 0.0656,  ..., 0.6156, 0.3028, 0.9303])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 2.2060210704803467 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 9519 -ss 30000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.79372525215149}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     3,  ..., 89997, 89999, 90000]),
-       col_indices=tensor([15244, 15936,  9998,  ..., 16898, 18863, 20836]),
-       values=tensor([0.4356, 0.9410, 0.0325,  ..., 0.8568, 0.9195, 0.8628]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.3669, 0.2405, 0.0914,  ..., 0.8449, 0.6451, 0.3598])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 21.79372525215149 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     3,  ..., 89997, 89999, 90000]),
-       col_indices=tensor([15244, 15936,  9998,  ..., 16898, 18863, 20836]),
-       values=tensor([0.4356, 0.9410, 0.0325,  ..., 0.8568, 0.9195, 0.8628]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.3669, 0.2405, 0.0914,  ..., 0.8449, 0.6451, 0.3598])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 21.79372525215149 seconds
-
-[20.48, 20.6, 20.6, 20.52, 20.52, 20.76, 20.88, 21.04, 21.0, 21.0]
-[20.96, 20.88, 23.92, 25.0, 27.36, 27.36, 28.16, 28.88, 25.92, 25.64, 24.4, 24.24, 24.2, 24.16, 24.24, 24.84, 24.92, 24.92, 24.6, 24.64, 24.64, 24.68, 24.56, 24.8, 24.96]
-25.32107639312744
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 9519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.79372525215149, 'TIME_S_1KI': 2.2894973476364626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 607.9340667724609, 'W': 24.009013571692563}
-[20.48, 20.6, 20.6, 20.52, 20.52, 20.76, 20.88, 21.04, 21.0, 21.0, 20.24, 20.28, 20.44, 20.36, 20.2, 20.24, 20.24, 20.44, 20.52, 20.48]
-369.74
-18.487000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 9519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.79372525215149, 'TIME_S_1KI': 2.2894973476364626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 607.9340667724609, 'W': 24.009013571692563, 'J_1KI': 63.86532900225454, 'W_1KI': 2.5222201462015508, 'W_D': 5.522013571692561, 'J_D': 139.82332749271384, 'W_D_1KI': 0.5801043777384768, 'J_D_1KI': 0.06094173523883567}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json
deleted file mode 100644
index d309fe1..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.699798345565796, "TIME_S_1KI": 20.699798345565796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 537.3931253051758, "W": 23.043866328673488, "J_1KI": 537.3931253051758, "W_1KI": 23.043866328673488, "W_D": 4.700866328673488, "J_D": 109.62627590250972, "W_D_1KI": 4.700866328673488, "J_D_1KI": 4.700866328673488}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output
deleted file mode 100644
index 67d8d59..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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 1000 -ss 30000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.699798345565796}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     33,     63,  ..., 899945, 899974,
-                            900000]),
-       col_indices=tensor([  547,  1664,  1767,  ..., 28485, 29124, 29514]),
-       values=tensor([0.5453, 0.3696, 0.4974,  ..., 0.8638, 0.8625, 0.2546]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.4458, 0.9665, 0.0852,  ..., 0.0100, 0.1262, 0.9671])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 20.699798345565796 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     33,     63,  ..., 899945, 899974,
-                            900000]),
-       col_indices=tensor([  547,  1664,  1767,  ..., 28485, 29124, 29514]),
-       values=tensor([0.5453, 0.3696, 0.4974,  ..., 0.8638, 0.8625, 0.2546]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.4458, 0.9665, 0.0852,  ..., 0.0100, 0.1262, 0.9671])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 20.699798345565796 seconds
-
-[20.6, 20.52, 20.4, 20.52, 20.64, 20.44, 20.4, 20.36, 20.16, 20.16]
-[20.16, 20.08, 20.2, 21.68, 22.88, 25.96, 26.92, 27.04, 26.52, 24.64, 24.28, 23.92, 24.12, 24.12, 24.48, 24.6, 24.4, 24.32, 24.28, 24.36, 24.16, 24.4, 24.32]
-23.320441007614136
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.699798345565796, 'TIME_S_1KI': 20.699798345565796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 537.3931253051758, 'W': 23.043866328673488}
-[20.6, 20.52, 20.4, 20.52, 20.64, 20.44, 20.4, 20.36, 20.16, 20.16, 20.44, 20.2, 20.16, 20.16, 20.4, 20.44, 20.56, 20.52, 20.28, 20.2]
-366.86
-18.343
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.699798345565796, 'TIME_S_1KI': 20.699798345565796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 537.3931253051758, 'W': 23.043866328673488, 'J_1KI': 537.3931253051758, 'W_1KI': 23.043866328673488, 'W_D': 4.700866328673488, 'J_D': 109.62627590250972, 'W_D_1KI': 4.700866328673488, 'J_D_1KI': 4.700866328673488}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.json
deleted file mode 100644
index e69de29..0000000
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output
deleted file mode 100644
index 054bc13..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output
+++ /dev/null
@@ -1 +0,0 @@
-['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 1000 -ss 30000 -sd 0.01 -c 16']
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json
deleted file mode 100644
index 00f9500..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52473, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.229777336120605, "TIME_S_1KI": 0.40458478333849035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 561.8060250091554, "W": 23.114903822014995, "J_1KI": 10.706573380770212, "W_1KI": 0.4405104305455185, "W_D": 4.660903822014994, "J_D": 113.28292210769662, "W_D_1KI": 0.08882480174594543, "J_D_1KI": 0.0016927715538647577}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output
deleted file mode 100644
index a0c66f4..0000000
--- a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['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 1000 -ss 30000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.4002048969268799}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 8999, 9000, 9000]),
-       col_indices=tensor([17165, 27151, 23572,  ..., 25119,  9148,  7528]),
-       values=tensor([0.4884, 0.2785, 0.9649,  ..., 0.5831, 0.3229, 0.8447]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.9734, 0.5614, 0.1566,  ..., 0.4974, 0.8204, 0.0911])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 0.4002048969268799 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 52473 -ss 30000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.229777336120605}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 8998, 9000, 9000]),
-       col_indices=tensor([25247, 22356, 16191,  ..., 29211, 15014, 22819]),
-       values=tensor([0.9864, 0.6356, 0.7247,  ..., 0.1858, 0.6120, 0.1833]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.8210, 0.2318, 0.4195,  ..., 0.3881, 0.9911, 0.4380])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 21.229777336120605 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 8998, 9000, 9000]),
-       col_indices=tensor([25247, 22356, 16191,  ..., 29211, 15014, 22819]),
-       values=tensor([0.9864, 0.6356, 0.7247,  ..., 0.1858, 0.6120, 0.1833]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.8210, 0.2318, 0.4195,  ..., 0.3881, 0.9911, 0.4380])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 21.229777336120605 seconds
-
-[20.6, 20.72, 20.72, 20.72, 20.72, 20.6, 20.28, 20.32, 20.44, 20.48]
-[20.48, 20.52, 20.6, 21.72, 23.52, 25.24, 26.32, 26.68, 25.64, 24.84, 24.92, 24.56, 24.64, 24.64, 24.44, 24.12, 24.32, 24.4, 24.32, 24.44, 24.28, 24.08, 24.04, 23.96]
-24.3049259185791
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.229777336120605, 'TIME_S_1KI': 0.40458478333849035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 561.8060250091554, 'W': 23.114903822014995}
-[20.6, 20.72, 20.72, 20.72, 20.72, 20.6, 20.28, 20.32, 20.44, 20.48, 20.76, 20.68, 20.68, 20.44, 20.48, 20.24, 20.24, 20.32, 20.36, 20.4]
-369.08000000000004
-18.454
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.229777336120605, 'TIME_S_1KI': 0.40458478333849035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 561.8060250091554, 'W': 23.114903822014995, 'J_1KI': 10.706573380770212, 'W_1KI': 0.4405104305455185, 'W_D': 4.660903822014994, 'J_D': 113.28292210769662, 'W_D_1KI': 0.08882480174594543, 'J_D_1KI': 0.0016927715538647577}
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 9b3aaee..f73c7ea 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": 66220, "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": 10.006447315216064, "TIME_S_1KI": 0.15110914097275843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1856.6265121269225, "W": 145.81, "J_1KI": 28.037247238401125, "W_1KI": 2.2019027484143763, "W_D": 109.5725, "J_D": 1395.2075200605393, "W_D_1KI": 1.6546738145575355, "J_D_1KI": 0.024987523626661668}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 63031, "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": 10.264819622039795, "TIME_S_1KI": 0.16285351052719765, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1907.2002365589144, "W": 143.8, "J_1KI": 30.25813070646054, "W_1KI": 2.2814170804842067, "W_D": 106.65425000000002, "J_D": 1414.5411045202616, "W_D_1KI": 1.6920919864828419, "J_D_1KI": 0.026845393322061237}
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 9d496c4..b2fc454 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
@@ -1,14 +1,54 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.20569086074829102}
+{"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": 0.20868682861328125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999978,
+tensor(crow_indices=tensor([      0,      11,      23,  ...,  999975,
+                             999990, 1000000]),
+       col_indices=tensor([ 1102,  1885,  5689,  ..., 70464, 82505, 82637]),
+       values=tensor([0.9145, 0.6563, 0.0210,  ..., 0.3467, 0.9517, 0.4307]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3954, 0.8531, 0.4592,  ..., 0.1653, 0.9288, 0.8508])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.20868682861328125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50314', '-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": 8.38151502609253}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      15,  ...,  999976,
+                             999987, 1000000]),
+       col_indices=tensor([ 9326, 16949, 19479,  ..., 70135, 76689, 93251]),
+       values=tensor([0.2491, 0.4486, 0.5526,  ..., 0.3620, 0.8491, 0.1510]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1294, 0.2549, 0.0676,  ..., 0.6377, 0.6452, 0.0657])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 8.38151502609253 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63031', '-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": 10.264819622039795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,
                              999986, 1000000]),
-       col_indices=tensor([ 4321, 11912, 13631,  ..., 82074, 92560, 99324]),
-       values=tensor([0.9071, 0.2919, 0.8193,  ..., 0.7739, 0.0445, 0.1624]),
+       col_indices=tensor([ 1906, 11602, 20474,  ..., 94634, 95193, 99629]),
+       values=tensor([0.7595, 0.5479, 0.3671,  ..., 0.3196, 0.4186, 0.5082]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6567, 0.9688, 0.9697,  ..., 0.6873, 0.4864, 0.9023])
+tensor([0.5397, 0.2720, 0.7091,  ..., 0.7919, 0.2241, 0.5973])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.20569086074829102 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '51047', '-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": 8.09404468536377}
+Time: 10.264819622039795 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      17,  ...,  999979,
-                             999988, 1000000]),
-       col_indices=tensor([15686, 48109, 49313,  ..., 51931, 56127, 66767]),
-       values=tensor([0.4545, 0.6496, 0.9508,  ..., 0.7270, 0.9957, 0.0621]),
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
+                             999986, 1000000]),
+       col_indices=tensor([ 1906, 11602, 20474,  ..., 94634, 95193, 99629]),
+       values=tensor([0.7595, 0.5479, 0.3671,  ..., 0.3196, 0.4186, 0.5082]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3660, 0.6002, 0.9317,  ..., 0.1977, 0.4107, 0.4541])
+tensor([0.5397, 0.2720, 0.7091,  ..., 0.7919, 0.2241, 0.5973])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,50 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 8.09404468536377 seconds
+Time: 10.264819622039795 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66220', '-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": 10.006447315216064}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999980,
-                             999990, 1000000]),
-       col_indices=tensor([ 4776, 21129, 24622,  ..., 75160, 86654, 97411]),
-       values=tensor([0.8410, 0.1609, 0.8553,  ..., 0.3742, 0.0938, 0.8797]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6086, 0.7634, 0.6649,  ..., 0.3430, 0.9091, 0.5785])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 10.006447315216064 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999980,
-                             999990, 1000000]),
-       col_indices=tensor([ 4776, 21129, 24622,  ..., 75160, 86654, 97411]),
-       values=tensor([0.8410, 0.1609, 0.8553,  ..., 0.3742, 0.0938, 0.8797]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6086, 0.7634, 0.6649,  ..., 0.3430, 0.9091, 0.5785])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 10.006447315216064 seconds
-
-[41.04, 40.85, 39.33, 39.23, 39.35, 39.32, 44.72, 39.63, 39.87, 39.35]
-[145.81]
-12.733190536499023
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66220, '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': 10.006447315216064, 'TIME_S_1KI': 0.15110914097275843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1856.6265121269225, 'W': 145.81}
-[41.04, 40.85, 39.33, 39.23, 39.35, 39.32, 44.72, 39.63, 39.87, 39.35, 40.83, 39.21, 39.3, 44.69, 39.3, 39.36, 39.77, 39.25, 41.13, 39.66]
-724.75
-36.2375
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66220, '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': 10.006447315216064, 'TIME_S_1KI': 0.15110914097275843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1856.6265121269225, 'W': 145.81, 'J_1KI': 28.037247238401125, 'W_1KI': 2.2019027484143763, 'W_D': 109.5725, 'J_D': 1395.2075200605393, 'W_D_1KI': 1.6546738145575355, 'J_D_1KI': 0.024987523626661668}
+[40.75, 39.66, 39.67, 39.19, 39.32, 45.15, 39.35, 39.19, 39.95, 39.54]
+[143.8]
+13.262866735458374
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 63031, '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': 10.264819622039795, 'TIME_S_1KI': 0.16285351052719765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1907.2002365589144, 'W': 143.8}
+[40.75, 39.66, 39.67, 39.19, 39.32, 45.15, 39.35, 39.19, 39.95, 39.54, 40.42, 44.44, 57.71, 39.25, 40.02, 40.75, 39.74, 39.58, 39.68, 39.82]
+742.915
+37.14575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 63031, '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': 10.264819622039795, 'TIME_S_1KI': 0.16285351052719765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1907.2002365589144, 'W': 143.8, 'J_1KI': 30.25813070646054, 'W_1KI': 2.2814170804842067, 'W_D': 106.65425000000002, 'J_D': 1414.5411045202616, 'W_D_1KI': 1.6920919864828419, 'J_D_1KI': 0.026845393322061237}
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
new file mode 100644
index 0000000..47e1eaa
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4290, "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": 10.725477457046509, "TIME_S_1KI": 2.500111295348837, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2014.465433692932, "W": 126.69, "J_1KI": 469.57236216618463, "W_1KI": 29.53146853146853, "W_D": 91.17699999999999, "J_D": 1449.7822625923156, "W_D_1KI": 21.25337995337995, "J_D_1KI": 4.954167821300688}
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
new file mode 100644
index 0000000..2b06cc0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_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-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 2.4475483894348145}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      211,  ...,  9999795,
+                             9999912, 10000000]),
+       col_indices=tensor([  147,  1138,  2699,  ..., 95915, 96101, 99505]),
+       values=tensor([0.5370, 0.7637, 0.8320,  ..., 0.1671, 0.6910, 0.1145]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0022, 0.6683, 0.3307,  ..., 0.4747, 0.3475, 0.4636])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 2.4475483894348145 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4290', '-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": 10.725477457046509}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      113,      209,  ...,  9999816,
+                             9999914, 10000000]),
+       col_indices=tensor([  524,  3053,  3097,  ..., 98248, 99944, 99996]),
+       values=tensor([0.2951, 0.6504, 0.4617,  ..., 0.6241, 0.9747, 0.0943]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1529, 0.0141, 0.4287,  ..., 0.1937, 0.2308, 0.9820])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.725477457046509 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      113,      209,  ...,  9999816,
+                             9999914, 10000000]),
+       col_indices=tensor([  524,  3053,  3097,  ..., 98248, 99944, 99996]),
+       values=tensor([0.2951, 0.6504, 0.4617,  ..., 0.6241, 0.9747, 0.0943]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1529, 0.0141, 0.4287,  ..., 0.1937, 0.2308, 0.9820])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.725477457046509 seconds
+
+[39.92, 39.19, 39.59, 39.28, 39.81, 39.63, 39.63, 39.45, 39.41, 39.18]
+[126.69]
+15.900745391845703
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4290, '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': 10.725477457046509, 'TIME_S_1KI': 2.500111295348837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2014.465433692932, 'W': 126.69}
+[39.92, 39.19, 39.59, 39.28, 39.81, 39.63, 39.63, 39.45, 39.41, 39.18, 40.85, 39.15, 39.3, 39.24, 39.74, 39.48, 39.55, 39.09, 39.1, 39.29]
+710.26
+35.513
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4290, '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': 10.725477457046509, 'TIME_S_1KI': 2.500111295348837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2014.465433692932, 'W': 126.69, 'J_1KI': 469.57236216618463, 'W_1KI': 29.53146853146853, 'W_D': 91.17699999999999, 'J_D': 1449.7822625923156, 'W_D_1KI': 21.25337995337995, 'J_D_1KI': 4.954167821300688}
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 b72a71c..7f413fc 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": 101854, "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": 13.331042528152466, "TIME_S_1KI": 0.13088383890816724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1504.6270782256126, "W": 115.31, "J_1KI": 14.77239065943029, "W_1KI": 1.1321106682113615, "W_D": 79.84075000000001, "J_D": 1041.8051721085908, "W_D_1KI": 0.7838744673748701, "J_D_1KI": 0.007696059726420858}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102924, "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": 10.60866904258728, "TIME_S_1KI": 0.103072840567674, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1542.0244372987747, "W": 115.47, "J_1KI": 14.982165843717448, "W_1KI": 1.121895767750962, "W_D": 79.97325000000001, "J_D": 1067.989138565898, "W_D_1KI": 0.7770126501107615, "J_D_1KI": 0.00754938255519375}
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 3cc7b50..fb29049 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', '1000', '-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": 0.1346125602722168}
+{"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": 0.12978029251098633}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      3,  ...,  99997,  99999,
+tensor(crow_indices=tensor([     0,      3,      4,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([50727, 53996, 86356,  ...,  6143, 63321, 22305]),
-       values=tensor([0.4164, 0.0014, 0.4337,  ..., 0.6487, 0.2549, 0.7487]),
+       col_indices=tensor([21616, 77637, 85619,  ..., 53732, 81470,  6094]),
+       values=tensor([0.4857, 0.1991, 0.9153,  ..., 0.9203, 0.8308, 0.8562]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9720, 0.1729, 0.4503,  ..., 0.2850, 0.8795, 0.9664])
+tensor([0.0197, 0.8164, 0.2872,  ..., 0.9903, 0.3891, 0.9778])
 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: 0.1346125602722168 seconds
+Time: 0.12978029251098633 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78001', '-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": 8.040945768356323}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '80905', '-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": 8.253613233566284}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([16049, 52557, 57673,  ..., 90883, 73385, 65676]),
-       values=tensor([0.2845, 0.3961, 0.0285,  ..., 0.0101, 0.6896, 0.8511]),
+       col_indices=tensor([18950, 61338, 17160,  ..., 57514, 79997, 96494]),
+       values=tensor([0.7220, 0.1840, 0.6067,  ..., 0.9597, 0.4652, 0.5228]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5851, 0.1832, 0.4128,  ..., 0.6645, 0.1519, 0.8981])
+tensor([0.0221, 0.6414, 0.1516,  ..., 0.3018, 0.8902, 0.3461])
 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: 8.040945768356323 seconds
+Time: 8.253613233566284 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '101854', '-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": 13.331042528152466}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102924', '-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": 10.60866904258728}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([  641, 46150, 85524,  ..., 87101, 55219, 61785]),
-       values=tensor([0.2560, 0.7953, 0.3517,  ..., 0.8505, 0.5170, 0.2719]),
+       col_indices=tensor([ 4611, 80501,  8771,  ..., 95435, 27789, 45343]),
+       values=tensor([0.8274, 0.0201, 0.6109,  ..., 0.4116, 0.6491, 0.0785]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1951, 0.6662, 0.1969,  ..., 0.4780, 0.9904, 0.5617])
+tensor([0.0461, 0.3256, 0.3375,  ..., 0.6234, 0.9526, 0.7301])
 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: 13.331042528152466 seconds
+Time: 10.60866904258728 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([  641, 46150, 85524,  ..., 87101, 55219, 61785]),
-       values=tensor([0.2560, 0.7953, 0.3517,  ..., 0.8505, 0.5170, 0.2719]),
+       col_indices=tensor([ 4611, 80501,  8771,  ..., 95435, 27789, 45343]),
+       values=tensor([0.8274, 0.0201, 0.6109,  ..., 0.4116, 0.6491, 0.0785]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1951, 0.6662, 0.1969,  ..., 0.4780, 0.9904, 0.5617])
+tensor([0.0461, 0.3256, 0.3375,  ..., 0.6234, 0.9526, 0.7301])
 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: 13.331042528152466 seconds
+Time: 10.60866904258728 seconds
 
-[42.16, 39.6, 39.44, 39.17, 39.28, 39.19, 39.17, 39.11, 40.48, 39.44]
-[115.31]
-13.048539400100708
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 101854, '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': 13.331042528152466, 'TIME_S_1KI': 0.13088383890816724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1504.6270782256126, 'W': 115.31}
-[42.16, 39.6, 39.44, 39.17, 39.28, 39.19, 39.17, 39.11, 40.48, 39.44, 39.93, 39.1, 39.15, 39.29, 39.15, 39.23, 39.19, 39.11, 39.43, 39.06]
-709.3849999999999
-35.469249999999995
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 101854, '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': 13.331042528152466, 'TIME_S_1KI': 0.13088383890816724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1504.6270782256126, 'W': 115.31, 'J_1KI': 14.77239065943029, 'W_1KI': 1.1321106682113615, 'W_D': 79.84075000000001, 'J_D': 1041.8051721085908, 'W_D_1KI': 0.7838744673748701, 'J_D_1KI': 0.007696059726420858}
+[39.93, 39.22, 39.43, 40.14, 39.51, 39.36, 39.3, 39.3, 39.26, 39.1]
+[115.47]
+13.354329586029053
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102924, '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': 10.60866904258728, 'TIME_S_1KI': 0.103072840567674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1542.0244372987747, 'W': 115.47}
+[39.93, 39.22, 39.43, 40.14, 39.51, 39.36, 39.3, 39.3, 39.26, 39.1, 41.76, 39.08, 39.78, 39.45, 39.66, 39.16, 39.27, 39.06, 39.08, 38.96]
+709.935
+35.49675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102924, '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': 10.60866904258728, 'TIME_S_1KI': 0.103072840567674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1542.0244372987747, 'W': 115.47, 'J_1KI': 14.982165843717448, 'W_1KI': 1.121895767750962, 'W_D': 79.97325000000001, 'J_D': 1067.989138565898, 'W_D_1KI': 0.7770126501107615, 'J_D_1KI': 0.00754938255519375}
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 9b9bfd5..cb11125 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": 282693, "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": 10.381328821182251, "TIME_S_1KI": 0.0367229780050523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.42685277462, "W": 97.9, "J_1KI": 4.5364648320779795, "W_1KI": 0.3463120770588589, "W_D": 62.39075000000001, "J_D": 817.2785818666817, "W_D_1KI": 0.22070143229581213, "J_D_1KI": 0.00078071063767342}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 278690, "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": 10.3841392993927, "TIME_S_1KI": 0.0372605378714439, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1292.3170569992064, "W": 98.52, "J_1KI": 4.63711312569237, "W_1KI": 0.3535110696472783, "W_D": 63.16824999999999, "J_D": 828.5973095390796, "W_D_1KI": 0.2266613441458251, "J_D_1KI": 0.0008133099291177477}
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 b8e2459..26aaa7d 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', '1000', '-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": 0.05349230766296387}
+{"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": 0.05305743217468262}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3626, 2250, 5764,  ..., 7539, 8316, 7972]),
-       values=tensor([0.1411, 0.7419, 0.4018,  ..., 0.4202, 0.3955, 0.4235]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([2207,  830, 7633,  ..., 2513, 8541, 2972]),
+       values=tensor([0.9417, 0.1071, 0.2127,  ..., 0.2034, 0.4535, 0.3737]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9736, 0.6802, 0.3390,  ..., 0.1575, 0.6861, 0.0446])
+tensor([0.2095, 0.5712, 0.5435,  ..., 0.2564, 0.5818, 0.1577])
 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: 0.05349230766296387 seconds
+Time: 0.05305743217468262 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '196289', '-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": 7.290691137313843}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '197898', '-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": 7.456049680709839}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9998, 10000, 10000]),
-       col_indices=tensor([ 763, 7857, 9582,  ..., 1442, 6306, 9133]),
-       values=tensor([0.7701, 0.8887, 0.1796,  ..., 0.1701, 0.0666, 0.3737]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([7930, 9951, 4041,  ..., 9045, 6420, 8503]),
+       values=tensor([0.2418, 0.2435, 0.4116,  ..., 0.5201, 0.9725, 0.0713]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4503, 0.2095, 0.3791,  ..., 0.5528, 0.9269, 0.0093])
+tensor([0.5895, 0.0291, 0.5304,  ..., 0.4324, 0.9976, 0.6205])
 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: 7.290691137313843 seconds
+Time: 7.456049680709839 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '282693', '-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": 10.381328821182251}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '278690', '-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": 10.3841392993927}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9997,  9998, 10000]),
-       col_indices=tensor([4956,  145,  658,  ..., 4096, 6098, 6574]),
-       values=tensor([0.3279, 0.7076, 0.5307,  ..., 0.3493, 0.0702, 0.3289]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([9574, 4944, 2003,  ..., 2641, 7523, 8416]),
+       values=tensor([0.4157, 0.2537, 0.8916,  ..., 0.3966, 0.1591, 0.0732]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0837, 0.8046, 0.5398,  ..., 0.6704, 0.0489, 0.7610])
+tensor([0.4368, 0.0363, 0.2687,  ..., 0.3029, 0.2331, 0.6830])
 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: 10.381328821182251 seconds
+Time: 10.3841392993927 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9997,  9998, 10000]),
-       col_indices=tensor([4956,  145,  658,  ..., 4096, 6098, 6574]),
-       values=tensor([0.3279, 0.7076, 0.5307,  ..., 0.3493, 0.0702, 0.3289]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([9574, 4944, 2003,  ..., 2641, 7523, 8416]),
+       values=tensor([0.4157, 0.2537, 0.8916,  ..., 0.3966, 0.1591, 0.0732]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0837, 0.8046, 0.5398,  ..., 0.6704, 0.0489, 0.7610])
+tensor([0.4368, 0.0363, 0.2687,  ..., 0.3029, 0.2331, 0.6830])
 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: 10.381328821182251 seconds
+Time: 10.3841392993927 seconds
 
-[39.48, 38.98, 44.27, 38.82, 39.35, 38.99, 39.28, 39.0, 38.75, 39.31]
-[97.9]
-13.099354982376099
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 282693, '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': 10.381328821182251, 'TIME_S_1KI': 0.0367229780050523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.42685277462, 'W': 97.9}
-[39.48, 38.98, 44.27, 38.82, 39.35, 38.99, 39.28, 39.0, 38.75, 39.31, 39.89, 39.14, 38.98, 38.75, 41.57, 38.58, 39.15, 38.62, 39.12, 38.99]
-710.185
-35.509249999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 282693, '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': 10.381328821182251, 'TIME_S_1KI': 0.0367229780050523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.42685277462, 'W': 97.9, 'J_1KI': 4.5364648320779795, 'W_1KI': 0.3463120770588589, 'W_D': 62.39075000000001, 'J_D': 817.2785818666817, 'W_D_1KI': 0.22070143229581213, 'J_D_1KI': 0.00078071063767342}
+[40.74, 38.88, 38.93, 39.04, 38.99, 38.97, 39.42, 39.32, 39.26, 38.71]
+[98.52]
+13.11730670928955
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 278690, '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': 10.3841392993927, 'TIME_S_1KI': 0.0372605378714439, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1292.3170569992064, 'W': 98.52}
+[40.74, 38.88, 38.93, 39.04, 38.99, 38.97, 39.42, 39.32, 39.26, 38.71, 39.37, 39.1, 39.73, 39.04, 39.15, 38.73, 39.2, 38.61, 38.78, 44.95]
+707.0350000000001
+35.35175
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 278690, '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': 10.3841392993927, 'TIME_S_1KI': 0.0372605378714439, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1292.3170569992064, 'W': 98.52, 'J_1KI': 4.63711312569237, 'W_1KI': 0.3535110696472783, 'W_D': 63.16824999999999, 'J_D': 828.5973095390796, 'W_D_1KI': 0.2266613441458251, 'J_D_1KI': 0.0008133099291177477}
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 d2eaa5f..9de9eeb 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": 189141, "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": 10.465899229049683, "TIME_S_1KI": 0.05533384738924761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1388.9149661660194, "W": 107.77, "J_1KI": 7.343278116146259, "W_1KI": 0.5697865613484121, "W_D": 72.38875, "J_D": 932.9295560643077, "W_D_1KI": 0.3827237352028381, "J_D_1KI": 0.002023483724855204}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 181643, "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": 10.058825254440308, "TIME_S_1KI": 0.05537689453730839, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1334.3994569778442, "W": 108.0, "J_1KI": 7.346275149484671, "W_1KI": 0.5945728709611711, "W_D": 72.86524999999999, "J_D": 900.2902780792116, "W_D_1KI": 0.4011453785722543, "J_D_1KI": 0.002208427401949177}
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 117af51..62ff1c2 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', '1000', '-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": 0.06988883018493652}
+{"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": 0.07387351989746094}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99977,  99988,
+tensor(crow_indices=tensor([     0,     11,     20,  ...,  99983,  99988,
                             100000]),
-       col_indices=tensor([ 768, 2423, 2910,  ..., 9615, 9787, 9788]),
-       values=tensor([0.1330, 0.2030, 0.8709,  ..., 0.6786, 0.0798, 0.8357]),
+       col_indices=tensor([2080, 2520, 2867,  ..., 8307, 8901, 9286]),
+       values=tensor([0.8261, 0.1055, 0.9939,  ..., 0.1447, 0.1951, 0.2617]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0016, 0.6011, 0.7478,  ..., 0.9565, 0.9755, 0.4110])
+tensor([0.7373, 0.8108, 0.8070,  ..., 0.3032, 0.8916, 0.0356])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,20 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.06988883018493652 seconds
+Time: 0.07387351989746094 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '150238', '-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": 8.34029221534729}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '142134', '-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": 8.216149806976318}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99979,  99987,
+tensor(crow_indices=tensor([     0,      8,     16,  ...,  99977,  99988,
                             100000]),
-       col_indices=tensor([ 978, 1327, 2112,  ..., 8470, 8534, 8708]),
-       values=tensor([0.4296, 0.3021, 0.5865,  ..., 0.4657, 0.4173, 0.7957]),
+       col_indices=tensor([ 929, 1145, 1167,  ..., 7253, 9439, 9881]),
+       values=tensor([3.5267e-01, 8.9746e-01, 4.0379e-01,  ...,
+                      8.5718e-04, 5.6681e-01, 4.6851e-01]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7639, 0.4914, 0.7736,  ..., 0.7926, 0.8542, 0.7117])
+tensor([0.4055, 0.0658, 0.7904,  ..., 0.2959, 0.0826, 0.7426])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +37,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 8.34029221534729 seconds
+Time: 8.216149806976318 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '189141', '-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": 10.465899229049683}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '181643', '-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": 10.058825254440308}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99985,  99995,
+tensor(crow_indices=tensor([     0,     10,     23,  ...,  99984,  99996,
                             100000]),
-       col_indices=tensor([ 277, 3135, 4455,  ..., 4161, 8684, 9934]),
-       values=tensor([0.4295, 0.8999, 0.7885,  ..., 0.8935, 0.6648, 0.4808]),
+       col_indices=tensor([2026, 2065, 2399,  ..., 4623, 7297, 9355]),
+       values=tensor([0.4157, 0.6883, 0.2119,  ..., 0.3441, 0.2622, 0.5721]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1477, 0.6711, 0.3568,  ..., 0.3604, 0.6617, 0.9866])
+tensor([0.4888, 0.3451, 0.6891,  ..., 0.9797, 0.8702, 0.1612])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +57,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.465899229049683 seconds
+Time: 10.058825254440308 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99985,  99995,
+tensor(crow_indices=tensor([     0,     10,     23,  ...,  99984,  99996,
                             100000]),
-       col_indices=tensor([ 277, 3135, 4455,  ..., 4161, 8684, 9934]),
-       values=tensor([0.4295, 0.8999, 0.7885,  ..., 0.8935, 0.6648, 0.4808]),
+       col_indices=tensor([2026, 2065, 2399,  ..., 4623, 7297, 9355]),
+       values=tensor([0.4157, 0.6883, 0.2119,  ..., 0.3441, 0.2622, 0.5721]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1477, 0.6711, 0.3568,  ..., 0.3604, 0.6617, 0.9866])
+tensor([0.4888, 0.3451, 0.6891,  ..., 0.9797, 0.8702, 0.1612])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +74,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.465899229049683 seconds
+Time: 10.058825254440308 seconds
 
-[39.55, 39.91, 39.11, 39.43, 38.85, 39.56, 39.33, 39.4, 38.87, 38.91]
-[107.77]
-12.887769937515259
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 189141, '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': 10.465899229049683, 'TIME_S_1KI': 0.05533384738924761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1388.9149661660194, 'W': 107.77}
-[39.55, 39.91, 39.11, 39.43, 38.85, 39.56, 39.33, 39.4, 38.87, 38.91, 39.56, 38.88, 39.03, 38.89, 39.75, 39.07, 39.18, 39.11, 38.8, 42.89]
-707.6249999999999
-35.381249999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 189141, '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': 10.465899229049683, 'TIME_S_1KI': 0.05533384738924761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1388.9149661660194, 'W': 107.77, 'J_1KI': 7.343278116146259, 'W_1KI': 0.5697865613484121, 'W_D': 72.38875, 'J_D': 932.9295560643077, 'W_D_1KI': 0.3827237352028381, 'J_D_1KI': 0.002023483724855204}
+[40.01, 39.91, 39.26, 39.32, 39.03, 38.69, 39.03, 38.68, 38.9, 38.67]
+[108.0]
+12.355550527572632
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 181643, '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': 10.058825254440308, 'TIME_S_1KI': 0.05537689453730839, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.3994569778442, 'W': 108.0}
+[40.01, 39.91, 39.26, 39.32, 39.03, 38.69, 39.03, 38.68, 38.9, 38.67, 40.03, 39.23, 39.15, 39.23, 38.85, 38.69, 38.72, 38.72, 38.62, 38.62]
+702.6950000000002
+35.13475000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 181643, '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': 10.058825254440308, 'TIME_S_1KI': 0.05537689453730839, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.3994569778442, 'W': 108.0, 'J_1KI': 7.346275149484671, 'W_1KI': 0.5945728709611711, 'W_D': 72.86524999999999, 'J_D': 900.2902780792116, 'W_D_1KI': 0.4011453785722543, 'J_D_1KI': 0.002208427401949177}
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 a1716dd..37875a8 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": 105256, "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": 10.995163202285767, "TIME_S_1KI": 0.10446115378017184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1749.1652185320856, "W": 133.05, "J_1KI": 16.61819961362854, "W_1KI": 1.264060956145018, "W_D": 97.78125000000001, "J_D": 1285.4983955249193, "W_D_1KI": 0.928985045983127, "J_D_1KI": 0.008825958101990642}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 104114, "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": 10.469164609909058, "TIME_S_1KI": 0.10055482077250953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1774.261237783432, "W": 135.86, "J_1KI": 17.041524077294426, "W_1KI": 1.304915765410992, "W_D": 100.35275000000001, "J_D": 1310.5549420725108, "W_D_1KI": 0.9638737345601938, "J_D_1KI": 0.009257868630157269}
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 a290fe5..0c99b95 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', '1000', '-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": 0.13490986824035645}
+{"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": 0.14159297943115234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     107,     208,  ...,  999789,
-                             999899, 1000000]),
-       col_indices=tensor([ 114,  296,  309,  ..., 9749, 9750, 9977]),
-       values=tensor([0.3507, 0.7412, 0.8612,  ..., 0.2456, 0.4049, 0.8296]),
+tensor(crow_indices=tensor([      0,     124,     236,  ...,  999773,
+                             999882, 1000000]),
+       col_indices=tensor([  35,   69,  144,  ..., 9773, 9862, 9873]),
+       values=tensor([0.1838, 0.7773, 0.5109,  ..., 0.8192, 0.8376, 0.6812]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8457, 0.6850, 0.0016,  ..., 0.7234, 0.0569, 0.9899])
+tensor([0.0358, 0.2032, 0.7087,  ..., 0.4931, 0.1706, 0.1726])
 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: 0.13490986824035645 seconds
+Time: 0.14159297943115234 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77829', '-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": 7.763918876647949}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '74156', '-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": 7.9134438037872314}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     200,  ...,  999783,
-                             999885, 1000000]),
-       col_indices=tensor([   5,   70,  184,  ..., 9826, 9903, 9930]),
-       values=tensor([0.4822, 0.0560, 0.4645,  ..., 0.7540, 0.5324, 0.2081]),
+tensor(crow_indices=tensor([      0,      93,     199,  ...,  999798,
+                             999892, 1000000]),
+       col_indices=tensor([  57,  323,  325,  ..., 9719, 9779, 9889]),
+       values=tensor([0.3339, 0.1610, 0.8675,  ..., 0.7107, 0.3615, 0.1870]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2499, 0.5119, 0.0857,  ..., 0.6236, 0.3822, 0.7230])
+tensor([0.9536, 0.3002, 0.1616,  ..., 0.3121, 0.8413, 0.9505])
 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: 7.763918876647949 seconds
+Time: 7.9134438037872314 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '105256', '-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": 10.995163202285767}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '98394', '-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": 9.923112392425537}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     112,     220,  ...,  999823,
-                             999909, 1000000]),
-       col_indices=tensor([  16,   85,  154,  ..., 9645, 9832, 9858]),
-       values=tensor([0.5111, 0.0405, 0.8270,  ..., 0.3072, 0.2885, 0.2472]),
+tensor(crow_indices=tensor([      0,      97,     191,  ...,  999779,
+                             999891, 1000000]),
+       col_indices=tensor([  18,   52,  269,  ..., 9883, 9995, 9999]),
+       values=tensor([0.5511, 0.2767, 0.8168,  ..., 0.6887, 0.5827, 0.0686]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6255, 0.9183, 0.8326,  ..., 0.9246, 0.2373, 0.5392])
+tensor([0.2767, 0.4380, 0.7945,  ..., 0.2102, 0.5547, 0.8740])
 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: 10.995163202285767 seconds
+Time: 9.923112392425537 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '104114', '-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": 10.469164609909058}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     112,     220,  ...,  999823,
-                             999909, 1000000]),
-       col_indices=tensor([  16,   85,  154,  ..., 9645, 9832, 9858]),
-       values=tensor([0.5111, 0.0405, 0.8270,  ..., 0.3072, 0.2885, 0.2472]),
+tensor(crow_indices=tensor([      0,     112,     221,  ...,  999805,
+                             999915, 1000000]),
+       col_indices=tensor([ 402,  501,  665,  ..., 9291, 9326, 9607]),
+       values=tensor([0.0486, 0.5637, 0.4384,  ..., 0.7973, 0.3634, 0.8351]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6255, 0.9183, 0.8326,  ..., 0.9246, 0.2373, 0.5392])
+tensor([0.7936, 0.9785, 0.9590,  ..., 0.6005, 0.0137, 0.6516])
 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: 10.995163202285767 seconds
+Time: 10.469164609909058 seconds
 
-[40.1, 38.91, 39.01, 39.37, 38.97, 38.99, 39.2, 38.87, 38.93, 38.8]
-[133.05]
-13.146675825119019
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105256, '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': 10.995163202285767, 'TIME_S_1KI': 0.10446115378017184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1749.1652185320856, 'W': 133.05}
-[40.1, 38.91, 39.01, 39.37, 38.97, 38.99, 39.2, 38.87, 38.93, 38.8, 40.08, 39.28, 39.91, 38.88, 38.99, 38.83, 39.08, 39.35, 39.95, 38.73]
-705.375
-35.26875
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105256, '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': 10.995163202285767, 'TIME_S_1KI': 0.10446115378017184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1749.1652185320856, 'W': 133.05, 'J_1KI': 16.61819961362854, 'W_1KI': 1.264060956145018, 'W_D': 97.78125000000001, 'J_D': 1285.4983955249193, 'W_D_1KI': 0.928985045983127, 'J_D_1KI': 0.008825958101990642}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     221,  ...,  999805,
+                             999915, 1000000]),
+       col_indices=tensor([ 402,  501,  665,  ..., 9291, 9326, 9607]),
+       values=tensor([0.0486, 0.5637, 0.4384,  ..., 0.7973, 0.3634, 0.8351]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7936, 0.9785, 0.9590,  ..., 0.6005, 0.0137, 0.6516])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.469164609909058 seconds
+
+[42.04, 39.8, 39.79, 39.52, 39.3, 39.73, 39.12, 39.49, 39.14, 38.98]
+[135.86]
+13.059482097625732
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 104114, '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': 10.469164609909058, 'TIME_S_1KI': 0.10055482077250953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1774.261237783432, 'W': 135.86}
+[42.04, 39.8, 39.79, 39.52, 39.3, 39.73, 39.12, 39.49, 39.14, 38.98, 39.91, 39.55, 39.06, 39.42, 39.43, 39.34, 38.96, 39.36, 38.94, 39.46]
+710.145
+35.50725
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 104114, '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': 10.469164609909058, 'TIME_S_1KI': 0.10055482077250953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1774.261237783432, 'W': 135.86, 'J_1KI': 17.041524077294426, 'W_1KI': 1.304915765410992, 'W_D': 100.35275000000001, 'J_D': 1310.5549420725108, 'W_D_1KI': 0.9638737345601938, 'J_D_1KI': 0.009257868630157269}
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 0aa6e19..1c24924 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": 27486, "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": 10.233055591583252, "TIME_S_1KI": 0.3723006472961963, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2077.8737717533113, "W": 151.69, "J_1KI": 75.59753226199925, "W_1KI": 5.518809575784036, "W_D": 115.92275, "J_D": 1587.9282864692211, "W_D_1KI": 4.217519828276212, "J_D_1KI": 0.1534424735602202}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27505, "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": 10.25606393814087, "TIME_S_1KI": 0.37287998320817556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2107.3890700149536, "W": 153.89000000000001, "J_1KI": 76.6183992006891, "W_1KI": 5.594982730412653, "W_D": 118.26825000000002, "J_D": 1619.580332573891, "W_D_1KI": 4.299881839665516, "J_D_1KI": 0.156330915821324}
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 a8a40a5..536ca18 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', '1000', '-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": 0.45975399017333984}
+{"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": 0.4628758430480957}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1006,  ..., 4999034,
-                            4999489, 5000000]),
-       col_indices=tensor([  23,   40,  103,  ..., 9927, 9976, 9991]),
-       values=tensor([0.6183, 0.2980, 0.3566,  ..., 0.0352, 0.5258, 0.0852]),
+tensor(crow_indices=tensor([      0,     508,     974,  ..., 4999019,
+                            4999492, 5000000]),
+       col_indices=tensor([   9,   34,   50,  ..., 9951, 9957, 9978]),
+       values=tensor([0.7868, 0.5776, 0.2287,  ..., 0.8734, 0.0439, 0.0393]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4623, 0.5953, 0.6862,  ..., 0.1082, 0.6720, 0.4260])
+tensor([0.6523, 0.3584, 0.2115,  ..., 0.2592, 0.0051, 0.7390])
 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: 0.45975399017333984 seconds
+Time: 0.4628758430480957 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22838', '-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": 8.724292278289795}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22684', '-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": 8.659529685974121}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1006,  ..., 4998953,
-                            4999498, 5000000]),
-       col_indices=tensor([  69,   83,  128,  ..., 9917, 9953, 9972]),
-       values=tensor([0.6637, 0.2623, 0.2360,  ..., 0.3507, 0.8119, 0.6229]),
+tensor(crow_indices=tensor([      0,     519,    1023,  ..., 4999047,
+                            4999545, 5000000]),
+       col_indices=tensor([   4,   44,   83,  ..., 9892, 9941, 9972]),
+       values=tensor([0.8741, 0.5769, 0.9569,  ..., 0.2090, 0.9404, 0.5070]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8552, 0.8520, 0.0158,  ..., 0.2551, 0.9127, 0.4905])
+tensor([0.1912, 0.0895, 0.2612,  ..., 0.6252, 0.2980, 0.9838])
 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: 8.724292278289795 seconds
+Time: 8.659529685974121 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27486', '-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": 10.233055591583252}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27505', '-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": 10.25606393814087}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     518,    1031,  ..., 4999018,
-                            4999521, 5000000]),
-       col_indices=tensor([   2,   29,   76,  ..., 9919, 9923, 9942]),
-       values=tensor([0.8327, 0.8899, 0.2406,  ..., 0.0134, 0.9622, 0.2874]),
+tensor(crow_indices=tensor([      0,     494,     944,  ..., 4998986,
+                            4999507, 5000000]),
+       col_indices=tensor([  48,   74,   75,  ..., 9915, 9966, 9976]),
+       values=tensor([0.3182, 0.9601, 0.3370,  ..., 0.9931, 0.0889, 0.2292]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5989, 0.5463, 0.4311,  ..., 0.3886, 0.2295, 0.1764])
+tensor([0.0431, 0.2285, 0.4438,  ..., 0.2766, 0.7465, 0.1407])
 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: 10.233055591583252 seconds
+Time: 10.25606393814087 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     518,    1031,  ..., 4999018,
-                            4999521, 5000000]),
-       col_indices=tensor([   2,   29,   76,  ..., 9919, 9923, 9942]),
-       values=tensor([0.8327, 0.8899, 0.2406,  ..., 0.0134, 0.9622, 0.2874]),
+tensor(crow_indices=tensor([      0,     494,     944,  ..., 4998986,
+                            4999507, 5000000]),
+       col_indices=tensor([  48,   74,   75,  ..., 9915, 9966, 9976]),
+       values=tensor([0.3182, 0.9601, 0.3370,  ..., 0.9931, 0.0889, 0.2292]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5989, 0.5463, 0.4311,  ..., 0.3886, 0.2295, 0.1764])
+tensor([0.0431, 0.2285, 0.4438,  ..., 0.2766, 0.7465, 0.1407])
 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: 10.233055591583252 seconds
+Time: 10.25606393814087 seconds
 
-[45.15, 39.18, 39.48, 39.74, 39.27, 40.48, 39.56, 39.63, 39.62, 39.49]
-[151.69]
-13.698159217834473
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27486, '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': 10.233055591583252, 'TIME_S_1KI': 0.3723006472961963, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2077.8737717533113, 'W': 151.69}
-[45.15, 39.18, 39.48, 39.74, 39.27, 40.48, 39.56, 39.63, 39.62, 39.49, 40.16, 40.86, 39.65, 39.13, 39.26, 39.59, 39.63, 39.11, 39.14, 39.23]
-715.345
-35.767250000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27486, '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': 10.233055591583252, 'TIME_S_1KI': 0.3723006472961963, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2077.8737717533113, 'W': 151.69, 'J_1KI': 75.59753226199925, 'W_1KI': 5.518809575784036, 'W_D': 115.92275, 'J_D': 1587.9282864692211, 'W_D_1KI': 4.217519828276212, 'J_D_1KI': 0.1534424735602202}
+[40.36, 40.12, 40.4, 39.54, 39.59, 39.1, 39.19, 40.07, 39.54, 39.22]
+[153.89]
+13.69412612915039
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27505, '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': 10.25606393814087, 'TIME_S_1KI': 0.37287998320817556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2107.3890700149536, 'W': 153.89000000000001}
+[40.36, 40.12, 40.4, 39.54, 39.59, 39.1, 39.19, 40.07, 39.54, 39.22, 40.73, 39.18, 39.33, 39.63, 39.65, 39.27, 39.19, 39.18, 39.78, 39.04]
+712.435
+35.62175
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27505, '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': 10.25606393814087, 'TIME_S_1KI': 0.37287998320817556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2107.3890700149536, 'W': 153.89000000000001, 'J_1KI': 76.6183992006891, 'W_1KI': 5.594982730412653, 'W_D': 118.26825000000002, 'J_D': 1619.580332573891, 'W_D_1KI': 4.299881839665516, 'J_D_1KI': 0.156330915821324}
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
new file mode 100644
index 0000000..3ce07d9
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4716, "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": 10.072229146957397, "TIME_S_1KI": 2.1357568165728154, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1865.214413022995, "W": 124.6, "J_1KI": 395.5077211668777, "W_1KI": 26.42069550466497, "W_D": 88.853, "J_D": 1330.0954754440784, "W_D_1KI": 18.84075487701442, "J_D_1KI": 3.9950710086968657}
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
new file mode 100644
index 0000000..3a1030a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 2.2264370918273926}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1050,     2086,  ...,  9997973,
+                             9998977, 10000000]),
+       col_indices=tensor([   7,   18,   19,  ..., 9986, 9989, 9991]),
+       values=tensor([0.4594, 0.3854, 0.2627,  ..., 0.1030, 0.9821, 0.5221]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7604, 0.6286, 0.0952,  ..., 0.1700, 0.1146, 0.5013])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 2.2264370918273926 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4716', '-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": 10.072229146957397}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2032,  ...,  9997988,
+                             9999031, 10000000]),
+       col_indices=tensor([   8,   19,   25,  ..., 9964, 9974, 9989]),
+       values=tensor([0.6442, 0.9503, 0.4324,  ..., 0.4734, 0.5264, 0.7582]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7346, 0.1525, 0.9122,  ..., 0.8135, 0.4141, 0.4880])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.072229146957397 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2032,  ...,  9997988,
+                             9999031, 10000000]),
+       col_indices=tensor([   8,   19,   25,  ..., 9964, 9974, 9989]),
+       values=tensor([0.6442, 0.9503, 0.4324,  ..., 0.4734, 0.5264, 0.7582]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7346, 0.1525, 0.9122,  ..., 0.8135, 0.4141, 0.4880])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.072229146957397 seconds
+
+[40.38, 39.18, 39.76, 39.52, 39.78, 39.24, 39.97, 39.14, 39.12, 39.09]
+[124.6]
+14.969618082046509
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4716, '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': 10.072229146957397, 'TIME_S_1KI': 2.1357568165728154, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1865.214413022995, 'W': 124.6}
+[40.38, 39.18, 39.76, 39.52, 39.78, 39.24, 39.97, 39.14, 39.12, 39.09, 39.77, 39.88, 39.63, 39.31, 39.15, 39.05, 39.3, 39.01, 43.67, 41.22]
+714.94
+35.747
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4716, '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': 10.072229146957397, 'TIME_S_1KI': 2.1357568165728154, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1865.214413022995, 'W': 124.6, 'J_1KI': 395.5077211668777, 'W_1KI': 26.42069550466497, 'W_D': 88.853, 'J_D': 1330.0954754440784, 'W_D_1KI': 18.84075487701442, 'J_D_1KI': 3.9950710086968657}
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 cda3d72..6beae0d 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": 375977, "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": 10.999524116516113, "TIME_S_1KI": 0.029255843087518954, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.755545105934, "W": 96.41999999999999, "J_1KI": 3.5208418203930933, "W_1KI": 0.25645185742744897, "W_D": 61.132499999999986, "J_D": 839.2914941006896, "W_D_1KI": 0.16259638222550846, "J_D_1KI": 0.0004324636406628822}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 355068, "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": 10.454679489135742, "TIME_S_1KI": 0.029444161369472165, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1283.4049695920944, "W": 97.06, "J_1KI": 3.614532905224054, "W_1KI": 0.27335608953777873, "W_D": 61.48125, "J_D": 812.9542735084892, "W_D_1KI": 0.17315345229646154, "J_D_1KI": 0.0004876627921875853}
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 e9eebf5..f912c9b 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
@@ -1,373 +1,266 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.04503059387207031}
+{"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": 0.1263408660888672}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4113,  614, 2519, 8527, 7416, 2734,  949, 6682,  484,
-                           5512, 2710, 4041, 4037, 3756, 4925, 4764, 8722, 3874,
-                           8352, 2481, 3774, 8294, 3589, 6629, 1741,  283, 3355,
-                           5134, 1636, 5257, 6914, 8377, 9479, 3405, 2807, 6603,
-                            730, 4849, 7221, 7178, 5773, 4547, 9572, 5072, 5733,
-                           8766, 8040, 7105, 6968, 6795, 4519, 4433, 7044, 2666,
-                           5807, 2089, 4272, 1275, 3276,  409, 2016, 5940, 4287,
-                           7005, 5810, 8597, 1286, 8246, 5523, 3085, 4475, 3444,
-                           5153, 3360, 5524, 9599, 3802, 5759, 6854, 9537, 9505,
-                           7933, 4849, 4073, 6294, 3565, 5654, 9049, 3619, 8438,
-                           2201, 1301,  373, 5050,  213, 3319, 2294, 9757, 2234,
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+                      0.7086, 0.4515, 0.4407, 0.9257, 0.9829, 0.8739, 0.9325,
+                      0.3778, 0.1118, 0.7555, 0.1293, 0.6795, 0.0619, 0.8626,
+                      0.7469, 0.8528, 0.2420, 0.3647, 0.2182, 0.8272, 0.9530,
+                      0.5556, 0.3834, 0.2160, 0.8095, 0.1449, 0.1013]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.9668, 0.9511, 0.4479,  ..., 0.0607, 0.6261, 0.2898])
+tensor([0.7754, 0.7775, 0.1646,  ..., 0.9136, 0.8074, 0.4658])
 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: 0.04503059387207031 seconds
+Time: 0.1263408660888672 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '233174', '-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": 7.088708162307739}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '83108', '-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": 2.457648515701294}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+                      7.3674e-01, 5.8085e-01, 3.4811e-01, 4.7061e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.2611, 0.6478, 0.1138,  ..., 0.3633, 0.3210, 0.9692])
+tensor([0.8610, 0.2031, 0.4276,  ..., 0.7862, 0.1485, 0.1233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,378 +647,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 7.088708162307739 seconds
+Time: 2.457648515701294 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '345384', '-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": 9.645604133605957}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '355068', '-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": 10.454679489135742}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7495, 0.5234, 0.3924,  ..., 0.9829, 0.4941, 0.4696])
+tensor([0.1885, 0.9110, 0.8668,  ..., 0.5988, 0.5354, 0.4490])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,378 +1026,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 9.645604133605957 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '375977', '-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": 10.999524116516113}
+Time: 10.454679489135742 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+                      1.2136e-01, 2.2091e-01, 6.2259e-01, 8.8417e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7103, 0.5485, 0.7256,  ..., 0.6866, 0.8523, 0.5088])
+tensor([0.1885, 0.9110, 0.8668,  ..., 0.5988, 0.5354, 0.4490])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1512,389 +1402,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.999524116516113 seconds
+Time: 10.454679489135742 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the 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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-                      4.3737e-01, 4.8044e-02, 6.7611e-01, 9.4603e-01]),
-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7103, 0.5485, 0.7256,  ..., 0.6866, 0.8523, 0.5088])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 10.999524116516113 seconds
-
-[39.86, 39.2, 39.16, 38.85, 39.2, 39.62, 38.88, 38.71, 38.95, 38.67]
-[96.42]
-13.729055643081665
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 375977, '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': 10.999524116516113, 'TIME_S_1KI': 0.029255843087518954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.755545105934, 'W': 96.41999999999999}
-[39.86, 39.2, 39.16, 38.85, 39.2, 39.62, 38.88, 38.71, 38.95, 38.67, 40.17, 39.41, 39.19, 38.69, 38.88, 39.53, 40.39, 39.3, 39.0, 38.88]
-705.75
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-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 375977, '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': 10.999524116516113, 'TIME_S_1KI': 0.029255843087518954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.755545105934, 'W': 96.41999999999999, 'J_1KI': 3.5208418203930933, 'W_1KI': 0.25645185742744897, 'W_D': 61.132499999999986, 'J_D': 839.2914941006896, 'W_D_1KI': 0.16259638222550846, 'J_D_1KI': 0.0004324636406628822}
+[39.64, 44.74, 39.71, 38.92, 39.06, 38.91, 39.09, 38.87, 39.14, 39.48]
+[97.06]
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+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 355068, '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': 10.454679489135742, 'TIME_S_1KI': 0.029444161369472165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.4049695920944, 'W': 97.06}
+[39.64, 44.74, 39.71, 38.92, 39.06, 38.91, 39.09, 38.87, 39.14, 39.48, 40.2, 40.78, 38.95, 38.87, 38.88, 38.91, 39.42, 39.12, 39.01, 39.07]
+711.575
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+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 355068, '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': 10.454679489135742, 'TIME_S_1KI': 0.029444161369472165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.4049695920944, 'W': 97.06, 'J_1KI': 3.614532905224054, 'W_1KI': 0.27335608953777873, 'W_D': 61.48125, 'J_D': 812.9542735084892, 'W_D_1KI': 0.17315345229646154, 'J_D_1KI': 0.0004876627921875853}
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 7e41519..98c0109 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": 21375, "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": 10.636817216873169, "TIME_S_1KI": 0.4976288756431892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2053.7217113614083, "W": 152.01000000000002, "J_1KI": 96.08054789994893, "W_1KI": 7.111578947368422, "W_D": 116.32275000000001, "J_D": 1571.5713255724313, "W_D_1KI": 5.442000000000001, "J_D_1KI": 0.2545964912280702}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21497, "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": 10.492619514465332, "TIME_S_1KI": 0.488096921173435, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2075.744820713997, "W": 155.25, "J_1KI": 96.55974418356035, "W_1KI": 7.221937944829511, "W_D": 119.6075, "J_D": 1599.1925838553905, "W_D_1KI": 5.5639158952411965, "J_D_1KI": 0.25882290064851826}
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 e90e199..4dbd4ff 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 +1,57 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.5442898273468018}
+{"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": 0.5443899631500244}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      11,  ..., 2499988,
+tensor(crow_indices=tensor([      0,       7,      13,  ..., 2499984,
+                            2499993, 2500000]),
+       col_indices=tensor([ 29642,  73796, 205405,  ..., 362365, 387524,
+                           440531]),
+       values=tensor([0.6565, 0.4150, 0.8341,  ..., 0.7997, 0.8212, 0.8706]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3188, 0.4041, 0.2486,  ..., 0.5189, 0.6175, 0.2446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.5443899631500244 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19287', '-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": 9.420541286468506}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499994,
+                            2499998, 2500000]),
+       col_indices=tensor([131466, 192610, 285983,  ..., 398857,   7127,
+                           216070]),
+       values=tensor([0.3766, 0.1095, 0.0818,  ..., 0.7673, 0.9998, 0.7256]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6672, 0.9862, 0.6354,  ..., 0.4943, 0.9100, 0.2548])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.420541286468506 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21497', '-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": 10.492619514465332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      12,  ..., 2499992,
                             2499996, 2500000]),
-       col_indices=tensor([ 37839,  98870, 148404,  ..., 161688, 445826,
-                           487462]),
-       values=tensor([0.2708, 0.4230, 0.0396,  ..., 0.5012, 0.9237, 0.4084]),
+       col_indices=tensor([124091, 157764, 160136,  ..., 120950, 171105,
+                           490445]),
+       values=tensor([0.1739, 0.8424, 0.0842,  ..., 0.2028, 0.9911, 0.7243]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6604, 0.4578, 0.9008,  ..., 0.1692, 0.6250, 0.2013])
+tensor([0.9645, 0.6044, 0.9036,  ..., 0.9779, 0.7664, 0.8298])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +59,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.5442898273468018 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19291', '-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": 9.475887298583984}
+Time: 10.492619514465332 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      11,  ..., 2499990,
-                            2499994, 2500000]),
-       col_indices=tensor([  2997,  16168, 106256,  ..., 284595, 359619,
-                           400100]),
-       values=tensor([0.5956, 0.5098, 0.7367,  ..., 0.1293, 0.8182, 0.3844]),
+tensor(crow_indices=tensor([      0,      10,      12,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([124091, 157764, 160136,  ..., 120950, 171105,
+                           490445]),
+       values=tensor([0.1739, 0.8424, 0.0842,  ..., 0.2028, 0.9911, 0.7243]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4741, 0.3124, 0.4103,  ..., 0.8230, 0.7925, 0.1055])
+tensor([0.9645, 0.6044, 0.9036,  ..., 0.9779, 0.7664, 0.8298])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,52 +77,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 9.475887298583984 seconds
+Time: 10.492619514465332 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21375', '-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": 10.636817216873169}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499987,
-                            2499990, 2500000]),
-       col_indices=tensor([ 69634, 109368, 119504,  ..., 397654, 413765,
-                           480494]),
-       values=tensor([0.1977, 0.6347, 0.9236,  ..., 0.5996, 0.0558, 0.7507]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2633, 0.4244, 0.4182,  ..., 0.0717, 0.3446, 0.9616])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 10.636817216873169 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499987,
-                            2499990, 2500000]),
-       col_indices=tensor([ 69634, 109368, 119504,  ..., 397654, 413765,
-                           480494]),
-       values=tensor([0.1977, 0.6347, 0.9236,  ..., 0.5996, 0.0558, 0.7507]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2633, 0.4244, 0.4182,  ..., 0.0717, 0.3446, 0.9616])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 10.636817216873169 seconds
-
-[39.92, 39.18, 39.71, 40.61, 40.15, 39.79, 39.41, 39.75, 39.22, 39.64]
-[152.01]
-13.510438203811646
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21375, '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': 10.636817216873169, 'TIME_S_1KI': 0.4976288756431892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2053.7217113614083, 'W': 152.01000000000002}
-[39.92, 39.18, 39.71, 40.61, 40.15, 39.79, 39.41, 39.75, 39.22, 39.64, 39.92, 39.78, 39.31, 39.34, 39.44, 40.15, 39.73, 39.14, 39.7, 39.19]
-713.7450000000001
-35.687250000000006
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21375, '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': 10.636817216873169, 'TIME_S_1KI': 0.4976288756431892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2053.7217113614083, 'W': 152.01000000000002, 'J_1KI': 96.08054789994893, 'W_1KI': 7.111578947368422, 'W_D': 116.32275000000001, 'J_D': 1571.5713255724313, 'W_D_1KI': 5.442000000000001, 'J_D_1KI': 0.2545964912280702}
+[41.35, 39.34, 39.96, 39.31, 39.6, 39.93, 39.84, 39.39, 39.3, 39.31]
+[155.25]
+13.370337009429932
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21497, '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': 10.492619514465332, 'TIME_S_1KI': 0.488096921173435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2075.744820713997, 'W': 155.25}
+[41.35, 39.34, 39.96, 39.31, 39.6, 39.93, 39.84, 39.39, 39.3, 39.31, 40.05, 40.08, 39.33, 39.83, 39.32, 39.88, 39.28, 39.25, 39.29, 39.13]
+712.85
+35.6425
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21497, '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': 10.492619514465332, 'TIME_S_1KI': 0.488096921173435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2075.744820713997, 'W': 155.25, 'J_1KI': 96.55974418356035, 'W_1KI': 7.221937944829511, 'W_D': 119.6075, 'J_D': 1599.1925838553905, 'W_D_1KI': 5.5639158952411965, 'J_D_1KI': 0.25882290064851826}
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 7a8ab5e..c71ff3b 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": 88993, "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": 10.460110664367676, "TIME_S_1KI": 0.11753857791475371, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1554.5358743476868, "W": 116.24, "J_1KI": 17.468069110465844, "W_1KI": 1.3061701482139043, "W_D": 80.32, "J_D": 1074.1596819305419, "W_D_1KI": 0.9025428966323192, "J_D_1KI": 0.010141729086920537}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91834, "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": 10.82576298713684, "TIME_S_1KI": 0.11788404062914433, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1647.9860616016388, "W": 116.97, "J_1KI": 17.945271485524305, "W_1KI": 1.273711261624235, "W_D": 80.83175, "J_D": 1138.8355760867596, "W_D_1KI": 0.8801941546703835, "J_D_1KI": 0.009584621759592129}
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 82a28ba..504044c 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', '1000', '-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": 0.1613328456878662}
+{"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": 0.14647722244262695}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249987, 249990,
+tensor(crow_indices=tensor([     0,      6,      8,  ..., 249988, 249995,
                             250000]),
-       col_indices=tensor([ 2831, 11435, 18332,  ..., 36257, 39398, 40541]),
-       values=tensor([0.1158, 0.5239, 0.2299,  ..., 0.2166, 0.7808, 0.4412]),
+       col_indices=tensor([  544,  6056, 19594,  ..., 16208, 31107, 37035]),
+       values=tensor([0.8576, 0.5005, 0.2810,  ..., 0.0063, 0.7171, 0.8258]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7586, 0.4736, 0.7326,  ..., 0.5631, 0.8162, 0.2413])
+tensor([0.4318, 0.7107, 0.2576,  ..., 0.8496, 0.3705, 0.3608])
 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: 0.1613328456878662 seconds
+Time: 0.14647722244262695 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65082', '-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": 8.079791784286499}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71683', '-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": 9.233005046844482}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249989, 249995,
+tensor(crow_indices=tensor([     0,      7,     10,  ..., 249988, 249994,
                             250000]),
-       col_indices=tensor([ 9506, 10457, 11174,  ..., 14178, 16522, 25750]),
-       values=tensor([0.5729, 0.5279, 0.3744,  ..., 0.1961, 0.5511, 0.6709]),
+       col_indices=tensor([ 4979, 12449, 23825,  ..., 32585, 40358, 48594]),
+       values=tensor([0.7825, 0.8569, 0.5029,  ..., 0.3250, 0.4106, 0.3303]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0404, 0.4787, 0.7701,  ..., 0.8815, 0.0868, 0.4305])
+tensor([0.8033, 0.4755, 0.5204,  ..., 0.8611, 0.9528, 0.0172])
 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: 8.079791784286499 seconds
+Time: 9.233005046844482 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84576', '-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": 9.978835582733154}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '81519', '-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": 9.805182695388794}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 249996,
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 249983, 249992,
                             250000]),
-       col_indices=tensor([26217, 28400, 13678,  ..., 15637, 35417, 48424]),
-       values=tensor([0.3837, 0.9571, 0.9616,  ..., 0.3970, 0.1960, 0.8766]),
+       col_indices=tensor([ 7422, 17911, 31055,  ..., 30707, 32021, 38558]),
+       values=tensor([0.7718, 0.8036, 0.8293,  ..., 0.2159, 0.0251, 0.0647]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6737, 0.6555, 0.0878,  ..., 0.0726, 0.6482, 0.1469])
+tensor([0.3183, 0.3041, 0.1046,  ..., 0.2603, 0.8118, 0.2097])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,19 +56,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 9.978835582733154 seconds
+Time: 9.805182695388794 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '88993', '-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": 10.460110664367676}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '87295', '-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": 9.980920553207397}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249992, 249993,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 249989, 249993,
                             250000]),
-       col_indices=tensor([ 7470, 20811, 24121,  ..., 36968, 38743, 40607]),
-       values=tensor([0.2685, 0.7271, 0.6618,  ..., 0.0403, 0.7886, 0.4035]),
+       col_indices=tensor([19530, 21432, 40127,  ..., 33319, 45642, 48654]),
+       values=tensor([0.8438, 0.0330, 0.2387,  ..., 0.6115, 0.5796, 0.5067]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0454, 0.0390, 0.3317,  ..., 0.3195, 0.9524, 0.5758])
+tensor([0.1992, 0.5617, 0.3460,  ..., 0.4818, 0.9372, 0.6597])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -76,16 +76,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.460110664367676 seconds
+Time: 9.980920553207397 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91834', '-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": 10.82576298713684}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249992, 249993,
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 249987, 249995,
                             250000]),
-       col_indices=tensor([ 7470, 20811, 24121,  ..., 36968, 38743, 40607]),
-       values=tensor([0.2685, 0.7271, 0.6618,  ..., 0.0403, 0.7886, 0.4035]),
+       col_indices=tensor([ 2714,  5631, 18387,  ..., 39061, 48792, 49070]),
+       values=tensor([0.1970, 0.9435, 0.9859,  ..., 0.7944, 0.6863, 0.0587]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0454, 0.0390, 0.3317,  ..., 0.3195, 0.9524, 0.5758])
+tensor([0.5128, 0.8861, 0.8900,  ..., 0.3721, 0.4809, 0.7353])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -93,13 +96,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.460110664367676 seconds
+Time: 10.82576298713684 seconds
 
-[40.38, 39.72, 39.57, 39.21, 40.98, 39.68, 39.65, 39.04, 39.23, 43.6]
-[116.24]
-13.373502016067505
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88993, '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': 10.460110664367676, 'TIME_S_1KI': 0.11753857791475371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1554.5358743476868, 'W': 116.24}
-[40.38, 39.72, 39.57, 39.21, 40.98, 39.68, 39.65, 39.04, 39.23, 43.6, 39.84, 40.48, 39.37, 39.22, 39.24, 38.98, 44.18, 39.09, 39.18, 39.34]
-718.4
-35.92
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88993, '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': 10.460110664367676, 'TIME_S_1KI': 0.11753857791475371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1554.5358743476868, 'W': 116.24, 'J_1KI': 17.468069110465844, 'W_1KI': 1.3061701482139043, 'W_D': 80.32, 'J_D': 1074.1596819305419, 'W_D_1KI': 0.9025428966323192, 'J_D_1KI': 0.010141729086920537}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249987, 249995,
+                            250000]),
+       col_indices=tensor([ 2714,  5631, 18387,  ..., 39061, 48792, 49070]),
+       values=tensor([0.1970, 0.9435, 0.9859,  ..., 0.7944, 0.6863, 0.0587]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5128, 0.8861, 0.8900,  ..., 0.3721, 0.4809, 0.7353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.82576298713684 seconds
+
+[40.37, 39.3, 39.3, 39.19, 39.7, 39.15, 40.14, 44.33, 39.43, 39.81]
+[116.97]
+14.088963508605957
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91834, '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': 10.82576298713684, 'TIME_S_1KI': 0.11788404062914433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1647.9860616016388, 'W': 116.97}
+[40.37, 39.3, 39.3, 39.19, 39.7, 39.15, 40.14, 44.33, 39.43, 39.81, 40.26, 39.09, 41.97, 44.11, 39.87, 39.01, 40.55, 38.99, 38.97, 38.89]
+722.7650000000001
+36.138250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91834, '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': 10.82576298713684, 'TIME_S_1KI': 0.11788404062914433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1647.9860616016388, 'W': 116.97, 'J_1KI': 17.945271485524305, 'W_1KI': 1.273711261624235, 'W_D': 80.83175, 'J_D': 1138.8355760867596, 'W_D_1KI': 0.8801941546703835, 'J_D_1KI': 0.009584621759592129}
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 19e233d..3676d30 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": 46287, "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": 11.077528476715088, "TIME_S_1KI": 0.23932267108940064, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2013.3453536748884, "W": 148.1, "J_1KI": 43.49699383573981, "W_1KI": 3.1996024801780196, "W_D": 112.52425, "J_D": 1529.7108434385657, "W_D_1KI": 2.4310119471989973, "J_D_1KI": 0.052520404156652996}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46775, "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": 10.762548208236694, "TIME_S_1KI": 0.2300918911434889, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2033.0924378275872, "W": 149.27, "J_1KI": 43.46536478519695, "W_1KI": 3.1912346338856232, "W_D": 113.87475, "J_D": 1551.0008245763183, "W_D_1KI": 2.434521646178514, "J_D_1KI": 0.052047496444222636}
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 a9b5d6b..3112aac 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', '1000', '-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": 0.2967829704284668}
+{"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": 0.29659008979797363}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     108,  ..., 2499894,
-                            2499951, 2500000]),
-       col_indices=tensor([  368,  1693,  4088,  ..., 44885, 46596, 47442]),
-       values=tensor([0.5982, 0.3592, 0.7042,  ..., 0.6155, 0.2314, 0.2925]),
+tensor(crow_indices=tensor([      0,      46,      83,  ..., 2499888,
+                            2499946, 2500000]),
+       col_indices=tensor([ 2168,  2264,  3614,  ..., 46868, 47216, 48811]),
+       values=tensor([0.2788, 0.0512, 0.3475,  ..., 0.9281, 0.1898, 0.0144]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7227, 0.5816, 0.4934,  ..., 0.3583, 0.6407, 0.9822])
+tensor([0.5080, 0.1629, 0.0847,  ..., 0.6599, 0.4582, 0.2341])
 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: 0.2967829704284668 seconds
+Time: 0.29659008979797363 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35379', '-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": 8.025555610656738}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35402', '-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": 7.946921348571777}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      96,  ..., 2499914,
-                            2499959, 2500000]),
-       col_indices=tensor([  123,   723,   909,  ..., 47588, 48779, 49819]),
-       values=tensor([0.6654, 0.3505, 0.8901,  ..., 0.8476, 0.5107, 0.1185]),
+tensor(crow_indices=tensor([      0,      54,     117,  ..., 2499905,
+                            2499953, 2500000]),
+       col_indices=tensor([ 1300,  1442,  2491,  ..., 47415, 49147, 49910]),
+       values=tensor([0.1149, 0.9707, 0.0968,  ..., 0.7933, 0.6392, 0.9343]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6658, 0.6242, 0.4020,  ..., 0.5009, 0.1451, 0.6481])
+tensor([0.2903, 0.7408, 0.0968,  ..., 0.3344, 0.5691, 0.3821])
 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: 8.025555610656738 seconds
+Time: 7.946921348571777 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46287', '-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": 11.077528476715088}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46775', '-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": 10.762548208236694}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499903,
-                            2499951, 2500000]),
-       col_indices=tensor([  506,  1320,  4404,  ..., 49283, 49651, 49966]),
-       values=tensor([0.0094, 0.0130, 0.0811,  ..., 0.5846, 0.7695, 0.1584]),
+tensor(crow_indices=tensor([      0,      65,     117,  ..., 2499903,
+                            2499954, 2500000]),
+       col_indices=tensor([ 2232,  2981,  3015,  ..., 49447, 49836, 49877]),
+       values=tensor([0.3281, 0.9452, 0.1004,  ..., 0.4282, 0.2346, 0.1167]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8952, 0.2999, 0.6108,  ..., 0.3758, 0.9662, 0.9596])
+tensor([0.5238, 0.6081, 0.9163,  ..., 0.2866, 0.2457, 0.9117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 11.077528476715088 seconds
+Time: 10.762548208236694 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499903,
-                            2499951, 2500000]),
-       col_indices=tensor([  506,  1320,  4404,  ..., 49283, 49651, 49966]),
-       values=tensor([0.0094, 0.0130, 0.0811,  ..., 0.5846, 0.7695, 0.1584]),
+tensor(crow_indices=tensor([      0,      65,     117,  ..., 2499903,
+                            2499954, 2500000]),
+       col_indices=tensor([ 2232,  2981,  3015,  ..., 49447, 49836, 49877]),
+       values=tensor([0.3281, 0.9452, 0.1004,  ..., 0.4282, 0.2346, 0.1167]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8952, 0.2999, 0.6108,  ..., 0.3758, 0.9662, 0.9596])
+tensor([0.5238, 0.6081, 0.9163,  ..., 0.2866, 0.2457, 0.9117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 11.077528476715088 seconds
+Time: 10.762548208236694 seconds
 
-[40.89, 39.29, 39.33, 39.28, 39.26, 39.19, 39.19, 40.42, 39.41, 39.34]
-[148.1]
-13.594499349594116
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46287, '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': 11.077528476715088, 'TIME_S_1KI': 0.23932267108940064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2013.3453536748884, 'W': 148.1}
-[40.89, 39.29, 39.33, 39.28, 39.26, 39.19, 39.19, 40.42, 39.41, 39.34, 40.01, 40.0, 39.66, 39.57, 39.25, 39.46, 39.33, 39.3, 39.25, 40.41]
-711.5149999999999
-35.57574999999999
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46287, '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': 11.077528476715088, 'TIME_S_1KI': 0.23932267108940064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2013.3453536748884, 'W': 148.1, 'J_1KI': 43.49699383573981, 'W_1KI': 3.1996024801780196, 'W_D': 112.52425, 'J_D': 1529.7108434385657, 'W_D_1KI': 2.4310119471989973, 'J_D_1KI': 0.052520404156652996}
+[41.04, 39.15, 39.26, 39.11, 39.17, 39.29, 39.32, 39.24, 39.56, 39.01]
+[149.27]
+13.620234727859497
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46775, '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': 10.762548208236694, 'TIME_S_1KI': 0.2300918911434889, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2033.0924378275872, 'W': 149.27}
+[41.04, 39.15, 39.26, 39.11, 39.17, 39.29, 39.32, 39.24, 39.56, 39.01, 39.97, 39.18, 39.31, 39.08, 39.46, 39.32, 39.13, 39.45, 39.15, 39.43]
+707.905
+35.39525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46775, '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': 10.762548208236694, 'TIME_S_1KI': 0.2300918911434889, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2033.0924378275872, 'W': 149.27, 'J_1KI': 43.46536478519695, 'W_1KI': 3.1912346338856232, 'W_D': 113.87475, 'J_D': 1551.0008245763183, 'W_D_1KI': 2.434521646178514, 'J_D_1KI': 0.052047496444222636}
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 1e1cae0..efee96e 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": 126164, "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": 10.496079683303833, "TIME_S_1KI": 0.08319393553869434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1339.4068178844452, "W": 103.41, "J_1KI": 10.616394675854009, "W_1KI": 0.8196474430106845, "W_D": 67.667, "J_D": 876.4494840517044, "W_D_1KI": 0.5363415871405472, "J_D_1KI": 0.0042511460253364455}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 128043, "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": 10.502545356750488, "TIME_S_1KI": 0.08202358080293722, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1345.7720102190972, "W": 103.43, "J_1KI": 10.510313021556017, "W_1KI": 0.8077755129136307, "W_D": 68.14325000000001, "J_D": 886.6409990850092, "W_D_1KI": 0.532190357926634, "J_D_1KI": 0.004156340900530557}
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 f5dabe4..1baca85 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', '1000', '-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": 0.13353276252746582}
+{"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": 0.1170039176940918}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([10989,  5739, 28866,  ..., 21823,  4005, 34886]),
-       values=tensor([0.4353, 0.4497, 0.0871,  ..., 0.0925, 0.2903, 0.5435]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([20669, 48572, 15521,  ...,  4942, 37440, 49163]),
+       values=tensor([0.4805, 0.0794, 0.3246,  ..., 0.3038, 0.8605, 0.6038]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7562, 0.8922, 0.4564,  ..., 0.1486, 0.4797, 0.4813])
+tensor([0.4235, 0.9189, 0.0697,  ..., 0.8234, 0.9093, 0.0251])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,37 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.13353276252746582 seconds
+Time: 0.1170039176940918 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78632', '-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": 6.544129848480225}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89740', '-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": 7.3589677810668945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([42797, 39277, 20964,  ..., 31232, 43143, 42518]),
+       values=tensor([0.7162, 0.4091, 0.9127,  ..., 0.7828, 0.7816, 0.8353]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2017, 0.4349, 0.5577,  ..., 0.2868, 0.8229, 0.7966])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 7.3589677810668945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '128043', '-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": 10.502545356750488}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 24999, 25000]),
-       col_indices=tensor([34114, 35224, 10296,  ..., 13464,   985,  3770]),
-       values=tensor([0.2384, 0.3975, 0.4000,  ..., 0.4541, 0.7785, 0.5313]),
+       col_indices=tensor([18076, 25567, 40242,  ..., 19386, 42443, 43843]),
+       values=tensor([0.1613, 0.4932, 0.9378,  ..., 0.7394, 0.5576, 0.1832]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2311, 0.0634, 0.6873,  ..., 0.2883, 0.1765, 0.0650])
+tensor([0.1994, 0.9899, 0.9038,  ..., 0.7869, 0.4416, 0.9952])
 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: 6.544129848480225 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '126164', '-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": 10.496079683303833}
+Time: 10.502545356750488 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 3707, 41195, 46820,  ..., 24919, 16438, 24153]),
-       values=tensor([0.4077, 0.2091, 0.6369,  ..., 0.9924, 0.1508, 0.5036]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([18076, 25567, 40242,  ..., 19386, 42443, 43843]),
+       values=tensor([0.1613, 0.4932, 0.9378,  ..., 0.7394, 0.5576, 0.1832]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4831, 0.5861, 0.9166,  ..., 0.7031, 0.1228, 0.1244])
+tensor([0.1994, 0.9899, 0.9038,  ..., 0.7869, 0.4416, 0.9952])
 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: 10.496079683303833 seconds
+Time: 10.502545356750488 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 3707, 41195, 46820,  ..., 24919, 16438, 24153]),
-       values=tensor([0.4077, 0.2091, 0.6369,  ..., 0.9924, 0.1508, 0.5036]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4831, 0.5861, 0.9166,  ..., 0.7031, 0.1228, 0.1244])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 10.496079683303833 seconds
-
-[41.91, 39.37, 39.94, 39.02, 39.81, 39.1, 40.39, 38.98, 39.08, 39.0]
-[103.41]
-12.952391624450684
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126164, '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': 10.496079683303833, 'TIME_S_1KI': 0.08319393553869434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.4068178844452, 'W': 103.41}
-[41.91, 39.37, 39.94, 39.02, 39.81, 39.1, 40.39, 38.98, 39.08, 39.0, 39.67, 39.09, 39.31, 39.83, 39.31, 39.25, 39.43, 38.89, 44.16, 39.22]
-714.86
-35.743
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126164, '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': 10.496079683303833, 'TIME_S_1KI': 0.08319393553869434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.4068178844452, 'W': 103.41, 'J_1KI': 10.616394675854009, 'W_1KI': 0.8196474430106845, 'W_D': 67.667, 'J_D': 876.4494840517044, 'W_D_1KI': 0.5363415871405472, 'J_D_1KI': 0.0042511460253364455}
+[40.3, 39.43, 39.29, 39.36, 38.95, 39.04, 39.78, 38.84, 39.12, 39.2]
+[103.43]
+13.011428117752075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128043, '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': 10.502545356750488, 'TIME_S_1KI': 0.08202358080293722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1345.7720102190972, 'W': 103.43}
+[40.3, 39.43, 39.29, 39.36, 38.95, 39.04, 39.78, 38.84, 39.12, 39.2, 40.2, 39.28, 39.13, 39.22, 38.89, 38.97, 38.85, 39.3, 39.03, 38.81]
+705.7349999999999
+35.28675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128043, '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': 10.502545356750488, 'TIME_S_1KI': 0.08202358080293722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1345.7720102190972, 'W': 103.43, 'J_1KI': 10.510313021556017, 'W_1KI': 0.8077755129136307, 'W_D': 68.14325000000001, 'J_D': 886.6409990850092, 'W_D_1KI': 0.532190357926634, 'J_D_1KI': 0.004156340900530557}
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
new file mode 100644
index 0000000..4891577
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 435807, "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": 10.511547565460205, "TIME_S_1KI": 0.024119730902578906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1232.545183532238, "W": 96.09, "J_1KI": 2.828190422669296, "W_1KI": 0.2204875093791518, "W_D": 60.76, "J_D": 779.3677318286896, "W_D_1KI": 0.1394195136838096, "J_D_1KI": 0.0003199111388385446}
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
new file mode 100644
index 0000000..0251f49
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.040769100189208984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4125, 1116, 4300,  ...,  690, 2880, 3382]),
+       values=tensor([0.0653, 0.6541, 0.1575,  ..., 0.5764, 0.0907, 0.6553]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7803, 0.2089, 0.7573,  ..., 0.7596, 0.3125, 0.6078])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.040769100189208984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '257547', '-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": 6.205128908157349}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1273, 2247, 4850,  ..., 2520, 1394, 3793]),
+       values=tensor([0.8733, 0.7089, 0.0515,  ..., 0.3445, 0.4099, 0.0495]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.0086, 0.7636, 0.4685,  ..., 0.9955, 0.7657, 0.7966])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.205128908157349 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '435807', '-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": 10.511547565460205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4068, 4690, 1058,  ..., 2571, 4364, 3391]),
+       values=tensor([0.9209, 0.6933, 0.9201,  ..., 0.0738, 0.0357, 0.7845]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3918, 0.7384, 0.9927,  ..., 0.9998, 0.6009, 0.1634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.511547565460205 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4068, 4690, 1058,  ..., 2571, 4364, 3391]),
+       values=tensor([0.9209, 0.6933, 0.9201,  ..., 0.0738, 0.0357, 0.7845]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3918, 0.7384, 0.9927,  ..., 0.9998, 0.6009, 0.1634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.511547565460205 seconds
+
+[39.78, 39.82, 44.51, 38.6, 40.39, 38.56, 38.63, 38.62, 38.67, 38.55]
+[96.09]
+12.826987028121948
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 435807, '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': 10.511547565460205, 'TIME_S_1KI': 0.024119730902578906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1232.545183532238, 'W': 96.09}
+[39.78, 39.82, 44.51, 38.6, 40.39, 38.56, 38.63, 38.62, 38.67, 38.55, 39.31, 38.89, 38.76, 38.73, 39.09, 38.53, 38.74, 39.13, 38.76, 38.7]
+706.6000000000001
+35.330000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 435807, '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': 10.511547565460205, 'TIME_S_1KI': 0.024119730902578906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1232.545183532238, 'W': 96.09, 'J_1KI': 2.828190422669296, 'W_1KI': 0.2204875093791518, 'W_D': 60.76, 'J_D': 779.3677318286896, 'W_D_1KI': 0.1394195136838096, 'J_D_1KI': 0.0003199111388385446}
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
new file mode 100644
index 0000000..fe8abca
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 245735, "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": 10.373013734817505, "TIME_S_1KI": 0.042212194985726516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1260.703432407379, "W": 98.11, "J_1KI": 5.130337283689255, "W_1KI": 0.39925122591409445, "W_D": 62.99425, "J_D": 809.4696483225822, "W_D_1KI": 0.2563503367448674, "J_D_1KI": 0.0010431983101506395}
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
new file mode 100644
index 0000000..79cf966
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.05801510810852051}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    14,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([2155, 3530, 3567,  ..., 2695, 4305, 4878]),
+       values=tensor([0.7077, 0.9384, 0.0254,  ..., 0.2116, 0.4863, 0.3277]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5025, 0.8306, 0.5455,  ..., 0.1180, 0.7485, 0.4884])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05801510810852051 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '180987', '-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": 7.7333667278289795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([ 162,  480,  815,  ..., 2232, 2732, 2847]),
+       values=tensor([0.8302, 0.2791, 0.7518,  ..., 0.7674, 0.4968, 0.3066]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5831, 0.9483, 0.7910,  ..., 0.0226, 0.1378, 0.9053])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.7333667278289795 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '245735', '-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": 10.373013734817505}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([1389, 1769, 1783,  ..., 2323, 3077, 3881]),
+       values=tensor([0.3893, 0.4927, 0.3928,  ..., 0.2440, 0.9871, 0.0384]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3455, 0.7497, 0.7321,  ..., 0.5403, 0.0178, 0.6295])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.373013734817505 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([1389, 1769, 1783,  ..., 2323, 3077, 3881]),
+       values=tensor([0.3893, 0.4927, 0.3928,  ..., 0.2440, 0.9871, 0.0384]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3455, 0.7497, 0.7321,  ..., 0.5403, 0.0178, 0.6295])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.373013734817505 seconds
+
+[40.07, 38.96, 38.85, 38.93, 38.85, 39.21, 39.52, 38.55, 38.74, 38.71]
+[98.11]
+12.849897384643555
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 245735, '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': 10.373013734817505, 'TIME_S_1KI': 0.042212194985726516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1260.703432407379, 'W': 98.11}
+[40.07, 38.96, 38.85, 38.93, 38.85, 39.21, 39.52, 38.55, 38.74, 38.71, 40.05, 38.72, 38.65, 39.11, 38.9, 39.03, 39.06, 38.96, 38.91, 39.9]
+702.3149999999999
+35.11575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 245735, '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': 10.373013734817505, 'TIME_S_1KI': 0.042212194985726516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1260.703432407379, 'W': 98.11, 'J_1KI': 5.130337283689255, 'W_1KI': 0.39925122591409445, 'W_D': 62.99425, 'J_D': 809.4696483225822, 'W_D_1KI': 0.2563503367448674, 'J_D_1KI': 0.0010431983101506395}
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
new file mode 100644
index 0000000..65befab
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 145666, "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": 10.058181524276733, "TIME_S_1KI": 0.06904961709854553, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1397.6345458984374, "W": 116.8, "J_1KI": 9.594789078428992, "W_1KI": 0.80183433333791, "W_D": 81.52975, "J_D": 975.5889993019105, "W_D_1KI": 0.5597033624867849, "J_D_1KI": 0.0038423747647823438}
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
new file mode 100644
index 0000000..930f25a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.10852384567260742}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     93,  ..., 249898, 249945,
+                            250000]),
+       col_indices=tensor([ 121,  263,  268,  ..., 4347, 4657, 4780]),
+       values=tensor([0.9155, 0.4457, 0.5767,  ..., 0.8561, 0.2482, 0.9078]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9909, 0.5337, 0.2877,  ..., 0.9413, 0.4687, 0.7116])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.10852384567260742 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '96752', '-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": 6.974123954772949}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    108,  ..., 249899, 249958,
+                            250000]),
+       col_indices=tensor([  30,   44,  230,  ..., 4553, 4620, 4987]),
+       values=tensor([0.7207, 0.9659, 0.8009,  ..., 0.1897, 0.2795, 0.9074]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9275, 0.8053, 0.7107,  ..., 0.1305, 0.9789, 0.9894])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 6.974123954772949 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '145666', '-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": 10.058181524276733}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    108,  ..., 249901, 249948,
+                            250000]),
+       col_indices=tensor([ 207,  226,  430,  ..., 4797, 4906, 4947]),
+       values=tensor([0.9242, 0.6665, 0.8223,  ..., 0.0998, 0.8618, 0.4766]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6439, 0.4458, 0.8465,  ..., 0.5021, 0.5940, 0.7614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.058181524276733 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    108,  ..., 249901, 249948,
+                            250000]),
+       col_indices=tensor([ 207,  226,  430,  ..., 4797, 4906, 4947]),
+       values=tensor([0.9242, 0.6665, 0.8223,  ..., 0.0998, 0.8618, 0.4766]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6439, 0.4458, 0.8465,  ..., 0.5021, 0.5940, 0.7614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.058181524276733 seconds
+
+[39.46, 38.8, 39.74, 39.98, 38.95, 39.2, 39.78, 38.76, 39.24, 38.86]
+[116.8]
+11.966049194335938
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 145666, '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': 10.058181524276733, 'TIME_S_1KI': 0.06904961709854553, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.6345458984374, 'W': 116.8}
+[39.46, 38.8, 39.74, 39.98, 38.95, 39.2, 39.78, 38.76, 39.24, 38.86, 39.39, 39.72, 39.13, 38.67, 39.47, 39.2, 39.27, 38.58, 38.78, 38.56]
+705.405
+35.27025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 145666, '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': 10.058181524276733, 'TIME_S_1KI': 0.06904961709854553, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.6345458984374, 'W': 116.8, 'J_1KI': 9.594789078428992, 'W_1KI': 0.80183433333791, 'W_D': 81.52975, 'J_D': 975.5889993019105, 'W_D_1KI': 0.5597033624867849, 'J_D_1KI': 0.0038423747647823438}
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
new file mode 100644
index 0000000..d2380a6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91710, "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": 10.455259799957275, "TIME_S_1KI": 0.11400348707836959, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1752.8075734996796, "W": 134.53, "J_1KI": 19.112502164427866, "W_1KI": 1.466906553265729, "W_D": 98.01950000000001, "J_D": 1277.107871483326, "W_D_1KI": 1.0687983862174244, "J_D_1KI": 0.011654109543315062}
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
new file mode 100644
index 0000000..16b0c97
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.15620112419128418}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     240,     508,  ..., 1249498,
+                            1249743, 1250000]),
+       col_indices=tensor([   1,    2,   46,  ..., 4888, 4964, 4980]),
+       values=tensor([0.7368, 0.9867, 0.0616,  ..., 0.4088, 0.7518, 0.0307]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2511, 0.5490, 0.8698,  ..., 0.0135, 0.9603, 0.4779])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.15620112419128418 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67221', '-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": 7.696179628372192}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     260,     523,  ..., 1249493,
+                            1249731, 1250000]),
+       col_indices=tensor([  11,   36,   51,  ..., 4933, 4983, 4999]),
+       values=tensor([0.1688, 0.6439, 0.5409,  ..., 0.9889, 0.0264, 0.5294]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2628, 0.4260, 0.4558,  ..., 0.6039, 0.8509, 0.7408])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 7.696179628372192 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91710', '-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": 10.455259799957275}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     271,     506,  ..., 1249448,
+                            1249721, 1250000]),
+       col_indices=tensor([  29,   30,   76,  ..., 4981, 4997, 4999]),
+       values=tensor([0.0426, 0.5256, 0.4347,  ..., 0.1903, 0.6901, 0.8658]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0426, 0.2115, 0.6413,  ..., 0.2013, 0.2155, 0.0145])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.455259799957275 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     271,     506,  ..., 1249448,
+                            1249721, 1250000]),
+       col_indices=tensor([  29,   30,   76,  ..., 4981, 4997, 4999]),
+       values=tensor([0.0426, 0.5256, 0.4347,  ..., 0.1903, 0.6901, 0.8658]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0426, 0.2115, 0.6413,  ..., 0.2013, 0.2155, 0.0145])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.455259799957275 seconds
+
+[40.22, 39.01, 39.13, 39.06, 38.89, 38.77, 38.89, 45.02, 39.49, 38.74]
+[134.53]
+13.029120445251465
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91710, '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': 10.455259799957275, 'TIME_S_1KI': 0.11400348707836959, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1752.8075734996796, 'W': 134.53}
+[40.22, 39.01, 39.13, 39.06, 38.89, 38.77, 38.89, 45.02, 39.49, 38.74, 39.45, 38.91, 38.91, 38.77, 45.05, 51.64, 42.38, 38.87, 38.81, 38.81]
+730.2099999999999
+36.51049999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91710, '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': 10.455259799957275, 'TIME_S_1KI': 0.11400348707836959, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1752.8075734996796, 'W': 134.53, 'J_1KI': 19.112502164427866, 'W_1KI': 1.466906553265729, 'W_D': 98.01950000000001, 'J_D': 1277.107871483326, 'W_D_1KI': 1.0687983862174244, 'J_D_1KI': 0.011654109543315062}
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
new file mode 100644
index 0000000..074b9e9
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53642, "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": 10.719228744506836, "TIME_S_1KI": 0.19982902845730652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1911.0154657673836, "W": 139.47, "J_1KI": 35.62535822242615, "W_1KI": 2.600014913687036, "W_D": 104.0105, "J_D": 1425.1500258277654, "W_D_1KI": 1.9389750568584316, "J_D_1KI": 0.036146583961418885}
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
new file mode 100644
index 0000000..687e6a4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.2534494400024414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     505,     999,  ..., 2498983,
+                            2499471, 2500000]),
+       col_indices=tensor([   5,   18,   40,  ..., 4969, 4978, 4986]),
+       values=tensor([0.5163, 0.4412, 0.3185,  ..., 0.4202, 0.6886, 0.7408]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4458, 0.7168, 0.1041,  ..., 0.9922, 0.1900, 0.6310])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.2534494400024414 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '41428', '-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": 8.109162092208862}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     993,  ..., 2498983,
+                            2499503, 2500000]),
+       col_indices=tensor([   3,   19,   21,  ..., 4946, 4955, 4989]),
+       values=tensor([0.8083, 0.9559, 0.4619,  ..., 0.5259, 0.1142, 0.6698]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9615, 0.1263, 0.8854,  ..., 0.2773, 0.4703, 0.0965])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 8.109162092208862 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53642', '-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": 10.719228744506836}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1060,  ..., 2499048,
+                            2499512, 2500000]),
+       col_indices=tensor([  10,   15,   21,  ..., 4947, 4988, 4996]),
+       values=tensor([0.5424, 0.5712, 0.8006,  ..., 0.9771, 0.7885, 0.2387]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8250, 0.9268, 0.6213,  ..., 0.2000, 0.5207, 0.9721])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.719228744506836 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1060,  ..., 2499048,
+                            2499512, 2500000]),
+       col_indices=tensor([  10,   15,   21,  ..., 4947, 4988, 4996]),
+       values=tensor([0.5424, 0.5712, 0.8006,  ..., 0.9771, 0.7885, 0.2387]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8250, 0.9268, 0.6213,  ..., 0.2000, 0.5207, 0.9721])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.719228744506836 seconds
+
+[40.33, 39.84, 39.58, 39.84, 39.14, 39.05, 39.58, 39.29, 39.44, 39.24]
+[139.47]
+13.701982259750366
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53642, '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': 10.719228744506836, 'TIME_S_1KI': 0.19982902845730652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1911.0154657673836, 'W': 139.47}
+[40.33, 39.84, 39.58, 39.84, 39.14, 39.05, 39.58, 39.29, 39.44, 39.24, 39.75, 39.09, 39.67, 39.12, 39.04, 39.58, 39.1, 39.15, 39.53, 38.98]
+709.19
+35.459500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53642, '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': 10.719228744506836, 'TIME_S_1KI': 0.19982902845730652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1911.0154657673836, 'W': 139.47, 'J_1KI': 35.62535822242615, 'W_1KI': 2.600014913687036, 'W_D': 104.0105, 'J_D': 1425.1500258277654, 'W_D_1KI': 1.9389750568584316, 'J_D_1KI': 0.036146583961418885}
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
new file mode 100644
index 0000000..f4429df
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 491380, "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": 10.363765478134155, "TIME_S_1KI": 0.021091142248634776, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1327.781383190155, "W": 95.12, "J_1KI": 2.7021477943549903, "W_1KI": 0.1935772721722496, "W_D": 60.282000000000004, "J_D": 841.4772638926506, "W_D_1KI": 0.12267898571370427, "J_D_1KI": 0.00024966214683891136}
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
new file mode 100644
index 0000000..5c32150
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,464 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.0375218391418457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4395, 1896, 1016, 4725, 4287, 4964, 4709, 2475, 4723,
+                           2193, 4334, 4011, 1534,  947, 2980, 1276, 2745, 2145,
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+                           3476, 3768,  384, 2163, 1378, 1422, 3827,   12, 4549,
+                           4524, 1374, 4468, 1024, 3152,  985, 3013]),
+       values=tensor([0.3589, 0.1660, 0.5969, 0.5688, 0.7752, 0.6324, 0.0921,
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+                      0.4774, 0.1186, 0.4945, 0.1561, 0.6695]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1727, 0.0592, 0.5429,  ..., 0.7822, 0.3152, 0.8983])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.0375218391418457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '279837', '-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": 6.313635349273682}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1000,  776, 2038, 1918, 4703, 1006, 4783,  345, 4138,
+                           3890, 4809, 2179, 2654,   39, 3277, 4397,  222, 2644,
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+                           4353, 1361, 3013, 1482, 4277, 3046,  277]),
+       values=tensor([0.1993, 0.0335, 0.2936, 0.2778, 0.6825, 0.6252, 0.3746,
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+                      0.9730, 0.0239, 0.4507, 0.6650, 0.1805]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7126, 0.1651, 0.2523,  ..., 0.5242, 0.8574, 0.9519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 6.313635349273682 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '465387', '-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": 9.944559097290039}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3699,  186,  998, 2786, 4646, 2125, 3758, 4753, 3164,
+                           2363,  875, 4485, 3467, 1146, 3713,   40, 3541, 4449,
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+                           4380, 4811, 1487, 4371, 1280, 1276, 2851]),
+       values=tensor([0.3089, 0.4336, 0.4888, 0.1926, 0.0728, 0.0243, 0.5274,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5720, 0.9223, 0.3340,  ..., 0.6697, 0.2837, 0.3607])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 9.944559097290039 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '491380', '-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": 10.363765478134155}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.363765478134155 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+Matrix Type: synthetic
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+NNZ: 250
+Density: 1e-05
+Time: 10.363765478134155 seconds
+
+[39.35, 38.55, 38.58, 38.65, 38.55, 38.56, 38.58, 38.95, 38.97, 38.84]
+[95.12]
+13.95901370048523
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 491380, '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': 10.363765478134155, 'TIME_S_1KI': 0.021091142248634776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1327.781383190155, 'W': 95.12}
+[39.35, 38.55, 38.58, 38.65, 38.55, 38.56, 38.58, 38.95, 38.97, 38.84, 39.11, 38.56, 38.47, 38.42, 38.44, 38.49, 38.99, 38.76, 39.03, 39.12]
+696.76
+34.838
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 491380, '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': 10.363765478134155, 'TIME_S_1KI': 0.021091142248634776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1327.781383190155, 'W': 95.12, 'J_1KI': 2.7021477943549903, 'W_1KI': 0.1935772721722496, 'W_D': 60.282000000000004, 'J_D': 841.4772638926506, 'W_D_1KI': 0.12267898571370427, 'J_D_1KI': 0.00024966214683891136}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json
deleted file mode 100644
index 62f730b..0000000
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 250038, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.432795524597168, "TIME_S_1KI": 0.08971754503154387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2606.0890457463265, "W": 109.03, "J_1KI": 10.422771921653215, "W_1KI": 0.4360537198345852, "W_D": 73.72525, "J_D": 1762.2174302477242, "W_D_1KI": 0.2948561818603573, "J_D_1KI": 0.0011792454821281456}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output
deleted file mode 100644
index 3701848..0000000
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output
+++ /dev/null
@@ -1,81 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.10453343391418457}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89993, 89997, 90000]),
-       col_indices=tensor([20651, 24290, 28771,  ..., 10287, 15356, 24487]),
-       values=tensor([0.1253, 0.8320, 0.5079,  ..., 0.2152, 0.2753, 0.6533]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.9310, 0.8886, 0.9050,  ..., 0.7990, 0.2751, 0.5722])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 0.10453343391418457 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '200892', '-ss', '30000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 16.872318267822266}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89991, 89992, 90000]),
-       col_indices=tensor([ 9009, 16842, 24312,  ..., 27764, 28622, 29005]),
-       values=tensor([0.8393, 0.9269, 0.8193,  ..., 0.0379, 0.8842, 0.8625]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.6604, 0.9619, 0.4104,  ..., 0.2632, 0.2079, 0.2105])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 16.872318267822266 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '250038', '-ss', '30000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.432795524597168}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89997, 89998, 90000]),
-       col_indices=tensor([12588, 20450, 20704,  ..., 21668, 10676, 12342]),
-       values=tensor([0.6372, 0.0652, 0.9949,  ..., 0.3492, 0.9239, 0.3604]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.0127, 0.8502, 0.1682,  ..., 0.0608, 0.3685, 0.2970])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 22.432795524597168 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89997, 89998, 90000]),
-       col_indices=tensor([12588, 20450, 20704,  ..., 21668, 10676, 12342]),
-       values=tensor([0.6372, 0.0652, 0.9949,  ..., 0.3492, 0.9239, 0.3604]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.0127, 0.8502, 0.1682,  ..., 0.0608, 0.3685, 0.2970])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 22.432795524597168 seconds
-
-[40.69, 39.01, 39.44, 38.94, 38.95, 39.35, 38.98, 39.05, 38.97, 38.87]
-[109.03]
-23.90249514579773
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 250038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.432795524597168, 'TIME_S_1KI': 0.08971754503154387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2606.0890457463265, 'W': 109.03}
-[40.69, 39.01, 39.44, 38.94, 38.95, 39.35, 38.98, 39.05, 38.97, 38.87, 40.47, 39.15, 39.52, 39.41, 39.16, 39.78, 39.02, 38.95, 38.92, 38.96]
-706.095
-35.30475
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 250038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.432795524597168, 'TIME_S_1KI': 0.08971754503154387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2606.0890457463265, 'W': 109.03, 'J_1KI': 10.422771921653215, 'W_1KI': 0.4360537198345852, 'W_D': 73.72525, 'J_D': 1762.2174302477242, 'W_D_1KI': 0.2948561818603573, 'J_D_1KI': 0.0011792454821281456}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.json
deleted file mode 100644
index e69de29..0000000
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output
deleted file mode 100644
index 8658000..0000000
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output
+++ /dev/null
@@ -1,21 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.1439976692199707}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     29,     67,  ..., 899936, 899964,
-                            900000]),
-       col_indices=tensor([   58,   341,  3959,  ..., 27670, 28034, 29816]),
-       values=tensor([0.8286, 0.0691, 0.1730,  ..., 0.2645, 0.7295, 0.5386]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.0558, 0.4553, 0.9674,  ..., 0.2366, 0.6209, 0.6160])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 0.1439976692199707 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '145835', '-ss', '30000', '-sd', '0.001', '-c', '16']
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json
deleted file mode 100644
index 9b8b9bd..0000000
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 321850, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.594725370407104, "TIME_S_1KI": 0.06398858278827747, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2327.1387594389917, "W": 101.41000000000001, "J_1KI": 7.230507253189348, "W_1KI": 0.3150846667702346, "W_D": 65.9145, "J_D": 1512.5942979887725, "W_D_1KI": 0.20479881932577287, "J_D_1KI": 0.0006363175992722476}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output
deleted file mode 100644
index e9d1ad8..0000000
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output
+++ /dev/null
@@ -1,81 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08333611488342285}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 9000, 9000, 9000]),
-       col_indices=tensor([13464, 15002, 12998,  ...,  1674,  7890,  9839]),
-       values=tensor([0.3937, 0.5826, 0.6728,  ..., 0.2443, 0.0810, 0.3168]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.3767, 0.3322, 0.0921,  ..., 0.4449, 0.8687, 0.6223])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 0.08333611488342285 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '251991', '-ss', '30000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.441835403442383}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 9000, 9000, 9000]),
-       col_indices=tensor([ 1592, 26221,  2007,  ...,  5499,  7511, 18290]),
-       values=tensor([0.1009, 0.0773, 0.0762,  ..., 0.6540, 0.2265, 0.9524]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.5719, 0.1239, 0.1698,  ..., 0.8424, 0.3509, 0.9636])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 16.441835403442383 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '321850', '-ss', '30000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.594725370407104}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 9000, 9000, 9000]),
-       col_indices=tensor([28655, 14046, 22660,  ..., 19793, 14001, 26576]),
-       values=tensor([0.0604, 0.3035, 0.4856,  ..., 0.8323, 0.7946, 0.0096]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.2670, 0.6630, 0.3861,  ..., 0.4215, 0.9031, 0.7574])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 20.594725370407104 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 9000, 9000, 9000]),
-       col_indices=tensor([28655, 14046, 22660,  ..., 19793, 14001, 26576]),
-       values=tensor([0.0604, 0.3035, 0.4856,  ..., 0.8323, 0.7946, 0.0096]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.2670, 0.6630, 0.3861,  ..., 0.4215, 0.9031, 0.7574])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 20.594725370407104 seconds
-
-[39.56, 39.04, 39.2, 38.58, 39.16, 39.36, 38.83, 40.63, 38.67, 39.16]
-[101.41]
-22.94782328605652
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 321850, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.594725370407104, 'TIME_S_1KI': 0.06398858278827747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2327.1387594389917, 'W': 101.41000000000001}
-[39.56, 39.04, 39.2, 38.58, 39.16, 39.36, 38.83, 40.63, 38.67, 39.16, 39.61, 44.11, 38.89, 39.27, 38.75, 38.81, 40.71, 38.62, 38.81, 38.61]
-709.91
-35.4955
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 321850, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.594725370407104, 'TIME_S_1KI': 0.06398858278827747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2327.1387594389917, 'W': 101.41000000000001, 'J_1KI': 7.230507253189348, 'W_1KI': 0.3150846667702346, 'W_D': 65.9145, 'J_D': 1512.5942979887725, 'W_D_1KI': 0.20479881932577287, 'J_D_1KI': 0.0006363175992722476}
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 092cf4b..174f326 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": 33012, "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": 10.519692420959473, "TIME_S_1KI": 0.318662680872394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.664799463749, "W": 88.39, "J_1KI": 37.09756450574788, "W_1KI": 2.677511208045559, "W_D": 72.108, "J_D": 999.0737567567826, "W_D_1KI": 2.184296619411123, "J_D_1KI": 0.06616674601390778}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 32214, "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": 10.624319791793823, "TIME_S_1KI": 0.3298044263920601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1308.5074545145035, "W": 90.38, "J_1KI": 40.619216940290045, "W_1KI": 2.8056124666294155, "W_D": 74.09325, "J_D": 1072.7104442819953, "W_D_1KI": 2.30003259452412, "J_D_1KI": 0.07139854083703111}
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 d5ef17d..48c062d 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', '1000', '-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": 0.3180568218231201}
+{"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": 0.3259446620941162}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999979,
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999982,
                              999991, 1000000]),
-       col_indices=tensor([10691, 12782, 14246,  ..., 70658, 88202, 93324]),
-       values=tensor([0.3844, 0.6658, 0.7124,  ..., 0.3153, 0.8920, 0.6509]),
+       col_indices=tensor([10285, 14477, 16251,  ..., 79839, 98536, 99886]),
+       values=tensor([0.0755, 0.8469, 0.4749,  ..., 0.2250, 0.2555, 0.2499]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9202, 0.9151, 0.8232,  ..., 0.5628, 0.6151, 0.8368])
+tensor([0.5289, 0.3805, 0.4649,  ..., 0.7570, 0.9550, 0.1372])
 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: 0.3180568218231201 seconds
+Time: 0.3259446620941162 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', '33012', '-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": 10.519692420959473}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '32214', '-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": 10.624319791793823}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      15,  ...,  999984,
-                             999990, 1000000]),
-       col_indices=tensor([ 7405, 49048, 69982,  ..., 87685, 98650, 99933]),
-       values=tensor([0.6053, 0.2022, 0.4562,  ..., 0.3977, 0.5709, 0.7435]),
+tensor(crow_indices=tensor([      0,       9,      15,  ...,  999974,
+                             999991, 1000000]),
+       col_indices=tensor([   27,  9769, 50112,  ..., 53126, 61224, 82066]),
+       values=tensor([0.2467, 0.4042, 0.1080,  ..., 0.3359, 0.4921, 0.7955]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0490, 0.1129, 0.5767,  ..., 0.3037, 0.9982, 0.0194])
+tensor([0.8754, 0.9877, 0.9510,  ..., 0.4555, 0.1143, 0.3690])
 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: 10.519692420959473 seconds
+Time: 10.624319791793823 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      15,  ...,  999984,
-                             999990, 1000000]),
-       col_indices=tensor([ 7405, 49048, 69982,  ..., 87685, 98650, 99933]),
-       values=tensor([0.6053, 0.2022, 0.4562,  ..., 0.3977, 0.5709, 0.7435]),
+tensor(crow_indices=tensor([      0,       9,      15,  ...,  999974,
+                             999991, 1000000]),
+       col_indices=tensor([   27,  9769, 50112,  ..., 53126, 61224, 82066]),
+       values=tensor([0.2467, 0.4042, 0.1080,  ..., 0.3359, 0.4921, 0.7955]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0490, 0.1129, 0.5767,  ..., 0.3037, 0.9982, 0.0194])
+tensor([0.8754, 0.9877, 0.9510,  ..., 0.4555, 0.1143, 0.3690])
 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: 10.519692420959473 seconds
+Time: 10.624319791793823 seconds
 
-[18.25, 17.99, 18.13, 17.81, 18.01, 17.82, 18.18, 18.25, 17.98, 18.75]
-[88.39]
-13.855241537094116
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33012, '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': 10.519692420959473, 'TIME_S_1KI': 0.318662680872394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.664799463749, 'W': 88.39}
-[18.25, 17.99, 18.13, 17.81, 18.01, 17.82, 18.18, 18.25, 17.98, 18.75, 18.55, 17.94, 18.05, 17.81, 18.35, 17.79, 18.28, 18.36, 18.2, 17.83]
-325.64
-16.282
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33012, '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': 10.519692420959473, 'TIME_S_1KI': 0.318662680872394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.664799463749, 'W': 88.39, 'J_1KI': 37.09756450574788, 'W_1KI': 2.677511208045559, 'W_D': 72.108, 'J_D': 999.0737567567826, 'W_D_1KI': 2.184296619411123, 'J_D_1KI': 0.06616674601390778}
+[18.22, 17.55, 18.1, 17.52, 17.64, 17.6, 18.51, 17.5, 17.7, 17.75]
+[90.38]
+14.477843046188354
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 32214, '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': 10.624319791793823, 'TIME_S_1KI': 0.3298044263920601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1308.5074545145035, 'W': 90.38}
+[18.22, 17.55, 18.1, 17.52, 17.64, 17.6, 18.51, 17.5, 17.7, 17.75, 18.28, 17.75, 17.59, 17.7, 17.88, 17.82, 18.16, 18.33, 22.4, 17.72]
+325.735
+16.28675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 32214, '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': 10.624319791793823, 'TIME_S_1KI': 0.3298044263920601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1308.5074545145035, 'W': 90.38, 'J_1KI': 40.619216940290045, 'W_1KI': 2.8056124666294155, 'W_D': 74.09325, 'J_D': 1072.7104442819953, 'W_D_1KI': 2.30003259452412, 'J_D_1KI': 0.07139854083703111}
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
new file mode 100644
index 0000000..be7a2cb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2697, "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": 10.332640647888184, "TIME_S_1KI": 3.8311607889833827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1417.5436856460572, "W": 83.44, "J_1KI": 525.6001800689867, "W_1KI": 30.938079347423063, "W_D": 67.3325, "J_D": 1143.8969344890118, "W_D_1KI": 24.965702632554688, "J_D_1KI": 9.25684191047634}
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
new file mode 100644
index 0000000..d59582d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 3.892810106277466}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      187,  ...,  9999795,
+                             9999888, 10000000]),
+       col_indices=tensor([ 1843,  1850,  4412,  ..., 98725, 98752, 98846]),
+       values=tensor([0.9343, 0.4740, 0.0577,  ..., 0.9099, 0.1721, 0.4592]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5874, 0.0844, 0.8298,  ..., 0.9009, 0.0712, 0.0168])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 3.892810106277466 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', '2697', '-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": 10.332640647888184}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       99,      214,  ...,  9999796,
+                             9999890, 10000000]),
+       col_indices=tensor([  133,   206,   762,  ..., 95508, 95519, 98505]),
+       values=tensor([0.7799, 0.5247, 0.9444,  ..., 0.2262, 0.0403, 0.9029]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3536, 0.8501, 0.0907,  ..., 0.0431, 0.6064, 0.5575])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.332640647888184 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       99,      214,  ...,  9999796,
+                             9999890, 10000000]),
+       col_indices=tensor([  133,   206,   762,  ..., 95508, 95519, 98505]),
+       values=tensor([0.7799, 0.5247, 0.9444,  ..., 0.2262, 0.0403, 0.9029]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3536, 0.8501, 0.0907,  ..., 0.0431, 0.6064, 0.5575])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.332640647888184 seconds
+
+[18.26, 17.98, 17.82, 17.72, 17.76, 17.93, 17.75, 17.95, 17.83, 18.05]
+[83.44]
+16.988778591156006
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2697, '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': 10.332640647888184, 'TIME_S_1KI': 3.8311607889833827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1417.5436856460572, 'W': 83.44}
+[18.26, 17.98, 17.82, 17.72, 17.76, 17.93, 17.75, 17.95, 17.83, 18.05, 18.55, 18.9, 17.56, 17.66, 17.87, 17.73, 17.82, 17.88, 17.78, 17.56]
+322.15
+16.107499999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2697, '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': 10.332640647888184, 'TIME_S_1KI': 3.8311607889833827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1417.5436856460572, 'W': 83.44, 'J_1KI': 525.6001800689867, 'W_1KI': 30.938079347423063, 'W_D': 67.3325, 'J_D': 1143.8969344890118, 'W_D_1KI': 24.965702632554688, 'J_D_1KI': 9.25684191047634}
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 d7ba913..60a31fb 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": 64591, "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": 10.66994047164917, "TIME_S_1KI": 0.16519237156336286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1159.8280617713929, "W": 82.8, "J_1KI": 17.9564964433341, "W_1KI": 1.2819123407285846, "W_D": 66.57124999999999, "J_D": 932.5024620434641, "W_D_1KI": 1.0306582960474369, "J_D_1KI": 0.015956685854800777}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 63032, "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": 10.23182988166809, "TIME_S_1KI": 0.1623275460348409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.4584797358514, "W": 89.17, "J_1KI": 19.473576591824017, "W_1KI": 1.4146782586622668, "W_D": 73.15375, "J_D": 1006.9887940111756, "W_D_1KI": 1.1605811333925626, "J_D_1KI": 0.018412570335584508}
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 ad36eba..14be47f 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', '1000', '-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": 0.17906904220581055}
+{"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": 0.17937397956848145}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      4,  ...,  99996, 100000,
                             100000]),
-       col_indices=tensor([85471,  5444, 13434,  ..., 17615, 87992, 83918]),
-       values=tensor([0.7119, 0.1219, 0.2242,  ..., 0.7199, 0.3920, 0.9751]),
+       col_indices=tensor([ 6463, 19403, 32975,  ..., 50312, 73566, 75866]),
+       values=tensor([0.6504, 0.4570, 0.8704,  ..., 0.7277, 0.1675, 0.6048]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8861, 0.1716, 0.8373,  ..., 0.2826, 0.6276, 0.0027])
+tensor([0.7096, 0.4020, 0.6001,  ..., 0.3911, 0.2531, 0.2591])
 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: 0.17906904220581055 seconds
+Time: 0.17937397956848145 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', '58636', '-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": 9.531909704208374}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58536', '-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": 9.751020431518555}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([28875, 86601,  1118,  ..., 53659, 98581, 89346]),
-       values=tensor([0.0170, 0.0837, 0.6677,  ..., 0.0775, 0.7543, 0.4196]),
+       col_indices=tensor([64186, 21974, 57698,  ..., 75952, 18460, 38945]),
+       values=tensor([0.5668, 0.1226, 0.0967,  ..., 0.2541, 0.6343, 0.4356]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4702, 0.4277, 0.7376,  ..., 0.9470, 0.3873, 0.6416])
+tensor([0.9872, 0.9595, 0.0420,  ..., 0.0153, 0.9518, 0.5571])
 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: 9.531909704208374 seconds
+Time: 9.751020431518555 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', '64591', '-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": 10.66994047164917}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '63032', '-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": 10.23182988166809}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      1,      2,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([32373, 45973, 94969,  ...,  5823, 12968, 35562]),
-       values=tensor([0.6698, 0.7885, 0.1863,  ..., 0.4943, 0.2796, 0.7613]),
+       col_indices=tensor([35835, 88904, 80345,  ..., 79801,  8127, 81515]),
+       values=tensor([0.8153, 0.8474, 0.9328,  ..., 0.8046, 0.4857, 0.5161]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4737, 0.5533, 0.8139,  ..., 0.3662, 0.3156, 0.7007])
+tensor([0.1493, 0.1613, 0.9905,  ..., 0.3209, 0.7704, 0.3686])
 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: 10.66994047164917 seconds
+Time: 10.23182988166809 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      1,      2,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([32373, 45973, 94969,  ...,  5823, 12968, 35562]),
-       values=tensor([0.6698, 0.7885, 0.1863,  ..., 0.4943, 0.2796, 0.7613]),
+       col_indices=tensor([35835, 88904, 80345,  ..., 79801,  8127, 81515]),
+       values=tensor([0.8153, 0.8474, 0.9328,  ..., 0.8046, 0.4857, 0.5161]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4737, 0.5533, 0.8139,  ..., 0.3662, 0.3156, 0.7007])
+tensor([0.1493, 0.1613, 0.9905,  ..., 0.3209, 0.7704, 0.3686])
 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: 10.66994047164917 seconds
+Time: 10.23182988166809 seconds
 
-[18.59, 17.9, 18.34, 17.96, 18.14, 17.94, 18.32, 17.79, 17.82, 17.71]
-[82.8]
-14.007585287094116
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64591, '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': 10.66994047164917, 'TIME_S_1KI': 0.16519237156336286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1159.8280617713929, 'W': 82.8}
-[18.59, 17.9, 18.34, 17.96, 18.14, 17.94, 18.32, 17.79, 17.82, 17.71, 18.29, 17.85, 18.6, 17.8, 18.13, 17.74, 17.99, 17.83, 18.16, 17.94]
-324.57500000000005
-16.22875
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64591, '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': 10.66994047164917, 'TIME_S_1KI': 0.16519237156336286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1159.8280617713929, 'W': 82.8, 'J_1KI': 17.9564964433341, 'W_1KI': 1.2819123407285846, 'W_D': 66.57124999999999, 'J_D': 932.5024620434641, 'W_D_1KI': 1.0306582960474369, 'J_D_1KI': 0.015956685854800777}
+[18.1, 17.83, 17.83, 17.64, 17.89, 17.76, 17.87, 17.83, 17.97, 17.52]
+[89.17]
+13.765374898910522
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 63032, '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': 10.23182988166809, 'TIME_S_1KI': 0.1623275460348409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.4584797358514, 'W': 89.17}
+[18.1, 17.83, 17.83, 17.64, 17.89, 17.76, 17.87, 17.83, 17.97, 17.52, 18.59, 17.72, 17.71, 17.51, 17.74, 17.62, 17.64, 18.03, 17.89, 17.48]
+320.32500000000005
+16.016250000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 63032, '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': 10.23182988166809, 'TIME_S_1KI': 0.1623275460348409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.4584797358514, 'W': 89.17, 'J_1KI': 19.473576591824017, 'W_1KI': 1.4146782586622668, 'W_D': 73.15375, 'J_D': 1006.9887940111756, 'W_D_1KI': 1.1605811333925626, 'J_D_1KI': 0.018412570335584508}
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 c1f8ab6..05a227c 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": 250193, "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": 10.988550901412964, "TIME_S_1KI": 0.043920297136262665, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.7657339859009, "W": 74.48, "J_1KI": 4.267768218878628, "W_1KI": 0.29769018317858614, "W_D": 58.048, "J_D": 832.1920693664551, "W_D_1KI": 0.23201288605196788, "J_D_1KI": 0.0009273356410929478}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253876, "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": 10.530851364135742, "TIME_S_1KI": 0.041480294963429955, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1171.4377240467072, "W": 80.68, "J_1KI": 4.614212150997759, "W_1KI": 0.317792938284832, "W_D": 64.302, "J_D": 933.636446847439, "W_D_1KI": 0.2532811293702437, "J_D_1KI": 0.0009976568457445514}
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 15dcb35..04a16aa 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', '1000', '-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": 0.06029987335205078}
+{"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": 0.05977439880371094}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9998,  9999, 10000]),
-       col_indices=tensor([9584, 2249, 9621,  ...,  267, 2843, 1232]),
-       values=tensor([0.1887, 0.8280, 0.8733,  ..., 0.6422, 0.8241, 0.9503]),
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([6615, 8991, 2810,  ..., 6295, 8510, 7610]),
+       values=tensor([0.3885, 0.8426, 0.7862,  ..., 0.5955, 0.1672, 0.2063]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2203, 0.8610, 0.9153,  ..., 0.2931, 0.9983, 0.3156])
+tensor([0.1595, 0.0624, 0.6993,  ..., 0.5987, 0.7271, 0.9533])
 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: 0.06029987335205078 seconds
+Time: 0.05977439880371094 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', '174129', '-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": 7.307769536972046}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '175660', '-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": 7.265056371688843}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([5050, 9096,  467,  ..., 6460, 6547, 2963]),
-       values=tensor([0.3312, 0.9984, 0.8182,  ..., 0.5509, 0.3722, 0.7285]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([6157, 6465, 6955,  ..., 9189, 5553, 9168]),
+       values=tensor([0.9492, 0.4977, 0.7776,  ..., 0.2833, 0.2034, 0.6430]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0543, 0.3720, 0.3677,  ..., 0.5280, 0.6433, 0.3148])
+tensor([0.2429, 0.7570, 0.9101,  ..., 0.6676, 0.5300, 0.9328])
 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: 7.307769536972046 seconds
+Time: 7.265056371688843 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', '250193', '-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": 10.988550901412964}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253876', '-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": 10.530851364135742}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([4233, 4275, 7541,  ..., 2248, 7833,  717]),
-       values=tensor([0.0347, 0.7995, 0.4404,  ..., 0.0217, 0.2651, 0.9390]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([ 868, 4014, 6169,  ..., 4688, 7367, 6538]),
+       values=tensor([0.9131, 0.0133, 0.5134,  ..., 0.5757, 0.9187, 0.1463]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4739, 0.4789, 0.6628,  ..., 0.7267, 0.9323, 0.5704])
+tensor([0.7710, 0.0750, 0.1717,  ..., 0.8123, 0.4992, 0.1144])
 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: 10.988550901412964 seconds
+Time: 10.530851364135742 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([4233, 4275, 7541,  ..., 2248, 7833,  717]),
-       values=tensor([0.0347, 0.7995, 0.4404,  ..., 0.0217, 0.2651, 0.9390]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([ 868, 4014, 6169,  ..., 4688, 7367, 6538]),
+       values=tensor([0.9131, 0.0133, 0.5134,  ..., 0.5757, 0.9187, 0.1463]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4739, 0.4789, 0.6628,  ..., 0.7267, 0.9323, 0.5704])
+tensor([0.7710, 0.0750, 0.1717,  ..., 0.8123, 0.4992, 0.1144])
 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: 10.988550901412964 seconds
+Time: 10.530851364135742 seconds
 
-[18.5, 18.04, 18.08, 20.55, 18.03, 18.27, 18.34, 17.92, 18.14, 18.0]
-[74.48]
-14.33627462387085
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250193, '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': 10.988550901412964, 'TIME_S_1KI': 0.043920297136262665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.7657339859009, 'W': 74.48}
-[18.5, 18.04, 18.08, 20.55, 18.03, 18.27, 18.34, 17.92, 18.14, 18.0, 18.31, 18.29, 18.5, 18.09, 18.0, 17.95, 17.89, 18.08, 18.14, 17.85]
-328.64
-16.432
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250193, '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': 10.988550901412964, 'TIME_S_1KI': 0.043920297136262665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.7657339859009, 'W': 74.48, 'J_1KI': 4.267768218878628, 'W_1KI': 0.29769018317858614, 'W_D': 58.048, 'J_D': 832.1920693664551, 'W_D_1KI': 0.23201288605196788, 'J_D_1KI': 0.0009273356410929478}
+[18.32, 17.72, 17.73, 18.28, 18.02, 18.18, 17.93, 18.12, 17.89, 21.22]
+[80.68]
+14.51955533027649
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253876, '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': 10.530851364135742, 'TIME_S_1KI': 0.041480294963429955, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1171.4377240467072, 'W': 80.68}
+[18.32, 17.72, 17.73, 18.28, 18.02, 18.18, 17.93, 18.12, 17.89, 21.22, 18.16, 17.68, 17.59, 17.54, 18.03, 22.12, 17.56, 17.65, 17.87, 17.6]
+327.56000000000006
+16.378000000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253876, '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': 10.530851364135742, 'TIME_S_1KI': 0.041480294963429955, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1171.4377240467072, 'W': 80.68, 'J_1KI': 4.614212150997759, 'W_1KI': 0.317792938284832, 'W_D': 64.302, 'J_D': 933.636446847439, 'W_D_1KI': 0.2532811293702437, 'J_D_1KI': 0.0009976568457445514}
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 dab286f..fdd9d92 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": 186516, "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": 10.228839635848999, "TIME_S_1KI": 0.054841620214078145, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1075.814607758522, "W": 79.58, "J_1KI": 5.767948099672533, "W_1KI": 0.4266658088314139, "W_D": 63.054, "J_D": 852.4053063282967, "W_D_1KI": 0.338062150164061, "J_D_1KI": 0.0018125101876732344}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 195071, "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": 10.382015228271484, "TIME_S_1KI": 0.05322172556798029, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.0013425803186, "W": 86.31, "J_1KI": 6.131107866265712, "W_1KI": 0.4424542858753992, "W_D": 70.10050000000001, "J_D": 971.3856113492252, "W_D_1KI": 0.35935890009278676, "J_D_1KI": 0.0018421954062509895}
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 fad6e92..5d9701a 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', '1000', '-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": 0.0709388256072998}
+{"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": 0.06870174407958984}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99974,  99988,
+tensor(crow_indices=tensor([     0,     10,     25,  ...,  99981,  99995,
                             100000]),
-       col_indices=tensor([1106, 1398, 2518,  ..., 6886, 7547, 8173]),
-       values=tensor([0.5902, 0.0057, 0.8492,  ..., 0.2608, 0.7269, 0.6940]),
+       col_indices=tensor([   3,  150,  370,  ..., 2691, 9535, 9749]),
+       values=tensor([0.2561, 0.9230, 0.8831,  ..., 0.2203, 0.7623, 0.4185]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8144, 0.0674, 0.1585,  ..., 0.0850, 0.2846, 0.5370])
+tensor([0.1427, 0.1860, 0.4972,  ..., 0.5058, 0.8744, 0.6551])
 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: 0.0709388256072998 seconds
+Time: 0.06870174407958984 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', '148014', '-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": 8.332475900650024}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '152834', '-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": 8.226486682891846}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     17,  ...,  99965,  99978,
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99982,  99990,
                             100000]),
-       col_indices=tensor([  77,  628, 3642,  ..., 8176, 8481, 9600]),
-       values=tensor([0.7580, 0.3721, 0.0885,  ..., 0.9345, 0.1388, 0.5730]),
+       col_indices=tensor([ 560, 3215, 3961,  ..., 6911, 7414, 7504]),
+       values=tensor([0.0904, 0.0706, 0.8224,  ..., 0.0963, 0.3127, 0.0052]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9678, 0.5744, 0.4262,  ..., 0.2115, 0.3242, 0.5272])
+tensor([0.8141, 0.4563, 0.6350,  ..., 0.0924, 0.8861, 0.1694])
 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: 8.332475900650024 seconds
+Time: 8.226486682891846 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', '186516', '-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": 10.228839635848999}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '195071', '-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": 10.382015228271484}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     30,  ...,  99982,  99990,
+tensor(crow_indices=tensor([     0,      4,     10,  ...,  99988,  99994,
                             100000]),
-       col_indices=tensor([ 298,  367, 1190,  ..., 3689, 6850, 7173]),
-       values=tensor([0.7086, 0.6908, 0.8648,  ..., 0.4576, 0.3199, 0.8368]),
+       col_indices=tensor([1742, 3653, 4110,  ..., 7414, 9186, 9217]),
+       values=tensor([0.4393, 0.0633, 0.6988,  ..., 0.9636, 0.3600, 0.6461]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7366, 0.0593, 0.8663,  ..., 0.2557, 0.4256, 0.5242])
+tensor([0.5362, 0.5145, 0.3988,  ..., 0.1543, 0.7121, 0.2032])
 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: 10.228839635848999 seconds
+Time: 10.382015228271484 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     30,  ...,  99982,  99990,
+tensor(crow_indices=tensor([     0,      4,     10,  ...,  99988,  99994,
                             100000]),
-       col_indices=tensor([ 298,  367, 1190,  ..., 3689, 6850, 7173]),
-       values=tensor([0.7086, 0.6908, 0.8648,  ..., 0.4576, 0.3199, 0.8368]),
+       col_indices=tensor([1742, 3653, 4110,  ..., 7414, 9186, 9217]),
+       values=tensor([0.4393, 0.0633, 0.6988,  ..., 0.9636, 0.3600, 0.6461]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7366, 0.0593, 0.8663,  ..., 0.2557, 0.4256, 0.5242])
+tensor([0.5362, 0.5145, 0.3988,  ..., 0.1543, 0.7121, 0.2032])
 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: 10.228839635848999 seconds
+Time: 10.382015228271484 seconds
 
-[18.35, 18.1, 18.16, 18.05, 17.94, 18.34, 18.01, 17.89, 17.93, 17.71]
-[79.58]
-13.51865553855896
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 186516, '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': 10.228839635848999, 'TIME_S_1KI': 0.054841620214078145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1075.814607758522, 'W': 79.58}
-[18.35, 18.1, 18.16, 18.05, 17.94, 18.34, 18.01, 17.89, 17.93, 17.71, 19.07, 18.27, 18.23, 17.94, 18.27, 18.14, 21.36, 18.53, 18.63, 18.33]
-330.52
-16.526
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 186516, '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': 10.228839635848999, 'TIME_S_1KI': 0.054841620214078145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1075.814607758522, 'W': 79.58, 'J_1KI': 5.767948099672533, 'W_1KI': 0.4266658088314139, 'W_D': 63.054, 'J_D': 852.4053063282967, 'W_D_1KI': 0.338062150164061, 'J_D_1KI': 0.0018125101876732344}
+[18.32, 18.17, 18.0, 18.26, 17.83, 17.86, 19.18, 17.87, 17.68, 18.06]
+[86.31]
+13.85704255104065
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195071, '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': 10.382015228271484, 'TIME_S_1KI': 0.05322172556798029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0013425803186, 'W': 86.31}
+[18.32, 18.17, 18.0, 18.26, 17.83, 17.86, 19.18, 17.87, 17.68, 18.06, 18.29, 17.98, 17.81, 17.75, 18.0, 18.2, 17.75, 17.66, 17.87, 17.97]
+324.18999999999994
+16.2095
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195071, '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': 10.382015228271484, 'TIME_S_1KI': 0.05322172556798029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0013425803186, 'W': 86.31, 'J_1KI': 6.131107866265712, 'W_1KI': 0.4424542858753992, 'W_D': 70.10050000000001, 'J_D': 971.3856113492252, 'W_D_1KI': 0.35935890009278676, 'J_D_1KI': 0.0018421954062509895}
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 27eac6d..5933eb4 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": 57497, "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": 10.399010181427002, "TIME_S_1KI": 0.18086178724849997, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1216.1904487371446, "W": 87.17000000000002, "J_1KI": 21.15224183413299, "W_1KI": 1.5160790997791191, "W_D": 70.89300000000001, "J_D": 989.0947514319422, "W_D_1KI": 1.2329860688383745, "J_D_1KI": 0.021444354815701245}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 53507, "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": 10.015070676803589, "TIME_S_1KI": 0.18717309280661576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1202.573525466919, "W": 88.64, "J_1KI": 22.47506915855718, "W_1KI": 1.6566056777617881, "W_D": 72.22725, "J_D": 979.9027376723885, "W_D_1KI": 1.3498654381669688, "J_D_1KI": 0.02522782884794455}
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 98e81fe..d725fe1 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 +1,14 @@
 ['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', '1000', '-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": 0.1964414119720459}
+{"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": 0.19623374938964844}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     210,  ...,  999804,
-                             999909, 1000000]),
-       col_indices=tensor([   4,  297,  328,  ..., 9417, 9717, 9744]),
-       values=tensor([0.3827, 0.2830, 0.2497,  ..., 0.1291, 0.2102, 0.5312]),
+tensor(crow_indices=tensor([      0,     102,     197,  ...,  999814,
+                             999918, 1000000]),
+       col_indices=tensor([  21,  221,  266,  ..., 9711, 9962, 9983]),
+       values=tensor([0.8240, 0.1342, 0.9347,  ..., 0.9531, 0.8710, 0.7315]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7948, 0.9855, 0.6473,  ..., 0.4205, 0.5296, 0.9253])
+tensor([0.2953, 0.0740, 0.7231,  ..., 0.2507, 0.0704, 0.5422])
 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: 0.1964414119720459 seconds
+Time: 0.19623374938964844 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', '53451', '-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": 9.761078357696533}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53507', '-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": 10.015070676803589}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     112,     220,  ...,  999796,
-                             999898, 1000000]),
-       col_indices=tensor([ 465,  658,  715,  ..., 9500, 9653, 9927]),
-       values=tensor([0.9513, 0.9158, 0.4499,  ..., 0.0775, 0.2496, 0.9759]),
+tensor(crow_indices=tensor([      0,     117,     202,  ...,  999813,
+                             999911, 1000000]),
+       col_indices=tensor([ 101,  231,  245,  ..., 9677, 9872, 9873]),
+       values=tensor([0.6066, 0.1771, 0.9671,  ..., 0.7083, 0.4630, 0.7862]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5799, 0.5098, 0.6156,  ..., 0.8166, 0.2331, 0.2979])
+tensor([0.1666, 0.0462, 0.0015,  ..., 0.3047, 0.2438, 0.6174])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 9.761078357696533 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', '57497', '-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": 10.399010181427002}
+Time: 10.015070676803589 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999802,
-                             999905, 1000000]),
-       col_indices=tensor([ 124,  157,  187,  ..., 9539, 9601, 9680]),
-       values=tensor([0.6532, 0.0603, 0.0418,  ..., 0.1935, 0.1125, 0.4778]),
+tensor(crow_indices=tensor([      0,     117,     202,  ...,  999813,
+                             999911, 1000000]),
+       col_indices=tensor([ 101,  231,  245,  ..., 9677, 9872, 9873]),
+       values=tensor([0.6066, 0.1771, 0.9671,  ..., 0.7083, 0.4630, 0.7862]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5307, 0.8097, 0.3092,  ..., 0.4937, 0.1856, 0.7516])
+tensor([0.1666, 0.0462, 0.0015,  ..., 0.3047, 0.2438, 0.6174])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.399010181427002 seconds
+Time: 10.015070676803589 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999802,
-                             999905, 1000000]),
-       col_indices=tensor([ 124,  157,  187,  ..., 9539, 9601, 9680]),
-       values=tensor([0.6532, 0.0603, 0.0418,  ..., 0.1935, 0.1125, 0.4778]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5307, 0.8097, 0.3092,  ..., 0.4937, 0.1856, 0.7516])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 10.399010181427002 seconds
-
-[18.23, 18.13, 17.96, 18.03, 17.94, 17.9, 17.91, 17.8, 18.15, 18.93]
-[87.17]
-13.951938152313232
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57497, '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': 10.399010181427002, 'TIME_S_1KI': 0.18086178724849997, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1216.1904487371446, 'W': 87.17000000000002}
-[18.23, 18.13, 17.96, 18.03, 17.94, 17.9, 17.91, 17.8, 18.15, 18.93, 18.34, 17.87, 18.21, 18.15, 18.42, 17.87, 18.22, 18.25, 18.03, 17.9]
-325.54
-16.277
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57497, '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': 10.399010181427002, 'TIME_S_1KI': 0.18086178724849997, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1216.1904487371446, 'W': 87.17000000000002, 'J_1KI': 21.15224183413299, 'W_1KI': 1.5160790997791191, 'W_D': 70.89300000000001, 'J_D': 989.0947514319422, 'W_D_1KI': 1.2329860688383745, 'J_D_1KI': 0.021444354815701245}
+[18.69, 17.91, 17.85, 17.85, 22.65, 17.8, 18.01, 17.72, 17.94, 17.78]
+[88.64]
+13.56693959236145
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 53507, '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': 10.015070676803589, 'TIME_S_1KI': 0.18717309280661576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1202.573525466919, 'W': 88.64}
+[18.69, 17.91, 17.85, 17.85, 22.65, 17.8, 18.01, 17.72, 17.94, 17.78, 20.07, 19.06, 18.03, 17.54, 17.79, 18.03, 17.73, 17.66, 17.6, 17.63]
+328.255
+16.41275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 53507, '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': 10.015070676803589, 'TIME_S_1KI': 0.18717309280661576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1202.573525466919, 'W': 88.64, 'J_1KI': 22.47506915855718, 'W_1KI': 1.6566056777617881, 'W_D': 72.22725, 'J_D': 979.9027376723885, 'W_D_1KI': 1.3498654381669688, 'J_D_1KI': 0.02522782884794455}
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 ae32267..31ef3d6 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": 9007, "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": 10.744792222976685, "TIME_S_1KI": 1.192937961915919, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1359.414791688919, "W": 84.86, "J_1KI": 150.92869897734195, "W_1KI": 9.421561008104806, "W_D": 68.55725000000001, "J_D": 1098.2528838971855, "W_D_1KI": 7.611552126124127, "J_D_1KI": 0.8450707367740786}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8765, "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": 10.565605640411377, "TIME_S_1KI": 1.2054313337605678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1349.8730677318572, "W": 85.96, "J_1KI": 154.0071954058023, "W_1KI": 9.807187678265828, "W_D": 69.70649999999999, "J_D": 1094.636191203475, "W_D_1KI": 7.952823730747289, "J_D_1KI": 0.9073387028804666}
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 c9ec716..34eb983 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', '1000', '-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": 1.1656646728515625}
+{"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": 1.1979267597198486}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     991,  ..., 4998989,
-                            4999492, 5000000]),
-       col_indices=tensor([  25,   30,   53,  ..., 9970, 9993, 9995]),
-       values=tensor([0.0157, 0.5603, 0.3033,  ..., 0.4419, 0.2413, 0.9606]),
+tensor(crow_indices=tensor([      0,     503,     997,  ..., 4999030,
+                            4999508, 5000000]),
+       col_indices=tensor([  13,   17,   19,  ..., 9920, 9929, 9953]),
+       values=tensor([0.9385, 0.6026, 0.1531,  ..., 0.7529, 0.2170, 0.3875]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4291, 0.9468, 0.9558,  ..., 0.3375, 0.0455, 0.9666])
+tensor([0.6172, 0.1221, 0.7807,  ..., 0.3915, 0.5006, 0.2223])
 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: 1.1656646728515625 seconds
+Time: 1.1979267597198486 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', '9007', '-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": 10.744792222976685}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8765', '-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": 10.565605640411377}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     491,    1013,  ..., 4998981,
-                            4999517, 5000000]),
-       col_indices=tensor([  61,   62,   77,  ..., 9979, 9982, 9988]),
-       values=tensor([0.6511, 0.9070, 0.7175,  ..., 0.4257, 0.4784, 0.0096]),
+tensor(crow_indices=tensor([      0,     511,     969,  ..., 4998985,
+                            4999485, 5000000]),
+       col_indices=tensor([  18,   30,   44,  ..., 9958, 9974, 9994]),
+       values=tensor([0.9183, 0.2043, 0.3929,  ..., 0.1798, 0.2421, 0.5984]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7046, 0.2172, 0.5779,  ..., 0.4690, 0.0165, 0.6122])
+tensor([0.9280, 0.7586, 0.0981,  ..., 0.8069, 0.8205, 0.0580])
 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: 10.744792222976685 seconds
+Time: 10.565605640411377 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     491,    1013,  ..., 4998981,
-                            4999517, 5000000]),
-       col_indices=tensor([  61,   62,   77,  ..., 9979, 9982, 9988]),
-       values=tensor([0.6511, 0.9070, 0.7175,  ..., 0.4257, 0.4784, 0.0096]),
+tensor(crow_indices=tensor([      0,     511,     969,  ..., 4998985,
+                            4999485, 5000000]),
+       col_indices=tensor([  18,   30,   44,  ..., 9958, 9974, 9994]),
+       values=tensor([0.9183, 0.2043, 0.3929,  ..., 0.1798, 0.2421, 0.5984]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7046, 0.2172, 0.5779,  ..., 0.4690, 0.0165, 0.6122])
+tensor([0.9280, 0.7586, 0.0981,  ..., 0.8069, 0.8205, 0.0580])
 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: 10.744792222976685 seconds
+Time: 10.565605640411377 seconds
 
-[18.37, 18.68, 18.13, 17.9, 18.06, 18.22, 18.04, 18.49, 17.9, 18.1]
-[84.86]
-16.019500255584717
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9007, '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': 10.744792222976685, 'TIME_S_1KI': 1.192937961915919, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.414791688919, 'W': 84.86}
-[18.37, 18.68, 18.13, 17.9, 18.06, 18.22, 18.04, 18.49, 17.9, 18.1, 18.25, 17.97, 18.05, 17.84, 17.92, 18.2, 17.96, 17.89, 18.19, 18.51]
-326.05499999999995
-16.302749999999996
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9007, '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': 10.744792222976685, 'TIME_S_1KI': 1.192937961915919, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.414791688919, 'W': 84.86, 'J_1KI': 150.92869897734195, 'W_1KI': 9.421561008104806, 'W_D': 68.55725000000001, 'J_D': 1098.2528838971855, 'W_D_1KI': 7.611552126124127, 'J_D_1KI': 0.8450707367740786}
+[18.27, 17.87, 17.87, 17.65, 18.34, 18.87, 17.99, 17.76, 18.35, 17.83]
+[85.96]
+15.703502416610718
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8765, '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': 10.565605640411377, 'TIME_S_1KI': 1.2054313337605678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1349.8730677318572, 'W': 85.96}
+[18.27, 17.87, 17.87, 17.65, 18.34, 18.87, 17.99, 17.76, 18.35, 17.83, 18.43, 17.64, 18.08, 18.62, 18.12, 17.89, 17.87, 17.65, 18.18, 18.11]
+325.07
+16.2535
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8765, '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': 10.565605640411377, 'TIME_S_1KI': 1.2054313337605678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1349.8730677318572, 'W': 85.96, 'J_1KI': 154.0071954058023, 'W_1KI': 9.807187678265828, 'W_D': 69.70649999999999, 'J_D': 1094.636191203475, 'W_D_1KI': 7.952823730747289, 'J_D_1KI': 0.9073387028804666}
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
new file mode 100644
index 0000000..916740d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2843, "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": 10.2704017162323, "TIME_S_1KI": 3.6125225874893774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1409.3875760412218, "W": 82.18, "J_1KI": 495.73956244854793, "W_1KI": 28.90608512135069, "W_D": 66.09875000000001, "J_D": 1133.5940258196, "W_D_1KI": 23.249648258881464, "J_D_1KI": 8.177857284165128}
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
new file mode 100644
index 0000000..f77d6ca
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 3.6929545402526855}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      984,     2024,  ...,  9998063,
+                             9998995, 10000000]),
+       col_indices=tensor([   8,   13,   17,  ..., 9976, 9985, 9991]),
+       values=tensor([0.9364, 0.6574, 0.1385,  ..., 0.6834, 0.0920, 0.4928]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2645, 0.6514, 0.1258,  ..., 0.8959, 0.1836, 0.1827])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 3.6929545402526855 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', '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": 10.2704017162323}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2005,  ...,  9997991,
+                             9998986, 10000000]),
+       col_indices=tensor([  33,   43,   63,  ..., 9975, 9988, 9994]),
+       values=tensor([0.7459, 0.7397, 0.9950,  ..., 0.6626, 0.6614, 0.8057]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0776, 0.4434, 0.3294,  ..., 0.9636, 0.8443, 0.5700])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.2704017162323 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2005,  ...,  9997991,
+                             9998986, 10000000]),
+       col_indices=tensor([  33,   43,   63,  ..., 9975, 9988, 9994]),
+       values=tensor([0.7459, 0.7397, 0.9950,  ..., 0.6626, 0.6614, 0.8057]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0776, 0.4434, 0.3294,  ..., 0.9636, 0.8443, 0.5700])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.2704017162323 seconds
+
+[18.3, 17.96, 17.98, 17.52, 17.65, 17.88, 17.7, 17.55, 17.72, 17.81]
+[82.18]
+17.150007009506226
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, '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': 10.2704017162323, 'TIME_S_1KI': 3.6125225874893774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.3875760412218, 'W': 82.18}
+[18.3, 17.96, 17.98, 17.52, 17.65, 17.88, 17.7, 17.55, 17.72, 17.81, 18.29, 17.77, 17.64, 17.69, 18.52, 18.77, 17.66, 17.68, 17.94, 17.59]
+321.625
+16.08125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, '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': 10.2704017162323, 'TIME_S_1KI': 3.6125225874893774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.3875760412218, 'W': 82.18, 'J_1KI': 495.73956244854793, 'W_1KI': 28.90608512135069, 'W_D': 66.09875000000001, 'J_D': 1133.5940258196, 'W_D_1KI': 23.249648258881464, 'J_D_1KI': 8.177857284165128}
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 3d91d0f..a7f50f8 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": 279705, "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": 10.192691802978516, "TIME_S_1KI": 0.03644086377783206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1002.2923643112182, "W": 73.24, "J_1KI": 3.5833909451429835, "W_1KI": 0.2618473034089487, "W_D": 56.983999999999995, "J_D": 779.8283463668822, "W_D_1KI": 0.20372892869272982, "J_D_1KI": 0.0007283707073263969}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 286739, "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": 10.441989183425903, "TIME_S_1KI": 0.036416354885194915, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1103.374533557892, "W": 79.9, "J_1KI": 3.8480099796605693, "W_1KI": 0.27865061955297327, "W_D": 63.605500000000006, "J_D": 878.3565568737985, "W_D_1KI": 0.22182367937392544, "J_D_1KI": 0.0007736083315277149}
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 d0774e1..03249c3 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
@@ -1,373 +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', '1000', '-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": 0.05549430847167969}
+{"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": 0.05496048927307129}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3370, 8033, 9994, 2466, 6901, 8760, 7929, 6009, 6694,
-                           5159, 1569, 4321, 2884, 3593, 7087,  277, 9865,  708,
-                            219, 1957, 2394, 9752, 9341, 4634, 7106, 8670, 5732,
-                            279, 8294, 2115, 4730, 6630, 1087, 3467,   99,  364,
-                           8115, 4267, 1834, 5621, 1569, 7117, 9388, 1669, 5931,
-                           9264, 3811, 5721, 3727,  135, 5730, 2995, 3406, 9737,
-                           8203, 4619, 3682, 7347,  200, 8973, 7753,  580, 2253,
-                           5338, 9810, 8027,  181, 7440, 8883, 5987, 8971,  592,
-                           4310, 5459, 5555, 5982, 2912, 5657, 5155, 5158, 2575,
-                           4534, 5426,  285, 2313,  564,  416, 9640, 2595, 4194,
-                            651, 1798, 5551, 7426, 7258, 3436, 2400, 6562, 5104,
-                           7010,  536, 2620, 9757,   68, 4487, 1288, 1752, 3582,
-                           4320, 2874, 3544, 5364, 8870,  570,  876, 9095, 9069,
-                           7054, 4172, 1984, 9030, 5728, 1404, 5844, 3846,  641,
-                           8291, 9336, 3061, 3478, 6348, 6870,  170, 9681, 4066,
-                            161, 4454, 1223, 5179, 2812, 3556, 5193, 2967, 5978,
-                            997,  659,  500, 7339, 9371, 8915, 9451, 2693, 6998,
-                           4462, 9723, 8519,  528, 8686, 4869, 1134, 5722, 3684,
-                           5254, 2008, 3928, 3524, 7467, 5611, 9303, 1848, 7513,
-                            144, 6865, 9315,  260, 6542, 8384, 7775, 9566, 1870,
-                           5432, 4007, 6545, 8496, 7112, 7663, 8511, 3654, 8360,
-                           7458, 7244, 9547,  237, 7595, 3067, 3897, 1148, 6044,
-                           9905, 3252, 3717, 2097, 8397, 5162,  880, 3643, 3669,
-                           3006, 4680, 1145, 5124, 2016, 4795, 1874, 2179, 4471,
-                           5866, 4135, 4797, 3701, 1368, 8916, 2162, 8724, 3185,
-                           2438, 7109, 8896, 7886, 7935, 5984, 6692, 9779,  356,
-                           8330, 7674, 7049, 1894, 9291,  438,  644, 5593, 4012,
-                           4092, 1074, 7656, 8497, 9936, 1003, 1428, 4946, 3098,
-                           3834, 5854, 9677, 3180, 1592, 4702, 1405, 2278,  844,
-                           3771,  581,  700, 6434, 4124, 9326,  859, 5258, 8507,
-                           5298, 8386, 9430, 4702, 2123, 1428, 3971, 9516, 3171,
-                            631, 9941,  183, 4223, 8243, 8550, 4681, 1680, 6085,
-                           9231, 7887, 2007, 9929,  319, 2262, 7173, 8741, 4368,
-                           7062, 7568, 7422, 6915, 6947, 8441, 2961, 6342, 2044,
-                           1447, 9597,  985, 8998, 9652, 4424, 5209,  960, 6967,
-                            167,  332, 8301, 5318, 8462, 9782, 1436, 5255, 8291,
-                           1679,  100, 7990, 7641, 5493, 6378, 4691, 7683, 2088,
-                           3372, 8060, 1057, 3230, 8284, 1196, 9763, 1017,  415,
-                           4982, 6524, 8257, 2213, 7932, 3442, 8568, 3619, 2201,
-                           8218, 4246, 4441, 4982, 2118,  427, 6086, 1745, 4209,
-                           7427, 2079, 9082, 6939, 9748, 4182, 4214, 8354, 8651,
-                           3029, 8143, 5135, 9139,  422, 6584, 4206, 3350, 3991,
-                            580, 2652, 5057, 3635, 5440, 7829, 2912, 7163, 3688,
-                            695, 1097, 7502, 9778, 6390, 2778, 5858, 8301, 3489,
-                           4549, 1884,  825, 5427,  443, 8921, 1023, 9620, 3942,
-                           4314,  675,  963, 9852, 4203, 3468, 8239,  870, 8839,
-                            968, 4232, 4985, 3353,   73, 2459, 5154, 1956, 1327,
-                           6990, 1883, 4040, 9749, 9910, 4672, 5452, 4110, 9189,
-                           5479,  140, 5133, 1656, 2426, 8412, 8268, 6211, 4670,
-                           4046,  220, 7783, 3136, 5529, 1519, 5090, 4609, 7290,
-                           6130, 3587, 3942, 1229,  552, 8186, 3250, 8456, 5207,
-                           6427, 6375, 7492, 5408, 4660,  134, 3661,   13, 8254,
-                           5276, 4385, 9507, 9434, 8019, 1686, 3007, 5215, 4722,
-                           2267,  549, 6567, 9286, 5206, 4726, 1559, 1580, 1843,
-                           9051, 1093, 6549,  227, 7062, 3358, 7788,  368, 1631,
-                           6978,  486, 1733, 2943, 2244, 1432, 7203, 6090, 6747,
-                           7011, 1946, 5223, 4474, 9388, 5043, 3504,  986, 4293,
-                           9704, 6750, 9397, 4917, 3565, 4328, 3764, 3459, 2307,
-                           2729,  635, 5686, 8216, 7948, 4213, 7647, 9654, 8289,
-                           3877, 9908, 2629, 3758, 2379, 7870, 1661, 2524, 2025,
-                           1815, 1665, 3312, 6095, 9851,  161, 2621, 4565, 7285,
-                           2137, 5689, 9717, 1329, 6660, 9626, 5019, 6708, 5239,
-                           2539, 7410, 9243,  432, 2490, 4568, 6231, 3113, 8655,
-                           7941, 5808, 4116, 1627, 1188, 3208, 6858, 4204, 4107,
-                           8587, 6451, 7251, 5391, 5116, 7261, 6410,  672, 1611,
-                           2152, 6577, 1898, 7871, 7055, 9067, 2195, 7972, 8151,
-                           3524, 2108, 1529,  903, 5082, 5551, 7367, 8808, 5241,
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+                      4.6489e-01, 3.7186e-01, 4.7115e-01, 3.1902e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.9318, 0.2708, 0.1659,  ..., 0.9519, 0.7638, 0.9831])
+tensor([0.0435, 0.9300, 0.8297,  ..., 0.3222, 0.7823, 0.3267])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,378 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.05549430847167969 seconds
+Time: 0.05496048927307129 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', '189208', '-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": 7.102759838104248}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '191046', '-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": 6.995845317840576}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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-                           4477, 7482,  497, 1165, 1179, 8217, 7349, 5272, 9560,
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+                      7.2234e-01, 4.5613e-01, 2.0993e-01, 3.8888e-01,
+                      3.4362e-01, 2.8391e-01, 4.7760e-01, 1.3578e-01,
+                      4.1355e-01, 5.4012e-01, 8.5277e-01, 2.8268e-01,
+                      2.1746e-01, 6.6617e-01, 3.8200e-01, 6.7181e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4272, 0.4478, 0.5565,  ..., 0.4220, 0.4867, 0.3940])
+tensor([0.7926, 0.8036, 0.9264,  ..., 0.8315, 0.2335, 0.7753])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,378 +754,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 7.102759838104248 seconds
+Time: 6.995845317840576 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', '279705', '-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": 10.192691802978516}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '286739', '-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": 10.441989183425903}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.3844, 0.6738, 0.8785,  ..., 0.1267, 0.6920, 0.7757])
+tensor([0.1121, 0.3867, 0.9452,  ..., 0.7986, 0.0788, 0.8344])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +1133,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.192691802978516 seconds
+Time: 10.441989183425903 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      7.8894e-01, 5.0686e-01, 1.0844e-02, 8.8476e-01,
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+                      1.2881e-01, 6.6073e-01, 6.6982e-01, 9.4311e-01,
+                      5.7920e-01, 3.4038e-01, 5.1929e-01, 6.6344e-01,
+                      3.6283e-01, 8.4071e-01, 8.3032e-02, 3.7224e-01,
+                      2.8825e-01, 2.1346e-01, 6.6572e-01, 9.1219e-01,
+                      7.5086e-01, 5.9660e-01, 6.4939e-01, 8.3636e-02,
+                      2.1276e-01, 8.1840e-01, 9.1241e-01, 4.1267e-01,
+                      1.2384e-01, 3.2306e-01, 1.8222e-01, 1.6973e-01,
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+                      4.9029e-01, 6.0270e-02, 9.5693e-01, 2.5910e-01,
+                      4.3595e-01, 3.2220e-01, 7.7687e-02, 8.6974e-01,
+                      4.2140e-01, 9.3963e-01, 1.5046e-02, 1.9120e-01,
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+                      3.8009e-01, 9.0525e-01, 3.1360e-01, 2.8014e-01,
+                      8.6922e-01, 8.0054e-02, 1.5774e-01, 7.8264e-01,
+                      5.5830e-01, 8.2156e-01, 5.4363e-02, 6.1422e-01,
+                      6.4211e-01, 1.8056e-01, 4.8874e-01, 2.7600e-01,
+                      7.3967e-01, 6.3099e-01, 9.2851e-01, 2.9826e-01,
+                      7.2043e-02, 1.9501e-01, 2.0723e-01, 4.8988e-01,
+                      1.1743e-01, 6.0389e-01, 2.4019e-01, 5.3080e-01,
+                      2.4116e-01, 6.4832e-01, 1.3206e-01, 5.7881e-01,
+                      2.4751e-01, 2.5004e-01, 1.0644e-01, 6.4024e-02,
+                      8.1402e-01, 4.5228e-01, 6.7548e-01, 9.3771e-01,
+                      2.0818e-01, 4.7722e-01, 2.0221e-01, 6.3364e-01,
+                      2.8929e-01, 5.9029e-01, 4.2652e-01, 9.1620e-01,
+                      2.2454e-01, 6.7134e-01, 7.9748e-02, 3.2710e-01,
+                      8.1974e-01, 1.5549e-02, 4.9868e-01, 5.7492e-01,
+                      7.2008e-02, 7.9730e-01, 1.5602e-01, 2.0940e-01,
+                      5.3816e-01, 1.9379e-01, 2.7025e-02, 4.9766e-01,
+                      6.0189e-01, 3.2022e-01, 8.5769e-01, 6.5720e-01,
+                      3.0809e-01, 1.1742e-01, 9.2720e-01, 1.9132e-01,
+                      3.9789e-01, 3.7697e-02, 6.0352e-01, 3.9733e-01,
+                      4.4928e-01, 1.5419e-01, 9.8145e-01, 4.1892e-01,
+                      7.0064e-01, 3.8871e-01, 6.2793e-01, 7.7207e-01,
+                      4.4089e-01, 3.6800e-01, 4.9602e-01, 7.8104e-01,
+                      1.0657e-02, 8.0591e-01, 9.9727e-01, 1.3379e-01,
+                      5.4798e-01, 7.6658e-01, 6.3649e-01, 2.8939e-01,
+                      8.1990e-01, 7.9348e-01, 8.2543e-01, 6.7492e-01,
+                      6.7563e-02, 9.7314e-01, 2.2021e-01, 2.0069e-01,
+                      8.7506e-01, 7.2409e-01, 3.6865e-01, 1.9488e-01,
+                      7.4566e-01, 9.7425e-01, 9.4802e-01, 3.3832e-01,
+                      8.4433e-01, 4.1426e-02, 2.6255e-01, 1.7428e-01,
+                      1.8207e-01, 1.8488e-01, 8.2856e-03, 1.1348e-01,
+                      9.2197e-01, 9.2179e-01, 6.1482e-01, 1.7686e-01,
+                      6.5075e-02, 1.5011e-01, 7.1724e-01, 1.9623e-01,
+                      7.7691e-01, 8.8594e-01, 5.6303e-01, 7.3530e-01,
+                      9.8857e-01, 7.4483e-01, 8.5336e-01, 7.1468e-01,
+                      3.4586e-01, 6.2753e-01, 3.4994e-01, 9.4314e-01,
+                      5.9523e-01, 2.4831e-01, 6.7030e-01, 7.2279e-01,
+                      7.4167e-01, 3.7774e-01, 9.7774e-01, 2.7848e-01,
+                      5.9253e-01, 2.2646e-01, 7.3907e-02, 1.8840e-01,
+                      1.6017e-01, 9.0665e-01, 7.6825e-01, 1.7432e-01,
+                      5.2899e-02, 4.0457e-01, 9.7040e-01, 9.0688e-02,
+                      5.8056e-01, 2.6439e-01, 8.2605e-01, 9.1309e-01,
+                      6.8101e-01, 8.0757e-01, 6.2304e-01, 9.9518e-01,
+                      4.7896e-01, 3.1904e-01, 7.0082e-01, 4.5077e-01,
+                      1.7951e-01, 3.2747e-02, 1.3088e-01, 9.1100e-01,
+                      2.8097e-01, 7.7241e-01, 2.7702e-01, 8.9268e-01,
+                      7.4964e-02, 4.5346e-01, 3.5535e-01, 7.4730e-01,
+                      1.0795e-01, 9.2068e-01, 2.5310e-01, 9.3425e-01,
+                      8.6243e-03, 3.4566e-01, 1.2041e-01, 8.5141e-02,
+                      6.8159e-01, 6.3087e-01, 4.5251e-02, 3.5864e-01,
+                      2.6782e-01, 7.8826e-02, 8.7299e-01, 6.6518e-01,
+                      7.0044e-01, 1.8992e-01, 8.0560e-01, 7.7462e-01,
+                      3.8679e-01, 6.1189e-01, 1.5892e-01, 6.3252e-01,
+                      6.9613e-01, 3.5041e-01, 8.1632e-01, 4.0743e-01,
+                      7.7173e-01, 4.3097e-01, 4.1902e-01, 7.2770e-01,
+                      8.4816e-01, 2.2977e-01, 7.1589e-01, 9.0275e-01,
+                      9.9999e-01, 4.1852e-01, 2.2491e-02, 7.9153e-01,
+                      9.7639e-01, 3.3819e-01, 7.4342e-01, 8.5250e-01,
+                      9.9385e-01, 6.3891e-02, 9.7464e-01, 3.0992e-01,
+                      9.9006e-01, 1.1081e-01, 7.5155e-01, 8.4844e-01,
+                      7.0384e-01, 4.6698e-01, 1.6751e-01, 6.1651e-01,
+                      4.3979e-01, 6.1587e-01, 3.4869e-01, 4.9449e-01,
+                      9.1105e-02, 9.8166e-01, 3.3675e-02, 3.1123e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3844, 0.6738, 0.8785,  ..., 0.1267, 0.6920, 0.7757])
+tensor([0.1121, 0.3867, 0.9452,  ..., 0.7986, 0.0788, 0.8344])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +1509,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.192691802978516 seconds
+Time: 10.441989183425903 seconds
 
-[18.55, 17.99, 18.16, 18.03, 18.22, 17.84, 17.97, 17.8, 18.22, 18.03]
-[73.24]
-13.685040473937988
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 279705, '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': 10.192691802978516, 'TIME_S_1KI': 0.03644086377783206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1002.2923643112182, 'W': 73.24}
-[18.55, 17.99, 18.16, 18.03, 18.22, 17.84, 17.97, 17.8, 18.22, 18.03, 18.98, 18.15, 18.06, 17.9, 17.97, 18.02, 18.2, 18.05, 17.87, 17.78]
-325.11999999999995
-16.255999999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 279705, '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': 10.192691802978516, 'TIME_S_1KI': 0.03644086377783206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1002.2923643112182, 'W': 73.24, 'J_1KI': 3.5833909451429835, 'W_1KI': 0.2618473034089487, 'W_D': 56.983999999999995, 'J_D': 779.8283463668822, 'W_D_1KI': 0.20372892869272982, 'J_D_1KI': 0.0007283707073263969}
+[18.13, 22.04, 18.79, 17.71, 17.77, 17.9, 17.83, 17.54, 17.97, 17.85]
+[79.9]
+13.809443473815918
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286739, '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': 10.441989183425903, 'TIME_S_1KI': 0.036416354885194915, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.374533557892, 'W': 79.9}
+[18.13, 22.04, 18.79, 17.71, 17.77, 17.9, 17.83, 17.54, 17.97, 17.85, 18.13, 17.63, 18.09, 17.81, 17.93, 17.7, 17.59, 17.82, 17.87, 17.69]
+325.89
+16.2945
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286739, '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': 10.441989183425903, 'TIME_S_1KI': 0.036416354885194915, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.374533557892, 'W': 79.9, 'J_1KI': 3.8480099796605693, 'W_1KI': 0.27865061955297327, 'W_D': 63.605500000000006, 'J_D': 878.3565568737985, 'W_D_1KI': 0.22182367937392544, 'J_D_1KI': 0.0007736083315277149}
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 8eef901..d6eca5a 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": 8355, "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": 10.90480637550354, "TIME_S_1KI": 1.305183288510298, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1307.563270778656, "W": 87.44, "J_1KI": 156.5006906976249, "W_1KI": 10.4655894673848, "W_D": 70.932, "J_D": 1060.7053742322921, "W_D_1KI": 8.489766606822261, "J_D_1KI": 1.0161300546765126}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 7939, "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": 10.723366260528564, "TIME_S_1KI": 1.350720022739459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1313.216650083065, "W": 88.63, "J_1KI": 165.41335811601778, "W_1KI": 11.163874543393375, "W_D": 72.31174999999999, "J_D": 1071.4317284964918, "W_D_1KI": 9.108420455976821, "J_D_1KI": 1.1473007250254217}
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 03135d2..2715834 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
@@ -1,15 +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', '1000', '-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": 1.2567212581634521}
+{"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": 1.3224358558654785}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
-                            2499996, 2500000]),
-       col_indices=tensor([164554, 277712, 289036,  ..., 389470, 409865,
-                           491502]),
-       values=tensor([0.0126, 0.9348, 0.8595,  ..., 0.3584, 0.7345, 0.5238]),
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499992,
+                            2499995, 2500000]),
+       col_indices=tensor([ 81446, 111347, 262323,  ...,  95785, 329641,
+                           405148]),
+       values=tensor([0.7472, 0.0566, 0.1215,  ..., 0.1323, 0.4741, 0.2377]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0175, 0.7668, 0.4852,  ..., 0.2657, 0.5513, 0.9738])
+tensor([0.0977, 0.7761, 0.5514,  ..., 0.9913, 0.3768, 0.8332])
 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: 1.2567212581634521 seconds
+Time: 1.3224358558654785 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', '8355', '-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": 10.90480637550354}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7939', '-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": 10.723366260528564}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      12,  ..., 2499995,
-                            2499997, 2500000]),
-       col_indices=tensor([ 72448,  73110, 121261,  ...,  13350, 176428,
-                           278854]),
-       values=tensor([0.1918, 0.3445, 0.8471,  ..., 0.5873, 0.4603, 0.6922]),
+tensor(crow_indices=tensor([      0,       1,       9,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([185711,  60363, 105088,  ..., 318731, 319175,
+                           323232]),
+       values=tensor([0.5920, 0.0659, 0.0171,  ..., 0.3410, 0.9352, 0.3450]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8369, 0.9252, 0.2721,  ..., 0.2352, 0.7861, 0.2173])
+tensor([0.5701, 0.8906, 0.4066,  ..., 0.2438, 0.9359, 0.5479])
 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: 10.90480637550354 seconds
+Time: 10.723366260528564 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      12,  ..., 2499995,
-                            2499997, 2500000]),
-       col_indices=tensor([ 72448,  73110, 121261,  ...,  13350, 176428,
-                           278854]),
-       values=tensor([0.1918, 0.3445, 0.8471,  ..., 0.5873, 0.4603, 0.6922]),
+tensor(crow_indices=tensor([      0,       1,       9,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([185711,  60363, 105088,  ..., 318731, 319175,
+                           323232]),
+       values=tensor([0.5920, 0.0659, 0.0171,  ..., 0.3410, 0.9352, 0.3450]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8369, 0.9252, 0.2721,  ..., 0.2352, 0.7861, 0.2173])
+tensor([0.5701, 0.8906, 0.4066,  ..., 0.2438, 0.9359, 0.5479])
 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: 10.90480637550354 seconds
+Time: 10.723366260528564 seconds
 
-[18.48, 21.5, 18.34, 18.43, 18.27, 18.03, 18.28, 18.13, 18.03, 18.78]
-[87.44]
-14.953834295272827
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8355, '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': 10.90480637550354, 'TIME_S_1KI': 1.305183288510298, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1307.563270778656, 'W': 87.44}
-[18.48, 21.5, 18.34, 18.43, 18.27, 18.03, 18.28, 18.13, 18.03, 18.78, 18.48, 18.09, 18.1, 17.9, 18.11, 18.02, 17.99, 17.84, 18.15, 18.16]
-330.15999999999997
-16.508
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8355, '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': 10.90480637550354, 'TIME_S_1KI': 1.305183288510298, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1307.563270778656, 'W': 87.44, 'J_1KI': 156.5006906976249, 'W_1KI': 10.4655894673848, 'W_D': 70.932, 'J_D': 1060.7053742322921, 'W_D_1KI': 8.489766606822261, 'J_D_1KI': 1.0161300546765126}
+[18.35, 18.0, 17.81, 18.15, 20.9, 17.73, 17.87, 17.99, 18.01, 17.97]
+[88.63]
+14.81684136390686
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7939, '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': 10.723366260528564, 'TIME_S_1KI': 1.350720022739459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1313.216650083065, 'W': 88.63}
+[18.35, 18.0, 17.81, 18.15, 20.9, 17.73, 17.87, 17.99, 18.01, 17.97, 18.83, 17.84, 18.13, 18.11, 17.94, 17.77, 17.81, 17.81, 18.09, 17.66]
+326.365
+16.31825
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7939, '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': 10.723366260528564, 'TIME_S_1KI': 1.350720022739459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1313.216650083065, 'W': 88.63, 'J_1KI': 165.41335811601778, 'W_1KI': 11.163874543393375, 'W_D': 72.31174999999999, 'J_D': 1071.4317284964918, 'W_D_1KI': 9.108420455976821, 'J_D_1KI': 1.1473007250254217}
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 035895d..c8d8a61 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": 77922, "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": 10.570462703704834, "TIME_S_1KI": 0.13565440701861906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.7638923931122, "W": 83.24, "J_1KI": 15.525318811030418, "W_1KI": 1.0682477349144015, "W_D": 66.53899999999999, "J_D": 967.0408413736818, "W_D_1KI": 0.8539180205846871, "J_D_1KI": 0.010958625556129042}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 78314, "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": 10.500975370407104, "TIME_S_1KI": 0.1340880988125636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1246.381588702202, "W": 89.06, "J_1KI": 15.91518232630439, "W_1KI": 1.1372168450085551, "W_D": 72.72325000000001, "J_D": 1017.7511775273682, "W_D_1KI": 0.9286111040171617, "J_D_1KI": 0.0118575363794106}
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 70de4f5..e0ea23e 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', '1000', '-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": 0.14919304847717285}
+{"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": 0.14957594871520996}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      9,  ..., 249989, 249995,
+tensor(crow_indices=tensor([     0,      8,     15,  ..., 249987, 249991,
                             250000]),
-       col_indices=tensor([ 8787, 10800, 12548,  ..., 22776, 32520, 35593]),
-       values=tensor([0.0395, 0.0216, 0.0459,  ..., 0.9233, 0.0886, 0.1442]),
+       col_indices=tensor([ 3249, 11393, 14942,  ..., 33826, 38027, 48849]),
+       values=tensor([0.4435, 0.3887, 0.6766,  ..., 0.7020, 0.9117, 0.7998]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0084, 0.2765, 0.2672,  ..., 0.0856, 0.1416, 0.8826])
+tensor([0.4072, 0.0290, 0.9610,  ..., 0.4695, 0.4913, 0.1254])
 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: 0.14919304847717285 seconds
+Time: 0.14957594871520996 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', '70378', '-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": 9.483437538146973}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70198', '-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": 9.41176462173462}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249988, 249997,
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 249984, 249993,
                             250000]),
-       col_indices=tensor([ 1665,  9567,  9654,  ...,  4112, 18670, 38091]),
-       values=tensor([0.4890, 0.0494, 0.7903,  ..., 0.9513, 0.0590, 0.1377]),
+       col_indices=tensor([  257,   837, 13772,  ..., 26625, 34572, 42693]),
+       values=tensor([0.6771, 0.0630, 0.4952,  ..., 0.2009, 0.3453, 0.0186]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5003, 0.9747, 0.2176,  ..., 0.9666, 0.4758, 0.9002])
+tensor([0.1005, 0.4396, 0.3760,  ..., 0.8175, 0.2613, 0.1136])
 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: 9.483437538146973 seconds
+Time: 9.41176462173462 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', '77922', '-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": 10.570462703704834}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78314', '-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": 10.500975370407104}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249995, 249999,
+tensor(crow_indices=tensor([     0,      4,     13,  ..., 249994, 249998,
                             250000]),
-       col_indices=tensor([18420, 40988,  3727,  ..., 33621, 36384, 44487]),
-       values=tensor([0.1861, 0.8144, 0.1628,  ..., 0.4774, 0.5715, 0.3216]),
+       col_indices=tensor([  417,  3050, 28352,  ..., 48782,  1625, 48386]),
+       values=tensor([0.9216, 0.4652, 0.6011,  ..., 0.6170, 0.6564, 0.4691]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6272, 0.7644, 0.0884,  ..., 0.9496, 0.3089, 0.8679])
+tensor([0.8482, 0.9835, 0.6846,  ..., 0.7970, 0.3559, 0.9710])
 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: 10.570462703704834 seconds
+Time: 10.500975370407104 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249995, 249999,
+tensor(crow_indices=tensor([     0,      4,     13,  ..., 249994, 249998,
                             250000]),
-       col_indices=tensor([18420, 40988,  3727,  ..., 33621, 36384, 44487]),
-       values=tensor([0.1861, 0.8144, 0.1628,  ..., 0.4774, 0.5715, 0.3216]),
+       col_indices=tensor([  417,  3050, 28352,  ..., 48782,  1625, 48386]),
+       values=tensor([0.9216, 0.4652, 0.6011,  ..., 0.6170, 0.6564, 0.4691]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6272, 0.7644, 0.0884,  ..., 0.9496, 0.3089, 0.8679])
+tensor([0.8482, 0.9835, 0.6846,  ..., 0.7970, 0.3559, 0.9710])
 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: 10.570462703704834 seconds
+Time: 10.500975370407104 seconds
 
-[18.59, 17.97, 19.57, 18.3, 18.09, 17.87, 18.02, 21.06, 18.56, 17.89]
-[83.24]
-14.533444166183472
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77922, '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': 10.570462703704834, 'TIME_S_1KI': 0.13565440701861906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.7638923931122, 'W': 83.24}
-[18.59, 17.97, 19.57, 18.3, 18.09, 17.87, 18.02, 21.06, 18.56, 17.89, 18.2, 17.85, 17.83, 21.57, 17.96, 18.06, 18.27, 18.3, 18.38, 18.04]
-334.02
-16.701
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77922, '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': 10.570462703704834, 'TIME_S_1KI': 0.13565440701861906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.7638923931122, 'W': 83.24, 'J_1KI': 15.525318811030418, 'W_1KI': 1.0682477349144015, 'W_D': 66.53899999999999, 'J_D': 967.0408413736818, 'W_D_1KI': 0.8539180205846871, 'J_D_1KI': 0.010958625556129042}
+[18.34, 17.82, 17.88, 21.83, 18.66, 17.91, 17.8, 17.7, 17.85, 17.95]
+[89.06]
+13.994852781295776
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78314, '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': 10.500975370407104, 'TIME_S_1KI': 0.1340880988125636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.381588702202, 'W': 89.06}
+[18.34, 17.82, 17.88, 21.83, 18.66, 17.91, 17.8, 17.7, 17.85, 17.95, 18.61, 18.02, 17.66, 18.02, 17.94, 17.65, 17.89, 18.0, 17.71, 17.89]
+326.735
+16.336750000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78314, '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': 10.500975370407104, 'TIME_S_1KI': 0.1340880988125636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.381588702202, 'W': 89.06, 'J_1KI': 15.91518232630439, 'W_1KI': 1.1372168450085551, 'W_D': 72.72325000000001, 'J_D': 1017.7511775273682, 'W_D_1KI': 0.9286111040171617, 'J_D_1KI': 0.0118575363794106}
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 f58be3b..2dcd593 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": 17357, "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": 10.690638303756714, "TIME_S_1KI": 0.6159266177194627, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.2395059108735, "W": 87.56, "J_1KI": 74.62346637730447, "W_1KI": 5.044650573255747, "W_D": 71.326, "J_D": 1055.0965394997595, "W_D_1KI": 4.109350694244396, "J_D_1KI": 0.23675466349279234}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 16503, "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": 10.453594446182251, "TIME_S_1KI": 0.6334360083731595, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1283.8258463859559, "W": 88.2, "J_1KI": 77.7934827840972, "W_1KI": 5.344482821305218, "W_D": 72.15925, "J_D": 1050.3391179798841, "W_D_1KI": 4.372492880082409, "J_D_1KI": 0.2649513955088414}
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 ec20a71..eb8fa27 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
@@ -1,14 +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', '1000', '-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": 0.6049323081970215}
+{"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": 0.6362464427947998}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     103,  ..., 2499893,
-                            2499950, 2500000]),
-       col_indices=tensor([  214,   217,  3424,  ..., 47339, 47927, 48505]),
-       values=tensor([0.8463, 0.5755, 0.1058,  ..., 0.4565, 0.0843, 0.4040]),
+tensor(crow_indices=tensor([      0,      51,     104,  ..., 2499889,
+                            2499946, 2500000]),
+       col_indices=tensor([  554,  2346,  3623,  ..., 48601, 49342, 49458]),
+       values=tensor([0.6346, 0.6039, 0.4681,  ..., 0.0926, 0.5934, 0.5905]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2070, 0.0126, 0.4112,  ..., 0.3463, 0.8132, 0.3234])
+tensor([0.3504, 0.0589, 0.7648,  ..., 0.3104, 0.5013, 0.0863])
 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: 0.6049323081970215 seconds
+Time: 0.6362464427947998 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', '17357', '-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": 10.690638303756714}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16503', '-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": 10.453594446182251}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     101,  ..., 2499901,
-                            2499949, 2500000]),
-       col_indices=tensor([  511,   725,   819,  ..., 47217, 48788, 49222]),
-       values=tensor([0.0511, 0.3894, 0.2647,  ..., 0.8233, 0.9615, 0.4045]),
+tensor(crow_indices=tensor([      0,      44,      95,  ..., 2499888,
+                            2499946, 2500000]),
+       col_indices=tensor([   29,    59,  1099,  ..., 49158, 49549, 49729]),
+       values=tensor([0.6925, 0.1264, 0.7717,  ..., 0.9011, 0.2629, 0.2267]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7118, 0.9063, 0.7110,  ..., 0.7333, 0.4959, 0.7807])
+tensor([0.0922, 0.7073, 0.7429,  ..., 0.1285, 0.2485, 0.0697])
 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: 10.690638303756714 seconds
+Time: 10.453594446182251 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     101,  ..., 2499901,
-                            2499949, 2500000]),
-       col_indices=tensor([  511,   725,   819,  ..., 47217, 48788, 49222]),
-       values=tensor([0.0511, 0.3894, 0.2647,  ..., 0.8233, 0.9615, 0.4045]),
+tensor(crow_indices=tensor([      0,      44,      95,  ..., 2499888,
+                            2499946, 2500000]),
+       col_indices=tensor([   29,    59,  1099,  ..., 49158, 49549, 49729]),
+       values=tensor([0.6925, 0.1264, 0.7717,  ..., 0.9011, 0.2629, 0.2267]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7118, 0.9063, 0.7110,  ..., 0.7333, 0.4959, 0.7807])
+tensor([0.0922, 0.7073, 0.7429,  ..., 0.1285, 0.2485, 0.0697])
 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: 10.690638303756714 seconds
+Time: 10.453594446182251 seconds
 
-[18.27, 17.74, 18.29, 17.77, 18.01, 17.85, 17.88, 17.79, 18.2, 17.88]
-[87.56]
-14.792593717575073
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17357, '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': 10.690638303756714, 'TIME_S_1KI': 0.6159266177194627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2395059108735, 'W': 87.56}
-[18.27, 17.74, 18.29, 17.77, 18.01, 17.85, 17.88, 17.79, 18.2, 17.88, 18.2, 18.34, 18.01, 17.89, 18.13, 18.46, 18.22, 17.9, 17.95, 18.15]
-324.68
-16.234
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17357, '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': 10.690638303756714, 'TIME_S_1KI': 0.6159266177194627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2395059108735, 'W': 87.56, 'J_1KI': 74.62346637730447, 'W_1KI': 5.044650573255747, 'W_D': 71.326, 'J_D': 1055.0965394997595, 'W_D_1KI': 4.109350694244396, 'J_D_1KI': 0.23675466349279234}
+[18.19, 17.67, 17.57, 17.63, 17.67, 17.54, 18.18, 18.16, 17.63, 17.67]
+[88.2]
+14.555848598480225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16503, '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': 10.453594446182251, 'TIME_S_1KI': 0.6334360083731595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.8258463859559, 'W': 88.2}
+[18.19, 17.67, 17.57, 17.63, 17.67, 17.54, 18.18, 18.16, 17.63, 17.67, 18.06, 18.43, 18.51, 17.47, 17.4, 17.92, 17.64, 17.73, 17.72, 17.97]
+320.81500000000005
+16.040750000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16503, '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': 10.453594446182251, 'TIME_S_1KI': 0.6334360083731595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.8258463859559, 'W': 88.2, 'J_1KI': 77.7934827840972, 'W_1KI': 5.344482821305218, 'W_D': 72.15925, 'J_D': 1050.3391179798841, 'W_D_1KI': 4.372492880082409, 'J_D_1KI': 0.2649513955088414}
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 3c6e8be..774716c 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": 112508, "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": 10.720516443252563, "TIME_S_1KI": 0.09528670355221462, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1090.2722121667862, "W": 75.82, "J_1KI": 9.69061944187779, "W_1KI": 0.6739076332349699, "W_D": 59.38549999999999, "J_D": 853.9483046113252, "W_D_1KI": 0.5278335762790201, "J_D_1KI": 0.004691520392141182}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111170, "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": 10.608571290969849, "TIME_S_1KI": 0.09542656553899297, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1151.6899712467193, "W": 82.52, "J_1KI": 10.35971909010272, "W_1KI": 0.7422865881083026, "W_D": 66.40424999999999, "J_D": 926.7705861992239, "W_D_1KI": 0.5973216695151569, "J_D_1KI": 0.005373047310561814}
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 b66333e..05a9200 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', '1000', '-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": 0.11333847045898438}
+{"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": 0.11198759078979492}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24996, 24999, 25000]),
-       col_indices=tensor([ 9502, 18497,  7204,  ..., 33396, 45910,   109]),
-       values=tensor([0.5325, 0.6011, 0.4727,  ..., 0.6967, 0.0269, 0.7415]),
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([42990, 31865, 45603,  ...,    32, 31145, 42502]),
+       values=tensor([0.2680, 0.8494, 0.1049,  ..., 0.3912, 0.0276, 0.1741]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7210, 0.8240, 0.5786,  ..., 0.5702, 0.4441, 0.2533])
+tensor([0.9789, 0.0522, 0.6759,  ..., 0.0240, 0.3185, 0.8367])
 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: 0.11333847045898438 seconds
+Time: 0.11198759078979492 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', '92642', '-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": 8.645956993103027}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '93760', '-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": 8.855576515197754}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([35285,  1305, 12700,  ...,  6399, 17561, 45264]),
-       values=tensor([0.6896, 0.7157, 0.5414,  ..., 0.3157, 0.2585, 0.8046]),
+       col_indices=tensor([  213, 39463,  2534,  ..., 21769, 20293, 48702]),
+       values=tensor([0.6944, 0.6922, 0.1012,  ..., 0.1071, 0.8204, 0.4025]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8892, 0.5178, 0.0901,  ..., 0.0600, 0.1718, 0.0275])
+tensor([0.8784, 0.5968, 0.0083,  ..., 0.0039, 0.6938, 0.6481])
 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: 8.645956993103027 seconds
+Time: 8.855576515197754 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', '112508', '-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": 10.720516443252563}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '111170', '-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": 10.608571290969849}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([27684, 39939,  2715,  ..., 47308, 11944, 42221]),
-       values=tensor([0.6561, 0.5911, 0.5622,  ..., 0.2806, 0.4491, 0.6100]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([37263, 14810, 49193,  ..., 22299, 19031, 40338]),
+       values=tensor([0.7995, 0.8033, 0.3510,  ..., 0.6585, 0.0621, 0.7519]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8957, 0.2813, 0.1993,  ..., 0.7019, 0.9944, 0.8970])
+tensor([0.9318, 0.0252, 0.9296,  ..., 0.2820, 0.1820, 0.1630])
 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: 10.720516443252563 seconds
+Time: 10.608571290969849 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([27684, 39939,  2715,  ..., 47308, 11944, 42221]),
-       values=tensor([0.6561, 0.5911, 0.5622,  ..., 0.2806, 0.4491, 0.6100]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([37263, 14810, 49193,  ..., 22299, 19031, 40338]),
+       values=tensor([0.7995, 0.8033, 0.3510,  ..., 0.6585, 0.0621, 0.7519]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8957, 0.2813, 0.1993,  ..., 0.7019, 0.9944, 0.8970])
+tensor([0.9318, 0.0252, 0.9296,  ..., 0.2820, 0.1820, 0.1630])
 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: 10.720516443252563 seconds
+Time: 10.608571290969849 seconds
 
-[18.48, 18.01, 18.17, 19.64, 18.03, 18.42, 18.45, 18.1, 18.26, 18.04]
-[75.82]
-14.379744291305542
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112508, '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': 10.720516443252563, 'TIME_S_1KI': 0.09528670355221462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1090.2722121667862, 'W': 75.82}
-[18.48, 18.01, 18.17, 19.64, 18.03, 18.42, 18.45, 18.1, 18.26, 18.04, 18.62, 18.47, 18.04, 18.06, 17.8, 18.08, 18.08, 18.67, 17.82, 18.04]
-328.69000000000005
-16.434500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112508, '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': 10.720516443252563, 'TIME_S_1KI': 0.09528670355221462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1090.2722121667862, 'W': 75.82, 'J_1KI': 9.69061944187779, 'W_1KI': 0.6739076332349699, 'W_D': 59.38549999999999, 'J_D': 853.9483046113252, 'W_D_1KI': 0.5278335762790201, 'J_D_1KI': 0.004691520392141182}
+[18.42, 17.72, 18.1, 17.98, 17.9, 18.19, 17.99, 17.99, 17.89, 17.81]
+[82.52]
+13.9564950466156
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111170, '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': 10.608571290969849, 'TIME_S_1KI': 0.09542656553899297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1151.6899712467193, 'W': 82.52}
+[18.42, 17.72, 18.1, 17.98, 17.9, 18.19, 17.99, 17.99, 17.89, 17.81, 18.18, 17.91, 17.74, 18.05, 17.74, 17.64, 17.73, 17.76, 17.96, 17.64]
+322.315
+16.11575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111170, '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': 10.608571290969849, 'TIME_S_1KI': 0.09542656553899297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1151.6899712467193, 'W': 82.52, 'J_1KI': 10.35971909010272, 'W_1KI': 0.7422865881083026, 'W_D': 66.40424999999999, 'J_D': 926.7705861992239, 'W_D_1KI': 0.5973216695151569, 'J_D_1KI': 0.005373047310561814}
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
new file mode 100644
index 0000000..0ba39e0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 323751, "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": 10.660384178161621, "TIME_S_1KI": 0.03292772587007182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1091.8019851422312, "W": 79.43, "J_1KI": 3.3723509275407064, "W_1KI": 0.24534287152781, "W_D": 62.86500000000001, "J_D": 864.1084199416639, "W_D_1KI": 0.19417700640306904, "J_D_1KI": 0.0005997726845726161}
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
new file mode 100644
index 0000000..9a09fc6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.04866957664489746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([2014, 2333, 4073,  ..., 1117, 3505, 2207]),
+       values=tensor([0.1339, 0.9980, 0.7024,  ..., 0.3782, 0.0544, 0.2308]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2751, 0.2895, 0.5101,  ..., 0.3933, 0.2935, 0.0678])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.04866957664489746 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', '215740', '-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": 6.996948957443237}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3059, 3492, 4969,  ..., 3863, 1265, 1575]),
+       values=tensor([0.7839, 0.7068, 0.1359,  ..., 0.6765, 0.7179, 0.7182]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2637, 0.1133, 0.2354,  ..., 0.5397, 0.9545, 0.7707])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.996948957443237 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', '323751', '-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": 10.660384178161621}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4531, 3967, 1182,  ..., 4005, 3234, 3449]),
+       values=tensor([0.1835, 0.1001, 0.2805,  ..., 0.8615, 0.2040, 0.1828]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2085, 0.7612, 0.9816,  ..., 0.7337, 0.6921, 0.5494])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.660384178161621 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4531, 3967, 1182,  ..., 4005, 3234, 3449]),
+       values=tensor([0.1835, 0.1001, 0.2805,  ..., 0.8615, 0.2040, 0.1828]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2085, 0.7612, 0.9816,  ..., 0.7337, 0.6921, 0.5494])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.660384178161621 seconds
+
+[18.46, 17.59, 17.71, 17.79, 18.06, 17.81, 22.16, 18.13, 17.88, 17.67]
+[79.43]
+13.745461225509644
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 323751, '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': 10.660384178161621, 'TIME_S_1KI': 0.03292772587007182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1091.8019851422312, 'W': 79.43}
+[18.46, 17.59, 17.71, 17.79, 18.06, 17.81, 22.16, 18.13, 17.88, 17.67, 18.24, 17.78, 22.87, 17.85, 17.88, 17.75, 18.09, 17.86, 17.93, 17.95]
+331.3
+16.565
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 323751, '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': 10.660384178161621, 'TIME_S_1KI': 0.03292772587007182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1091.8019851422312, 'W': 79.43, 'J_1KI': 3.3723509275407064, 'W_1KI': 0.24534287152781, 'W_D': 62.86500000000001, 'J_D': 864.1084199416639, 'W_D_1KI': 0.19417700640306904, 'J_D_1KI': 0.0005997726845726161}
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
new file mode 100644
index 0000000..9b486c5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 244536, "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": 10.47565221786499, "TIME_S_1KI": 0.04283889577757463, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1122.3107054543495, "W": 80.61, "J_1KI": 4.589552071900863, "W_1KI": 0.32964471488860536, "W_D": 64.2855, "J_D": 895.0292129448652, "W_D_1KI": 0.2628876729806654, "J_D_1KI": 0.0010750469173482246}
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
new file mode 100644
index 0000000..a34514c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.05913829803466797}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([  91, 1225, 4183,  ..., 1260, 1498, 1816]),
+       values=tensor([0.4538, 0.5289, 0.0869,  ..., 0.3885, 0.0043, 0.2412]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3917, 0.0968, 0.9015,  ..., 0.9180, 0.2586, 0.0822])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05913829803466797 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', '177549', '-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": 7.6236653327941895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24989, 24994, 25000]),
+       col_indices=tensor([ 412, 1102, 1155,  ...,  695, 1250, 1499]),
+       values=tensor([0.5017, 0.7691, 0.1146,  ..., 0.5300, 0.6967, 0.6559]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7930, 0.9227, 0.2342,  ..., 0.4335, 0.3949, 0.6803])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.6236653327941895 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', '244536', '-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": 10.47565221786499}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([ 194,  369, 2258,  ..., 1755, 2835, 2987]),
+       values=tensor([0.8194, 0.2005, 0.5023,  ..., 0.6221, 0.3751, 0.8448]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5474, 0.2694, 0.2646,  ..., 0.5254, 0.5763, 0.9998])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.47565221786499 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([ 194,  369, 2258,  ..., 1755, 2835, 2987]),
+       values=tensor([0.8194, 0.2005, 0.5023,  ..., 0.6221, 0.3751, 0.8448]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5474, 0.2694, 0.2646,  ..., 0.5254, 0.5763, 0.9998])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.47565221786499 seconds
+
+[18.33, 18.0, 17.95, 18.79, 18.11, 18.49, 17.87, 17.96, 17.71, 17.91]
+[80.61]
+13.922723054885864
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 244536, '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': 10.47565221786499, 'TIME_S_1KI': 0.04283889577757463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1122.3107054543495, 'W': 80.61}
+[18.33, 18.0, 17.95, 18.79, 18.11, 18.49, 17.87, 17.96, 17.71, 17.91, 18.2, 17.81, 17.83, 18.46, 18.26, 17.74, 18.1, 18.0, 17.87, 20.64]
+326.49
+16.3245
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 244536, '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': 10.47565221786499, 'TIME_S_1KI': 0.04283889577757463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1122.3107054543495, 'W': 80.61, 'J_1KI': 4.589552071900863, 'W_1KI': 0.32964471488860536, 'W_D': 64.2855, 'J_D': 895.0292129448652, 'W_D_1KI': 0.2628876729806654, 'J_D_1KI': 0.0010750469173482246}
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
new file mode 100644
index 0000000..6d5d9d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 162920, "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": 10.414216756820679, "TIME_S_1KI": 0.06392227324343652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1267.1163623809814, "W": 89.2, "J_1KI": 7.7775372107843195, "W_1KI": 0.5475079793763811, "W_D": 72.96625, "J_D": 1036.5104178988934, "W_D_1KI": 0.44786551681807024, "J_D_1KI": 0.0027489904052177155}
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
new file mode 100644
index 0000000..548b49c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/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', '1000', '-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": 0.08090949058532715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     56,     99,  ..., 249898, 249957,
+                            250000]),
+       col_indices=tensor([  27,  423,  607,  ..., 4371, 4379, 4963]),
+       values=tensor([0.2630, 0.0898, 0.5767,  ..., 0.9425, 0.5823, 0.3558]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9446, 0.5109, 0.8342,  ..., 0.1182, 0.7217, 0.5335])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.08090949058532715 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', '129774', '-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": 8.36373782157898}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    109,  ..., 249903, 249955,
+                            250000]),
+       col_indices=tensor([ 168,  371,  372,  ..., 4708, 4876, 4879]),
+       values=tensor([0.3469, 0.2972, 0.5901,  ..., 0.0640, 0.2331, 0.9267]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6978, 0.0250, 0.3323,  ..., 0.6356, 0.0847, 0.1678])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.36373782157898 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', '162920', '-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": 10.414216756820679}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    103,  ..., 249909, 249962,
+                            250000]),
+       col_indices=tensor([  86,  107,  119,  ..., 4571, 4629, 4973]),
+       values=tensor([0.3206, 0.5923, 0.4852,  ..., 0.3807, 0.1641, 0.9581]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7369, 0.6267, 0.7979,  ..., 0.0231, 0.0899, 0.6643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.414216756820679 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    103,  ..., 249909, 249962,
+                            250000]),
+       col_indices=tensor([  86,  107,  119,  ..., 4571, 4629, 4973]),
+       values=tensor([0.3206, 0.5923, 0.4852,  ..., 0.3807, 0.1641, 0.9581]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7369, 0.6267, 0.7979,  ..., 0.0231, 0.0899, 0.6643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.414216756820679 seconds
+
+[18.27, 17.91, 18.14, 17.62, 17.8, 17.9, 19.35, 17.84, 17.83, 17.75]
+[89.2]
+14.205340385437012
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 162920, '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': 10.414216756820679, 'TIME_S_1KI': 0.06392227324343652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.1163623809814, 'W': 89.2}
+[18.27, 17.91, 18.14, 17.62, 17.8, 17.9, 19.35, 17.84, 17.83, 17.75, 18.16, 17.99, 17.94, 17.95, 17.67, 17.72, 18.79, 18.01, 18.17, 17.91]
+324.675
+16.23375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 162920, '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': 10.414216756820679, 'TIME_S_1KI': 0.06392227324343652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.1163623809814, 'W': 89.2, 'J_1KI': 7.7775372107843195, 'W_1KI': 0.5475079793763811, 'W_D': 72.96625, 'J_D': 1036.5104178988934, 'W_D_1KI': 0.44786551681807024, 'J_D_1KI': 0.0027489904052177155}
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
new file mode 100644
index 0000000..c4982b3
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 43553, "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": 10.298628091812134, "TIME_S_1KI": 0.23646196798870647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.7661000442506, "W": 89.26, "J_1KI": 28.993779993209436, "W_1KI": 2.0494569834454572, "W_D": 72.47675000000001, "J_D": 1025.3325447163584, "W_D_1KI": 1.664104654099603, "J_D_1KI": 0.03820872624387764}
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
new file mode 100644
index 0000000..c9d208e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.24108004570007324}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     254,     488,  ..., 1249514,
+                            1249753, 1250000]),
+       col_indices=tensor([  20,  116,  133,  ..., 4920, 4936, 4946]),
+       values=tensor([0.1564, 0.7439, 0.0267,  ..., 0.8153, 0.5940, 0.0091]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2737, 0.8794, 0.7768,  ..., 0.6794, 0.5883, 0.1555])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.24108004570007324 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', '43553', '-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": 10.298628091812134}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     228,     477,  ..., 1249472,
+                            1249753, 1250000]),
+       col_indices=tensor([  33,   68,  106,  ..., 4915, 4934, 4973]),
+       values=tensor([0.4796, 0.5786, 0.7704,  ..., 0.3679, 0.0791, 0.9103]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4175, 0.6924, 0.0772,  ..., 0.0345, 0.5597, 0.1347])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.298628091812134 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     228,     477,  ..., 1249472,
+                            1249753, 1250000]),
+       col_indices=tensor([  33,   68,  106,  ..., 4915, 4934, 4973]),
+       values=tensor([0.4796, 0.5786, 0.7704,  ..., 0.3679, 0.0791, 0.9103]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4175, 0.6924, 0.0772,  ..., 0.0345, 0.5597, 0.1347])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.298628091812134 seconds
+
+[18.22, 18.23, 17.9, 17.56, 17.94, 17.95, 17.83, 17.93, 21.91, 17.95]
+[89.26]
+14.147054672241211
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43553, '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': 10.298628091812134, 'TIME_S_1KI': 0.23646196798870647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.7661000442506, 'W': 89.26}
+[18.22, 18.23, 17.9, 17.56, 17.94, 17.95, 17.83, 17.93, 21.91, 17.95, 18.27, 17.9, 17.95, 22.05, 18.3, 22.03, 18.06, 18.09, 17.91, 17.81]
+335.66499999999996
+16.78325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43553, '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': 10.298628091812134, 'TIME_S_1KI': 0.23646196798870647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.7661000442506, 'W': 89.26, 'J_1KI': 28.993779993209436, 'W_1KI': 2.0494569834454572, 'W_D': 72.47675000000001, 'J_D': 1025.3325447163584, 'W_D_1KI': 1.664104654099603, 'J_D_1KI': 0.03820872624387764}
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
new file mode 100644
index 0000000..901a9df
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19209, "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": 10.589914798736572, "TIME_S_1KI": 0.5512996407276054, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1296.501946372986, "W": 88.72, "J_1KI": 67.49450499104513, "W_1KI": 4.618668332552449, "W_D": 72.5775, "J_D": 1060.6049370253086, "W_D_1KI": 3.778307043573325, "J_D_1KI": 0.19669462458083842}
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
new file mode 100644
index 0000000..ac30e09
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.5466129779815674}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1026,  ..., 2498984,
+                            2499497, 2500000]),
+       col_indices=tensor([   4,   26,   34,  ..., 4975, 4994, 4997]),
+       values=tensor([0.5421, 0.0550, 0.0297,  ..., 0.2626, 0.0439, 0.1648]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7995, 0.3197, 0.3485,  ..., 0.5295, 0.0131, 0.9723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.5466129779815674 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', '19209', '-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": 10.589914798736572}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1003,  ..., 2498928,
+                            2499468, 2500000]),
+       col_indices=tensor([   6,    8,   21,  ..., 4933, 4958, 4973]),
+       values=tensor([0.5143, 0.8442, 0.2205,  ..., 0.0567, 0.9724, 0.7726]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8237, 0.2559, 0.0746,  ..., 0.2976, 0.1284, 0.3075])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.589914798736572 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1003,  ..., 2498928,
+                            2499468, 2500000]),
+       col_indices=tensor([   6,    8,   21,  ..., 4933, 4958, 4973]),
+       values=tensor([0.5143, 0.8442, 0.2205,  ..., 0.0567, 0.9724, 0.7726]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8237, 0.2559, 0.0746,  ..., 0.2976, 0.1284, 0.3075])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.589914798736572 seconds
+
+[18.45, 18.0, 17.83, 17.78, 17.91, 17.57, 18.07, 17.82, 17.87, 17.88]
+[88.72]
+14.613412380218506
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19209, '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': 10.589914798736572, 'TIME_S_1KI': 0.5512996407276054, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.501946372986, 'W': 88.72}
+[18.45, 18.0, 17.83, 17.78, 17.91, 17.57, 18.07, 17.82, 17.87, 17.88, 18.16, 17.97, 18.01, 17.95, 17.55, 18.45, 18.51, 17.65, 17.73, 17.87]
+322.85
+16.142500000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19209, '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': 10.589914798736572, 'TIME_S_1KI': 0.5512996407276054, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.501946372986, 'W': 88.72, 'J_1KI': 67.49450499104513, 'W_1KI': 4.618668332552449, 'W_D': 72.5775, 'J_D': 1060.6049370253086, 'W_D_1KI': 3.778307043573325, 'J_D_1KI': 0.19669462458083842}
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
new file mode 100644
index 0000000..5c599da
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 353197, "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": 10.226954936981201, "TIME_S_1KI": 0.0289553844935863, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1121.6130926513672, "W": 79.36, "J_1KI": 3.1756019803434548, "W_1KI": 0.22469047019085664, "W_D": 63.1995, "J_D": 893.2130374120474, "W_D_1KI": 0.1789355515477198, "J_D_1KI": 0.0005066168499384757}
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
new file mode 100644
index 0000000..bd4d211
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.04602789878845215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 578,  489, 2035, 2602, 4011, 1806, 4187, 4466, 1701,
+                           3439, 1855, 2207, 4634, 4913, 4351, 2699, 4950, 1482,
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+                           2488, 2445,  979, 1019, 4241, 1675, 1170, 2324, 2271,
+                           3633, 2309, 4715, 1380, 4338, 2573, 2764]),
+       values=tensor([0.6984, 0.1478, 0.0323, 0.8260, 0.6827, 0.1000, 0.4915,
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+                      0.7479, 0.4857, 0.1928, 0.9739, 0.1023]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.6347, 0.6451, 0.4713,  ..., 0.2060, 0.2664, 0.4890])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04602789878845215 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', '228122', '-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": 6.781703472137451}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4412, 3431, 2377, 4102, 3105,  469, 2716, 4410, 2733,
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+                      0.4486, 0.2404, 0.7355, 0.7961, 0.7130]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8182, 0.2605, 0.1489,  ..., 0.1484, 0.3699, 0.6778])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 6.781703472137451 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', '353197', '-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": 10.226954936981201}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1961, 1566,  179,  628, 4168, 3230,   47, 1058,  848,
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+                      0.6829, 0.9316, 0.0223, 0.3259, 0.0955]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8734, 0.8080, 0.1055,  ..., 0.8475, 0.7666, 0.2333])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.226954936981201 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1961, 1566,  179,  628, 4168, 3230,   47, 1058,  848,
+                           1307,  863, 3163,  497, 3237, 3835, 4225, 1765, 2385,
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+                           2173, 3550, 3891, 1058, 4695, 4487, 1810, 3555, 3979,
+                           4408, 2688,  366, 1825, 2362, 2165,  528]),
+       values=tensor([0.4900, 0.1519, 0.0910, 0.3336, 0.1203, 0.0899, 0.6181,
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+                      0.7706, 0.7962, 0.5827, 0.3612, 0.1219, 0.5026, 0.1788,
+                      0.6829, 0.9316, 0.0223, 0.3259, 0.0955]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8734, 0.8080, 0.1055,  ..., 0.8475, 0.7666, 0.2333])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.226954936981201 seconds
+
+[18.35, 17.76, 18.03, 17.72, 17.87, 18.0, 18.15, 17.7, 17.8, 17.85]
+[79.36]
+14.133229494094849
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 353197, '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': 10.226954936981201, 'TIME_S_1KI': 0.0289553844935863, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1121.6130926513672, 'W': 79.36}
+[18.35, 17.76, 18.03, 17.72, 17.87, 18.0, 18.15, 17.7, 17.8, 17.85, 18.33, 17.94, 18.05, 17.86, 17.95, 18.18, 18.05, 17.92, 18.13, 17.67]
+323.21
+16.1605
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 353197, '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': 10.226954936981201, 'TIME_S_1KI': 0.0289553844935863, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1121.6130926513672, 'W': 79.36, 'J_1KI': 3.1756019803434548, 'W_1KI': 0.22469047019085664, 'W_D': 63.1995, 'J_D': 893.2130374120474, 'W_D_1KI': 0.1789355515477198, 'J_D_1KI': 0.0005066168499384757}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json
deleted file mode 100644
index 1a2b755..0000000
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 234425, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.312235116958618, "TIME_S_1KI": 0.09091280843322434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2054.109435153008, "W": 83.45000000000002, "J_1KI": 8.762330959381499, "W_1KI": 0.3559773914898156, "W_D": 67.21450000000002, "J_D": 1654.4749985511307, "W_D_1KI": 0.2867206995840888, "J_D_1KI": 0.0012230807276702091}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output
deleted file mode 100644
index 06a3a12..0000000
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output
+++ /dev/null
@@ -1,81 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.10643196105957031}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89992, 89998, 90000]),
-       col_indices=tensor([ 7924, 12206, 12582,  ..., 21107, 10373, 19571]),
-       values=tensor([0.8274, 0.6462, 0.9289,  ..., 0.2542, 0.4328, 0.6143]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.4141, 0.4229, 0.5665,  ..., 0.1440, 0.7095, 0.1472])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 0.10643196105957031 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', '197309', '-ss', '30000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 17.675063133239746}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89992, 89999, 90000]),
-       col_indices=tensor([  929,  2315, 11088,  ..., 21381, 23338, 19838]),
-       values=tensor([0.3872, 0.2873, 0.0227,  ..., 0.4746, 0.4839, 0.3522]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.1013, 0.5431, 0.3309,  ..., 0.2751, 0.1147, 0.0007])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 17.675063133239746 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', '234425', '-ss', '30000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.312235116958618}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89994, 89997, 90000]),
-       col_indices=tensor([ 6200, 14122, 21980,  ..., 11781, 19689, 21155]),
-       values=tensor([0.5859, 0.7824, 0.3581,  ..., 0.7747, 0.1479, 0.5181]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.3266, 0.0767, 0.6789,  ..., 0.9087, 0.9799, 0.8849])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 21.312235116958618 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89994, 89997, 90000]),
-       col_indices=tensor([ 6200, 14122, 21980,  ..., 11781, 19689, 21155]),
-       values=tensor([0.5859, 0.7824, 0.3581,  ..., 0.7747, 0.1479, 0.5181]),
-       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
-tensor([0.3266, 0.0767, 0.6789,  ..., 0.9087, 0.9799, 0.8849])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 90000
-Density: 0.0001
-Time: 21.312235116958618 seconds
-
-[18.37, 18.49, 18.06, 17.95, 17.95, 17.71, 18.07, 17.8, 17.94, 17.81]
-[83.45]
-24.61485242843628
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 234425, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.312235116958618, 'TIME_S_1KI': 0.09091280843322434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.109435153008, 'W': 83.45000000000002}
-[18.37, 18.49, 18.06, 17.95, 17.95, 17.71, 18.07, 17.8, 17.94, 17.81, 18.28, 18.06, 18.16, 17.86, 18.06, 17.97, 18.34, 17.89, 18.12, 18.1]
-324.71
-16.2355
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 234425, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.312235116958618, 'TIME_S_1KI': 0.09091280843322434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.109435153008, 'W': 83.45000000000002, 'J_1KI': 8.762330959381499, 'W_1KI': 0.3559773914898156, 'W_D': 67.21450000000002, 'J_D': 1654.4749985511307, 'W_D_1KI': 0.2867206995840888, 'J_D_1KI': 0.0012230807276702091}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.json
deleted file mode 100644
index e69de29..0000000
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output
deleted file mode 100644
index 355226c..0000000
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output
+++ /dev/null
@@ -1,77 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2140212059020996}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     34,     62,  ..., 899934, 899967,
-                            900000]),
-       col_indices=tensor([ 1559,  1711,  3295,  ..., 29804, 29893, 29964]),
-       values=tensor([0.7225, 0.7366, 0.0675,  ..., 0.3495, 0.2204, 0.5611]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.1783, 0.4759, 0.5239,  ..., 0.8363, 0.1566, 0.5506])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 0.2140212059020996 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', '98121', '-ss', '30000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 19.3143093585968}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     29,     64,  ..., 899940, 899966,
-                            900000]),
-       col_indices=tensor([  612,   701,  1017,  ..., 29770, 29777, 29834]),
-       values=tensor([0.4034, 0.5977, 0.8788,  ..., 0.6466, 0.3405, 0.9207]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.7678, 0.0123, 0.5496,  ..., 0.4589, 0.2646, 0.8857])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 19.3143093585968 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', '106684', '-ss', '30000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.90600872039795}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     51,  ..., 899936, 899963,
-                            900000]),
-       col_indices=tensor([  855,  2329,  2453,  ..., 28070, 28293, 29379]),
-       values=tensor([0.2478, 0.6443, 0.3087,  ..., 0.2033, 0.4619, 0.6203]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.3161, 0.0015, 0.4480,  ..., 0.6517, 0.7843, 0.6370])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 20.90600872039795 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     51,  ..., 899936, 899963,
-                            900000]),
-       col_indices=tensor([  855,  2329,  2453,  ..., 28070, 28293, 29379]),
-       values=tensor([0.2478, 0.6443, 0.3087,  ..., 0.2033, 0.4619, 0.6203]),
-       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
-tensor([0.3161, 0.0015, 0.4480,  ..., 0.6517, 0.7843, 0.6370])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 900000
-Density: 0.001
-Time: 20.90600872039795 seconds
-
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json
deleted file mode 100644
index 21aeca6..0000000
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 303288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.079484939575195, "TIME_S_1KI": 0.06950319478375404, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1922.1091361045835, "W": 78.6, "J_1KI": 6.337570679039671, "W_1KI": 0.2591596106670887, "W_D": 62.18274999999999, "J_D": 1520.636537953019, "W_D_1KI": 0.20502871857772148, "J_D_1KI": 0.0006760198839971298}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output
deleted file mode 100644
index d8910fb..0000000
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output
+++ /dev/null
@@ -1,81 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08539462089538574}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 9000, 9000, 9000]),
-       col_indices=tensor([ 8168, 26166, 15021,  ...,  3965, 14348,  3180]),
-       values=tensor([0.0414, 0.9204, 0.6909,  ..., 0.5705, 0.2524, 0.4947]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.9721, 0.7014, 0.8881,  ..., 0.4193, 0.5170, 0.9013])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 0.08539462089538574 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', '245917', '-ss', '30000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 17.02755308151245}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 9000, 9000, 9000]),
-       col_indices=tensor([ 9352, 11930, 17471,  ..., 19597, 20552,  1111]),
-       values=tensor([0.4298, 0.4908, 0.5157,  ..., 0.6454, 0.4570, 0.2738]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.3622, 0.2189, 0.3857,  ..., 0.2935, 0.6447, 0.7890])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 17.02755308151245 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', '303288', '-ss', '30000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.079484939575195}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 8999, 9000, 9000]),
-       col_indices=tensor([20060, 11216, 16521,  ..., 22127, 15786,  9820]),
-       values=tensor([0.3604, 0.7216, 0.9721,  ..., 0.8443, 0.2707, 0.5761]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.4467, 0.9917, 0.2567,  ..., 0.4911, 0.0150, 0.9779])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 21.079484939575195 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 8999, 9000, 9000]),
-       col_indices=tensor([20060, 11216, 16521,  ..., 22127, 15786,  9820]),
-       values=tensor([0.3604, 0.7216, 0.9721,  ..., 0.8443, 0.2707, 0.5761]),
-       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
-tensor([0.4467, 0.9917, 0.2567,  ..., 0.4911, 0.0150, 0.9779])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([30000, 30000])
-Rows: 30000
-Size: 900000000
-NNZ: 9000
-Density: 1e-05
-Time: 21.079484939575195 seconds
-
-[18.69, 17.92, 17.95, 17.83, 18.17, 17.93, 17.96, 17.93, 18.28, 17.85]
-[78.6]
-24.454314708709717
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 303288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.079484939575195, 'TIME_S_1KI': 0.06950319478375404, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1922.1091361045835, 'W': 78.6}
-[18.69, 17.92, 17.95, 17.83, 18.17, 17.93, 17.96, 17.93, 18.28, 17.85, 18.57, 17.96, 18.0, 18.17, 18.14, 18.05, 18.17, 20.58, 18.66, 18.18]
-328.345
-16.417250000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 303288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.079484939575195, 'TIME_S_1KI': 0.06950319478375404, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1922.1091361045835, 'W': 78.6, 'J_1KI': 6.337570679039671, 'W_1KI': 0.2591596106670887, 'W_D': 62.18274999999999, 'J_D': 1520.636537953019, 'W_D_1KI': 0.20502871857772148, 'J_D_1KI': 0.0006760198839971298}
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 27d9946..59c1962 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": 1000, "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": 24.691206455230713, "TIME_S_1KI": 24.691206455230713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 647.4072245025635, "W": 22.90104020202067, "J_1KI": 647.4072245025635, "W_1KI": 22.90104020202067, "W_D": 3.140040202020675, "J_D": 88.76822598814977, "W_D_1KI": 3.140040202020675, "J_D_1KI": 3.140040202020675}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 23.50609064102173, "TIME_S_1KI": 23.50609064102173, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 609.4492170715331, "W": 22.389903061636694, "J_1KI": 609.4492170715331, "W_1KI": 22.389903061636694, "W_D": 3.917903061636693, "J_D": 106.64463114929183, "W_D_1KI": 3.9179030616366926, "J_D_1KI": 3.9179030616366926}
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 d5446f7..81f61e7 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 1000 -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": 24.691206455230713}
+{"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": 23.50609064102173}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      21,  ...,  999980,
+tensor(crow_indices=tensor([      0,       6,      16,  ...,  999976,
                              999990, 1000000]),
-       col_indices=tensor([ 5106, 13656, 15471,  ..., 68202, 79637, 95576]),
-       values=tensor([0.9862, 0.5796, 0.7870,  ..., 0.3201, 0.7080, 0.2748]),
+       col_indices=tensor([35450, 44241, 45004,  ..., 57756, 61659, 92730]),
+       values=tensor([0.6041, 0.1643, 0.4254,  ..., 0.8911, 0.3600, 0.5834]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6105, 0.8083, 0.7150,  ..., 0.7011, 0.0810, 0.6416])
+tensor([0.3461, 0.8147, 0.1835,  ..., 0.7972, 0.9198, 0.6224])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 24.691206455230713 seconds
+Time: 23.50609064102173 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      21,  ...,  999980,
+tensor(crow_indices=tensor([      0,       6,      16,  ...,  999976,
                              999990, 1000000]),
-       col_indices=tensor([ 5106, 13656, 15471,  ..., 68202, 79637, 95576]),
-       values=tensor([0.9862, 0.5796, 0.7870,  ..., 0.3201, 0.7080, 0.2748]),
+       col_indices=tensor([35450, 44241, 45004,  ..., 57756, 61659, 92730]),
+       values=tensor([0.6041, 0.1643, 0.4254,  ..., 0.8911, 0.3600, 0.5834]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6105, 0.8083, 0.7150,  ..., 0.7011, 0.0810, 0.6416])
+tensor([0.3461, 0.8147, 0.1835,  ..., 0.7972, 0.9198, 0.6224])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 24.691206455230713 seconds
+Time: 23.50609064102173 seconds
 
-[20.64, 20.52, 20.48, 20.68, 20.48, 20.48, 20.52, 20.48, 20.28, 20.36]
-[20.64, 20.84, 21.24, 24.56, 26.2, 27.16, 28.16, 26.08, 25.68, 24.72, 24.72, 24.48, 24.6, 24.6, 24.72, 24.68, 24.6, 24.52, 24.52, 24.8, 24.72, 24.6, 24.48, 24.48, 24.52, 24.44, 24.64]
-28.269773721694946
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 24.691206455230713, 'TIME_S_1KI': 24.691206455230713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 647.4072245025635, 'W': 22.90104020202067}
-[20.64, 20.52, 20.48, 20.68, 20.48, 20.48, 20.52, 20.48, 20.28, 20.36, 20.6, 20.56, 20.64, 22.68, 24.64, 25.4, 25.4, 25.36, 24.48, 22.68]
-395.2199999999999
-19.760999999999996
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 24.691206455230713, 'TIME_S_1KI': 24.691206455230713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 647.4072245025635, 'W': 22.90104020202067, 'J_1KI': 647.4072245025635, 'W_1KI': 22.90104020202067, 'W_D': 3.140040202020675, 'J_D': 88.76822598814977, 'W_D_1KI': 3.140040202020675, 'J_D_1KI': 3.140040202020675}
+[20.48, 20.4, 20.48, 20.48, 20.28, 20.4, 20.28, 20.28, 20.4, 20.44]
+[20.28, 20.36, 20.36, 21.44, 23.4, 25.2, 26.04, 26.28, 25.2, 24.36, 24.32, 24.36, 24.52, 24.6, 24.68, 24.76, 24.68, 24.48, 24.52, 24.52, 24.48, 24.76, 24.72, 24.72, 24.64, 24.88]
+27.219823837280273
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 23.50609064102173, 'TIME_S_1KI': 23.50609064102173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 609.4492170715331, 'W': 22.389903061636694}
+[20.48, 20.4, 20.48, 20.48, 20.28, 20.4, 20.28, 20.28, 20.4, 20.44, 20.6, 20.64, 20.72, 21.0, 20.92, 20.76, 20.68, 20.44, 20.28, 20.48]
+369.44000000000005
+18.472
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 23.50609064102173, 'TIME_S_1KI': 23.50609064102173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 609.4492170715331, 'W': 22.389903061636694, 'J_1KI': 609.4492170715331, 'W_1KI': 22.389903061636694, 'W_D': 3.917903061636693, 'J_D': 106.64463114929183, 'W_D_1KI': 3.9179030616366926, 'J_D_1KI': 3.9179030616366926}
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
new file mode 100644
index 0000000..05631c7
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 227.44817399978638, "TIME_S_1KI": 227.44817399978638, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5651.356887254718, "W": 23.406634424755232, "J_1KI": 5651.356887254718, "W_1KI": 23.406634424755232, "W_D": 5.256634424755234, "J_D": 1269.1750817751913, "W_D_1KI": 5.256634424755234, "J_D_1KI": 5.256634424755234}
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
new file mode 100644
index 0000000..4847746
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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 1000 -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": 227.44817399978638}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      105,      212,  ...,  9999786,
+                             9999896, 10000000]),
+       col_indices=tensor([  310,  1031,  2044,  ..., 96924, 97369, 99264]),
+       values=tensor([0.4389, 0.5701, 0.8338,  ..., 0.1266, 0.7107, 0.7989]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1044, 0.8564, 0.8953,  ..., 0.3136, 0.0570, 0.9535])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 227.44817399978638 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      105,      212,  ...,  9999786,
+                             9999896, 10000000]),
+       col_indices=tensor([  310,  1031,  2044,  ..., 96924, 97369, 99264]),
+       values=tensor([0.4389, 0.5701, 0.8338,  ..., 0.1266, 0.7107, 0.7989]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1044, 0.8564, 0.8953,  ..., 0.3136, 0.0570, 0.9535])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 227.44817399978638 seconds
+
+[20.56, 20.48, 20.48, 20.36, 20.4, 20.32, 20.32, 19.96, 20.0, 20.04]
+[20.12, 20.12, 20.4, 21.88, 23.4, 25.08, 27.48, 28.24, 27.96, 27.08, 26.24, 25.68, 24.72, 24.48, 24.4, 24.28, 24.36, 24.4, 24.48, 24.68, 24.72, 24.72, 24.8, 24.88, 24.76, 24.68, 24.64, 24.48, 24.44, 24.28, 24.44, 24.64, 24.76, 24.8, 24.84, 24.72, 24.44, 24.36, 24.32, 24.48, 24.48, 24.6, 24.76, 24.76, 24.68, 24.64, 24.72, 24.64, 24.4, 24.4, 24.68, 24.52, 24.6, 24.56, 24.48, 24.28, 24.32, 24.28, 24.32, 24.52, 24.52, 24.64, 24.76, 24.76, 24.68, 24.68, 24.6, 24.56, 24.72, 24.52, 24.76, 24.76, 24.68, 24.56, 24.48, 24.24, 24.4, 24.6, 24.76, 24.76, 24.8, 24.64, 24.64, 24.56, 24.76, 24.72, 24.8, 24.8, 24.8, 24.8, 24.6, 24.56, 24.44, 24.68, 24.72, 24.72, 24.68, 24.72, 24.68, 24.88, 24.92, 24.84, 24.8, 24.8, 25.0, 25.08, 25.0, 24.92, 24.8, 24.8, 24.6, 24.72, 24.84, 25.0, 25.0, 24.88, 24.96, 24.92, 24.96, 24.92, 24.92, 24.64, 24.56, 24.44, 24.44, 24.36, 24.6, 24.44, 24.52, 24.88, 25.12, 25.12, 25.2, 25.32, 24.96, 24.96, 24.8, 24.56, 24.64, 24.52, 24.44, 24.48, 24.4, 24.28, 24.56, 24.52, 24.48, 24.6, 24.52, 24.6, 24.64, 24.88, 24.8, 24.8, 24.76, 24.76, 24.76, 24.4, 24.28, 24.28, 24.28, 24.32, 24.8, 25.04, 24.92, 24.8, 24.8, 24.56, 24.52, 24.52, 24.48, 24.64, 24.52, 24.64, 24.68, 24.68, 24.72, 24.56, 24.56, 24.96, 24.96, 24.88, 24.72, 24.4, 24.32, 24.36, 24.4, 24.64, 24.92, 24.8, 24.76, 24.72, 24.64, 24.52, 24.8, 24.72, 24.76, 24.76, 24.72, 25.04, 24.96, 24.64, 24.32, 24.16, 24.12, 24.36, 24.44, 24.32, 24.16, 24.04, 24.32, 24.68, 24.56, 24.68, 24.72, 24.28, 24.4, 24.36, 24.36, 24.36, 24.44, 24.44, 24.16, 24.32, 24.44, 24.36, 24.6, 24.68, 24.8, 24.76]
+241.44252371788025
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 227.44817399978638, 'TIME_S_1KI': 227.44817399978638, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5651.356887254718, 'W': 23.406634424755232}
+[20.56, 20.48, 20.48, 20.36, 20.4, 20.32, 20.32, 19.96, 20.0, 20.04, 20.08, 20.32, 20.08, 20.0, 19.84, 19.84, 19.92, 20.08, 20.08, 20.36]
+363.0
+18.15
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 227.44817399978638, 'TIME_S_1KI': 227.44817399978638, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5651.356887254718, 'W': 23.406634424755232, 'J_1KI': 5651.356887254718, 'W_1KI': 23.406634424755232, 'W_D': 5.256634424755234, 'J_D': 1269.1750817751913, 'W_D_1KI': 5.256634424755234, 'J_D_1KI': 5.256634424755234}
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 75a4f33..4c6f614 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": 3170, "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": 10.487157583236694, "TIME_S_1KI": 3.3082516035446985, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.83638416290285, "W": 22.27932516765204, "J_1KI": 102.78750289050564, "W_1KI": 7.028178286325565, "W_D": 3.710325167652041, "J_D": 54.263714344978354, "W_D_1KI": 1.170449579700959, "J_D_1KI": 0.36922699675109116}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3195, "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": 10.793879747390747, "TIME_S_1KI": 3.3783661181191698, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 338.5230771541595, "W": 23.150052849773353, "J_1KI": 105.95401475873538, "W_1KI": 7.245712942026088, "W_D": 4.780052849773355, "J_D": 69.89868274450302, "W_D_1KI": 1.4961041783328186, "J_D_1KI": 0.46826421857052225}
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 2086467..a9ff479 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 1000 -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": 3.3119447231292725}
+{"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": 3.2854795455932617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([34080, 20424, 38945,  ..., 64155, 47978, 44736]),
-       values=tensor([0.5824, 0.7466, 0.8758,  ..., 0.8278, 0.8938, 0.7712]),
+       col_indices=tensor([96494, 10713, 51050,  ..., 77096, 58241, 39394]),
+       values=tensor([0.9472, 0.0468, 0.6571,  ..., 0.2815, 0.5696, 0.0055]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9015, 0.6308, 0.7799,  ..., 0.6045, 0.4908, 0.8218])
+tensor([0.9254, 0.6847, 0.8457,  ..., 0.6275, 0.7476, 0.1010])
 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: 3.3119447231292725 seconds
+Time: 3.2854795455932617 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 3170 -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": 10.487157583236694}
+['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 3195 -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": 10.793879747390747}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99997,  99999,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997, 100000,
                             100000]),
-       col_indices=tensor([62540, 50524, 43651,  ..., 12394, 59846, 74659]),
-       values=tensor([0.6601, 0.8101, 0.4564,  ..., 0.4320, 0.9061, 0.8749]),
+       col_indices=tensor([41532, 61839,  3968,  ..., 19432, 54156, 77664]),
+       values=tensor([0.2018, 0.3494, 0.0819,  ..., 0.4942, 0.5843, 0.6732]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2783, 0.8812, 0.6091,  ..., 0.5557, 0.0745, 0.6879])
+tensor([0.4618, 0.8259, 0.6206,  ..., 0.7480, 0.2960, 0.5870])
 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: 10.487157583236694 seconds
+Time: 10.793879747390747 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99997,  99999,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997, 100000,
                             100000]),
-       col_indices=tensor([62540, 50524, 43651,  ..., 12394, 59846, 74659]),
-       values=tensor([0.6601, 0.8101, 0.4564,  ..., 0.4320, 0.9061, 0.8749]),
+       col_indices=tensor([41532, 61839,  3968,  ..., 19432, 54156, 77664]),
+       values=tensor([0.2018, 0.3494, 0.0819,  ..., 0.4942, 0.5843, 0.6732]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2783, 0.8812, 0.6091,  ..., 0.5557, 0.0745, 0.6879])
+tensor([0.4618, 0.8259, 0.6206,  ..., 0.7480, 0.2960, 0.5870])
 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: 10.487157583236694 seconds
+Time: 10.793879747390747 seconds
 
-[20.76, 20.76, 20.76, 20.8, 20.84, 20.72, 20.6, 20.36, 20.36, 20.32]
-[20.32, 20.36, 20.48, 22.0, 23.24, 25.44, 26.04, 26.48, 26.08, 24.6, 24.44, 24.44, 24.4, 24.6]
-14.625056266784668
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3170, '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': 10.487157583236694, 'TIME_S_1KI': 3.3082516035446985, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.83638416290285, 'W': 22.27932516765204}
-[20.76, 20.76, 20.76, 20.8, 20.84, 20.72, 20.6, 20.36, 20.36, 20.32, 20.04, 20.16, 20.24, 20.6, 20.72, 20.72, 20.88, 20.72, 21.08, 21.0]
-371.38
-18.569
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3170, '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': 10.487157583236694, 'TIME_S_1KI': 3.3082516035446985, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.83638416290285, 'W': 22.27932516765204, 'J_1KI': 102.78750289050564, 'W_1KI': 7.028178286325565, 'W_D': 3.710325167652041, 'J_D': 54.263714344978354, 'W_D_1KI': 1.170449579700959, 'J_D_1KI': 0.36922699675109116}
+[20.88, 20.84, 20.52, 20.4, 20.32, 20.2, 20.2, 20.44, 20.48, 20.8]
+[20.92, 20.8, 21.0, 22.96, 24.6, 25.56, 26.6, 27.0, 26.4, 25.96, 26.12, 26.12, 25.88, 25.88]
+14.62299370765686
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3195, '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': 10.793879747390747, 'TIME_S_1KI': 3.3783661181191698, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.5230771541595, 'W': 23.150052849773353}
+[20.88, 20.84, 20.52, 20.4, 20.32, 20.2, 20.2, 20.44, 20.48, 20.8, 20.4, 20.4, 20.2, 20.36, 20.52, 20.36, 20.4, 20.36, 20.2, 20.32]
+367.4
+18.369999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3195, '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': 10.793879747390747, 'TIME_S_1KI': 3.3783661181191698, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.5230771541595, 'W': 23.150052849773353, 'J_1KI': 105.95401475873538, 'W_1KI': 7.245712942026088, 'W_D': 4.780052849773355, 'J_D': 69.89868274450302, 'W_D_1KI': 1.4961041783328186, 'J_D_1KI': 0.46826421857052225}
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 25fcf70..8de09b8 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": 32170, "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": 10.42804479598999, "TIME_S_1KI": 0.32415432999658034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 291.8528582954407, "W": 21.456480473872652, "J_1KI": 9.07220572879828, "W_1KI": 0.666971727506144, "W_D": 3.1474804738726547, "J_D": 42.81229504752161, "W_D_1KI": 0.09783899514680307, "J_D_1KI": 0.0030413116303016183}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32341, "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": 10.187894582748413, "TIME_S_1KI": 0.3150148289399961, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.80105960845947, "W": 22.074332714462717, "J_1KI": 9.981171256561623, "W_1KI": 0.6825494794367124, "W_D": 3.644332714462717, "J_D": 53.29241327524185, "W_D_1KI": 0.11268460203650836, "J_D_1KI": 0.0034842646187968327}
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 034e4a3..a571cff 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 1000 -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": 0.3263826370239258}
+{"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": 0.3246574401855469}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([1982,  558, 3662,  ...,  629, 5634, 6549]),
-       values=tensor([0.5250, 0.9307, 0.0448,  ..., 0.0150, 0.4421, 0.4831]),
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([ 702,  590, 2393,  ..., 5106, 4251, 5881]),
+       values=tensor([0.8131, 0.4443, 0.5032,  ..., 0.0454, 0.7892, 0.7021]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5546, 0.0630, 0.8785,  ..., 0.4779, 0.8090, 0.6189])
+tensor([0.5617, 0.3540, 0.6665,  ..., 0.2887, 0.4752, 0.2274])
 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: 0.3263826370239258 seconds
+Time: 0.3246574401855469 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 32170 -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": 10.42804479598999}
+['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 32341 -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": 10.187894582748413}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9996,  9999, 10000]),
-       col_indices=tensor([8155, 9480, 4094,  ..., 6796, 6921, 3902]),
-       values=tensor([0.0915, 0.3699, 0.5728,  ..., 0.9057, 0.8661, 0.7356]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([5513, 4819, 4488,  ..., 7223, 1569, 1749]),
+       values=tensor([0.7502, 0.9864, 0.0219,  ..., 0.7577, 0.3030, 0.6500]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3327, 0.6532, 0.3155,  ..., 0.1421, 0.0155, 0.6755])
+tensor([0.4735, 0.0629, 0.6403,  ..., 0.2218, 0.6036, 0.6062])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,15 +34,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.42804479598999 seconds
+Time: 10.187894582748413 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9996,  9999, 10000]),
-       col_indices=tensor([8155, 9480, 4094,  ..., 6796, 6921, 3902]),
-       values=tensor([0.0915, 0.3699, 0.5728,  ..., 0.9057, 0.8661, 0.7356]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([5513, 4819, 4488,  ..., 7223, 1569, 1749]),
+       values=tensor([0.7502, 0.9864, 0.0219,  ..., 0.7577, 0.3030, 0.6500]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3327, 0.6532, 0.3155,  ..., 0.1421, 0.0155, 0.6755])
+tensor([0.4735, 0.0629, 0.6403,  ..., 0.2218, 0.6036, 0.6062])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -50,13 +50,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.42804479598999 seconds
+Time: 10.187894582748413 seconds
 
-[20.24, 20.16, 19.96, 20.2, 20.32, 20.28, 20.4, 20.32, 20.32, 20.36]
-[20.36, 20.24, 20.48, 22.52, 23.04, 24.76, 25.6, 25.52, 24.28, 23.12, 23.12, 23.16, 23.44]
-13.602084398269653
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32170, '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': 10.42804479598999, 'TIME_S_1KI': 0.32415432999658034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.8528582954407, 'W': 21.456480473872652}
-[20.24, 20.16, 19.96, 20.2, 20.32, 20.28, 20.4, 20.32, 20.32, 20.36, 20.52, 20.48, 20.64, 20.48, 20.48, 20.32, 20.48, 20.36, 20.32, 20.2]
-366.17999999999995
-18.308999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32170, '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': 10.42804479598999, 'TIME_S_1KI': 0.32415432999658034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.8528582954407, 'W': 21.456480473872652, 'J_1KI': 9.07220572879828, 'W_1KI': 0.666971727506144, 'W_D': 3.1474804738726547, 'J_D': 42.81229504752161, 'W_D_1KI': 0.09783899514680307, 'J_D_1KI': 0.0030413116303016183}
+[20.16, 20.36, 20.44, 20.64, 20.64, 20.64, 20.68, 20.0, 19.96, 20.04]
+[20.04, 20.36, 20.64, 22.2, 24.32, 25.36, 25.96, 26.0, 25.44, 24.12, 24.0, 23.8, 23.8, 23.84]
+14.623366594314575
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32341, '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': 10.187894582748413, 'TIME_S_1KI': 0.3150148289399961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.80105960845947, 'W': 22.074332714462717}
+[20.16, 20.36, 20.44, 20.64, 20.64, 20.64, 20.68, 20.0, 19.96, 20.04, 19.96, 20.28, 20.36, 20.44, 20.48, 20.48, 20.72, 20.76, 20.96, 21.36]
+368.6
+18.43
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32341, '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': 10.187894582748413, 'TIME_S_1KI': 0.3150148289399961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.80105960845947, 'W': 22.074332714462717, 'J_1KI': 9.981171256561623, 'W_1KI': 0.6825494794367124, 'W_D': 3.644332714462717, 'J_D': 53.29241327524185, 'W_D_1KI': 0.11268460203650836, 'J_D_1KI': 0.0034842646187968327}
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 6de6107..f0f5b59 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": 4747, "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": 10.586360931396484, "TIME_S_1KI": 2.2301160588574858, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.0044006347656, "W": 22.3162197944069, "J_1KI": 68.67587963656321, "W_1KI": 4.701120664505352, "W_D": 3.9862197944068996, "J_D": 58.23231742858882, "W_D_1KI": 0.8397345258914893, "J_D_1KI": 0.17689794099251935}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4681, "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": 10.577015399932861, "TIME_S_1KI": 2.2595632129743346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.1403603744507, "W": 22.94669291080223, "J_1KI": 71.38226028080554, "W_1KI": 4.902092055287809, "W_D": 4.40869291080223, "J_D": 64.19758366584783, "W_D_1KI": 0.9418271546255567, "J_D_1KI": 0.20120212660234066}
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 0876b19..7485b00 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 1000 -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": 2.2116076946258545}
+{"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": 2.242969274520874}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  99984,  99990,
+tensor(crow_indices=tensor([     0,     10,     21,  ...,  99980,  99990,
                             100000]),
-       col_indices=tensor([ 365,  990, 1421,  ..., 6204, 7506, 8345]),
-       values=tensor([0.4012, 0.2163, 0.0214,  ..., 0.4427, 0.7190, 0.8381]),
+       col_indices=tensor([ 158,  243, 1021,  ..., 9060, 9386, 9562]),
+       values=tensor([0.4026, 0.0672, 0.1618,  ..., 0.9478, 0.4676, 0.6061]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6373, 0.6560, 0.2779,  ..., 0.6662, 0.5919, 0.8676])
+tensor([0.1276, 0.9367, 0.3121,  ..., 0.3681, 0.2222, 0.5819])
 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: 2.2116076946258545 seconds
+Time: 2.242969274520874 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 4747 -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": 10.586360931396484}
+['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 4681 -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": 10.577015399932861}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     23,  ...,  99976,  99989,
+tensor(crow_indices=tensor([     0,     13,     23,  ...,  99978,  99989,
                             100000]),
-       col_indices=tensor([ 145,  447,  695,  ..., 7955, 8009, 9128]),
-       values=tensor([0.3182, 0.0478, 0.7097,  ..., 0.3986, 0.2793, 0.7202]),
+       col_indices=tensor([1463, 2229, 2458,  ..., 6913, 8671, 9837]),
+       values=tensor([0.1583, 0.2191, 0.0082,  ..., 0.3537, 0.5043, 0.1355]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3264, 0.5290, 0.7390,  ..., 0.4961, 0.6761, 0.4965])
+tensor([0.7982, 0.2389, 0.8535,  ..., 0.4532, 0.2540, 0.6422])
 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: 10.586360931396484 seconds
+Time: 10.577015399932861 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     23,  ...,  99976,  99989,
+tensor(crow_indices=tensor([     0,     13,     23,  ...,  99978,  99989,
                             100000]),
-       col_indices=tensor([ 145,  447,  695,  ..., 7955, 8009, 9128]),
-       values=tensor([0.3182, 0.0478, 0.7097,  ..., 0.3986, 0.2793, 0.7202]),
+       col_indices=tensor([1463, 2229, 2458,  ..., 6913, 8671, 9837]),
+       values=tensor([0.1583, 0.2191, 0.0082,  ..., 0.3537, 0.5043, 0.1355]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3264, 0.5290, 0.7390,  ..., 0.4961, 0.6761, 0.4965])
+tensor([0.7982, 0.2389, 0.8535,  ..., 0.4532, 0.2540, 0.6422])
 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: 10.586360931396484 seconds
+Time: 10.577015399932861 seconds
 
-[20.56, 20.48, 20.36, 20.48, 20.4, 20.2, 20.32, 20.48, 20.52, 20.6]
-[20.44, 20.44, 20.44, 21.8, 24.32, 26.12, 27.12, 27.16, 25.36, 24.28, 24.24, 24.12, 23.96, 23.84]
-14.608406066894531
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4747, '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': 10.586360931396484, 'TIME_S_1KI': 2.2301160588574858, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.0044006347656, 'W': 22.3162197944069}
-[20.56, 20.48, 20.36, 20.48, 20.4, 20.2, 20.32, 20.48, 20.52, 20.6, 20.28, 20.08, 20.4, 20.32, 20.2, 20.36, 20.36, 20.4, 20.28, 20.48]
-366.6
-18.330000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4747, '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': 10.586360931396484, 'TIME_S_1KI': 2.2301160588574858, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.0044006347656, 'W': 22.3162197944069, 'J_1KI': 68.67587963656321, 'W_1KI': 4.701120664505352, 'W_D': 3.9862197944068996, 'J_D': 58.23231742858882, 'W_D_1KI': 0.8397345258914893, 'J_D_1KI': 0.17689794099251935}
+[20.84, 20.96, 20.84, 20.76, 20.88, 20.8, 20.72, 20.8, 20.8, 20.72]
+[20.72, 21.0, 21.04, 25.64, 26.56, 27.96, 28.44, 25.92, 25.0, 24.0, 23.88, 24.12, 24.36, 24.36]
+14.561591148376465
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4681, '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': 10.577015399932861, 'TIME_S_1KI': 2.2595632129743346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.1403603744507, 'W': 22.94669291080223}
+[20.84, 20.96, 20.84, 20.76, 20.88, 20.8, 20.72, 20.8, 20.8, 20.72, 20.52, 20.24, 20.24, 20.2, 20.44, 20.16, 20.56, 20.64, 20.52, 20.32]
+370.76
+18.538
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4681, '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': 10.577015399932861, 'TIME_S_1KI': 2.2595632129743346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.1403603744507, 'W': 22.94669291080223, 'J_1KI': 71.38226028080554, 'W_1KI': 4.902092055287809, 'W_D': 4.40869291080223, 'J_D': 64.19758366584783, 'W_D_1KI': 0.9418271546255567, 'J_D_1KI': 0.20120212660234066}
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 9af62e4..7587c79 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": 1000, "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": 21.214847326278687, "TIME_S_1KI": 21.214847326278687, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 593.507265138626, "W": 22.813207511083125, "J_1KI": 593.507265138626, "W_1KI": 22.813207511083125, "W_D": 4.622207511083129, "J_D": 120.25111933398253, "W_D_1KI": 4.622207511083129, "J_D_1KI": 4.622207511083129}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 21.648348808288574, "TIME_S_1KI": 21.648348808288574, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 597.1462204551697, "W": 23.932854010192194, "J_1KI": 597.1462204551697, "W_1KI": 23.932854010192194, "W_D": 5.369854010192196, "J_D": 133.98268443942072, "W_D_1KI": 5.369854010192196, "J_D_1KI": 5.369854010192196}
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 a8afaf9..ed3ac8e 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
@@ -1,14 +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 1000 -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": 21.214847326278687}
+{"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": 21.648348808288574}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     111,     190,  ...,  999805,
-                             999902, 1000000]),
-       col_indices=tensor([   3,  255,  407,  ..., 9480, 9499, 9966]),
-       values=tensor([0.6179, 0.1045, 0.6429,  ..., 0.5216, 0.7550, 0.7148]),
+tensor(crow_indices=tensor([      0,      93,     181,  ...,  999807,
+                             999904, 1000000]),
+       col_indices=tensor([  20,   39,  173,  ..., 9424, 9617, 9690]),
+       values=tensor([0.7771, 0.0078, 0.5851,  ..., 0.0250, 0.0076, 0.8688]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6572, 0.8503, 0.2699,  ..., 0.6176, 0.8577, 0.2518])
+tensor([0.6163, 0.0977, 0.8617,  ..., 0.7477, 0.6432, 0.7227])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.214847326278687 seconds
+Time: 21.648348808288574 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     111,     190,  ...,  999805,
-                             999902, 1000000]),
-       col_indices=tensor([   3,  255,  407,  ..., 9480, 9499, 9966]),
-       values=tensor([0.6179, 0.1045, 0.6429,  ..., 0.5216, 0.7550, 0.7148]),
+tensor(crow_indices=tensor([      0,      93,     181,  ...,  999807,
+                             999904, 1000000]),
+       col_indices=tensor([  20,   39,  173,  ..., 9424, 9617, 9690]),
+       values=tensor([0.7771, 0.0078, 0.5851,  ..., 0.0250, 0.0076, 0.8688]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6572, 0.8503, 0.2699,  ..., 0.6176, 0.8577, 0.2518])
+tensor([0.6163, 0.0977, 0.8617,  ..., 0.7477, 0.6432, 0.7227])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.214847326278687 seconds
+Time: 21.648348808288574 seconds
 
-[20.56, 20.64, 20.64, 20.52, 20.36, 20.4, 19.92, 19.96, 20.0, 20.08]
-[20.04, 20.08, 23.16, 25.4, 27.72, 28.8, 28.8, 29.48, 25.64, 25.4, 24.0, 23.96, 23.64, 23.72, 23.92, 24.04, 24.32, 24.36, 24.04, 24.0, 23.84, 24.08, 24.28, 24.28, 24.28]
-26.015949964523315
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 21.214847326278687, 'TIME_S_1KI': 21.214847326278687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 593.507265138626, 'W': 22.813207511083125}
-[20.56, 20.64, 20.64, 20.52, 20.36, 20.4, 19.92, 19.96, 20.0, 20.08, 19.76, 19.76, 19.96, 20.28, 20.4, 20.36, 20.4, 20.0, 19.92, 20.2]
-363.81999999999994
-18.190999999999995
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 21.214847326278687, 'TIME_S_1KI': 21.214847326278687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 593.507265138626, 'W': 22.813207511083125, 'J_1KI': 593.507265138626, 'W_1KI': 22.813207511083125, 'W_D': 4.622207511083129, 'J_D': 120.25111933398253, 'W_D_1KI': 4.622207511083129, 'J_D_1KI': 4.622207511083129}
+[20.48, 20.52, 20.6, 20.6, 20.6, 20.72, 20.48, 20.56, 20.64, 20.72]
+[20.72, 21.08, 23.92, 25.96, 27.92, 28.72, 29.32, 26.52, 26.52, 25.36, 24.24, 24.4, 24.24, 24.44, 24.16, 24.12, 24.16, 24.12, 24.16, 24.2, 24.32, 24.36, 24.68, 24.72]
+24.95089888572693
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 21.648348808288574, 'TIME_S_1KI': 21.648348808288574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.1462204551697, 'W': 23.932854010192194}
+[20.48, 20.52, 20.6, 20.6, 20.6, 20.72, 20.48, 20.56, 20.64, 20.72, 20.68, 20.72, 20.52, 20.52, 20.64, 20.76, 20.64, 20.8, 20.68, 20.64]
+371.26
+18.563
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 21.648348808288574, 'TIME_S_1KI': 21.648348808288574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.1462204551697, 'W': 23.932854010192194, 'J_1KI': 597.1462204551697, 'W_1KI': 23.932854010192194, 'W_D': 5.369854010192196, 'J_D': 133.98268443942072, 'W_D_1KI': 5.369854010192196, 'J_D_1KI': 5.369854010192196}
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 4f5ecb3..e29f054 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": 1000, "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": 106.68757820129395, "TIME_S_1KI": 106.68757820129395, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2614.243714923859, "W": 23.06903562044379, "J_1KI": 2614.243714923859, "W_1KI": 23.06903562044379, "W_D": 4.456035620443789, "J_D": 504.9696617529395, "W_D_1KI": 4.456035620443789, "J_D_1KI": 4.456035620443789}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 106.87029075622559, "TIME_S_1KI": 106.87029075622559, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2633.659623832703, "W": 23.232350154828893, "J_1KI": 2633.659623832703, "W_1KI": 23.232350154828893, "W_D": 4.519350154828892, "J_D": 512.3213944957257, "W_D_1KI": 4.519350154828892, "J_D_1KI": 4.519350154828892}
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 4e8e799..5edda98 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 1000 -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": 106.68757820129395}
+{"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": 106.87029075622559}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     529,    1008,  ..., 4999026,
-                            4999478, 5000000]),
-       col_indices=tensor([  75,  122,  128,  ..., 9908, 9909, 9916]),
-       values=tensor([0.8571, 0.2596, 0.0411,  ..., 0.7048, 0.9398, 0.3732]),
+tensor(crow_indices=tensor([      0,     497,     970,  ..., 4998958,
+                            4999495, 5000000]),
+       col_indices=tensor([   3,   19,   30,  ..., 9933, 9939, 9986]),
+       values=tensor([0.6521, 0.8632, 0.3100,  ..., 0.6388, 0.4505, 0.0265]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2354, 0.1436, 0.6485,  ..., 0.5167, 0.9065, 0.2719])
+tensor([0.1776, 0.4739, 0.9893,  ..., 0.4929, 0.9525, 0.7109])
 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: 106.68757820129395 seconds
+Time: 106.87029075622559 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     529,    1008,  ..., 4999026,
-                            4999478, 5000000]),
-       col_indices=tensor([  75,  122,  128,  ..., 9908, 9909, 9916]),
-       values=tensor([0.8571, 0.2596, 0.0411,  ..., 0.7048, 0.9398, 0.3732]),
+tensor(crow_indices=tensor([      0,     497,     970,  ..., 4998958,
+                            4999495, 5000000]),
+       col_indices=tensor([   3,   19,   30,  ..., 9933, 9939, 9986]),
+       values=tensor([0.6521, 0.8632, 0.3100,  ..., 0.6388, 0.4505, 0.0265]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2354, 0.1436, 0.6485,  ..., 0.5167, 0.9065, 0.2719])
+tensor([0.1776, 0.4739, 0.9893,  ..., 0.4929, 0.9525, 0.7109])
 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: 106.68757820129395 seconds
+Time: 106.87029075622559 seconds
 
-[20.52, 20.6, 20.8, 20.84, 20.8, 20.6, 20.68, 20.68, 20.4, 20.56]
-[20.56, 20.64, 20.92, 22.0, 23.8, 25.56, 26.6, 26.56, 26.4, 25.36, 24.52, 24.6, 24.56, 24.6, 24.32, 24.4, 24.4, 24.32, 24.24, 24.44, 24.6, 24.36, 24.32, 24.36, 24.48, 24.6, 24.76, 24.72, 24.64, 24.6, 24.48, 24.56, 24.64, 24.64, 24.44, 24.48, 24.36, 24.16, 24.16, 24.24, 24.28, 24.16, 24.2, 24.36, 24.44, 24.44, 24.32, 24.0, 24.0, 24.0, 24.28, 24.44, 24.56, 24.48, 24.48, 24.32, 24.52, 24.52, 24.36, 24.4, 24.4, 24.32, 24.36, 24.32, 24.68, 24.72, 24.6, 24.6, 24.64, 24.6, 24.72, 24.64, 24.64, 24.68, 24.68, 24.52, 24.4, 24.32, 24.2, 24.16, 24.24, 24.2, 24.2, 24.4, 24.52, 24.56, 24.8, 24.8, 24.56, 24.44, 24.4, 23.84, 23.76, 23.88, 24.0, 24.0, 24.16, 24.2, 24.36, 24.2, 24.16, 24.2, 24.24, 24.16, 24.16, 24.4, 24.32, 24.56]
-113.32262682914734
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 106.68757820129395, 'TIME_S_1KI': 106.68757820129395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.243714923859, 'W': 23.06903562044379}
-[20.52, 20.6, 20.8, 20.84, 20.8, 20.6, 20.68, 20.68, 20.4, 20.56, 20.4, 20.52, 20.72, 20.84, 20.96, 20.84, 20.8, 20.52, 20.64, 20.56]
-372.26
-18.613
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 106.68757820129395, 'TIME_S_1KI': 106.68757820129395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.243714923859, 'W': 23.06903562044379, 'J_1KI': 2614.243714923859, 'W_1KI': 23.06903562044379, 'W_D': 4.456035620443789, 'J_D': 504.9696617529395, 'W_D_1KI': 4.456035620443789, 'J_D_1KI': 4.456035620443789}
+[20.76, 20.72, 20.52, 20.48, 20.68, 20.6, 20.6, 20.56, 20.68, 20.6]
+[20.64, 20.72, 20.72, 24.72, 25.96, 28.32, 29.48, 30.12, 26.68, 25.6, 25.08, 24.6, 24.6, 24.6, 24.8, 24.8, 24.88, 24.92, 24.84, 24.8, 24.72, 24.52, 24.52, 24.52, 24.6, 24.56, 24.4, 24.48, 24.32, 24.16, 24.28, 24.36, 24.48, 24.64, 24.68, 24.64, 24.4, 24.68, 24.72, 24.72, 24.56, 24.64, 24.48, 24.32, 24.12, 24.12, 24.2, 24.52, 24.4, 24.56, 24.68, 24.48, 24.28, 24.24, 24.2, 24.04, 23.92, 24.04, 24.28, 24.12, 24.28, 24.36, 24.28, 24.44, 24.52, 24.6, 24.72, 24.72, 24.88, 24.84, 24.72, 24.44, 24.16, 24.2, 24.0, 24.2, 24.44, 24.32, 24.2, 24.2, 24.16, 24.12, 24.24, 24.2, 24.12, 24.16, 24.2, 24.16, 24.4, 24.4, 24.36, 24.2, 24.28, 24.52, 24.12, 24.36, 24.64, 24.6, 24.6, 24.52, 24.48, 24.2, 24.4, 24.4, 24.4, 24.52, 24.4, 24.16]
+113.36173939704895
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 106.87029075622559, 'TIME_S_1KI': 106.87029075622559, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2633.659623832703, 'W': 23.232350154828893}
+[20.76, 20.72, 20.52, 20.48, 20.68, 20.6, 20.6, 20.56, 20.68, 20.6, 20.52, 20.76, 20.76, 21.2, 21.2, 21.28, 21.12, 21.0, 20.88, 20.56]
+374.26
+18.713
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 106.87029075622559, 'TIME_S_1KI': 106.87029075622559, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2633.659623832703, 'W': 23.232350154828893, 'J_1KI': 2633.659623832703, 'W_1KI': 23.232350154828893, 'W_D': 4.519350154828892, 'J_D': 512.3213944957257, 'W_D_1KI': 4.519350154828892, 'J_D_1KI': 4.519350154828892}
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
new file mode 100644
index 0000000..8cd094e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 210.98000812530518, "TIME_S_1KI": 210.98000812530518, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5224.394376831054, "W": 23.586192508859664, "J_1KI": 5224.394376831054, "W_1KI": 23.586192508859664, "W_D": 5.122192508859662, "J_D": 1134.5770933685287, "W_D_1KI": 5.122192508859662, "J_D_1KI": 5.122192508859662}
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
new file mode 100644
index 0000000..d1e3dfe
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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 1000 -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": 210.98000812530518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      993,     1975,  ...,  9997956,
+                             9998997, 10000000]),
+       col_indices=tensor([  19,   22,   26,  ..., 9979, 9989, 9990]),
+       values=tensor([0.9746, 0.4059, 0.0503,  ..., 0.3598, 0.3506, 0.0768]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8784, 0.5931, 0.4456,  ..., 0.6081, 0.2914, 0.4121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.98000812530518 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      993,     1975,  ...,  9997956,
+                             9998997, 10000000]),
+       col_indices=tensor([  19,   22,   26,  ..., 9979, 9989, 9990]),
+       values=tensor([0.9746, 0.4059, 0.0503,  ..., 0.3598, 0.3506, 0.0768]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8784, 0.5931, 0.4456,  ..., 0.6081, 0.2914, 0.4121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.98000812530518 seconds
+
+[20.72, 20.64, 20.6, 20.6, 20.48, 20.44, 20.4, 20.36, 20.28, 20.28]
+[20.2, 20.36, 21.16, 23.28, 25.44, 27.76, 29.48, 29.48, 28.08, 28.08, 26.32, 25.08, 24.24, 24.16, 24.44, 24.76, 24.64, 24.68, 24.72, 24.64, 24.56, 24.64, 24.68, 24.64, 24.76, 24.76, 24.64, 24.56, 24.52, 24.48, 24.6, 24.6, 24.64, 24.96, 24.88, 25.04, 24.84, 24.68, 24.6, 24.48, 24.64, 24.52, 24.56, 24.4, 24.6, 24.6, 24.68, 24.84, 25.0, 24.68, 24.68, 24.68, 24.68, 24.68, 24.92, 24.68, 24.96, 25.12, 24.88, 24.8, 24.92, 24.72, 24.6, 24.64, 24.64, 24.96, 25.12, 25.0, 24.92, 24.88, 24.6, 24.48, 24.32, 24.48, 24.52, 24.52, 24.6, 24.76, 24.84, 24.76, 25.0, 24.72, 24.6, 24.92, 24.88, 24.88, 24.84, 24.92, 25.12, 25.2, 25.2, 25.12, 24.96, 24.52, 24.52, 24.32, 24.4, 24.4, 24.48, 24.36, 24.32, 24.28, 24.2, 24.16, 24.0, 24.08, 24.32, 24.36, 24.88, 25.12, 25.12, 25.08, 24.76, 25.0, 25.2, 24.84, 25.24, 25.16, 24.96, 24.96, 25.08, 25.24, 25.04, 25.12, 25.24, 25.16, 25.12, 25.24, 25.44, 25.64, 25.68, 25.44, 26.16, 26.24, 26.0, 26.24, 26.4, 25.56, 25.68, 25.56, 25.4, 25.4, 25.32, 25.24, 25.4, 25.6, 25.36, 25.16, 24.84, 24.52, 24.4, 24.24, 24.44, 24.48, 24.4, 24.56, 24.36, 24.24, 24.24, 24.44, 24.52, 24.68, 24.72, 24.72, 24.88, 24.76, 24.64, 24.36, 24.68, 24.84, 24.6, 24.84, 24.56, 24.28, 24.36, 24.52, 24.32, 24.4, 24.36, 24.4, 24.44, 24.44, 24.72, 24.64, 24.76, 24.76, 24.64, 24.52, 24.76, 24.68, 24.56, 24.72, 24.36, 24.44, 24.48, 24.88, 24.88, 25.0, 25.0, 24.68, 24.4, 24.44, 24.52, 24.36, 24.6, 24.52, 24.56, 24.56, 24.56, 24.64, 24.32]
+221.50223588943481
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 210.98000812530518, 'TIME_S_1KI': 210.98000812530518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5224.394376831054, 'W': 23.586192508859664}
+[20.72, 20.64, 20.6, 20.6, 20.48, 20.44, 20.4, 20.36, 20.28, 20.28, 20.24, 20.44, 20.68, 20.92, 21.04, 20.8, 20.44, 20.28, 20.2, 20.12]
+369.28000000000003
+18.464000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 210.98000812530518, 'TIME_S_1KI': 210.98000812530518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5224.394376831054, 'W': 23.586192508859664, 'J_1KI': 5224.394376831054, 'W_1KI': 23.586192508859664, 'W_D': 5.122192508859662, 'J_D': 1134.5770933685287, 'W_D_1KI': 5.122192508859662, 'J_D_1KI': 5.122192508859662}
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 69bee1d..e2472aa 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": 145400, "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": 10.376285076141357, "TIME_S_1KI": 0.07136372129395707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.9616888427734, "W": 22.159348523127505, "J_1KI": 2.2349497169379187, "W_1KI": 0.15240267209853856, "W_D": 3.711348523127505, "J_D": 54.42606233215331, "W_D_1KI": 0.02552509300637899, "J_D_1KI": 0.00017555084598610036}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 142368, "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": 10.641618490219116, "TIME_S_1KI": 0.07474726406368788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.11982940673823, "W": 23.739150118754246, "J_1KI": 2.445211209026876, "W_1KI": 0.16674498566218704, "W_D": 4.927150118754245, "J_D": 72.25358322525018, "W_D_1KI": 0.03460855050821986, "J_D_1KI": 0.00024309220125463487}
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 d251499..4bc129d 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
@@ -1,373 +1,266 @@
 ['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 1000 -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": 0.08243966102600098}
+{"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": 0.0838630199432373}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([ 252, 7839, 5648, 3465, 7349, 4902, 9434, 7529, 7692,
-                            165, 3611,  104,  550, 6486, 7084, 9069, 7958, 6919,
-                            690, 9278, 3067, 6601, 7528, 1640, 3373, 4102, 2924,
-                           2640, 1739,  407, 8622, 7009, 7252, 6788, 1851, 3757,
-                           6304, 8203, 5332, 7635,  594, 3806, 4878, 4044, 1441,
-                            999, 1148, 5958, 9975, 4945, 2434, 1204,   59,  181,
-                           7425,  800, 8678, 5796, 5760,  120, 6846,  442, 3920,
-                           1463, 5374, 6614, 1071, 5654, 6755, 4329, 2096, 3557,
-                           3459, 2406, 5557, 9403, 8210, 6660,  740, 4513, 3423,
-                           2395, 8647, 3341,  136, 1978, 4301,  975, 3977, 9483,
-                           1644, 1238, 3590, 4407,  378,  953, 4885, 3832, 7590,
-                            727, 9280, 2092, 6016, 2681, 4198, 2877, 6915, 4242,
-                           6915, 8581, 5016, 2122, 9650, 9146, 4295, 9411, 1035,
-                           3607, 4089, 1201, 5045, 5545, 7311, 3130, 7563, 2568,
-                           6650, 8830, 9967,  763, 8604, 7974, 6093, 2055, 9735,
-                           2084, 6764, 9924, 9982, 8233, 9788, 2760, 7451,  647,
-                           9876, 3730, 1454, 7105, 9740,    3, 6735, 3817, 6148,
-                           2672, 8936, 3502,   36,  122, 8671, 6286,   16, 4468,
-                           7863, 6117, 5323, 3322, 1830, 4682, 2100, 8360, 6810,
-                           1598, 8824,  932, 5248, 3917, 7002, 3906, 3017, 2692,
-                           1181, 3736, 4511, 4850, 7042,  514, 3936, 2631, 7634,
-                           8605, 7530, 2136, 1830, 5351, 6593, 8222, 4992,  702,
-                           8215, 7622, 3843, 1766, 8771, 4771, 6546, 8907, 5810,
-                           4223, 4783, 1749,  808,  748, 8530,  510, 4005, 9341,
-                           9392, 5211, 8047, 1297, 1483, 2102, 9250, 9844, 5843,
-                           7781, 5823, 5125, 7934, 6365, 4344, 2486, 5379, 5512,
-                           1500, 5968, 9635, 2436,  343,  691, 5998, 6974, 5014,
-                           8797, 2209,  662, 5712,  468, 4740, 3465, 7884, 1157,
-                           5482, 4513, 3540, 1871, 3557, 4818,  294, 9373, 9392,
-                           6804,  446, 4018, 9572, 2746, 8821, 3101, 5524, 4011,
-                           6392, 4612, 6933, 5523, 6755, 5391, 9534, 6269, 2247,
-                             26, 3621, 8701, 6455, 4517, 2157, 6077, 8811, 8299,
-                           1793, 7712, 8935, 4953, 4593, 2233, 1763, 2638, 6120,
-                           3919, 6685, 8170, 8912, 8472, 2086, 4535, 5619,  680,
-                           4842, 7676, 6910, 6323, 7473, 2912, 6983, 1773, 3647,
-                           1488, 4405, 9243, 4961, 5147, 4030, 6029, 7737, 9786,
-                           8179,  920, 4796, 5800, 5579, 8198, 6536, 9822, 7168,
-                           4169, 9541, 5653, 1326, 4959, 4059,   87, 6667, 6591,
-                           1202, 4268,  583, 2119, 8178, 8386, 2346, 9152,  480,
-                           8393, 1347,  969, 5740,  199, 2706, 9179, 9925, 1569,
-                           3911,  617, 7052, 7551, 8066, 3096,  422, 6701, 9877,
-                           1422, 9102,  806, 5587, 9274, 2597, 1463, 9111, 4690,
-                           6223, 1110, 1661, 1854, 9509, 4300, 4820,  680, 8309,
-                             83,  406,  145, 2909, 4808, 5417, 5546, 8440, 6714,
-                           8204, 2476, 3404, 3107, 4637, 7287, 2700, 6764, 9687,
-                           3766, 7158, 1579, 1642, 2888, 9988, 8451, 7261, 7908,
-                           9760, 6495,  868, 8055, 2229, 6718, 6854, 7101, 3460,
-                           1480, 5908, 1710, 1311, 7394, 1535, 5767, 9382, 6035,
-                           6352, 9940, 3068, 1099, 9199,  844, 2345, 7539, 5311,
-                           1344,  795, 1299, 1427, 2359, 6210, 1940, 4014, 9343,
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+                      0.2966, 0.3305, 0.0533, 0.9515, 0.3749, 0.2751, 0.9848,
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+                      0.8399, 0.9753, 0.8351, 0.3146, 0.2432, 0.5413]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8709, 0.3477, 0.9071,  ..., 0.3290, 0.2447, 0.6100])
+tensor([0.2336, 0.7811, 0.2916,  ..., 0.9209, 0.8685, 0.4951])
 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: 0.08243966102600098 seconds
+Time: 0.0838630199432373 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 127365 -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": 9.197555303573608}
+['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 125204 -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": 9.234081745147705}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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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+                      0.0176, 0.0647, 0.9873, 0.6484, 0.3203, 0.2129, 0.9498,
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+                      0.3238, 0.4139, 0.4523, 0.9314, 0.2645, 0.3099]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1139, 0.8384, 0.4762,  ..., 0.6681, 0.9732, 0.3908])
+tensor([0.9394, 0.5361, 0.0926,  ..., 0.1333, 0.7033, 0.7122])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,378 +540,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 9.197555303573608 seconds
+Time: 9.234081745147705 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 145400 -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": 10.376285076141357}
+['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 142368 -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": 10.641618490219116}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+                      4.0205e-02, 5.1058e-03, 5.9797e-01, 3.2629e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.6283, 0.6554, 0.1926,  ..., 0.5716, 0.9993, 0.6492])
+tensor([0.5424, 0.9332, 0.7035,  ..., 0.9872, 0.5484, 0.9353])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +919,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.376285076141357 seconds
+Time: 10.641618490219116 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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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+                      9.4683e-01, 8.0727e-01, 2.3938e-01, 5.1948e-01,
+                      4.7982e-01, 5.9710e-01, 1.9899e-01, 5.7719e-01,
+                      9.9101e-01, 8.2375e-01, 4.2012e-01, 4.5169e-01,
+                      4.0205e-02, 5.1058e-03, 5.9797e-01, 3.2629e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.6283, 0.6554, 0.1926,  ..., 0.5716, 0.9993, 0.6492])
+tensor([0.5424, 0.9332, 0.7035,  ..., 0.9872, 0.5484, 0.9353])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.376285076141357 seconds
+Time: 10.641618490219116 seconds
 
-[20.4, 20.32, 20.32, 20.44, 20.32, 20.32, 20.52, 20.6, 20.96, 21.36]
-[21.4, 21.52, 21.84, 22.84, 24.24, 24.88, 25.24, 25.24, 24.84, 24.56, 23.72, 23.8, 23.96, 23.68]
-14.664767265319824
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 145400, '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': 10.376285076141357, 'TIME_S_1KI': 0.07136372129395707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9616888427734, 'W': 22.159348523127505}
-[20.4, 20.32, 20.32, 20.44, 20.32, 20.32, 20.52, 20.6, 20.96, 21.36, 20.68, 20.64, 20.6, 20.52, 20.32, 20.28, 20.32, 20.32, 20.6, 20.68]
-368.96000000000004
-18.448
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 145400, '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': 10.376285076141357, 'TIME_S_1KI': 0.07136372129395707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9616888427734, 'W': 22.159348523127505, 'J_1KI': 2.2349497169379187, 'W_1KI': 0.15240267209853856, 'W_D': 3.711348523127505, 'J_D': 54.42606233215331, 'W_D_1KI': 0.02552509300637899, 'J_D_1KI': 0.00017555084598610036}
+[21.44, 21.48, 21.48, 21.12, 21.2, 21.24, 21.16, 21.48, 21.6, 21.92]
+[21.92, 21.88, 24.96, 26.8, 28.08, 28.64, 29.24, 29.24, 26.08, 24.52, 23.6, 23.56, 23.36, 23.36]
+14.664376258850098
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 142368, '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': 10.641618490219116, 'TIME_S_1KI': 0.07474726406368788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.11982940673823, 'W': 23.739150118754246}
+[21.44, 21.48, 21.48, 21.12, 21.2, 21.24, 21.16, 21.48, 21.6, 21.92, 20.56, 20.56, 20.2, 20.2, 20.08, 20.04, 20.44, 20.72, 20.72, 21.12]
+376.24
+18.812
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 142368, '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': 10.641618490219116, 'TIME_S_1KI': 0.07474726406368788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.11982940673823, 'W': 23.739150118754246, 'J_1KI': 2.445211209026876, 'W_1KI': 0.16674498566218704, 'W_D': 4.927150118754245, 'J_D': 72.25358322525018, 'W_D_1KI': 0.03460855050821986, 'J_D_1KI': 0.00024309220125463487}
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 c0e7e6f..7ae266f 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": 1000, "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": 98.37349367141724, "TIME_S_1KI": 98.37349367141724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2530.5661976623533, "W": 23.87864246176097, "J_1KI": 2530.5661976623533, "W_1KI": 23.87864246176097, "W_D": 5.392642461760968, "J_D": 571.491396617889, "W_D_1KI": 5.392642461760968, "J_D_1KI": 5.392642461760968}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 101.81685495376587, "TIME_S_1KI": 101.81685495376587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2509.431913032532, "W": 23.91615643501538, "J_1KI": 2509.431913032532, "W_1KI": 23.91615643501538, "W_D": 5.455156435015379, "J_D": 572.3889491109848, "W_D_1KI": 5.455156435015379, "J_D_1KI": 5.455156435015379}
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 3a0a0f2..06c1b57 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 1000 -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": 98.37349367141724}
+{"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": 101.81685495376587}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       6,  ..., 2499994,
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499992,
                             2499997, 2500000]),
-       col_indices=tensor([ 13104,  56490,  58201,  ...,  30329, 136735,
-                           267614]),
-       values=tensor([0.2415, 0.0022, 0.5702,  ..., 0.5534, 0.4567, 0.6374]),
+       col_indices=tensor([  7138,  74289, 101345,  ...,  58125, 215534,
+                           230533]),
+       values=tensor([0.6785, 0.9079, 0.1725,  ..., 0.1754, 0.6680, 0.6302]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5993, 0.2850, 0.9957,  ..., 0.8791, 0.8991, 0.2848])
+tensor([0.7654, 0.8855, 0.1287,  ..., 0.1047, 0.9719, 0.8120])
 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: 98.37349367141724 seconds
+Time: 101.81685495376587 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,       6,  ..., 2499994,
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499992,
                             2499997, 2500000]),
-       col_indices=tensor([ 13104,  56490,  58201,  ...,  30329, 136735,
-                           267614]),
-       values=tensor([0.2415, 0.0022, 0.5702,  ..., 0.5534, 0.4567, 0.6374]),
+       col_indices=tensor([  7138,  74289, 101345,  ...,  58125, 215534,
+                           230533]),
+       values=tensor([0.6785, 0.9079, 0.1725,  ..., 0.1754, 0.6680, 0.6302]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5993, 0.2850, 0.9957,  ..., 0.8791, 0.8991, 0.2848])
+tensor([0.7654, 0.8855, 0.1287,  ..., 0.1047, 0.9719, 0.8120])
 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: 98.37349367141724 seconds
+Time: 101.81685495376587 seconds
 
-[20.36, 20.36, 20.48, 20.36, 20.56, 20.68, 20.6, 20.64, 20.6, 20.52]
-[20.56, 20.56, 20.56, 21.88, 23.2, 25.36, 26.72, 27.0, 26.4, 25.72, 25.04, 25.04, 25.2, 25.32, 25.2, 25.0, 25.12, 24.96, 24.88, 25.0, 24.92, 24.92, 24.96, 25.08, 25.24, 25.28, 25.48, 25.28, 25.28, 25.44, 25.44, 25.4, 25.2, 25.12, 24.96, 25.12, 25.32, 25.52, 25.8, 25.72, 25.44, 25.08, 25.0, 25.0, 24.96, 25.0, 25.04, 25.12, 25.12, 25.2, 25.2, 25.16, 25.04, 24.88, 24.96, 25.16, 25.16, 25.24, 25.24, 25.4, 25.2, 25.32, 25.16, 25.16, 25.2, 25.2, 25.0, 25.16, 25.28, 25.28, 25.28, 25.16, 25.2, 25.2, 25.04, 25.2, 25.36, 25.32, 25.32, 25.52, 25.44, 25.4, 25.36, 25.4, 25.32, 25.24, 25.04, 25.04, 24.92, 24.96, 24.96, 25.12, 25.32, 25.24, 25.2, 25.04, 24.92, 25.08, 25.0, 24.96, 24.88]
-105.97613334655762
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 98.37349367141724, 'TIME_S_1KI': 98.37349367141724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2530.5661976623533, 'W': 23.87864246176097}
-[20.36, 20.36, 20.48, 20.36, 20.56, 20.68, 20.6, 20.64, 20.6, 20.52, 20.68, 20.4, 20.36, 20.52, 20.4, 20.6, 20.68, 20.68, 20.64, 20.76]
-369.72
-18.486
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 98.37349367141724, 'TIME_S_1KI': 98.37349367141724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2530.5661976623533, 'W': 23.87864246176097, 'J_1KI': 2530.5661976623533, 'W_1KI': 23.87864246176097, 'W_D': 5.392642461760968, 'J_D': 571.491396617889, 'W_D_1KI': 5.392642461760968, 'J_D_1KI': 5.392642461760968}
+[20.36, 20.36, 20.32, 20.32, 20.32, 20.56, 20.64, 20.8, 21.0, 21.0]
+[20.96, 20.8, 21.96, 21.96, 22.8, 24.92, 25.88, 26.52, 26.32, 25.68, 25.2, 25.44, 25.36, 25.12, 25.04, 25.08, 25.04, 25.24, 25.36, 25.12, 25.28, 25.28, 25.08, 24.96, 25.04, 25.04, 25.2, 25.32, 25.2, 25.36, 25.08, 24.88, 24.96, 25.12, 25.24, 25.4, 25.4, 25.4, 25.2, 25.16, 25.08, 25.04, 25.24, 25.28, 25.36, 25.48, 25.48, 25.4, 25.36, 25.48, 25.52, 25.4, 25.28, 25.04, 24.84, 24.88, 25.12, 25.68, 26.12, 26.12, 25.76, 25.32, 24.96, 24.84, 24.92, 24.96, 24.92, 24.92, 24.92, 25.28, 25.08, 25.08, 25.16, 25.12, 25.04, 25.12, 25.2, 24.92, 24.92, 24.96, 25.08, 25.4, 25.4, 25.48, 25.44, 25.24, 25.28, 25.48, 25.72, 25.56, 25.56, 25.52, 25.44, 25.2, 25.08, 25.12, 25.08, 25.28, 25.44, 25.32]
+104.92622089385986
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 101.81685495376587, 'TIME_S_1KI': 101.81685495376587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2509.431913032532, 'W': 23.91615643501538}
+[20.36, 20.36, 20.32, 20.32, 20.32, 20.56, 20.64, 20.8, 21.0, 21.0, 20.52, 20.48, 20.28, 20.48, 20.52, 20.64, 20.52, 20.44, 20.4, 20.4]
+369.22
+18.461000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 101.81685495376587, 'TIME_S_1KI': 101.81685495376587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2509.431913032532, 'W': 23.91615643501538, 'J_1KI': 2509.431913032532, 'W_1KI': 23.91615643501538, 'W_D': 5.455156435015379, 'J_D': 572.3889491109848, 'W_D_1KI': 5.455156435015379, 'J_D_1KI': 5.455156435015379}
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 05b637f..99da0bf 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": 1803, "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": 10.916261672973633, "TIME_S_1KI": 6.054498986674227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.36130602836613, "W": 23.871230542861202, "J_1KI": 193.21203883991467, "W_1KI": 13.239728531814311, "W_D": 5.286230542861201, "J_D": 77.14383104681971, "W_D_1KI": 2.931908232313478, "J_D_1KI": 1.6261276940174587}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1750, "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": 10.16480827331543, "TIME_S_1KI": 5.80846187046596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 350.615839214325, "W": 23.96705841957935, "J_1KI": 200.35190812247143, "W_1KI": 13.695461954045342, "W_D": 5.563058419579352, "J_D": 81.38238586616524, "W_D_1KI": 3.178890525473916, "J_D_1KI": 1.8165088716993805}
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 a1e0ae8..38bf734 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 1000 -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": 5.821210145950317}
+{"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": 5.999283075332642}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 249996, 249998,
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 249990, 249995,
                             250000]),
-       col_indices=tensor([12413, 12946, 15415,  ..., 25881, 14227, 42249]),
-       values=tensor([0.3226, 0.4714, 0.3498,  ..., 0.9478, 0.5271, 0.1593]),
+       col_indices=tensor([13962, 18394, 22949,  ..., 14595, 37415, 49220]),
+       values=tensor([0.3721, 0.9393, 0.0895,  ..., 0.9714, 0.3434, 0.8212]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8728, 0.8759, 0.3915,  ..., 0.5486, 0.7678, 0.2723])
+tensor([0.7511, 0.6955, 0.0801,  ..., 0.5808, 0.0034, 0.8132])
 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: 5.821210145950317 seconds
+Time: 5.999283075332642 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 1803 -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": 10.916261672973633}
+['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 1750 -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": 10.16480827331543}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 249988, 249991,
+tensor(crow_indices=tensor([     0,      8,     10,  ..., 249989, 249996,
                             250000]),
-       col_indices=tensor([ 7415, 12339, 19287,  ..., 32647, 33814, 45500]),
-       values=tensor([0.8370, 0.0969, 0.8316,  ..., 0.1944, 0.4025, 0.6344]),
+       col_indices=tensor([  581, 19518, 20111,  ..., 13396, 34309, 44743]),
+       values=tensor([0.2810, 0.4140, 0.9885,  ..., 0.7044, 0.0704, 0.4209]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2154, 0.6825, 0.0342,  ..., 0.6227, 0.4225, 0.9397])
+tensor([0.2162, 0.8403, 0.5346,  ..., 0.6143, 0.9627, 0.5199])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.916261672973633 seconds
+Time: 10.16480827331543 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 249988, 249991,
+tensor(crow_indices=tensor([     0,      8,     10,  ..., 249989, 249996,
                             250000]),
-       col_indices=tensor([ 7415, 12339, 19287,  ..., 32647, 33814, 45500]),
-       values=tensor([0.8370, 0.0969, 0.8316,  ..., 0.1944, 0.4025, 0.6344]),
+       col_indices=tensor([  581, 19518, 20111,  ..., 13396, 34309, 44743]),
+       values=tensor([0.2810, 0.4140, 0.9885,  ..., 0.7044, 0.0704, 0.4209]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2154, 0.6825, 0.0342,  ..., 0.6227, 0.4225, 0.9397])
+tensor([0.2162, 0.8403, 0.5346,  ..., 0.6143, 0.9627, 0.5199])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.916261672973633 seconds
+Time: 10.16480827331543 seconds
 
-[20.84, 20.76, 20.76, 20.6, 20.4, 20.28, 20.24, 20.52, 20.56, 20.44]
-[20.44, 20.32, 23.6, 25.4, 27.36, 28.36, 28.36, 29.32, 26.76, 26.0, 25.28, 24.92, 25.04, 25.08]
-14.593353509902954
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1803, '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': 10.916261672973633, 'TIME_S_1KI': 6.054498986674227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.36130602836613, 'W': 23.871230542861202}
-[20.84, 20.76, 20.76, 20.6, 20.4, 20.28, 20.24, 20.52, 20.56, 20.44, 20.2, 20.24, 20.32, 20.44, 20.72, 20.92, 20.92, 21.36, 21.28, 21.28]
-371.7
-18.585
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1803, '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': 10.916261672973633, 'TIME_S_1KI': 6.054498986674227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.36130602836613, 'W': 23.871230542861202, 'J_1KI': 193.21203883991467, 'W_1KI': 13.239728531814311, 'W_D': 5.286230542861201, 'J_D': 77.14383104681971, 'W_D_1KI': 2.931908232313478, 'J_D_1KI': 1.6261276940174587}
+[20.36, 20.36, 20.36, 20.4, 20.24, 20.28, 20.32, 20.8, 21.08, 20.92]
+[21.16, 21.04, 24.12, 26.48, 28.28, 28.28, 29.04, 29.84, 25.88, 24.76, 24.76, 24.72, 24.88, 24.76]
+14.629072666168213
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1750, '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': 10.16480827331543, 'TIME_S_1KI': 5.80846187046596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.615839214325, 'W': 23.96705841957935}
+[20.36, 20.36, 20.36, 20.4, 20.24, 20.28, 20.32, 20.8, 21.08, 20.92, 20.4, 20.24, 20.24, 20.08, 20.2, 20.32, 20.56, 20.68, 20.72, 20.72]
+368.0799999999999
+18.403999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1750, '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': 10.16480827331543, 'TIME_S_1KI': 5.80846187046596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.615839214325, 'W': 23.96705841957935, 'J_1KI': 200.35190812247143, 'W_1KI': 13.695461954045342, 'W_D': 5.563058419579352, 'J_D': 81.38238586616524, 'W_D_1KI': 3.178890525473916, 'J_D_1KI': 1.8165088716993805}
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 62a0664..295c278 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": 1000, "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": 53.93572449684143, "TIME_S_1KI": 53.93572449684143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1405.257185211182, "W": 23.49413775375655, "J_1KI": 1405.257185211182, "W_1KI": 23.49413775375655, "W_D": 4.945137753756551, "J_D": 295.78401357650813, "W_D_1KI": 4.945137753756551, "J_D_1KI": 4.945137753756551}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 54.368531465530396, "TIME_S_1KI": 54.368531465530396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1384.197800512314, "W": 23.562014123499935, "J_1KI": 1384.197800512314, "W_1KI": 23.562014123499935, "W_D": 4.987014123499936, "J_D": 292.97215190053004, "W_D_1KI": 4.987014123499936, "J_D_1KI": 4.987014123499936}
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 cbf5bdc..ea77cdc 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 +1,14 @@
 ['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 1000 -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": 53.93572449684143}
+{"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": 54.368531465530396}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      63,     121,  ..., 2499897,
-                            2499959, 2500000]),
-       col_indices=tensor([  158,  1232,  2736,  ..., 48449, 48581, 49575]),
-       values=tensor([0.0263, 0.9327, 0.9651,  ..., 0.1558, 0.2228, 0.0301]),
+tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499903,
+                            2499953, 2500000]),
+       col_indices=tensor([  180,   933,  1739,  ..., 48224, 48432, 48665]),
+       values=tensor([0.2331, 0.6137, 0.9488,  ..., 0.3126, 0.9414, 0.7411]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0174, 0.1708, 0.2801,  ..., 0.8892, 0.6468, 0.1800])
+tensor([0.7249, 0.6013, 0.3531,  ..., 0.7563, 0.1447, 0.0341])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 53.93572449684143 seconds
+Time: 54.368531465530396 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      63,     121,  ..., 2499897,
-                            2499959, 2500000]),
-       col_indices=tensor([  158,  1232,  2736,  ..., 48449, 48581, 49575]),
-       values=tensor([0.0263, 0.9327, 0.9651,  ..., 0.1558, 0.2228, 0.0301]),
+tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499903,
+                            2499953, 2500000]),
+       col_indices=tensor([  180,   933,  1739,  ..., 48224, 48432, 48665]),
+       values=tensor([0.2331, 0.6137, 0.9488,  ..., 0.3126, 0.9414, 0.7411]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0174, 0.1708, 0.2801,  ..., 0.8892, 0.6468, 0.1800])
+tensor([0.7249, 0.6013, 0.3531,  ..., 0.7563, 0.1447, 0.0341])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 53.93572449684143 seconds
+Time: 54.368531465530396 seconds
 
-[20.72, 20.76, 20.76, 20.8, 20.72, 20.72, 20.24, 20.36, 20.72, 20.72]
-[20.76, 21.16, 21.32, 25.56, 26.68, 28.72, 29.48, 27.44, 25.8, 24.96, 24.56, 24.52, 24.56, 24.64, 24.6, 24.52, 24.56, 24.56, 24.6, 24.6, 24.68, 24.68, 24.4, 24.48, 24.4, 24.56, 24.52, 24.8, 24.64, 24.8, 24.68, 24.88, 24.56, 24.56, 24.36, 24.48, 24.64, 24.68, 24.56, 24.72, 24.48, 24.48, 24.44, 24.92, 25.08, 24.92, 24.96, 24.76, 24.76, 24.56, 24.44, 24.36, 24.36, 24.44, 24.36, 24.44, 24.52]
-59.8130989074707
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 53.93572449684143, 'TIME_S_1KI': 53.93572449684143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.257185211182, 'W': 23.49413775375655}
-[20.72, 20.76, 20.76, 20.8, 20.72, 20.72, 20.24, 20.36, 20.72, 20.72, 20.2, 20.6, 20.56, 20.68, 20.8, 20.8, 20.64, 20.56, 20.36, 20.16]
-370.97999999999996
-18.549
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 53.93572449684143, 'TIME_S_1KI': 53.93572449684143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.257185211182, 'W': 23.49413775375655, 'J_1KI': 1405.257185211182, 'W_1KI': 23.49413775375655, 'W_D': 4.945137753756551, 'J_D': 295.78401357650813, 'W_D_1KI': 4.945137753756551, 'J_D_1KI': 4.945137753756551}
+[20.28, 20.28, 20.28, 20.52, 20.52, 20.52, 20.84, 20.84, 20.8, 20.84]
+[20.8, 20.8, 24.12, 26.04, 26.04, 28.44, 29.44, 30.08, 26.76, 25.32, 24.56, 24.68, 24.84, 24.84, 24.76, 24.52, 24.68, 24.72, 24.96, 24.76, 24.96, 24.84, 25.0, 24.88, 24.88, 24.64, 24.64, 24.44, 24.28, 24.24, 24.28, 24.28, 24.52, 24.72, 24.84, 24.64, 24.68, 24.68, 24.72, 24.72, 24.8, 24.76, 24.56, 24.52, 24.2, 24.24, 24.24, 24.24, 24.24, 24.4, 24.52, 24.52, 24.4, 24.44, 24.4, 24.12]
+58.74700665473938
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 54.368531465530396, 'TIME_S_1KI': 54.368531465530396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.197800512314, 'W': 23.562014123499935}
+[20.28, 20.28, 20.28, 20.52, 20.52, 20.52, 20.84, 20.84, 20.8, 20.84, 20.4, 20.36, 20.4, 20.28, 20.56, 20.56, 20.96, 21.12, 21.28, 21.24]
+371.5
+18.575
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 54.368531465530396, 'TIME_S_1KI': 54.368531465530396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.197800512314, 'W': 23.562014123499935, 'J_1KI': 1384.197800512314, 'W_1KI': 23.562014123499935, 'W_D': 4.987014123499936, 'J_D': 292.97215190053004, 'W_D_1KI': 4.987014123499936, 'J_D_1KI': 4.987014123499936}
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 8adab40..79e75a2 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": 10285, "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": 10.171167612075806, "TIME_S_1KI": 0.9889321936874872, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.4937496185303, "W": 23.99041156544644, "J_1KI": 31.647423395092883, "W_1KI": 2.332563107967568, "W_D": 5.591411565446439, "J_D": 75.86237156176564, "W_D_1KI": 0.5436472110302809, "J_D_1KI": 0.052858260673824105}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10740, "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": 10.677687883377075, "TIME_S_1KI": 0.9941981269438619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.0521117687225, "W": 22.70459223140718, "J_1KI": 30.91732884252537, "W_1KI": 2.11402162303605, "W_D": 4.278592231407178, "J_D": 62.57393091917032, "W_D_1KI": 0.3983791649354914, "J_D_1KI": 0.03709303211689864}
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 12e8b6c..0bf1f45 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 1000 -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": 1.020900011062622}
+{"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": 1.031747817993164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 24999, 25000]),
-       col_indices=tensor([43592, 45763, 41730,  ...,  2923, 32227, 39553]),
-       values=tensor([0.0398, 0.4210, 0.0283,  ..., 0.1409, 0.8695, 0.8837]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([40215,  7884, 10043,  ..., 30495, 28697, 40914]),
+       values=tensor([0.0776, 0.0144, 0.1627,  ..., 0.8046, 0.8736, 0.3953]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8554, 0.8486, 0.8747,  ..., 0.5244, 0.7497, 0.0831])
+tensor([0.9279, 0.0068, 0.0286,  ..., 0.3265, 0.6131, 0.7632])
 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: 1.020900011062622 seconds
+Time: 1.031747817993164 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 10285 -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": 10.171167612075806}
+['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 10176 -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": 9.948124408721924}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([14664, 43703, 46520,  ...,  7061, 31497, 43987]),
-       values=tensor([0.1911, 0.5487, 0.9416,  ..., 0.5242, 0.5616, 0.0900]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([36670,  6571, 29568,  ..., 18627, 41427, 17079]),
+       values=tensor([0.2785, 0.5861, 0.6450,  ..., 0.6094, 0.8660, 0.4536]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7733, 0.9528, 0.6124,  ..., 0.0354, 0.2670, 0.0752])
+tensor([0.5003, 0.3455, 0.7125,  ..., 0.5405, 0.2393, 0.4201])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,15 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.171167612075806 seconds
+Time: 9.948124408721924 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 10740 -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": 10.677687883377075}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([14664, 43703, 46520,  ...,  7061, 31497, 43987]),
-       values=tensor([0.1911, 0.5487, 0.9416,  ..., 0.5242, 0.5616, 0.0900]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([16841, 18429, 37212,  ..., 30943,  4364, 38003]),
+       values=tensor([0.6614, 0.5763, 0.4032,  ..., 0.0279, 0.2406, 0.7956]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7733, 0.9528, 0.6124,  ..., 0.0354, 0.2670, 0.0752])
+tensor([0.8404, 0.4278, 0.6904,  ..., 0.0651, 0.6749, 0.6556])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -50,13 +53,29 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.171167612075806 seconds
+Time: 10.677687883377075 seconds
 
-[20.44, 20.44, 20.36, 20.08, 20.36, 20.36, 20.4, 20.44, 20.6, 20.52]
-[20.52, 20.72, 24.24, 25.88, 27.44, 27.44, 28.48, 29.32, 25.96, 26.28, 25.8, 26.0, 25.92]
-13.567660093307495
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10285, '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': 10.171167612075806, 'TIME_S_1KI': 0.9889321936874872, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.4937496185303, 'W': 23.99041156544644}
-[20.44, 20.44, 20.36, 20.08, 20.36, 20.36, 20.4, 20.44, 20.6, 20.52, 20.56, 20.36, 20.28, 20.12, 20.2, 20.6, 20.92, 20.8, 20.6, 20.6]
-367.98
-18.399
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10285, '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': 10.171167612075806, 'TIME_S_1KI': 0.9889321936874872, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.4937496185303, 'W': 23.99041156544644, 'J_1KI': 31.647423395092883, 'W_1KI': 2.332563107967568, 'W_D': 5.591411565446439, 'J_D': 75.86237156176564, 'W_D_1KI': 0.5436472110302809, 'J_D_1KI': 0.052858260673824105}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([16841, 18429, 37212,  ..., 30943,  4364, 38003]),
+       values=tensor([0.6614, 0.5763, 0.4032,  ..., 0.0279, 0.2406, 0.7956]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8404, 0.4278, 0.6904,  ..., 0.0651, 0.6749, 0.6556])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.677687883377075 seconds
+
+[20.72, 20.68, 20.64, 20.72, 20.72, 20.64, 20.68, 20.52, 20.52, 20.6]
+[21.04, 21.2, 21.2, 21.92, 23.48, 24.12, 25.48, 25.92, 26.2, 25.8, 25.88, 25.6, 25.8, 25.72]
+14.624887704849243
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10740, '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': 10.677687883377075, 'TIME_S_1KI': 0.9941981269438619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.0521117687225, 'W': 22.70459223140718}
+[20.72, 20.68, 20.64, 20.72, 20.72, 20.64, 20.68, 20.52, 20.52, 20.6, 20.2, 20.12, 20.24, 20.24, 20.28, 20.28, 20.4, 20.4, 20.44, 20.48]
+368.52000000000004
+18.426000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10740, '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': 10.677687883377075, 'TIME_S_1KI': 0.9941981269438619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.0521117687225, 'W': 22.70459223140718, 'J_1KI': 30.91732884252537, 'W_1KI': 2.11402162303605, 'W_D': 4.278592231407178, 'J_D': 62.57393091917032, 'W_D_1KI': 0.3983791649354914, 'J_D_1KI': 0.03709303211689864}
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
new file mode 100644
index 0000000..f8f615f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 96826, "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": 10.588202953338623, "TIME_S_1KI": 0.10935289027057425, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 358.2542047309875, "W": 24.50573052785985, "J_1KI": 3.699979393251683, "W_1KI": 0.2530903943967514, "W_D": 4.6827305278598494, "J_D": 68.45777967405314, "W_D_1KI": 0.04836232548963966, "J_D_1KI": 0.000499476643563089}
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
new file mode 100644
index 0000000..4f006ee
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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 1000 -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": 0.1145937442779541}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 2495, 2499, 2500]),
+       col_indices=tensor([2458, 4485, 3264,  ..., 1767, 2577, 3633]),
+       values=tensor([0.5111, 0.1865, 0.4486,  ..., 0.9187, 0.4905, 0.6857]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1036, 0.8585, 0.3762,  ..., 0.6219, 0.4226, 0.3195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.1145937442779541 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 91628 -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": 9.936307668685913}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1700, 3040, 4129,  ..., 4083, 2058, 3930]),
+       values=tensor([0.1350, 0.7186, 0.2594,  ..., 0.2124, 0.0344, 0.1244]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7260, 0.9332, 0.2146,  ..., 0.1697, 0.4017, 0.1867])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 9.936307668685913 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 96826 -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": 10.588202953338623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 2500, 2500]),
+       col_indices=tensor([3997, 2967, 4931,  ..., 3835, 1314, 3597]),
+       values=tensor([0.7356, 0.2235, 0.2006,  ..., 0.5232, 0.0695, 0.2889]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7175, 0.9581, 0.9907,  ..., 0.9548, 0.8349, 0.6616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.588202953338623 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 2500, 2500]),
+       col_indices=tensor([3997, 2967, 4931,  ..., 3835, 1314, 3597]),
+       values=tensor([0.7356, 0.2235, 0.2006,  ..., 0.5232, 0.0695, 0.2889]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7175, 0.9581, 0.9907,  ..., 0.9548, 0.8349, 0.6616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.588202953338623 seconds
+
+[20.4, 20.56, 20.56, 20.6, 20.44, 20.64, 20.68, 20.96, 21.52, 22.56]
+[23.28, 23.64, 26.88, 28.2, 29.6, 29.52, 29.52, 29.72, 25.72, 24.92, 23.8, 23.72, 24.2, 24.28]
+14.619201183319092
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 96826, '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': 10.588202953338623, 'TIME_S_1KI': 0.10935289027057425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.2542047309875, 'W': 24.50573052785985}
+[20.4, 20.56, 20.56, 20.6, 20.44, 20.64, 20.68, 20.96, 21.52, 22.56, 22.96, 23.28, 23.32, 23.36, 23.32, 23.28, 23.36, 23.12, 23.0, 23.0]
+396.46
+19.823
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 96826, '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': 10.588202953338623, 'TIME_S_1KI': 0.10935289027057425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.2542047309875, 'W': 24.50573052785985, 'J_1KI': 3.699979393251683, 'W_1KI': 0.2530903943967514, 'W_D': 4.6827305278598494, 'J_D': 68.45777967405314, 'W_D_1KI': 0.04836232548963966, 'J_D_1KI': 0.000499476643563089}
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
new file mode 100644
index 0000000..7e96f33
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17363, "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": 10.667346239089966, "TIME_S_1KI": 0.6143722996653784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.18905796051024, "W": 22.76067676218484, "J_1KI": 19.1896019098376, "W_1KI": 1.3108723585892323, "W_D": 3.0516767621848366, "J_D": 44.67289422965043, "W_D_1KI": 0.17575745909029755, "J_D_1KI": 0.010122528312520737}
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
new file mode 100644
index 0000000..ba00b65
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,62 @@
+['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 1000 -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": 0.6047096252441406}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    11,  ..., 24983, 24992, 25000]),
+       col_indices=tensor([ 225,  408, 1943,  ..., 2555, 2651, 2712]),
+       values=tensor([0.7906, 0.4816, 0.2276,  ..., 0.2718, 0.8003, 0.8712]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6660, 0.8709, 0.7078,  ..., 0.4840, 0.5828, 0.2928])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.6047096252441406 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 17363 -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": 10.667346239089966}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     7,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([  85,  195, 4187,  ..., 2991, 3287, 4675]),
+       values=tensor([0.1915, 0.0298, 0.9128,  ..., 0.0482, 0.8260, 0.8063]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9668, 0.6018, 0.4153,  ..., 0.6117, 0.1974, 0.6733])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.667346239089966 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     7,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([  85,  195, 4187,  ..., 2991, 3287, 4675]),
+       values=tensor([0.1915, 0.0298, 0.9128,  ..., 0.0482, 0.8260, 0.8063]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9668, 0.6018, 0.4153,  ..., 0.6117, 0.1974, 0.6733])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.667346239089966 seconds
+
+[22.96, 22.64, 22.44, 22.84, 23.2, 23.48, 24.08, 23.88, 23.04, 22.2]
+[21.52, 21.52, 20.88, 24.32, 25.36, 27.04, 27.68, 28.12, 25.12, 23.88, 23.84, 23.84, 23.68, 23.84]
+14.638802766799927
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17363, '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': 10.667346239089966, 'TIME_S_1KI': 0.6143722996653784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.18905796051024, 'W': 22.76067676218484}
+[22.96, 22.64, 22.44, 22.84, 23.2, 23.48, 24.08, 23.88, 23.04, 22.2, 20.48, 20.6, 20.52, 20.76, 20.72, 20.68, 20.68, 20.76, 20.64, 20.8]
+394.18000000000006
+19.709000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17363, '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': 10.667346239089966, 'TIME_S_1KI': 0.6143722996653784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.18905796051024, 'W': 22.76067676218484, 'J_1KI': 19.1896019098376, 'W_1KI': 1.3108723585892323, 'W_D': 3.0516767621848366, 'J_D': 44.67289422965043, 'W_D_1KI': 0.17575745909029755, 'J_D_1KI': 0.010122528312520737}
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
new file mode 100644
index 0000000..a5d181f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1948, "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": 10.5094473361969, "TIME_S_1KI": 5.394993499074384, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 321.46233737945556, "W": 22.060099154306354, "J_1KI": 165.02173376768766, "W_1KI": 11.324486218843099, "W_D": 3.783099154306356, "J_D": 55.127762036561975, "W_D_1KI": 1.9420426870155834, "J_D_1KI": 0.9969418311168293}
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
new file mode 100644
index 0000000..80c2fbe
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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 1000 -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": 5.389868259429932}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     96,  ..., 249907, 249949,
+                            250000]),
+       col_indices=tensor([ 123,  345,  399,  ..., 4711, 4879, 4988]),
+       values=tensor([0.4250, 0.5468, 0.7620,  ..., 0.1883, 0.2040, 0.8985]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2612, 0.9268, 0.9416,  ..., 0.0698, 0.1077, 0.5090])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 5.389868259429932 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 1948 -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": 10.5094473361969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    115,  ..., 249893, 249944,
+                            250000]),
+       col_indices=tensor([  73,  135,  475,  ..., 4575, 4723, 4971]),
+       values=tensor([0.1739, 0.5180, 0.0955,  ..., 0.3924, 0.5566, 0.2573]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5075, 0.6044, 0.6141,  ..., 0.4161, 0.9554, 0.0515])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.5094473361969 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    115,  ..., 249893, 249944,
+                            250000]),
+       col_indices=tensor([  73,  135,  475,  ..., 4575, 4723, 4971]),
+       values=tensor([0.1739, 0.5180, 0.0955,  ..., 0.3924, 0.5566, 0.2573]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5075, 0.6044, 0.6141,  ..., 0.4161, 0.9554, 0.0515])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.5094473361969 seconds
+
+[20.56, 20.36, 20.36, 20.92, 20.64, 20.52, 20.44, 20.2, 20.0, 20.24]
+[20.4, 20.4, 20.96, 22.24, 24.0, 24.56, 25.32, 25.32, 25.28, 24.8, 24.08, 24.24, 24.16, 24.16]
+14.572116613388062
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1948, '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': 10.5094473361969, 'TIME_S_1KI': 5.394993499074384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.46233737945556, 'W': 22.060099154306354}
+[20.56, 20.36, 20.36, 20.92, 20.64, 20.52, 20.44, 20.2, 20.0, 20.24, 20.16, 20.08, 20.04, 20.12, 20.12, 20.16, 20.24, 20.2, 20.44, 20.44]
+365.53999999999996
+18.276999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1948, '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': 10.5094473361969, 'TIME_S_1KI': 5.394993499074384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.46233737945556, 'W': 22.060099154306354, 'J_1KI': 165.02173376768766, 'W_1KI': 11.324486218843099, 'W_D': 3.783099154306356, 'J_D': 55.127762036561975, 'W_D_1KI': 1.9420426870155834, 'J_D_1KI': 0.9969418311168293}
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
new file mode 100644
index 0000000..496e602
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 28.093817949295044, "TIME_S_1KI": 28.093817949295044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 689.2963391876219, "W": 22.82953703239495, "J_1KI": 689.2963391876219, "W_1KI": 22.82953703239495, "W_D": 4.334537032394952, "J_D": 130.87346030116072, "W_D_1KI": 4.334537032394952, "J_D_1KI": 4.334537032394952}
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
new file mode 100644
index 0000000..9c2fd18
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_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 1000 -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": 28.093817949295044}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     249,     484,  ..., 1249498,
+                            1249755, 1250000]),
+       col_indices=tensor([   8,   31,   46,  ..., 4934, 4976, 4984]),
+       values=tensor([0.2044, 0.4643, 0.3912,  ..., 0.8352, 0.2191, 0.4950]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7092, 0.7480, 0.2063,  ..., 0.9775, 0.7055, 0.9981])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 28.093817949295044 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     249,     484,  ..., 1249498,
+                            1249755, 1250000]),
+       col_indices=tensor([   8,   31,   46,  ..., 4934, 4976, 4984]),
+       values=tensor([0.2044, 0.4643, 0.3912,  ..., 0.8352, 0.2191, 0.4950]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7092, 0.7480, 0.2063,  ..., 0.9775, 0.7055, 0.9981])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 28.093817949295044 seconds
+
+[20.56, 20.6, 20.48, 20.56, 20.4, 20.48, 20.68, 20.72, 20.92, 21.08]
+[20.92, 20.92, 20.56, 23.36, 25.52, 27.28, 28.24, 28.72, 25.44, 24.36, 24.6, 24.76, 24.64, 24.52, 24.36, 24.44, 24.24, 24.4, 24.44, 24.16, 24.16, 24.12, 24.2, 24.2, 24.08, 24.12, 24.24, 24.12, 24.0]
+30.193180799484253
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 28.093817949295044, 'TIME_S_1KI': 28.093817949295044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.2963391876219, 'W': 22.82953703239495}
+[20.56, 20.6, 20.48, 20.56, 20.4, 20.48, 20.68, 20.72, 20.92, 21.08, 20.52, 20.6, 20.36, 20.4, 20.52, 20.44, 20.6, 20.44, 20.44, 20.36]
+369.9
+18.494999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 28.093817949295044, 'TIME_S_1KI': 28.093817949295044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.2963391876219, 'W': 22.82953703239495, 'J_1KI': 689.2963391876219, 'W_1KI': 22.82953703239495, 'W_D': 4.334537032394952, 'J_D': 130.87346030116072, 'W_D_1KI': 4.334537032394952, 'J_D_1KI': 4.334537032394952}
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
new file mode 100644
index 0000000..fff9557
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "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": 53.78093886375427, "TIME_S_1KI": 53.78093886375427, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1352.5172874259947, "W": 23.03061139017129, "J_1KI": 1352.5172874259947, "W_1KI": 23.03061139017129, "W_D": 4.417611390171292, "J_D": 259.4327902595995, "W_D_1KI": 4.417611390171292, "J_D_1KI": 4.417611390171292}
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
new file mode 100644
index 0000000..c5ce197
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_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 1000 -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": 53.78093886375427}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    1044,  ..., 2498992,
+                            2499517, 2500000]),
+       col_indices=tensor([   3,   19,   25,  ..., 4971, 4983, 4990]),
+       values=tensor([0.2124, 0.6762, 0.6770,  ..., 0.5380, 0.6783, 0.2658]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0160, 0.9125, 0.7128,  ..., 0.4183, 0.3158, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.78093886375427 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    1044,  ..., 2498992,
+                            2499517, 2500000]),
+       col_indices=tensor([   3,   19,   25,  ..., 4971, 4983, 4990]),
+       values=tensor([0.2124, 0.6762, 0.6770,  ..., 0.5380, 0.6783, 0.2658]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0160, 0.9125, 0.7128,  ..., 0.4183, 0.3158, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.78093886375427 seconds
+
+[20.56, 20.72, 20.76, 20.76, 20.64, 20.88, 20.6, 20.52, 20.72, 20.48]
+[20.36, 20.24, 20.8, 22.0, 24.0, 25.2, 26.08, 25.8, 25.8, 25.44, 24.16, 24.32, 24.32, 24.4, 24.36, 24.4, 24.52, 24.88, 24.68, 24.64, 24.52, 24.28, 24.04, 24.08, 24.08, 24.08, 24.36, 24.4, 24.48, 24.6, 24.6, 24.64, 24.56, 24.52, 24.56, 24.32, 24.04, 24.32, 24.36, 24.24, 24.28, 24.28, 24.28, 24.48, 24.52, 24.76, 24.56, 24.24, 24.16, 24.04, 24.12, 24.12, 24.44, 24.48, 24.52, 24.4]
+58.726938009262085
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 53.78093886375427, 'TIME_S_1KI': 53.78093886375427, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.5172874259947, 'W': 23.03061139017129}
+[20.56, 20.72, 20.76, 20.76, 20.64, 20.88, 20.6, 20.52, 20.72, 20.48, 20.84, 20.6, 20.72, 20.72, 20.72, 20.52, 20.6, 20.72, 20.76, 20.72]
+372.26
+18.613
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, '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': 53.78093886375427, 'TIME_S_1KI': 53.78093886375427, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.5172874259947, 'W': 23.03061139017129, 'J_1KI': 1352.5172874259947, 'W_1KI': 23.03061139017129, 'W_D': 4.417611390171292, 'J_D': 259.4327902595995, 'W_D_1KI': 4.417611390171292, 'J_D_1KI': 4.417611390171292}
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
new file mode 100644
index 0000000..a523326
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 289284, "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": 10.336281061172485, "TIME_S_1KI": 0.035730566022222056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 302.8508556365967, "W": 22.269333190276484, "J_1KI": 1.0468980504853247, "W_1KI": 0.07698086721103305, "W_D": 3.542333190276487, "J_D": 48.17381052494056, "W_D_1KI": 0.012245174950140648, "J_D_1KI": 4.232925066765064e-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
new file mode 100644
index 0000000..2a5bc74
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,356 @@
+['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 1000 -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": 0.04395866394042969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([ 439, 4351,  241,  500, 1004,  350, 1803, 3065, 3191,
+                           3136, 4095,  405, 1027,  870, 1417, 1360, 1534, 1342,
+                           3091, 3442, 2499, 1358, 1636, 3780, 2825, 2256, 4221,
+                            891, 2908, 3121, 1626, 2038,  883, 1037,  495, 2079,
+                            274,  937, 1868, 1488, 2903, 1523, 2167,  269, 3946,
+                           4053, 3008, 3702, 2193, 1563,  433, 1763, 2812, 3707,
+                           1886, 3013, 1511,  241, 1937, 2889, 1518, 4490, 4205,
+                           2026, 1673,  448,  986, 4061, 3094, 3985, 2097, 1213,
+                           4129, 3540, 2913, 1842, 3281, 3579, 2699, 1582, 1926,
+                           2137, 2888,  530, 3516, 2878,   57, 3238, 1656,  156,
+                           3904, 1121,  616, 2128,  426, 4846, 2365, 4030,  347,
+                           3690,  867, 1324, 1005, 4649, 3492, 4358,   47,  220,
+                           4307,  708, 2842, 3336, 1686, 1004, 4195, 3767,  332,
+                             43, 2809, 3452, 1463, 2172, 1464, 3770, 1880, 2042,
+                           3777, 2498, 3420, 1112, 4060, 4103, 4825, 1440, 4448,
+                             99, 2245, 3060,   27, 3733,  457, 3987, 3747, 1652,
+                           2522,  757, 4125, 2250, 2724, 3925, 2338, 3816, 1409,
+                           2282, 4242,  682, 3683, 4310, 1582, 4330,  601,  544,
+                           2289, 2874, 3966, 1136,  681, 4257, 2516, 3237, 2677,
+                           2257, 2771, 3675, 3168, 1248, 4288, 3632, 3599,  280,
+                           4551, 4047, 3577, 2662, 2281, 1968, 3402,  454, 1141,
+                           3366,  354, 2291, 4168, 3523, 2296, 2127, 2248, 4229,
+                           2140,  736, 3393,  640, 4820, 2236, 1416, 4815, 3234,
+                           1042, 1979, 4637, 2323,  138, 2380, 3226, 1859, 3342,
+                           2378,  803,  349, 3172, 4960, 4660, 4480, 3337,  245,
+                           4128, 3649, 2732,  968,  771, 3445, 3899,  644,   16,
+                           3599, 1029, 1799, 4502,  366, 4843, 2859, 2949,  545,
+                            645, 3511, 4843,  251, 2988, 2387,  946]),
+       values=tensor([9.4296e-01, 5.2301e-01, 8.9037e-01, 1.8262e-01,
+                      9.3621e-01, 5.6553e-01, 9.8721e-01, 6.5141e-01,
+                      2.8305e-01, 8.9567e-01, 7.1276e-04, 4.5788e-01,
+                      1.3154e-01, 7.7912e-01, 2.1464e-01, 9.3572e-01,
+                      4.0199e-01, 1.4579e-01, 1.5259e-01, 5.2311e-01,
+                      6.3620e-01, 8.3700e-01, 3.7813e-01, 1.4289e-01,
+                      6.8630e-01, 9.7120e-01, 7.6830e-01, 1.8723e-01,
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+                      9.8689e-01, 3.5349e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.0440, 0.7352, 0.2145,  ..., 0.3780, 0.1332, 0.0924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04395866394042969 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 238860 -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": 8.669770956039429}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([3091, 2173, 2760, 4828, 4497, 2021, 4336, 1372, 2593,
+                           4578, 2353, 1617, 2286, 4843,  611, 3842,  780, 3798,
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+                      0.6070, 0.5609, 0.7211, 0.9777, 0.7339]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3130, 0.9247, 0.8789,  ..., 0.8987, 0.6939, 0.4674])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 8.669770956039429 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 289284 -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": 10.336281061172485}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1127, 2914,  651, 3868, 3478, 2616,  630, 2816, 2915,
+                           3943, 3548, 2263, 4542, 4912, 1799, 1521, 4830, 1959,
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+                      0.1589, 0.3357, 0.6754, 0.9423, 0.7719]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4104, 0.7044, 0.9040,  ..., 0.0726, 0.3479, 0.6465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.336281061172485 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1127, 2914,  651, 3868, 3478, 2616,  630, 2816, 2915,
+                           3943, 3548, 2263, 4542, 4912, 1799, 1521, 4830, 1959,
+                            565, 4046,  352,  708,  388, 1948, 1601,  580, 4884,
+                           1273, 2391, 2767, 3934, 3369, 4073, 1550,  863, 4309,
+                            666, 1592, 2221,  566, 3749, 4816,  269,  465,  352,
+                           1056, 3923, 2996, 3908, 4028, 1098, 3401, 2466,  323,
+                           4554, 3723, 4598, 3095, 2628, 4872, 2114, 3081, 3773,
+                           3425, 1731, 1262, 1917, 2900, 2481, 4272, 4608, 2685,
+                           4012, 3949,  546,  721, 2719, 2060, 3934, 2047,  319,
+                           1177, 4368,  590,  919, 2939, 1268, 3254, 3134,  888,
+                            658, 3560, 3243, 4771, 1870, 2190, 3032, 1145, 3921,
+                           3093,  240,  195, 4761,   94, 4383, 2739,  425, 1280,
+                           2618, 2549, 2332, 4924, 2783, 3566,  338, 1395, 3128,
+                           1333, 3138, 4314, 4739, 2917, 1017,  709,  300, 2533,
+                           3360,  999,  395, 2920,  889, 1982, 4806, 1821, 1887,
+                           3776, 1083,  112,  254, 1671, 1524, 3260, 3015, 2718,
+                           1436, 4393, 4051, 3480, 2230, 4054, 2670,  395, 2759,
+                           4796,  849, 4168, 1575, 4853, 1261, 4275, 1866, 3556,
+                           3417, 1020, 4282,  584, 3689, 3874, 1509, 4083,  263,
+                           1550,  171, 3186, 1466, 1336, 4936, 3512, 2418,  944,
+                            325, 1694,  930, 2377, 1839,  621, 2680, 2742, 1537,
+                           4859, 1103, 3522, 1157,  158, 4629, 2357,  873, 4934,
+                           2882, 1458, 3703,  572, 1916, 2812, 1567, 1471, 1134,
+                            673, 1170, 2394,  135, 1008, 3492,  716, 2043, 4892,
+                           1753, 1218,  680, 2404, 2996, 3897, 4680,  298, 3550,
+                           1169,  883, 1691, 2497, 4937, 4137, 2804, 4987, 4765,
+                           1784, 3581, 2966, 4679, 4779,   60, 1363, 4249,  709,
+                           3283, 2433,  962, 3692, 1587, 4377, 2820]),
+       values=tensor([0.9574, 0.0088, 0.5020, 0.9141, 0.2863, 0.0911, 0.1607,
+                      0.4081, 0.1489, 0.0577, 0.6602, 0.5319, 0.6687, 0.7359,
+                      0.7218, 0.5265, 0.2843, 0.5255, 0.7975, 0.9675, 0.4955,
+                      0.9458, 0.7420, 0.1283, 0.0140, 0.5968, 0.2693, 0.9592,
+                      0.8530, 0.7750, 0.2021, 0.3487, 0.7218, 0.6129, 0.8420,
+                      0.1328, 0.0258, 0.3482, 0.2496, 0.9070, 0.1335, 0.8930,
+                      0.3961, 0.0685, 0.4593, 0.3228, 0.0085, 0.1698, 0.1363,
+                      0.2353, 0.4054, 0.4337, 0.7557, 0.8715, 0.1886, 0.6545,
+                      0.5162, 0.7325, 0.3336, 0.6877, 0.8204, 0.5811, 0.3075,
+                      0.6798, 0.4051, 0.0597, 0.5326, 0.8458, 0.4272, 0.2826,
+                      0.4719, 0.5396, 0.3388, 0.9973, 0.4187, 0.6234, 0.2698,
+                      0.3492, 0.8857, 0.1489, 0.1998, 0.2289, 0.4451, 0.0379,
+                      0.1988, 0.2113, 0.3738, 0.7193, 0.5213, 0.9072, 0.0613,
+                      0.4005, 0.3523, 0.0709, 0.5596, 0.7335, 0.6383, 0.0887,
+                      0.5692, 0.4603, 0.6272, 0.2553, 0.8985, 0.3462, 0.0407,
+                      0.6936, 0.4412, 0.0627, 0.2562, 0.5155, 0.3465, 0.4292,
+                      0.4385, 0.0812, 0.3872, 0.5207, 0.2559, 0.2581, 0.6221,
+                      0.7181, 0.1019, 0.8605, 0.1756, 0.2609, 0.7394, 0.4792,
+                      0.5099, 0.8831, 0.7934, 0.9746, 0.6748, 0.9066, 0.6080,
+                      0.5057, 0.1054, 0.3619, 0.1974, 0.9928, 0.4111, 0.7540,
+                      0.7143, 0.9147, 0.9579, 0.7958, 0.4523, 0.7894, 0.2118,
+                      0.3648, 0.9673, 0.5837, 0.0431, 0.7582, 0.2735, 0.6036,
+                      0.6216, 0.5076, 0.9183, 0.8897, 0.4081, 0.7880, 0.2381,
+                      0.5085, 0.3796, 0.6662, 0.3146, 0.0575, 0.2385, 0.6086,
+                      0.9934, 0.6888, 0.1889, 0.0438, 0.3261, 0.3882, 0.4169,
+                      0.8627, 0.9997, 0.2070, 0.7356, 0.5145, 0.1752, 0.6555,
+                      0.6684, 0.9501, 0.6473, 0.8531, 0.7478, 0.1401, 0.2317,
+                      0.3747, 0.6467, 0.8854, 0.0360, 0.9037, 0.4674, 0.5830,
+                      0.9597, 0.0900, 0.4875, 0.2138, 0.3988, 0.5880, 0.0152,
+                      0.7769, 0.9566, 0.4429, 0.9222, 0.4459, 0.5489, 0.2798,
+                      0.1520, 0.0578, 0.0988, 0.1282, 0.5238, 0.4828, 0.8259,
+                      0.8455, 0.5457, 0.6118, 0.8302, 0.6716, 0.4292, 0.3306,
+                      0.7331, 0.1640, 0.1078, 0.2534, 0.3387, 0.7022, 0.6433,
+                      0.1056, 0.7198, 0.6256, 0.4771, 0.9207, 0.9076, 0.7974,
+                      0.8755, 0.5354, 0.1002, 0.2943, 0.2911, 0.1894, 0.3903,
+                      0.1589, 0.3357, 0.6754, 0.9423, 0.7719]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4104, 0.7044, 0.9040,  ..., 0.0726, 0.3479, 0.6465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.336281061172485 seconds
+
+[20.72, 20.68, 20.72, 20.88, 21.04, 21.0, 21.04, 20.76, 20.76, 20.8]
+[20.88, 21.0, 21.28, 23.68, 24.48, 25.8, 26.4, 26.0, 24.92, 23.92, 24.12, 24.16, 24.24]
+13.599457740783691
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289284, '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': 10.336281061172485, 'TIME_S_1KI': 0.035730566022222056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 302.8508556365967, 'W': 22.269333190276484}
+[20.72, 20.68, 20.72, 20.88, 21.04, 21.0, 21.04, 20.76, 20.76, 20.8, 21.04, 20.92, 20.64, 20.48, 20.48, 20.48, 20.68, 20.96, 21.16, 21.16]
+374.53999999999996
+18.726999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289284, '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': 10.336281061172485, 'TIME_S_1KI': 0.035730566022222056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 302.8508556365967, 'W': 22.269333190276484, 'J_1KI': 1.0468980504853247, 'W_1KI': 0.07698086721103305, 'W_D': 3.542333190276487, 'J_D': 48.17381052494056, 'W_D_1KI': 0.012245174950140648, 'J_D_1KI': 4.232925066765064e-05}
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 e1dfb7e..4c97264 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": 6038, "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": 10.267276763916016, "TIME_S_1KI": 1.7004433196283564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.7928693008422, "W": 66.02, "J_1KI": 141.9001108481024, "W_1KI": 10.934084133819145, "W_D": 30.906499999999994, "J_D": 401.09767971897116, "W_D_1KI": 5.1186651209009595, "J_D_1KI": 0.8477418219445113}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6154, "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": 10.661418676376343, "TIME_S_1KI": 1.732437223980556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.1215419101716, "W": 66.29, "J_1KI": 144.31614265683646, "W_1KI": 10.771855703607411, "W_D": 31.486250000000005, "J_D": 421.8376361286641, "W_D_1KI": 5.116387715307118, "J_D_1KI": 0.8313922189319334}
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 ba4be77..3c6753a 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', '1000', '-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": 1.7388477325439453}
+{"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": 1.7059962749481201}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      28,  ...,  999983,
-                             999988, 1000000]),
-       col_indices=tensor([ 2300,  3196, 10757,  ..., 92248, 95895, 96660]),
-       values=tensor([0.0937, 0.5944, 0.4639,  ..., 0.5292, 0.3684, 0.5963]),
+tensor(crow_indices=tensor([      0,      10,      28,  ...,  999981,
+                             999989, 1000000]),
+       col_indices=tensor([10839, 13780, 19162,  ..., 70763, 71204, 84111]),
+       values=tensor([0.3862, 0.3703, 0.4692,  ..., 0.8959, 0.7094, 0.8230]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9252, 0.4601, 0.1039,  ..., 0.3841, 0.9664, 0.4740])
+tensor([0.6738, 0.0568, 0.8510,  ..., 0.5567, 0.5192, 0.1431])
 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: 1.7388477325439453 seconds
+Time: 1.7059962749481201 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6038', '-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": 10.267276763916016}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6154', '-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": 10.661418676376343}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      25,  ...,  999976,
-                             999990, 1000000]),
-       col_indices=tensor([ 7337,  9006, 37341,  ..., 86240, 86867, 93776]),
-       values=tensor([0.1177, 0.4165, 0.7590,  ..., 0.7494, 0.7065, 0.3766]),
+tensor(crow_indices=tensor([      0,      19,      22,  ...,  999983,
+                             999992, 1000000]),
+       col_indices=tensor([ 4495, 11307, 13629,  ..., 46229, 59792, 89876]),
+       values=tensor([0.8364, 0.7832, 0.5169,  ..., 0.6963, 0.9299, 0.6811]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1410, 0.1591, 0.3967,  ..., 0.8959, 0.7085, 0.3739])
+tensor([0.2491, 0.3919, 0.1225,  ..., 0.6201, 0.7425, 0.7393])
 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: 10.267276763916016 seconds
+Time: 10.661418676376343 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      25,  ...,  999976,
-                             999990, 1000000]),
-       col_indices=tensor([ 7337,  9006, 37341,  ..., 86240, 86867, 93776]),
-       values=tensor([0.1177, 0.4165, 0.7590,  ..., 0.7494, 0.7065, 0.3766]),
+tensor(crow_indices=tensor([      0,      19,      22,  ...,  999983,
+                             999992, 1000000]),
+       col_indices=tensor([ 4495, 11307, 13629,  ..., 46229, 59792, 89876]),
+       values=tensor([0.8364, 0.7832, 0.5169,  ..., 0.6963, 0.9299, 0.6811]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1410, 0.1591, 0.3967,  ..., 0.8959, 0.7085, 0.3739])
+tensor([0.2491, 0.3919, 0.1225,  ..., 0.6201, 0.7425, 0.7393])
 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: 10.267276763916016 seconds
+Time: 10.661418676376343 seconds
 
-[39.74, 38.35, 38.46, 38.36, 38.8, 38.69, 39.25, 38.82, 38.81, 38.76]
-[66.02]
-12.977777481079102
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6038, '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': 10.267276763916016, 'TIME_S_1KI': 1.7004433196283564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7928693008422, 'W': 66.02}
-[39.74, 38.35, 38.46, 38.36, 38.8, 38.69, 39.25, 38.82, 38.81, 38.76, 39.02, 38.96, 38.6, 38.78, 38.41, 38.79, 38.39, 38.7, 43.94, 38.8]
-702.27
-35.1135
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6038, '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': 10.267276763916016, 'TIME_S_1KI': 1.7004433196283564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7928693008422, 'W': 66.02, 'J_1KI': 141.9001108481024, 'W_1KI': 10.934084133819145, 'W_D': 30.906499999999994, 'J_D': 401.09767971897116, 'W_D_1KI': 5.1186651209009595, 'J_D_1KI': 0.8477418219445113}
+[39.48, 39.2, 39.2, 38.44, 38.38, 38.41, 38.43, 38.34, 38.9, 38.82]
+[66.29]
+13.3975191116333
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6154, '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': 10.661418676376343, 'TIME_S_1KI': 1.732437223980556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1215419101716, 'W': 66.29}
+[39.48, 39.2, 39.2, 38.44, 38.38, 38.41, 38.43, 38.34, 38.9, 38.82, 39.78, 38.22, 38.35, 38.27, 38.77, 38.41, 38.95, 38.84, 38.8, 38.25]
+696.075
+34.80375
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6154, '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': 10.661418676376343, 'TIME_S_1KI': 1.732437223980556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1215419101716, 'W': 66.29, 'J_1KI': 144.31614265683646, 'W_1KI': 10.771855703607411, 'W_D': 31.486250000000005, 'J_D': 421.8376361286641, 'W_D_1KI': 5.116387715307118, 'J_D_1KI': 0.8313922189319334}
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
new file mode 100644
index 0000000..28fc497
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "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": 16.51292133331299, "TIME_S_1KI": 16.51292133331299, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1675.715212612152, "W": 77.36, "J_1KI": 1675.715212612152, "W_1KI": 77.36, "W_D": 42.0475, "J_D": 910.8019054073095, "W_D_1KI": 42.0475, "J_D_1KI": 42.0475}
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
new file mode 100644
index 0000000..d1f2742
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_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-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 16.51292133331299}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       85,      184,  ...,  9999802,
+                             9999894, 10000000]),
+       col_indices=tensor([ 1647,  2383,  2584,  ..., 98263, 98777, 99734]),
+       values=tensor([0.1681, 0.5843, 0.2619,  ..., 0.7600, 0.0011, 0.9501]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3789, 0.7363, 0.6915,  ..., 0.8879, 0.6465, 0.7586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 16.51292133331299 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       85,      184,  ...,  9999802,
+                             9999894, 10000000]),
+       col_indices=tensor([ 1647,  2383,  2584,  ..., 98263, 98777, 99734]),
+       values=tensor([0.1681, 0.5843, 0.2619,  ..., 0.7600, 0.0011, 0.9501]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3789, 0.7363, 0.6915,  ..., 0.8879, 0.6465, 0.7586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 16.51292133331299 seconds
+
+[39.67, 38.57, 40.75, 44.07, 38.52, 39.19, 39.95, 38.76, 38.89, 38.37]
+[77.36]
+21.661261796951294
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, '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': 16.51292133331299, 'TIME_S_1KI': 16.51292133331299, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1675.715212612152, 'W': 77.36}
+[39.67, 38.57, 40.75, 44.07, 38.52, 39.19, 39.95, 38.76, 38.89, 38.37, 39.54, 38.41, 38.76, 38.62, 39.0, 38.84, 38.88, 38.44, 38.6, 38.42]
+706.25
+35.3125
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, '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': 16.51292133331299, 'TIME_S_1KI': 16.51292133331299, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1675.715212612152, 'W': 77.36, 'J_1KI': 1675.715212612152, 'W_1KI': 77.36, 'W_D': 42.0475, 'J_D': 910.8019054073095, 'W_D_1KI': 42.0475, 'J_D_1KI': 42.0475}
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 fbd54c6..8f7419f 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": 12169, "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": 10.374937295913696, "TIME_S_1KI": 0.8525710654871967, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 840.8859812259674, "W": 64.58, "J_1KI": 69.10066408299511, "W_1KI": 5.306927438573425, "W_D": 29.637499999999996, "J_D": 385.90520700812334, "W_D_1KI": 2.4354918234859064, "J_D_1KI": 0.20013902732236885}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12077, "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": 10.322007656097412, "TIME_S_1KI": 0.8546830881922176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 838.5464090538024, "W": 64.47, "J_1KI": 69.43333684307382, "W_1KI": 5.3382462532085775, "W_D": 29.621750000000006, "J_D": 385.28326496648793, "W_D_1KI": 2.452740746874224, "J_D_1KI": 0.20309188928328428}
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 cc678f8..d689d8c 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', '1000', '-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": 0.8628060817718506}
+{"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": 0.8693974018096924}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([15542, 51530, 32014,  ..., 17183, 69417, 75150]),
-       values=tensor([0.6948, 0.1030, 0.8530,  ..., 0.6511, 0.2631, 0.7718]),
+       col_indices=tensor([57795, 90642, 37628,  ..., 28610,   559, 98027]),
+       values=tensor([0.1696, 0.5341, 0.5606,  ..., 0.7529, 0.5749, 0.6066]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3720, 0.8026, 0.8839,  ..., 0.1725, 0.9607, 0.0788])
+tensor([0.7238, 0.7083, 0.7900,  ..., 0.2093, 0.5825, 0.4482])
 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: 0.8628060817718506 seconds
+Time: 0.8693974018096924 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12169', '-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": 10.374937295913696}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12077', '-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": 10.322007656097412}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99998,  99998,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([38450, 44184, 11395,  ...,  3206,  2272, 42747]),
-       values=tensor([0.8156, 0.6388, 0.3060,  ..., 0.5932, 0.6977, 0.4008]),
+       col_indices=tensor([ 3486, 41765,  3206,  ..., 33238, 50080, 42417]),
+       values=tensor([0.6049, 0.2829, 0.2416,  ..., 0.9238, 0.5292, 0.5723]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7706, 0.5998, 0.9728,  ..., 0.9827, 0.6551, 0.5654])
+tensor([0.4597, 0.0749, 0.9185,  ..., 0.4582, 0.2319, 0.2322])
 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: 10.374937295913696 seconds
+Time: 10.322007656097412 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99998,  99998,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([38450, 44184, 11395,  ...,  3206,  2272, 42747]),
-       values=tensor([0.8156, 0.6388, 0.3060,  ..., 0.5932, 0.6977, 0.4008]),
+       col_indices=tensor([ 3486, 41765,  3206,  ..., 33238, 50080, 42417]),
+       values=tensor([0.6049, 0.2829, 0.2416,  ..., 0.9238, 0.5292, 0.5723]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7706, 0.5998, 0.9728,  ..., 0.9827, 0.6551, 0.5654])
+tensor([0.4597, 0.0749, 0.9185,  ..., 0.4582, 0.2319, 0.2322])
 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: 10.374937295913696 seconds
+Time: 10.322007656097412 seconds
 
-[39.65, 38.92, 38.96, 38.86, 38.63, 38.6, 38.47, 38.43, 38.43, 38.83]
-[64.58]
-13.0208420753479
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12169, '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': 10.374937295913696, 'TIME_S_1KI': 0.8525710654871967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.8859812259674, 'W': 64.58}
-[39.65, 38.92, 38.96, 38.86, 38.63, 38.6, 38.47, 38.43, 38.43, 38.83, 40.06, 39.01, 39.04, 38.86, 38.59, 38.53, 38.46, 38.45, 40.01, 38.66]
-698.85
-34.9425
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12169, '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': 10.374937295913696, 'TIME_S_1KI': 0.8525710654871967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.8859812259674, 'W': 64.58, 'J_1KI': 69.10066408299511, 'W_1KI': 5.306927438573425, 'W_D': 29.637499999999996, 'J_D': 385.90520700812334, 'W_D_1KI': 2.4354918234859064, 'J_D_1KI': 0.20013902732236885}
+[39.73, 38.57, 38.91, 38.66, 38.65, 38.36, 38.94, 39.73, 38.42, 38.47]
+[64.47]
+13.006769180297852
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12077, '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': 10.322007656097412, 'TIME_S_1KI': 0.8546830881922176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 838.5464090538024, 'W': 64.47}
+[39.73, 38.57, 38.91, 38.66, 38.65, 38.36, 38.94, 39.73, 38.42, 38.47, 39.26, 38.5, 38.85, 38.41, 38.98, 38.35, 38.75, 38.41, 38.37, 38.75]
+696.9649999999999
+34.84824999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12077, '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': 10.322007656097412, 'TIME_S_1KI': 0.8546830881922176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 838.5464090538024, 'W': 64.47, 'J_1KI': 69.43333684307382, 'W_1KI': 5.3382462532085775, 'W_D': 29.621750000000006, 'J_D': 385.28326496648793, 'W_D_1KI': 2.452740746874224, 'J_D_1KI': 0.20309188928328428}
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 3886b8d..9dc0eed 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": 237950, "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": 10.479950666427612, "TIME_S_1KI": 0.044042658820876705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.0966372108459, "W": 64.88, "J_1KI": 3.5599774625377005, "W_1KI": 0.2726623240176507, "W_D": 29.35949999999999, "J_D": 383.32820160591587, "W_D_1KI": 0.12338516495061984, "J_D_1KI": 0.0005185339985317077}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 238697, "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": 10.480466604232788, "TIME_S_1KI": 0.04390698921324017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1152.02081230402, "W": 58.89, "J_1KI": 4.826289447726699, "W_1KI": 0.24671445388924035, "W_D": 23.781499999999994, "J_D": 465.21961195123185, "W_D_1KI": 0.09963049388974303, "J_D_1KI": 0.0004173931548772839}
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 4b3b779..9b8fd23 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', '1000', '-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": 0.055043935775756836}
+{"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": 0.05536341667175293}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([2489, 8082, 1798,  ..., 7687, 8784, 7173]),
-       values=tensor([0.0419, 0.2217, 0.5372,  ..., 0.9380, 0.6037, 0.5878]),
+tensor(crow_indices=tensor([    0,     1,     3,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([8403, 1214, 9126,  ..., 1351, 3891, 9766]),
+       values=tensor([0.6664, 0.5402, 0.6356,  ..., 0.4443, 0.7393, 0.7343]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.6995, 0.5522, 0.6987,  ..., 0.2479, 0.0646, 0.0677])
+tensor([0.3881, 0.9820, 0.4323,  ..., 0.4549, 0.5025, 0.0926])
 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: 0.055043935775756836 seconds
+Time: 0.05536341667175293 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '190756', '-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": 8.417458295822144}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '189655', '-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": 8.342687606811523}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([6369, 8699, 1454,  ..., 3376, 4538, 4463]),
-       values=tensor([0.7752, 0.1565, 0.1050,  ..., 0.8742, 0.0228, 0.3625]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9997, 10000]),
+       col_indices=tensor([1328, 2584, 2989,  ..., 4729, 4835, 7640]),
+       values=tensor([0.4337, 0.1976, 0.1440,  ..., 0.2725, 0.2860, 0.2817]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2878, 0.2325, 0.9670,  ..., 0.8581, 0.8156, 0.4801])
+tensor([0.7295, 0.2766, 0.3418,  ..., 0.0114, 0.7550, 0.8307])
 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: 8.417458295822144 seconds
+Time: 8.342687606811523 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237950', '-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": 10.479950666427612}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '238697', '-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": 10.480466604232788}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  9998, 10000]),
-       col_indices=tensor([2568, 4888, 9428,  ..., 1921, 2148, 9872]),
-       values=tensor([0.1473, 0.4194, 0.4025,  ..., 0.4119, 0.3062, 0.3667]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9997, 10000]),
+       col_indices=tensor([9286, 7396,  732,  ..., 1484, 5299, 9027]),
+       values=tensor([0.3440, 0.6043, 0.5062,  ..., 0.2355, 0.1186, 0.4561]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9989, 0.1547, 0.2140,  ..., 0.5569, 0.3690, 0.8580])
+tensor([0.0996, 0.8226, 0.2068,  ..., 0.2572, 0.9962, 0.0083])
 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: 10.479950666427612 seconds
+Time: 10.480466604232788 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  9998, 10000]),
-       col_indices=tensor([2568, 4888, 9428,  ..., 1921, 2148, 9872]),
-       values=tensor([0.1473, 0.4194, 0.4025,  ..., 0.4119, 0.3062, 0.3667]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9997, 10000]),
+       col_indices=tensor([9286, 7396,  732,  ..., 1484, 5299, 9027]),
+       values=tensor([0.3440, 0.6043, 0.5062,  ..., 0.2355, 0.1186, 0.4561]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9989, 0.1547, 0.2140,  ..., 0.5569, 0.3690, 0.8580])
+tensor([0.0996, 0.8226, 0.2068,  ..., 0.2572, 0.9962, 0.0083])
 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: 10.479950666427612 seconds
+Time: 10.480466604232788 seconds
 
-[40.47, 39.33, 38.5, 38.74, 38.63, 38.5, 43.74, 38.79, 39.18, 38.53]
-[64.88]
-13.056360006332397
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237950, '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': 10.479950666427612, 'TIME_S_1KI': 0.044042658820876705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.0966372108459, 'W': 64.88}
-[40.47, 39.33, 38.5, 38.74, 38.63, 38.5, 43.74, 38.79, 39.18, 38.53, 39.54, 38.39, 38.83, 43.63, 38.88, 38.94, 39.01, 39.89, 38.82, 38.68]
-710.4100000000001
-35.520500000000006
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237950, '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': 10.479950666427612, 'TIME_S_1KI': 0.044042658820876705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.0966372108459, 'W': 64.88, 'J_1KI': 3.5599774625377005, 'W_1KI': 0.2726623240176507, 'W_D': 29.35949999999999, 'J_D': 383.32820160591587, 'W_D_1KI': 0.12338516495061984, 'J_D_1KI': 0.0005185339985317077}
+[40.64, 38.78, 39.02, 39.34, 38.69, 38.38, 38.42, 38.49, 38.47, 38.38]
+[58.89]
+19.562248468399048
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 238697, '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': 10.480466604232788, 'TIME_S_1KI': 0.04390698921324017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.02081230402, 'W': 58.89}
+[40.64, 38.78, 39.02, 39.34, 38.69, 38.38, 38.42, 38.49, 38.47, 38.38, 44.89, 39.96, 38.48, 39.7, 39.01, 38.35, 38.7, 38.38, 38.58, 38.93]
+702.1700000000001
+35.10850000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 238697, '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': 10.480466604232788, 'TIME_S_1KI': 0.04390698921324017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.02081230402, 'W': 58.89, 'J_1KI': 4.826289447726699, 'W_1KI': 0.24671445388924035, 'W_D': 23.781499999999994, 'J_D': 465.21961195123185, 'W_D_1KI': 0.09963049388974303, 'J_D_1KI': 0.0004173931548772839}
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 4c5c61e..80c52ca 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": 75505, "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": 11.45979928970337, "TIME_S_1KI": 0.15177536970668656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 869.2745451879501, "W": 66.09, "J_1KI": 11.512807697343886, "W_1KI": 0.8753062711078737, "W_D": 31.095250000000007, "J_D": 408.9924239863158, "W_D_1KI": 0.4118303423614331, "J_D_1KI": 0.00545434530642253}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75618, "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": 10.541181087493896, "TIME_S_1KI": 0.13940042169184447, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.5316159009933, "W": 66.47, "J_1KI": 12.437932977611062, "W_1KI": 0.8790235129202042, "W_D": 31.600500000000004, "J_D": 447.1380973112584, "W_D_1KI": 0.41789653257160997, "J_D_1KI": 0.005526416098965987}
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 afe840d..9b49c17 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', '1000', '-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": 0.15292811393737793}
+{"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": 0.16539597511291504}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99983,  99994,
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99980,  99988,
                             100000]),
-       col_indices=tensor([ 267, 3923, 5616,  ..., 4271, 7755, 9973]),
-       values=tensor([0.9283, 0.7846, 0.2151,  ..., 0.9447, 0.6120, 0.1119]),
+       col_indices=tensor([2312, 2519, 3298,  ..., 9035, 9400, 9910]),
+       values=tensor([0.1410, 0.2218, 0.1849,  ..., 0.4652, 0.0649, 0.3640]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9192, 0.5849, 0.9579,  ..., 0.9586, 0.7879, 0.6201])
+tensor([0.4363, 0.0084, 0.9005,  ..., 0.6999, 0.4782, 0.9424])
 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: 0.15292811393737793 seconds
+Time: 0.16539597511291504 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68659', '-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": 9.547868490219116}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63484', '-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": 8.8150315284729}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     21,  ...,  99972,  99985,
+tensor(crow_indices=tensor([     0,     11,     21,  ...,  99981,  99989,
                             100000]),
-       col_indices=tensor([ 305,  380,  962,  ..., 8769, 9180, 9915]),
-       values=tensor([0.5782, 0.8638, 0.8069,  ..., 0.1223, 0.7033, 0.9891]),
+       col_indices=tensor([ 457, 1232, 2417,  ..., 8600, 9856, 9966]),
+       values=tensor([0.5653, 0.7705, 0.0640,  ..., 0.9989, 0.3761, 0.2052]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4135, 0.4566, 0.8532,  ..., 0.7837, 0.5944, 0.7679])
+tensor([0.7731, 0.4840, 0.8355,  ..., 0.4086, 0.2552, 0.3939])
 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: 9.547868490219116 seconds
+Time: 8.8150315284729 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75505', '-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": 11.45979928970337}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75618', '-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": 10.541181087493896}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     21,  ...,  99982,  99992,
+tensor(crow_indices=tensor([     0,      8,     15,  ...,  99981,  99989,
                             100000]),
-       col_indices=tensor([1022, 1138, 1407,  ..., 6223, 7233, 9402]),
-       values=tensor([0.9484, 0.5958, 0.7782,  ..., 0.0863, 0.6723, 0.0562]),
+       col_indices=tensor([ 812, 4021, 6538,  ..., 8729, 9196, 9676]),
+       values=tensor([0.8795, 0.6481, 0.9606,  ..., 0.0277, 0.7911, 0.3727]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0389, 0.1147, 0.5260,  ..., 0.1033, 0.1694, 0.2810])
+tensor([0.1908, 0.4704, 0.2059,  ..., 0.1529, 0.3275, 0.9276])
 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: 11.45979928970337 seconds
+Time: 10.541181087493896 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     21,  ...,  99982,  99992,
+tensor(crow_indices=tensor([     0,      8,     15,  ...,  99981,  99989,
                             100000]),
-       col_indices=tensor([1022, 1138, 1407,  ..., 6223, 7233, 9402]),
-       values=tensor([0.9484, 0.5958, 0.7782,  ..., 0.0863, 0.6723, 0.0562]),
+       col_indices=tensor([ 812, 4021, 6538,  ..., 8729, 9196, 9676]),
+       values=tensor([0.8795, 0.6481, 0.9606,  ..., 0.0277, 0.7911, 0.3727]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0389, 0.1147, 0.5260,  ..., 0.1033, 0.1694, 0.2810])
+tensor([0.1908, 0.4704, 0.2059,  ..., 0.1529, 0.3275, 0.9276])
 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: 11.45979928970337 seconds
+Time: 10.541181087493896 seconds
 
-[39.42, 38.37, 38.5, 39.45, 39.07, 38.36, 39.56, 38.91, 39.08, 38.45]
-[66.09]
-13.152890682220459
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75505, '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': 11.45979928970337, 'TIME_S_1KI': 0.15177536970668656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 869.2745451879501, 'W': 66.09}
-[39.42, 38.37, 38.5, 39.45, 39.07, 38.36, 39.56, 38.91, 39.08, 38.45, 39.1, 38.46, 38.46, 39.62, 38.45, 39.31, 38.8, 38.73, 38.89, 38.78]
-699.895
-34.994749999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75505, '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': 11.45979928970337, 'TIME_S_1KI': 0.15177536970668656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 869.2745451879501, 'W': 66.09, 'J_1KI': 11.512807697343886, 'W_1KI': 0.8753062711078737, 'W_D': 31.095250000000007, 'J_D': 408.9924239863158, 'W_D_1KI': 0.4118303423614331, 'J_D_1KI': 0.00545434530642253}
+[38.98, 38.52, 38.36, 38.43, 38.45, 38.57, 38.52, 38.55, 38.55, 38.4]
+[66.47]
+14.149715900421143
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75618, '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': 10.541181087493896, 'TIME_S_1KI': 0.13940042169184447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.5316159009933, 'W': 66.47}
+[38.98, 38.52, 38.36, 38.43, 38.45, 38.57, 38.52, 38.55, 38.55, 38.4, 39.0, 38.48, 38.51, 39.07, 38.73, 38.62, 38.94, 38.66, 38.36, 43.76]
+697.3899999999999
+34.869499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75618, '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': 10.541181087493896, 'TIME_S_1KI': 0.13940042169184447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.5316159009933, 'W': 66.47, 'J_1KI': 12.437932977611062, 'W_1KI': 0.8790235129202042, 'W_D': 31.600500000000004, 'J_D': 447.1380973112584, 'W_D_1KI': 0.41789653257160997, 'J_D_1KI': 0.005526416098965987}
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 4467311..e580351 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": 10051, "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": 10.396085023880005, "TIME_S_1KI": 1.0343334020376087, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 865.9663576483727, "W": 66.05, "J_1KI": 86.1572338720896, "W_1KI": 6.571485424335887, "W_D": 30.772, "J_D": 403.44461404323573, "W_D_1KI": 3.061585911849567, "J_D_1KI": 0.3046051051486983}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10094, "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": 10.424894571304321, "TIME_S_1KI": 1.0327813127902044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 874.2127872061731, "W": 66.43, "J_1KI": 86.60717131030047, "W_1KI": 6.581137309292649, "W_D": 31.351250000000007, "J_D": 412.5796122971178, "W_D_1KI": 3.1059292649098484, "J_D_1KI": 0.3077005414018078}
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 ec9215b..c679aff 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', '1000', '-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": 1.0446221828460693}
+{"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": 1.040170669555664}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      93,     186,  ...,  999791,
-                             999885, 1000000]),
-       col_indices=tensor([  85,  646,  706,  ..., 9852, 9875, 9886]),
-       values=tensor([0.7433, 0.1282, 0.1316,  ..., 0.9681, 0.9495, 0.6187]),
+tensor(crow_indices=tensor([      0,      84,     184,  ...,  999814,
+                             999899, 1000000]),
+       col_indices=tensor([ 171,  251,  472,  ..., 9843, 9880, 9941]),
+       values=tensor([0.4805, 0.3615, 0.2747,  ..., 0.6607, 0.4074, 0.0301]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5006, 0.3207, 0.7634,  ..., 0.1693, 0.2023, 0.9705])
+tensor([0.4780, 0.2256, 0.5818,  ..., 0.3209, 0.4621, 0.5747])
 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: 1.0446221828460693 seconds
+Time: 1.040170669555664 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10051', '-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": 10.396085023880005}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10094', '-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": 10.424894571304321}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     194,  ...,  999830,
-                             999924, 1000000]),
-       col_indices=tensor([ 207,  248,  391,  ..., 9735, 9842, 9886]),
-       values=tensor([0.2382, 0.1304, 0.8275,  ..., 0.9132, 0.3101, 0.1677]),
+tensor(crow_indices=tensor([      0,     106,     205,  ...,  999816,
+                             999911, 1000000]),
+       col_indices=tensor([  83,   89,  669,  ..., 9640, 9974, 9983]),
+       values=tensor([0.6432, 0.8453, 0.7190,  ..., 0.8302, 0.0770, 0.7390]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0491, 0.4304, 0.0195,  ..., 0.4012, 0.5324, 0.0059])
+tensor([0.4670, 0.3263, 0.5346,  ..., 0.9779, 0.3626, 0.9957])
 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: 10.396085023880005 seconds
+Time: 10.424894571304321 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     194,  ...,  999830,
-                             999924, 1000000]),
-       col_indices=tensor([ 207,  248,  391,  ..., 9735, 9842, 9886]),
-       values=tensor([0.2382, 0.1304, 0.8275,  ..., 0.9132, 0.3101, 0.1677]),
+tensor(crow_indices=tensor([      0,     106,     205,  ...,  999816,
+                             999911, 1000000]),
+       col_indices=tensor([  83,   89,  669,  ..., 9640, 9974, 9983]),
+       values=tensor([0.6432, 0.8453, 0.7190,  ..., 0.8302, 0.0770, 0.7390]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0491, 0.4304, 0.0195,  ..., 0.4012, 0.5324, 0.0059])
+tensor([0.4670, 0.3263, 0.5346,  ..., 0.9779, 0.3626, 0.9957])
 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: 10.396085023880005 seconds
+Time: 10.424894571304321 seconds
 
-[40.88, 38.45, 38.83, 38.72, 44.02, 38.37, 38.57, 38.7, 39.57, 38.48]
-[66.05]
-13.110769987106323
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10051, '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': 10.396085023880005, 'TIME_S_1KI': 1.0343334020376087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.9663576483727, 'W': 66.05}
-[40.88, 38.45, 38.83, 38.72, 44.02, 38.37, 38.57, 38.7, 39.57, 38.48, 42.6, 39.8, 38.39, 38.73, 39.03, 38.72, 38.63, 38.42, 38.48, 38.3]
-705.56
-35.278
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10051, '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': 10.396085023880005, 'TIME_S_1KI': 1.0343334020376087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.9663576483727, 'W': 66.05, 'J_1KI': 86.1572338720896, 'W_1KI': 6.571485424335887, 'W_D': 30.772, 'J_D': 403.44461404323573, 'W_D_1KI': 3.061585911849567, 'J_D_1KI': 0.3046051051486983}
+[39.35, 40.11, 43.57, 38.24, 39.46, 38.59, 38.37, 38.38, 38.28, 38.71]
+[66.43]
+13.15990948677063
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10094, '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': 10.424894571304321, 'TIME_S_1KI': 1.0327813127902044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.2127872061731, 'W': 66.43}
+[39.35, 40.11, 43.57, 38.24, 39.46, 38.59, 38.37, 38.38, 38.28, 38.71, 40.11, 38.48, 38.37, 38.48, 38.37, 38.22, 38.39, 39.48, 38.33, 38.74]
+701.575
+35.07875
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10094, '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': 10.424894571304321, 'TIME_S_1KI': 1.0327813127902044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.2127872061731, 'W': 66.43, 'J_1KI': 86.60717131030047, 'W_1KI': 6.581137309292649, 'W_D': 31.351250000000007, 'J_D': 412.5796122971178, 'W_D_1KI': 3.1059292649098484, 'J_D_1KI': 0.3077005414018078}
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 9a92bbf..73d3b47 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": 1760, "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": 10.496549844741821, "TIME_S_1KI": 5.963948775421489, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1063.6957297325134, "W": 75.3, "J_1KI": 604.3725737116553, "W_1KI": 42.784090909090914, "W_D": 40.305749999999996, "J_D": 569.3632690393924, "W_D_1KI": 22.900994318181816, "J_D_1KI": 13.011928589876032}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1758, "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": 10.496397256851196, "TIME_S_1KI": 5.970646903783388, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1131.7024735164641, "W": 74.58, "J_1KI": 643.7442966532789, "W_1KI": 42.42320819112628, "W_D": 39.617, "J_D": 601.1619320635796, "W_D_1KI": 22.535267349260522, "J_D_1KI": 12.818695875574814}
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 f82f04e..78fc6b7 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', '1000', '-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": 5.962578296661377}
+{"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": 5.972491502761841}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     983,  ..., 4998985,
-                            4999482, 5000000]),
-       col_indices=tensor([   6,   14,   63,  ..., 9975, 9976, 9988]),
-       values=tensor([0.1343, 0.9147, 0.2964,  ..., 0.8307, 0.6480, 0.1778]),
+tensor(crow_indices=tensor([      0,     519,     993,  ..., 4998959,
+                            4999496, 5000000]),
+       col_indices=tensor([  17,   61,   73,  ..., 9901, 9911, 9920]),
+       values=tensor([0.3098, 0.8299, 0.3979,  ..., 0.3415, 0.7398, 0.5378]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4820, 0.9526, 0.2470,  ..., 0.0414, 0.1724, 0.7388])
+tensor([0.6888, 0.9764, 0.3608,  ..., 0.4208, 0.9222, 0.1586])
 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: 5.962578296661377 seconds
+Time: 5.972491502761841 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1760', '-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": 10.496549844741821}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1758', '-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": 10.496397256851196}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     494,    1024,  ..., 4999026,
-                            4999505, 5000000]),
-       col_indices=tensor([  14,   81,  111,  ..., 9976, 9994, 9996]),
-       values=tensor([0.8750, 0.2097, 0.6973,  ..., 0.7142, 0.2835, 0.0523]),
+tensor(crow_indices=tensor([      0,     496,     998,  ..., 4999016,
+                            4999498, 5000000]),
+       col_indices=tensor([  25,   29,   69,  ..., 9894, 9911, 9997]),
+       values=tensor([0.8031, 0.3187, 0.9076,  ..., 0.2949, 0.8412, 0.6618]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6644, 0.2536, 0.5514,  ..., 0.5924, 0.6712, 0.0391])
+tensor([0.4316, 0.0196, 0.7556,  ..., 0.1123, 0.7172, 0.6330])
 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: 10.496549844741821 seconds
+Time: 10.496397256851196 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     494,    1024,  ..., 4999026,
-                            4999505, 5000000]),
-       col_indices=tensor([  14,   81,  111,  ..., 9976, 9994, 9996]),
-       values=tensor([0.8750, 0.2097, 0.6973,  ..., 0.7142, 0.2835, 0.0523]),
+tensor(crow_indices=tensor([      0,     496,     998,  ..., 4999016,
+                            4999498, 5000000]),
+       col_indices=tensor([  25,   29,   69,  ..., 9894, 9911, 9997]),
+       values=tensor([0.8031, 0.3187, 0.9076,  ..., 0.2949, 0.8412, 0.6618]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6644, 0.2536, 0.5514,  ..., 0.5924, 0.6712, 0.0391])
+tensor([0.4316, 0.0196, 0.7556,  ..., 0.1123, 0.7172, 0.6330])
 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: 10.496549844741821 seconds
+Time: 10.496397256851196 seconds
 
-[39.19, 39.0, 39.0, 38.91, 38.55, 38.97, 38.49, 38.52, 38.9, 38.82]
-[75.3]
-14.126105308532715
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1760, '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': 10.496549844741821, 'TIME_S_1KI': 5.963948775421489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1063.6957297325134, 'W': 75.3}
-[39.19, 39.0, 39.0, 38.91, 38.55, 38.97, 38.49, 38.52, 38.9, 38.82, 39.55, 38.59, 39.72, 38.89, 38.53, 38.82, 38.62, 39.33, 38.84, 38.85]
-699.885
-34.99425
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1760, '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': 10.496549844741821, 'TIME_S_1KI': 5.963948775421489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1063.6957297325134, 'W': 75.3, 'J_1KI': 604.3725737116553, 'W_1KI': 42.784090909090914, 'W_D': 40.305749999999996, 'J_D': 569.3632690393924, 'W_D_1KI': 22.900994318181816, 'J_D_1KI': 13.011928589876032}
+[39.62, 38.84, 38.53, 38.4, 38.53, 38.95, 38.67, 38.93, 38.95, 38.34]
+[74.58]
+15.174342632293701
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1758, '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': 10.496397256851196, 'TIME_S_1KI': 5.970646903783388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.7024735164641, 'W': 74.58}
+[39.62, 38.84, 38.53, 38.4, 38.53, 38.95, 38.67, 38.93, 38.95, 38.34, 39.14, 38.64, 38.84, 38.7, 38.86, 38.26, 38.63, 38.26, 38.4, 44.64]
+699.26
+34.963
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1758, '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': 10.496397256851196, 'TIME_S_1KI': 5.970646903783388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.7024735164641, 'W': 74.58, 'J_1KI': 643.7442966532789, 'W_1KI': 42.42320819112628, 'W_D': 39.617, 'J_D': 601.1619320635796, 'W_D_1KI': 22.535267349260522, 'J_D_1KI': 12.818695875574814}
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
new file mode 100644
index 0000000..6bba238
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "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": 14.862756252288818, "TIME_S_1KI": 14.862756252288818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1509.8221988201142, "W": 77.42, "J_1KI": 1509.8221988201142, "W_1KI": 77.42, "W_D": 42.230500000000006, "J_D": 823.5668608534337, "W_D_1KI": 42.230500000000006, "J_D_1KI": 42.230500000000006}
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
new file mode 100644
index 0000000..550e30b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_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-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 14.862756252288818}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2054,  ...,  9998064,
+                             9999016, 10000000]),
+       col_indices=tensor([   8,   12,   13,  ..., 9969, 9975, 9983]),
+       values=tensor([0.6048, 0.0895, 0.3093,  ..., 0.2729, 0.9589, 0.2791]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8686, 0.6857, 0.7903,  ..., 0.7591, 0.3670, 0.6215])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 14.862756252288818 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2054,  ...,  9998064,
+                             9999016, 10000000]),
+       col_indices=tensor([   8,   12,   13,  ..., 9969, 9975, 9983]),
+       values=tensor([0.6048, 0.0895, 0.3093,  ..., 0.2729, 0.9589, 0.2791]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8686, 0.6857, 0.7903,  ..., 0.7591, 0.3670, 0.6215])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 14.862756252288818 seconds
+
+[44.78, 38.35, 39.08, 38.71, 38.47, 39.2, 39.52, 39.83, 39.49, 40.27]
+[77.42]
+19.501707553863525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, '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': 14.862756252288818, 'TIME_S_1KI': 14.862756252288818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.8221988201142, 'W': 77.42}
+[44.78, 38.35, 39.08, 38.71, 38.47, 39.2, 39.52, 39.83, 39.49, 40.27, 40.17, 38.57, 38.51, 38.69, 38.5, 38.97, 38.48, 38.73, 38.88, 38.4]
+703.79
+35.189499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, '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': 14.862756252288818, 'TIME_S_1KI': 14.862756252288818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.8221988201142, 'W': 77.42, 'J_1KI': 1509.8221988201142, 'W_1KI': 77.42, 'W_D': 42.230500000000006, 'J_D': 823.5668608534337, 'W_D_1KI': 42.230500000000006, 'J_D_1KI': 42.230500000000006}
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 5067ef9..721cd64 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": 363782, "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": 10.585279941558838, "TIME_S_1KI": 0.029097866143896176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3318356752395, "W": 64.69, "J_1KI": 2.3512208841428097, "W_1KI": 0.17782628057462985, "W_D": 29.81474999999999, "J_D": 394.2109266918896, "W_D_1KI": 0.08195773842576046, "J_D_1KI": 0.00022529355060382441}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 361507, "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": 10.549095392227173, "TIME_S_1KI": 0.029180888315377497, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.0974852204322, "W": 65.27, "J_1KI": 2.3847324815852313, "W_1KI": 0.18054975422329303, "W_D": 30.177249999999994, "J_D": 398.58635415762654, "W_D_1KI": 0.08347625357185336, "J_D_1KI": 0.0002309118594435332}
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 198dd99..4ba6f6a 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
@@ -1,373 +1,266 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.08438587188720703}
+{"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": 0.043045759201049805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 654, 6772, 3481, 2535,  125, 3792, 8070, 9757, 6184,
-                           2668,   22, 1611, 8038, 5477, 9185, 5993, 9592, 3939,
-                           8639, 7553,  398, 5715, 4399, 2570, 9973, 3035, 1537,
-                           7004, 5611, 9891, 2622, 9818,  312, 6105, 1848, 1339,
-                           7382, 4214, 8955, 9892, 6474, 3909, 9732,  690, 3371,
-                           4869,  387, 3460, 9149, 5467, 6478, 5618, 6583, 2381,
-                           1542, 8342, 3787, 7463, 3823, 6427,  315, 6985,  523,
-                           5901, 9665, 9643, 5095, 3067, 2951, 9816, 6719, 6640,
-                           4349, 9622, 9227,  394, 8600, 2210, 9007, 6794, 6193,
-                           3591, 3763, 8848,  712, 2600, 6953,  345, 8176, 4284,
-                           2762, 1429, 3052, 9077, 9247, 8084, 9368, 8295, 4882,
-                           1029, 4128, 2221, 4457,  136, 1060, 5650, 2149,  979,
-                            879,  252, 4258, 4991, 6954, 9684, 5762, 3304, 4194,
-                           5738, 4881, 2067, 4630, 3102, 4373, 4364, 3467, 3904,
-                           2703, 9367, 5744, 1003, 7348, 7382, 2923, 6743, 2716,
-                           6005, 8885, 8781, 4688, 3208, 8165, 7655, 2105, 9594,
-                            413, 1022, 9740, 9415, 4440, 7624, 1850, 8157, 8564,
-                           2131, 4847, 5156,  223, 2070, 8741, 4432, 2158, 1202,
-                           3074, 6959, 1256, 8497, 7709, 1898, 7099, 7257, 3557,
-                           1556, 6637, 1724, 6284, 8045, 1698, 1347, 5955, 3324,
-                           2660, 5510, 9500,  461, 4931, 3894, 7627, 3132, 4579,
-                           5941, 9535, 3710, 3467, 3922,   66, 8349, 6253, 7489,
-                           3867, 9837, 4357, 5889, 1429, 2174, 5793, 5103, 9209,
-                           7044, 5165, 1550, 2922, 7404,  724, 8019, 1723, 9892,
-                           8178,  326, 8495, 1261, 5156, 3647,   48, 9679, 8317,
-                            258, 3999, 7172, 9917, 1368, 9356, 8091, 2920, 4194,
-                           9220,  533, 1856, 4184, 9970,  585,  888, 7980,  502,
-                           3237, 7414, 5484, 1198, 5107, 6730, 1701,  778, 9576,
-                           9847, 7139, 5046, 9505, 4273, 3181, 3701, 7837, 6438,
-                           5446, 5489, 3750, 7620, 3013, 2520, 4866, 2256, 1226,
-                           4389,  803, 6623,  207, 8091, 9177, 5502, 7927, 5893,
-                           7286, 4995, 7523, 7718,  638, 1691, 5859, 7604, 7499,
-                           5376, 8321,  497, 2691, 7462, 1832, 7626, 4908, 8743,
-                           2814, 8920, 2170, 2237, 5900, 1192, 1906, 5324, 8567,
-                            433, 7307, 4785, 7781, 8369, 5137, 3139, 6244, 3003,
-                           5146, 5779,  963, 5834, 8307, 1509, 6851, 2879, 9153,
-                           5590, 2199, 7088, 4851, 1689, 3978, 7052, 9728, 7823,
-                           6554, 7099, 3919, 9251, 3170, 4952, 3743,  753, 4966,
-                           8769, 5930, 1536, 3883, 3398, 3809, 4230, 4021, 4625,
-                            911, 3578, 6189,  791, 2646, 9155, 6564, 5024, 7172,
-                           6929, 9364, 1592, 9019, 2813, 8778, 1820,  106, 4324,
-                           1259, 4349, 3192, 4715, 9115,  966, 2560, 8066, 1744,
-                           3149, 6003, 1498, 3413, 7870, 8393, 6128, 5648, 3764,
-                           4120,   43, 1077, 5168, 6447,  193, 4043, 7134, 3629,
-                           5140, 5639, 2285, 6521, 4820, 2512, 2929, 3156, 8218,
-                           1304, 6623, 4398, 4748,  475, 2397, 4221, 1663, 9429,
-                           2056, 5484, 6397,  578, 8710, 4074, 3345, 5055, 8296,
-                           3426, 9590, 5354, 2960, 4081, 7610, 9414, 6545, 7162,
-                           2284, 3632, 5177, 9745,   61, 2148, 4065, 1241, 1790,
-                           2890,  291, 8273, 8068, 1391, 1258, 2802,  698, 6995,
-                           5305, 5523, 4749, 4449, 5337, 2346,  555, 1299, 4206,
-                           1176, 3831, 8077, 1268, 5459, 4259, 5701, 3655, 9166,
-                           7235, 9918, 5806, 8346, 4150, 3003, 3079, 6459, 1439,
-                            629, 2696, 6049, 7779, 4964, 9219, 6464, 1735, 1160,
-                           3784, 6373,  384, 1808, 5241, 8372, 1677, 4788, 4963,
-                           9409, 1508, 9724, 8760, 7710,  589, 3951,  700, 1849,
-                           1678,  543, 3229, 9558, 3592, 1739, 3704,  323, 6030,
-                           5632, 5396, 5373, 2268, 8829,  915, 2928, 6822, 4320,
-                           9393, 3081, 6996,  900, 1244, 9959, 6457, 3034, 4242,
-                           8879, 6148, 3658, 2641, 9655, 1273, 2175, 1409, 2889,
-                           4549, 2132, 2423, 3494, 1302, 6844, 8913, 6052, 6682,
-                           4676, 5897, 8113, 7646, 9008,  815, 8636, 2168, 8534,
-                           9398, 7841, 9924, 5887, 3514, 8309, 8574, 9316, 1868,
-                           8906, 4664, 9990,   98, 7132,  708, 8628, 2818,  642,
-                           3643,  213,  310, 8853, 5074, 7350, 4260, 4345, 2685,
-                           6155, 1114, 3294, 3439, 9572,  673, 6218, 2900, 8049,
-                           3443, 3297, 3657, 8180, 8304, 4095, 9553, 4268, 1084,
-                           5217, 7168, 3140, 2957, 3846, 8019, 7828,  163, 1006,
-                           9946, 9671, 9272, 3197, 4715, 2133, 6480, 2603, 3711,
-                           3059, 7614, 7252, 8503, 5168, 3017, 8178, 6997, 7469,
-                           6817, 2311, 2400, 2604, 5422, 4020, 1626, 3307, 5947,
-                           5172, 1638, 3688,   98, 1611, 1810, 3312, 7402, 2064,
-                           7049, 8774, 3516, 1326, 7757, 8495, 5985, 6522, 6285,
-                            926, 4463, 4966,  488, 3014, 7145, 2639, 4866, 7635,
-                           9111,  404, 6974,  425,  470, 1597, 9938, 8633, 1112,
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-                           9921, 3502, 5025,  968, 6760, 6708, 7717, 9632, 2776,
-                           8251, 2002, 6046, 5073, 6290, 4377, 8849, 6636, 2619,
-                           7154, 9845, 1431, 4845, 3786,  826, 5185, 5466, 9894,
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-                           3957, 3075, 9983, 7984, 3352, 4559, 4032, 6062, 2428,
-                           9519, 4774, 5203, 4905, 2366, 6001, 4187, 2803, 6224,
-                           8937,  871,  887,  753, 6766, 7729, 6343, 5371, 7478,
-                           4377, 2616,  562, 5625, 2629, 6739, 1582, 3555, 3157,
-                           6267, 2853, 1658, 4524,   81, 9456, 3168, 7244, 1072,
-                           7992, 7229, 3997, 2559, 7595, 3257,  822, 3803, 9044,
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-                           9018, 4356, 4519, 9343, 3566, 2087, 9695, 1990,  982,
-                           2457, 7819, 9049, 8393, 7518, 4215, 5670,  386, 7510,
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-                           5849, 7627, 2767, 9267, 1855, 5384, 4327, 4418, 1591,
-                           1122, 4195, 5132, 5368, 1200, 7062, 7024, 6158,  423,
-                           5711, 1019, 1328, 9184, 2842, 6447, 7948, 7654, 5641,
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-                           1604, 5981, 8914,  190, 1062, 5126, 1309, 3331,  553,
-                           4330, 7219, 4988, 2671, 2168, 5519, 3181, 9637, 5051,
-                           8302, 2204, 7909, 6777, 1005, 6145, 5110, 6838, 9076,
-                            177,  899, 6196, 6318, 1764, 1175, 4772, 8191, 8417,
-                           6357, 9011,  697, 8083, 6982, 8213, 8974, 7616,   72,
-                           1182, 2829, 5015, 8973, 9205,  383, 6201, 1455, 2354,
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4435, 0.0773, 0.0249,  ..., 0.0070, 0.7023, 0.2387])
+tensor([0.8647, 0.0597, 0.7485,  ..., 0.2087, 0.0137, 0.4402])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,757 +268,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.08438587188720703 seconds
+Time: 0.043045759201049805 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '124428', '-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": 3.5914108753204346}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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-                      4.2317e-01, 1.4961e-01, 4.3533e-01, 9.4417e-01]),
-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.2136, 0.3814, 0.1034,  ..., 0.1098, 0.3191, 0.2700])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 3.5914108753204346 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '363782', '-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": 10.585279941558838}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '243926', '-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": 7.084842920303345}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([7943, 4488, 6154, 9391,  155, 1146, 4006, 8134, 8781,
-                           7101, 3276, 7191, 9320, 2859, 4578, 2750, 6596, 4201,
-                           2541, 6640, 9961, 1286, 5879, 9740, 3536, 2151, 9582,
-                           2021, 7827, 8693, 2313,  555, 3377, 7363,  334, 2888,
-                           9782, 3162, 5677, 4519, 3889, 4828,  247, 2616,  279,
-                           8565, 2538, 9525, 8485, 2616, 1166, 2089, 7055, 6468,
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3920, 0.2913, 0.8672,  ..., 0.9245, 0.8812, 0.1957])
+tensor([0.8197, 0.8953, 0.9337,  ..., 0.6014, 0.8565, 0.4467])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +540,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.585279941558838 seconds
+Time: 7.084842920303345 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '361507', '-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": 10.549095392227173}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      5.9301e-01, 8.8253e-01, 8.2784e-01, 4.4139e-01,
+                      6.6233e-01, 5.8030e-01, 7.4490e-01, 9.5820e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3920, 0.2913, 0.8672,  ..., 0.9245, 0.8812, 0.1957])
+tensor([0.1332, 0.3872, 0.9921,  ..., 0.6563, 0.0596, 0.5136])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +919,389 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.585279941558838 seconds
+Time: 10.549095392227173 seconds
 
-[39.18, 40.08, 38.95, 38.39, 39.05, 38.86, 38.48, 38.43, 38.49, 38.42]
-[64.69]
-13.222010135650635
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363782, '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': 10.585279941558838, 'TIME_S_1KI': 0.029097866143896176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3318356752395, 'W': 64.69}
-[39.18, 40.08, 38.95, 38.39, 39.05, 38.86, 38.48, 38.43, 38.49, 38.42, 40.2, 38.44, 38.35, 38.82, 38.9, 38.53, 38.83, 38.37, 38.44, 38.39]
-697.5050000000001
-34.87525000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363782, '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': 10.585279941558838, 'TIME_S_1KI': 0.029097866143896176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3318356752395, 'W': 64.69, 'J_1KI': 2.3512208841428097, 'W_1KI': 0.17782628057462985, 'W_D': 29.81474999999999, 'J_D': 394.2109266918896, 'W_D_1KI': 0.08195773842576046, 'J_D_1KI': 0.00022529355060382441}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  999, 1000]),
+       col_indices=tensor([3175, 1540, 6513, 4566, 9706, 3242, 7522,  361, 3563,
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+                      1.2616e-01, 9.6273e-01, 2.5433e-01, 7.5316e-01,
+                      9.7344e-01, 2.8688e-01, 7.9705e-01, 3.3331e-01,
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+                      3.2711e-02, 8.9001e-01, 1.9633e-02, 3.7308e-01,
+                      5.9301e-01, 8.8253e-01, 8.2784e-01, 4.4139e-01,
+                      6.6233e-01, 5.8030e-01, 7.4490e-01, 9.5820e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1332, 0.3872, 0.9921,  ..., 0.6563, 0.0596, 0.5136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.549095392227173 seconds
+
+[45.65, 38.89, 39.88, 38.76, 38.37, 38.3, 38.7, 38.8, 39.08, 38.56]
+[65.27]
+13.208173513412476
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 361507, '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': 10.549095392227173, 'TIME_S_1KI': 0.029180888315377497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.0974852204322, 'W': 65.27}
+[45.65, 38.89, 39.88, 38.76, 38.37, 38.3, 38.7, 38.8, 39.08, 38.56, 39.02, 38.54, 38.45, 38.34, 38.8, 39.14, 38.83, 39.15, 38.35, 39.72]
+701.855
+35.09275
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 361507, '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': 10.549095392227173, 'TIME_S_1KI': 0.029180888315377497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.0974852204322, 'W': 65.27, 'J_1KI': 2.3847324815852313, 'W_1KI': 0.18054975422329303, 'W_D': 30.177249999999994, 'J_D': 398.58635415762654, 'W_D_1KI': 0.08347625357185336, 'J_D_1KI': 0.0002309118594435332}
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 f3fd74b..98e0f55 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": 1366, "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": 10.481968879699707, "TIME_S_1KI": 7.673476485870942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 980.9324438238144, "W": 72.78, "J_1KI": 718.1057421843444, "W_1KI": 53.2796486090776, "W_D": 37.9805, "J_D": 511.9030596681833, "W_D_1KI": 27.804172767203514, "J_D_1KI": 20.35444565681077}
+{"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": 10.362020254135132, "TIME_S_1KI": 7.635976605847555, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.3382936573029, "W": 74.92, "J_1KI": 734.9582119803264, "W_1KI": 55.21002210759028, "W_D": 39.366, "J_D": 524.0419016032218, "W_D_1KI": 29.00957995578482, "J_D_1KI": 21.377730254815635}
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 170f45c..f772ea4 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', '1000', '-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": 7.686005115509033}
+{"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": 7.737008094787598}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499986,
-                            2499989, 2500000]),
-       col_indices=tensor([176994, 249617, 373837,  ..., 283997, 343168,
-                           447931]),
-       values=tensor([0.4576, 0.5348, 0.2572,  ..., 0.1314, 0.2229, 0.5974]),
+tensor(crow_indices=tensor([      0,       6,       9,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 13538,  14404, 124427,  ..., 299545,  64656,
+                           263709]),
+       values=tensor([0.6726, 0.7704, 0.5503,  ..., 0.8434, 0.2560, 0.2989]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1382, 0.9782, 0.8741,  ..., 0.2337, 0.6569, 0.8329])
+tensor([0.7902, 0.8995, 0.9133,  ..., 0.8775, 0.6765, 0.9460])
 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: 7.686005115509033 seconds
+Time: 7.737008094787598 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1366', '-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": 10.481968879699707}
+['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": 10.362020254135132}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499988,
+tensor(crow_indices=tensor([      0,       7,      12,  ..., 2499987,
                             2499995, 2500000]),
-       col_indices=tensor([ 13301,  29016, 299078,  ..., 480591, 481476,
-                           496604]),
-       values=tensor([0.4578, 0.5414, 0.1917,  ..., 0.8449, 0.5002, 0.9459]),
+       col_indices=tensor([ 74385, 156503, 312661,  ..., 102229, 341067,
+                           464580]),
+       values=tensor([0.2383, 0.0369, 0.7603,  ..., 0.0658, 0.9688, 0.3918]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0252, 0.3938, 0.2908,  ..., 0.4459, 0.5549, 0.8752])
+tensor([0.4224, 0.2766, 0.2547,  ..., 0.2726, 0.8333, 0.3690])
 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: 10.481968879699707 seconds
+Time: 10.362020254135132 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499988,
+tensor(crow_indices=tensor([      0,       7,      12,  ..., 2499987,
                             2499995, 2500000]),
-       col_indices=tensor([ 13301,  29016, 299078,  ..., 480591, 481476,
-                           496604]),
-       values=tensor([0.4578, 0.5414, 0.1917,  ..., 0.8449, 0.5002, 0.9459]),
+       col_indices=tensor([ 74385, 156503, 312661,  ..., 102229, 341067,
+                           464580]),
+       values=tensor([0.2383, 0.0369, 0.7603,  ..., 0.0658, 0.9688, 0.3918]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0252, 0.3938, 0.2908,  ..., 0.4459, 0.5549, 0.8752])
+tensor([0.4224, 0.2766, 0.2547,  ..., 0.2726, 0.8333, 0.3690])
 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: 10.481968879699707 seconds
+Time: 10.362020254135132 seconds
 
-[39.21, 38.47, 38.56, 38.59, 38.63, 38.48, 38.95, 38.74, 38.52, 38.83]
-[72.78]
-13.478049516677856
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1366, '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': 10.481968879699707, 'TIME_S_1KI': 7.673476485870942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 980.9324438238144, 'W': 72.78}
-[39.21, 38.47, 38.56, 38.59, 38.63, 38.48, 38.95, 38.74, 38.52, 38.83, 39.07, 38.47, 38.63, 38.57, 38.58, 38.77, 38.83, 38.82, 38.46, 38.73]
-695.99
-34.7995
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1366, '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': 10.481968879699707, 'TIME_S_1KI': 7.673476485870942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 980.9324438238144, 'W': 72.78, 'J_1KI': 718.1057421843444, 'W_1KI': 53.2796486090776, 'W_D': 37.9805, 'J_D': 511.9030596681833, 'W_D_1KI': 27.804172767203514, 'J_D_1KI': 20.35444565681077}
+[40.3, 38.79, 39.14, 38.79, 39.0, 38.98, 38.53, 44.02, 38.67, 38.42]
+[74.92]
+13.31204342842102
+{'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': 10.362020254135132, 'TIME_S_1KI': 7.635976605847555, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3382936573029, 'W': 74.92}
+[40.3, 38.79, 39.14, 38.79, 39.0, 38.98, 38.53, 44.02, 38.67, 38.42, 39.44, 38.36, 38.82, 38.43, 45.76, 38.31, 39.93, 38.5, 38.79, 38.36]
+711.08
+35.554
+{'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': 10.362020254135132, 'TIME_S_1KI': 7.635976605847555, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3382936573029, 'W': 74.92, 'J_1KI': 734.9582119803264, 'W_1KI': 55.21002210759028, 'W_D': 39.366, 'J_D': 524.0419016032218, 'W_D_1KI': 29.00957995578482, 'J_D_1KI': 21.377730254815635}
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 3949dab..0569add 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": 15344, "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": 10.396661281585693, "TIME_S_1KI": 0.6775717727832178, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.4673494148254, "W": 65.08, "J_1KI": 55.166015994188314, "W_1KI": 4.241397288842545, "W_D": 30.18325, "J_D": 392.5804490507841, "W_D_1KI": 1.9671044056308655, "J_D_1KI": 0.1282002349863703}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15401, "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": 10.70950984954834, "TIME_S_1KI": 0.6953775631159236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.8573481559754, "W": 65.04, "J_1KI": 54.987166298031, "W_1KI": 4.223102395948315, "W_D": 30.268000000000008, "J_D": 394.10636860370647, "W_D_1KI": 1.9653269268229343, "J_D_1KI": 0.12761034522582523}
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 4ae3041..5835407 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', '1000', '-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": 0.6842620372772217}
+{"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": 0.6817739009857178}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 249995,
+tensor(crow_indices=tensor([     0,      3,     11,  ..., 249990, 249996,
                             250000]),
-       col_indices=tensor([ 5258, 47122, 48422,  ..., 30033, 41208, 46342]),
-       values=tensor([0.6499, 0.7211, 0.6182,  ..., 0.7244, 0.8782, 0.8107]),
+       col_indices=tensor([22352, 25754, 44016,  ..., 24187, 38739, 43878]),
+       values=tensor([0.9987, 0.7536, 0.3762,  ..., 0.2868, 0.8081, 0.6848]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4190, 0.1278, 0.1748,  ..., 0.3464, 0.8679, 0.1666])
+tensor([0.2548, 0.4461, 0.9076,  ..., 0.8528, 0.8836, 0.6180])
 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: 0.6842620372772217 seconds
+Time: 0.6817739009857178 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15344', '-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": 10.396661281585693}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15401', '-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": 10.70950984954834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      9,  ..., 249992, 249997,
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249994,
                             250000]),
-       col_indices=tensor([10534, 13796, 13942,  ..., 20381, 35132, 47921]),
-       values=tensor([0.7820, 0.3755, 0.2967,  ..., 0.2418, 0.5762, 0.2824]),
+       col_indices=tensor([21278, 27457, 27912,  ..., 25636, 33177, 40764]),
+       values=tensor([0.5508, 0.6259, 0.1639,  ..., 0.1456, 0.5920, 0.1745]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5105, 0.5604, 0.4598,  ..., 0.4891, 0.0194, 0.7500])
+tensor([0.3112, 0.1298, 0.2276,  ..., 0.1739, 0.6060, 0.6815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.396661281585693 seconds
+Time: 10.70950984954834 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      9,  ..., 249992, 249997,
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249994,
                             250000]),
-       col_indices=tensor([10534, 13796, 13942,  ..., 20381, 35132, 47921]),
-       values=tensor([0.7820, 0.3755, 0.2967,  ..., 0.2418, 0.5762, 0.2824]),
+       col_indices=tensor([21278, 27457, 27912,  ..., 25636, 33177, 40764]),
+       values=tensor([0.5508, 0.6259, 0.1639,  ..., 0.1456, 0.5920, 0.1745]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5105, 0.5604, 0.4598,  ..., 0.4891, 0.0194, 0.7500])
+tensor([0.3112, 0.1298, 0.2276,  ..., 0.1739, 0.6060, 0.6815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.396661281585693 seconds
+Time: 10.70950984954834 seconds
 
-[39.48, 38.54, 38.4, 39.05, 39.65, 38.93, 38.49, 38.76, 38.48, 39.28]
-[65.08]
-13.006566524505615
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15344, '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': 10.396661281585693, 'TIME_S_1KI': 0.6775717727832178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.4673494148254, 'W': 65.08}
-[39.48, 38.54, 38.4, 39.05, 39.65, 38.93, 38.49, 38.76, 38.48, 39.28, 39.19, 38.51, 38.7, 38.61, 39.0, 38.52, 39.05, 38.49, 38.55, 38.46]
-697.935
-34.89675
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15344, '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': 10.396661281585693, 'TIME_S_1KI': 0.6775717727832178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.4673494148254, 'W': 65.08, 'J_1KI': 55.166015994188314, 'W_1KI': 4.241397288842545, 'W_D': 30.18325, 'J_D': 392.5804490507841, 'W_D_1KI': 1.9671044056308655, 'J_D_1KI': 0.1282002349863703}
+[39.37, 38.33, 39.38, 39.01, 38.5, 38.38, 38.36, 38.51, 38.52, 38.33]
+[65.04]
+13.020561933517456
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15401, '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': 10.70950984954834, 'TIME_S_1KI': 0.6953775631159236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.8573481559754, 'W': 65.04}
+[39.37, 38.33, 39.38, 39.01, 38.5, 38.38, 38.36, 38.51, 38.52, 38.33, 40.16, 38.83, 38.77, 38.25, 38.69, 38.32, 38.3, 38.47, 38.28, 39.22]
+695.4399999999999
+34.772
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15401, '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': 10.70950984954834, 'TIME_S_1KI': 0.6953775631159236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.8573481559754, 'W': 65.04, 'J_1KI': 54.987166298031, 'W_1KI': 4.223102395948315, 'W_D': 30.268000000000008, 'J_D': 394.10636860370647, 'W_D_1KI': 1.9653269268229343, 'J_D_1KI': 0.12761034522582523}
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 bde3932..4fe2ca5 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": 3489, "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": 10.437294721603394, "TIME_S_1KI": 2.991486019376152, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 933.3928768968582, "W": 69.22, "J_1KI": 267.52447030577764, "W_1KI": 19.83949555746632, "W_D": 34.06175, "J_D": 459.30359469288595, "W_D_1KI": 9.762611063341934, "J_D_1KI": 2.798111511419299}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3498, "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": 10.43606948852539, "TIME_S_1KI": 2.983438961842593, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 938.4889250850676, "W": 69.66, "J_1KI": 268.29300316897303, "W_1KI": 19.914236706689536, "W_D": 34.37075, "J_D": 463.0572526825667, "W_D_1KI": 9.82582904516867, "J_D_1KI": 2.8089848613975614}
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 4299e13..08291b1 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', '1000', '-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": 3.0088562965393066}
+{"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": 3.0015740394592285}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      34,      93,  ..., 2499916,
-                            2499957, 2500000]),
-       col_indices=tensor([  603,  3952,  4942,  ..., 45684, 45744, 47378]),
-       values=tensor([0.2755, 0.3359, 0.2897,  ..., 0.6537, 0.9903, 0.6398]),
+tensor(crow_indices=tensor([      0,      44,     103,  ..., 2499905,
+                            2499956, 2500000]),
+       col_indices=tensor([  226,  2395,  3856,  ..., 46208, 48736, 49649]),
+       values=tensor([0.2794, 0.3289, 0.9047,  ..., 0.2004, 0.4257, 0.7682]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0226, 0.9900, 0.4586,  ..., 0.9619, 0.5778, 0.7456])
+tensor([0.4960, 0.6719, 0.9417,  ..., 0.9330, 0.7654, 0.9120])
 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: 3.0088562965393066 seconds
+Time: 3.0015740394592285 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3489', '-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": 10.437294721603394}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3498', '-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": 10.43606948852539}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499916,
-                            2499955, 2500000]),
-       col_indices=tensor([   84,    88,  1962,  ..., 43229, 45310, 46070]),
-       values=tensor([0.8625, 0.0720, 0.1202,  ..., 0.4148, 0.7410, 0.3059]),
+tensor(crow_indices=tensor([      0,      53,     101,  ..., 2499890,
+                            2499947, 2500000]),
+       col_indices=tensor([    1,   302,   356,  ..., 47860, 48391, 48616]),
+       values=tensor([0.1949, 0.9610, 0.6433,  ..., 0.8236, 0.0074, 0.9971]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1590, 0.6012, 0.6850,  ..., 0.6120, 0.4384, 0.7195])
+tensor([0.6285, 0.6234, 0.6444,  ..., 0.5791, 0.7727, 0.1804])
 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: 10.437294721603394 seconds
+Time: 10.43606948852539 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499916,
-                            2499955, 2500000]),
-       col_indices=tensor([   84,    88,  1962,  ..., 43229, 45310, 46070]),
-       values=tensor([0.8625, 0.0720, 0.1202,  ..., 0.4148, 0.7410, 0.3059]),
+tensor(crow_indices=tensor([      0,      53,     101,  ..., 2499890,
+                            2499947, 2500000]),
+       col_indices=tensor([    1,   302,   356,  ..., 47860, 48391, 48616]),
+       values=tensor([0.1949, 0.9610, 0.6433,  ..., 0.8236, 0.0074, 0.9971]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1590, 0.6012, 0.6850,  ..., 0.6120, 0.4384, 0.7195])
+tensor([0.6285, 0.6234, 0.6444,  ..., 0.5791, 0.7727, 0.1804])
 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: 10.437294721603394 seconds
+Time: 10.43606948852539 seconds
 
-[39.1, 38.45, 44.52, 38.52, 39.08, 38.38, 40.78, 38.54, 38.4, 38.35]
-[69.22]
-13.484439134597778
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3489, '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': 10.437294721603394, 'TIME_S_1KI': 2.991486019376152, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 933.3928768968582, 'W': 69.22}
-[39.1, 38.45, 44.52, 38.52, 39.08, 38.38, 40.78, 38.54, 38.4, 38.35, 39.37, 38.65, 38.68, 38.35, 38.4, 38.76, 38.81, 38.76, 38.48, 38.39]
-703.165
-35.158249999999995
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3489, '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': 10.437294721603394, 'TIME_S_1KI': 2.991486019376152, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 933.3928768968582, 'W': 69.22, 'J_1KI': 267.52447030577764, 'W_1KI': 19.83949555746632, 'W_D': 34.06175, 'J_D': 459.30359469288595, 'W_D_1KI': 9.762611063341934, 'J_D_1KI': 2.798111511419299}
+[38.98, 38.56, 38.59, 38.25, 38.75, 38.31, 38.43, 38.27, 38.33, 43.32]
+[69.66]
+13.472422122955322
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3498, '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': 10.43606948852539, 'TIME_S_1KI': 2.983438961842593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 938.4889250850676, 'W': 69.66}
+[38.98, 38.56, 38.59, 38.25, 38.75, 38.31, 38.43, 38.27, 38.33, 43.32, 39.04, 38.73, 38.9, 38.87, 38.44, 45.59, 38.97, 38.44, 40.32, 38.73]
+705.785
+35.289249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3498, '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': 10.43606948852539, 'TIME_S_1KI': 2.983438961842593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 938.4889250850676, 'W': 69.66, 'J_1KI': 268.29300316897303, 'W_1KI': 19.914236706689536, 'W_D': 34.37075, 'J_D': 463.0572526825667, 'W_D_1KI': 9.82582904516867, 'J_D_1KI': 2.8089848613975614}
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 eb380f5..5c43412 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": 35734, "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": 10.471917629241943, "TIME_S_1KI": 0.2930519289539918, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 841.987050704956, "W": 64.44, "J_1KI": 23.562630847510942, "W_1KI": 1.803324564840208, "W_D": 29.634499999999996, "J_D": 387.2108202066421, "W_D_1KI": 0.8293082218615323, "J_D_1KI": 0.023207819495761246}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35695, "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": 10.507647275924683, "TIME_S_1KI": 0.2943730851918947, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 842.3661928725243, "W": 64.41, "J_1KI": 23.598996858734395, "W_1KI": 1.8044544053789044, "W_D": 29.134750000000004, "J_D": 381.0297847817541, "W_D_1KI": 0.8162137554279313, "J_D_1KI": 0.02286633297178684}
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 96eed5a..c6e8d71 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', '1000', '-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": 0.3157460689544678}
+{"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": 0.3123207092285156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 4062,  9525, 48228,  ..., 39550, 26780, 46383]),
-       values=tensor([0.9682, 0.2653, 0.7546,  ..., 0.8059, 0.5876, 0.9597]),
+tensor(crow_indices=tensor([    0,     3,     3,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 1731,  4163, 39043,  ..., 48142,  1105, 32715]),
+       values=tensor([0.9730, 0.5233, 0.5883,  ..., 0.0098, 0.9466, 0.3610]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6423, 0.4854, 0.6493,  ..., 0.6821, 0.6803, 0.2283])
+tensor([0.3233, 0.5001, 0.4757,  ..., 0.9452, 0.0190, 0.8013])
 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: 0.3157460689544678 seconds
+Time: 0.3123207092285156 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '33254', '-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": 9.771223545074463}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '33619', '-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": 9.88913083076477}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([27980, 12083,  1659,  ..., 17852, 35908, 47898]),
-       values=tensor([0.9789, 0.4410, 0.2389,  ..., 0.6711, 0.3630, 0.6906]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([10235, 29693, 19116,  ..., 40289, 44691, 23523]),
+       values=tensor([0.1639, 0.2137, 0.2836,  ..., 0.1546, 0.8297, 0.2686]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9986, 0.5860, 0.4640,  ..., 0.2646, 0.6800, 0.7666])
+tensor([0.0511, 0.8204, 0.3831,  ..., 0.1304, 0.0964, 0.0598])
 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: 9.771223545074463 seconds
+Time: 9.88913083076477 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35734', '-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": 10.471917629241943}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35695', '-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": 10.507647275924683}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 25000, 25000]),
-       col_indices=tensor([14210,  9782, 13262,  ..., 32699, 48019, 38373]),
-       values=tensor([0.8162, 0.2704, 0.1597,  ..., 0.7469, 0.6704, 0.2691]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([19065, 20351, 39842,  ..., 40423,  9509, 47347]),
+       values=tensor([0.9158, 0.3839, 0.2352,  ..., 0.6644, 0.6974, 0.4594]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5208, 0.6581, 0.5659,  ..., 0.1337, 0.4152, 0.4244])
+tensor([0.0381, 0.0022, 0.0479,  ..., 0.9299, 0.2975, 0.9449])
 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: 10.471917629241943 seconds
+Time: 10.507647275924683 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 25000, 25000]),
-       col_indices=tensor([14210,  9782, 13262,  ..., 32699, 48019, 38373]),
-       values=tensor([0.8162, 0.2704, 0.1597,  ..., 0.7469, 0.6704, 0.2691]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([19065, 20351, 39842,  ..., 40423,  9509, 47347]),
+       values=tensor([0.9158, 0.3839, 0.2352,  ..., 0.6644, 0.6974, 0.4594]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5208, 0.6581, 0.5659,  ..., 0.1337, 0.4152, 0.4244])
+tensor([0.0381, 0.0022, 0.0479,  ..., 0.9299, 0.2975, 0.9449])
 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: 10.471917629241943 seconds
+Time: 10.507647275924683 seconds
 
-[39.24, 38.48, 38.44, 38.43, 38.45, 38.48, 38.5, 39.07, 38.96, 38.4]
-[64.44]
-13.066217422485352
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35734, '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': 10.471917629241943, 'TIME_S_1KI': 0.2930519289539918, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.987050704956, 'W': 64.44}
-[39.24, 38.48, 38.44, 38.43, 38.45, 38.48, 38.5, 39.07, 38.96, 38.4, 39.76, 38.41, 38.52, 38.56, 38.6, 38.96, 38.81, 38.77, 38.7, 38.54]
-696.11
-34.8055
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35734, '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': 10.471917629241943, 'TIME_S_1KI': 0.2930519289539918, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.987050704956, 'W': 64.44, 'J_1KI': 23.562630847510942, 'W_1KI': 1.803324564840208, 'W_D': 29.634499999999996, 'J_D': 387.2108202066421, 'W_D_1KI': 0.8293082218615323, 'J_D_1KI': 0.023207819495761246}
+[39.22, 44.39, 40.14, 38.47, 39.94, 38.41, 38.49, 38.91, 39.41, 38.82]
+[64.41]
+13.078189611434937
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35695, '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': 10.507647275924683, 'TIME_S_1KI': 0.2943730851918947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 842.3661928725243, 'W': 64.41}
+[39.22, 44.39, 40.14, 38.47, 39.94, 38.41, 38.49, 38.91, 39.41, 38.82, 39.08, 38.6, 38.48, 38.48, 38.48, 38.38, 38.83, 39.42, 38.84, 38.55]
+705.5049999999999
+35.27524999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35695, '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': 10.507647275924683, 'TIME_S_1KI': 0.2943730851918947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 842.3661928725243, 'W': 64.41, 'J_1KI': 23.598996858734395, 'W_1KI': 1.8044544053789044, 'W_D': 29.134750000000004, 'J_D': 381.0297847817541, 'W_D_1KI': 0.8162137554279313, 'J_D_1KI': 0.02286633297178684}
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
new file mode 100644
index 0000000..cbaf9ec
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 478217, "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": 10.855157613754272, "TIME_S_1KI": 0.022699229876299402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.2491577148438, "W": 64.87, "J_1KI": 1.7905033859416202, "W_1KI": 0.1356497155057223, "W_D": 29.804500000000004, "J_D": 393.40339172363286, "W_D_1KI": 0.06232421683043473, "J_D_1KI": 0.00013032622602382335}
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
new file mode 100644
index 0000000..dc99150
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 0.03418374061584473}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3258, 3666,  785,  ...,  592, 2528, 4295]),
+       values=tensor([0.0745, 0.3346, 0.7433,  ..., 0.4561, 0.1450, 0.7729]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6815, 0.4251, 0.0154,  ..., 0.8636, 0.4620, 0.2584])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.03418374061584473 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '307163', '-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": 6.744239568710327}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1557, 2371, 1241,  ..., 4745,  784, 3444]),
+       values=tensor([0.6224, 0.1480, 0.3479,  ..., 0.3226, 0.4259, 0.8584]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6292, 0.0071, 0.7726,  ..., 0.8443, 0.3847, 0.4326])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.744239568710327 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '478217', '-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": 10.855157613754272}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 537,  942, 2250,  ..., 4421, 3640, 3689]),
+       values=tensor([0.2431, 0.3591, 0.7204,  ..., 0.2868, 0.0163, 0.2334]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9258, 0.9006, 0.7252,  ..., 0.7255, 0.3779, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.855157613754272 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 537,  942, 2250,  ..., 4421, 3640, 3689]),
+       values=tensor([0.2431, 0.3591, 0.7204,  ..., 0.2868, 0.0163, 0.2334]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9258, 0.9006, 0.7252,  ..., 0.7255, 0.3779, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.855157613754272 seconds
+
+[39.25, 38.14, 38.36, 38.18, 38.58, 38.56, 38.4, 38.47, 38.48, 38.68]
+[64.87]
+13.199462890625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 478217, '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': 10.855157613754272, 'TIME_S_1KI': 0.022699229876299402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.2491577148438, 'W': 64.87}
+[39.25, 38.14, 38.36, 38.18, 38.58, 38.56, 38.4, 38.47, 38.48, 38.68, 39.07, 38.8, 38.23, 39.53, 38.54, 38.21, 38.59, 38.49, 41.46, 47.58]
+701.31
+35.0655
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 478217, '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': 10.855157613754272, 'TIME_S_1KI': 0.022699229876299402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.2491577148438, 'W': 64.87, 'J_1KI': 1.7905033859416202, 'W_1KI': 0.1356497155057223, 'W_D': 29.804500000000004, 'J_D': 393.40339172363286, 'W_D_1KI': 0.06232421683043473, 'J_D_1KI': 0.00013032622602382335}
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
new file mode 100644
index 0000000..34a1b0c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 248678, "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": 10.619725465774536, "TIME_S_1KI": 0.04270472444596843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 878.5904247951509, "W": 65.93, "J_1KI": 3.5330444381696444, "W_1KI": 0.2651219649506591, "W_D": 31.137750000000004, "J_D": 414.9450781080723, "W_D_1KI": 0.12521312701565881, "J_D_1KI": 0.0005035150958897}
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
new file mode 100644
index 0000000..c454cf5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 0.05426168441772461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([1287, 1316, 2359,  ..., 1751, 2298, 3529]),
+       values=tensor([0.1773, 0.9664, 0.4947,  ..., 0.2806, 0.9364, 0.2474]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5449, 0.4697, 0.1251,  ..., 0.6031, 0.3711, 0.9109])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05426168441772461 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193506', '-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": 8.17044973373413}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24989, 24995, 25000]),
+       col_indices=tensor([ 563, 1432, 1628,  ..., 3910, 4925, 4964]),
+       values=tensor([0.0779, 0.2473, 0.4860,  ..., 0.8752, 0.7145, 0.0936]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5862, 0.8689, 0.7521,  ..., 0.3378, 0.8388, 0.0430])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 8.17044973373413 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '248678', '-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": 10.619725465774536}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24993, 25000]),
+       col_indices=tensor([  49,  355,  745,  ..., 2877, 3597, 4425]),
+       values=tensor([0.2389, 0.4883, 0.4431,  ..., 0.9568, 0.0569, 0.8170]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9647, 0.9839, 0.1030,  ..., 0.7979, 0.9168, 0.5702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.619725465774536 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24993, 25000]),
+       col_indices=tensor([  49,  355,  745,  ..., 2877, 3597, 4425]),
+       values=tensor([0.2389, 0.4883, 0.4431,  ..., 0.9568, 0.0569, 0.8170]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9647, 0.9839, 0.1030,  ..., 0.7979, 0.9168, 0.5702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.619725465774536 seconds
+
+[39.55, 38.57, 38.5, 38.49, 39.15, 38.5, 38.44, 38.52, 38.74, 38.66]
+[65.93]
+13.326109886169434
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 248678, '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': 10.619725465774536, 'TIME_S_1KI': 0.04270472444596843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.5904247951509, 'W': 65.93}
+[39.55, 38.57, 38.5, 38.49, 39.15, 38.5, 38.44, 38.52, 38.74, 38.66, 39.58, 38.56, 38.38, 38.87, 38.31, 38.44, 38.39, 38.74, 38.99, 38.72]
+695.845
+34.79225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 248678, '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': 10.619725465774536, 'TIME_S_1KI': 0.04270472444596843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.5904247951509, 'W': 65.93, 'J_1KI': 3.5330444381696444, 'W_1KI': 0.2651219649506591, 'W_D': 31.137750000000004, 'J_D': 414.9450781080723, 'W_D_1KI': 0.12521312701565881, 'J_D_1KI': 0.0005035150958897}
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
new file mode 100644
index 0000000..163b6dc
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 39651, "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": 10.074631690979004, "TIME_S_1KI": 0.25408266351363157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 909.2396620059013, "W": 71.91, "J_1KI": 22.931065093084698, "W_1KI": 1.8135734281607019, "W_D": 21.342999999999996, "J_D": 269.8637478263378, "W_D_1KI": 0.5382714181231241, "J_D_1KI": 0.013575229328973395}
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
new file mode 100644
index 0000000..5c37580
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_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-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.26480770111083984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     43,     97,  ..., 249898, 249949,
+                            250000]),
+       col_indices=tensor([  46,  106,  224,  ..., 4804, 4890, 4986]),
+       values=tensor([0.9512, 0.1564, 0.8337,  ..., 0.0764, 0.6147, 0.8806]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5067, 0.1013, 0.0742,  ..., 0.2212, 0.5429, 0.9437])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.26480770111083984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '39651', '-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": 10.074631690979004}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    105,  ..., 249900, 249947,
+                            250000]),
+       col_indices=tensor([ 129,  155,  285,  ..., 4713, 4736, 4825]),
+       values=tensor([0.9050, 0.4779, 0.3101,  ..., 0.9077, 0.5485, 0.2382]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7723, 0.0685, 0.7362,  ..., 0.7986, 0.1054, 0.6909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.074631690979004 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    105,  ..., 249900, 249947,
+                            250000]),
+       col_indices=tensor([ 129,  155,  285,  ..., 4713, 4736, 4825]),
+       values=tensor([0.9050, 0.4779, 0.3101,  ..., 0.9077, 0.5485, 0.2382]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7723, 0.0685, 0.7362,  ..., 0.7986, 0.1054, 0.6909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.074631690979004 seconds
+
+[39.13, 38.4, 39.4, 38.95, 39.01, 38.64, 38.39, 62.19, 64.39, 63.32]
+[71.91]
+12.644133806228638
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 39651, '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': 10.074631690979004, 'TIME_S_1KI': 0.25408266351363157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 909.2396620059013, 'W': 71.91}
+[39.13, 38.4, 39.4, 38.95, 39.01, 38.64, 38.39, 62.19, 64.39, 63.32, 68.24, 64.54, 66.6, 65.57, 64.04, 68.19, 66.94, 66.24, 69.2, 70.61]
+1011.34
+50.567
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 39651, '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': 10.074631690979004, 'TIME_S_1KI': 0.25408266351363157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 909.2396620059013, 'W': 71.91, 'J_1KI': 22.931065093084698, 'W_1KI': 1.8135734281607019, 'W_D': 21.342999999999996, 'J_D': 269.8637478263378, 'W_D_1KI': 0.5382714181231241, 'J_D_1KI': 0.013575229328973395}
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
new file mode 100644
index 0000000..c6f366b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8104, "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": 10.399449348449707, "TIME_S_1KI": 1.2832489324345642, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 868.1994806170463, "W": 65.85, "J_1KI": 107.132216265677, "W_1KI": 8.125616979269497, "W_D": 30.541749999999993, "J_D": 402.67777505141487, "W_D_1KI": 3.768725320829219, "J_D_1KI": 0.46504507907566867}
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
new file mode 100644
index 0000000..440ed84
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_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-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 1.2955126762390137}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     222,     488,  ..., 1249497,
+                            1249743, 1250000]),
+       col_indices=tensor([   0,    1,   24,  ..., 4925, 4934, 4978]),
+       values=tensor([0.4956, 0.3294, 0.5952,  ..., 0.4990, 0.9373, 0.9148]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4962, 0.1920, 0.2421,  ..., 0.8601, 0.2392, 0.4151])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 1.2955126762390137 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8104', '-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": 10.399449348449707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     238,     495,  ..., 1249467,
+                            1249713, 1250000]),
+       col_indices=tensor([   4,    6,   45,  ..., 4913, 4952, 4965]),
+       values=tensor([0.6573, 0.3725, 0.2540,  ..., 0.9752, 0.8782, 0.5831]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7880, 0.7423, 0.8544,  ..., 0.3557, 0.5396, 0.2540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.399449348449707 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     238,     495,  ..., 1249467,
+                            1249713, 1250000]),
+       col_indices=tensor([   4,    6,   45,  ..., 4913, 4952, 4965]),
+       values=tensor([0.6573, 0.3725, 0.2540,  ..., 0.9752, 0.8782, 0.5831]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7880, 0.7423, 0.8544,  ..., 0.3557, 0.5396, 0.2540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.399449348449707 seconds
+
+[39.35, 38.88, 44.16, 39.47, 38.8, 39.17, 38.52, 38.75, 38.59, 39.07]
+[65.85]
+13.184502363204956
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8104, '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': 10.399449348449707, 'TIME_S_1KI': 1.2832489324345642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 868.1994806170463, 'W': 65.85}
+[39.35, 38.88, 44.16, 39.47, 38.8, 39.17, 38.52, 38.75, 38.59, 39.07, 39.04, 39.89, 38.99, 38.43, 38.88, 38.64, 38.45, 39.36, 39.15, 38.61]
+706.165
+35.30825
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8104, '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': 10.399449348449707, 'TIME_S_1KI': 1.2832489324345642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 868.1994806170463, 'W': 65.85, 'J_1KI': 107.132216265677, 'W_1KI': 8.125616979269497, 'W_D': 30.541749999999993, 'J_D': 402.67777505141487, 'W_D_1KI': 3.768725320829219, 'J_D_1KI': 0.46504507907566867}
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
new file mode 100644
index 0000000..f20d05f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3588, "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": 10.469439029693604, "TIME_S_1KI": 2.9179038544296554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 926.2283216476441, "W": 68.56, "J_1KI": 258.1461320088194, "W_1KI": 19.108138238573023, "W_D": 33.73800000000001, "J_D": 455.7918774175645, "W_D_1KI": 9.403010033444819, "J_D_1KI": 2.620682840982391}
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
new file mode 100644
index 0000000..3e39a46
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 2.925701141357422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1004,  ..., 2498982,
+                            2499482, 2500000]),
+       col_indices=tensor([  18,   27,   28,  ..., 4963, 4979, 4987]),
+       values=tensor([0.5744, 0.1591, 0.4039,  ..., 0.3146, 0.5536, 0.6554]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8562, 0.0559, 0.5751,  ..., 0.9013, 0.4689, 0.3374])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 2.925701141357422 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3588', '-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": 10.469439029693604}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1006,  ..., 2498987,
+                            2499524, 2500000]),
+       col_indices=tensor([   6,   12,   25,  ..., 4979, 4985, 4986]),
+       values=tensor([0.9526, 0.5714, 0.7457,  ..., 0.5995, 0.2741, 0.0768]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9940, 0.0288, 0.0030,  ..., 0.3299, 0.0903, 0.2227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.469439029693604 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1006,  ..., 2498987,
+                            2499524, 2500000]),
+       col_indices=tensor([   6,   12,   25,  ..., 4979, 4985, 4986]),
+       values=tensor([0.9526, 0.5714, 0.7457,  ..., 0.5995, 0.2741, 0.0768]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9940, 0.0288, 0.0030,  ..., 0.3299, 0.0903, 0.2227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.469439029693604 seconds
+
+[39.61, 38.42, 39.72, 38.79, 38.63, 38.61, 38.72, 38.24, 38.41, 38.4]
+[68.56]
+13.509747982025146
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3588, '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': 10.469439029693604, 'TIME_S_1KI': 2.9179038544296554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.2283216476441, 'W': 68.56}
+[39.61, 38.42, 39.72, 38.79, 38.63, 38.61, 38.72, 38.24, 38.41, 38.4, 39.84, 38.26, 38.74, 38.59, 38.81, 38.31, 38.94, 38.63, 38.58, 38.23]
+696.4399999999999
+34.821999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3588, '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': 10.469439029693604, 'TIME_S_1KI': 2.9179038544296554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.2283216476441, 'W': 68.56, 'J_1KI': 258.1461320088194, 'W_1KI': 19.108138238573023, 'W_D': 33.73800000000001, 'J_D': 455.7918774175645, 'W_D_1KI': 9.403010033444819, 'J_D_1KI': 2.620682840982391}
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
new file mode 100644
index 0000000..d170a6b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 565598, "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": 10.406220436096191, "TIME_S_1KI": 0.018398616041952396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.5254345631599, "W": 64.13, "J_1KI": 1.498459037272338, "W_1KI": 0.11338441790812556, "W_D": 29.180249999999987, "J_D": 385.6386100407241, "W_D_1KI": 0.05159185499241509, "J_D_1KI": 9.121647352433192e-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
new file mode 100644
index 0000000..4a0b8d8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 0.055680274963378906}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([4927,  850,  790,  511,    1, 4275, 3659, 3202, 4099,
+                           3346, 1589,  716, 4620, 4989, 3861, 2882, 2487,  356,
+                           3163, 4196, 2032,  713,  507, 4615, 4269, 4035, 1320,
+                            655, 4926, 1128, 2992, 3058, 2439, 4007, 3555, 1710,
+                           2353,  655, 2875,  397, 2586, 4948,  858, 1089,  783,
+                           1767, 1975, 2378, 3541, 1407,  868, 4760, 4954, 4948,
+                           2154, 3756,  192, 4715, 2175,  343, 3413,  855, 3051,
+                           4256, 4765, 3143, 3774, 3357, 1362, 3915, 3187, 3177,
+                           3730, 4948, 4331, 2972, 3797,  963, 1487, 1791, 3014,
+                           3104, 4150, 4779, 2304, 1176, 1597, 3268, 4290, 3867,
+                           1778, 4097, 4190, 1835, 2167, 1131, 4492, 3907, 2098,
+                           4204, 4273, 3262, 2220, 4871, 3645, 4702, 4344, 3548,
+                            398, 3919,  762, 3209,  941, 2587, 4871, 1294,  846,
+                           4270, 1587,  490, 3776,  205, 4893, 4944, 3389, 1241,
+                            319, 1205,  149, 2679,  835,  185, 1679,  305,  803,
+                           3987, 4919, 1049, 2984,  150, 2222, 3548, 4559, 2082,
+                            773, 3809,  333, 4072, 2819,  773, 1940, 3544, 2429,
+                           4213, 3874, 3370, 3390, 3737, 2306, 2576, 3944, 3962,
+                           2700, 3672, 1959, 2924, 1160, 2820,  201, 3021, 1400,
+                           2786, 3009, 3104, 1799, 1722, 1307, 4435, 3240, 3490,
+                           3514, 3928, 2870,  339,  280, 3127,  278,   43, 1063,
+                           3176, 1262, 2341, 4542, 3316, 4835, 2103, 3750, 2839,
+                           1642, 4880, 4963, 1368, 4924, 2484, 1087,   26, 3186,
+                           4671, 3346, 1979,  748,  800,  144,   54, 3361, 3955,
+                           4948, 2768, 2175,  216,    0,  934, 3902, 3054,  854,
+                           1551,  310,  382, 1750,  779, 4286, 2768, 4550, 2371,
+                           2027, 2115, 2210, 4053, 3461, 4944,  349, 2236, 2467,
+                           2141, 1730,   73, 1349, 3773, 2561, 2961]),
+       values=tensor([0.0052, 0.9685, 0.5552, 0.5554, 0.3769, 0.8417, 0.2484,
+                      0.8557, 0.2810, 0.1770, 0.3815, 0.5491, 0.2804, 0.7014,
+                      0.4668, 0.6665, 0.6885, 0.4406, 0.0793, 0.0505, 0.2168,
+                      0.2768, 0.8793, 0.5292, 0.6124, 0.8331, 0.8520, 0.8953,
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+                      0.4711, 0.1941, 0.9435, 0.7325, 0.9932, 0.9457, 0.1546,
+                      0.7522, 0.6262, 0.4856, 0.7356, 0.9269]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4907, 0.7631, 0.4016,  ..., 0.1364, 0.7839, 0.0874])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.055680274963378906 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '188576', '-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": 3.5007991790771484}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1945, 1023, 4059, 2482, 4205,  303, 4777,  854, 2860,
+                           3128, 1003, 4735, 2788, 4977, 4888, 1184, 1747, 1500,
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+                           2619, 1855,  848, 4820,   42, 3478, 2615, 4379, 3969,
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+                             24, 2376, 2290, 3244,  118, 4586, 1505, 1122, 1321,
+                           3378, 2663, 1121, 2193, 4996, 4050, 1149, 1171,  674,
+                             98,  868, 2491, 2360, 3984, 4243, 3717]),
+       values=tensor([0.8923, 0.2170, 0.4055, 0.4662, 0.6388, 0.1130, 0.3558,
+                      0.8111, 0.3477, 0.3800, 0.1079, 0.8330, 0.9521, 0.2703,
+                      0.3856, 0.0011, 0.5451, 0.8270, 0.6026, 0.6871, 0.2987,
+                      0.0297, 0.9583, 0.5169, 0.4017, 0.2171, 0.4756, 0.5607,
+                      0.0472, 0.1280, 0.3544, 0.8497, 0.3044, 0.7975, 0.4038,
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+                      0.4821, 0.5628, 0.3527, 0.5347, 0.9985, 0.4438, 0.9458,
+                      0.8619, 0.6814, 0.4148, 0.2273, 0.3882, 0.1003, 0.0543,
+                      0.4150, 0.9185, 0.0166, 0.8297, 0.5190, 0.1538, 0.6141,
+                      0.2637, 0.0598, 0.8180, 0.7469, 0.0453, 0.5538, 0.8701,
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+                      0.4220, 0.0019, 0.8210, 0.7450, 0.1671, 0.2691, 0.2129,
+                      0.6046, 0.1184, 0.5733, 0.5791, 0.6764]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1655, 0.0894, 0.3335,  ..., 0.5896, 0.4748, 0.7424])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.5007991790771484 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '565598', '-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": 10.406220436096191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3711, 2509, 1480, 2246, 4155, 2306,  315, 3219,  781,
+                           3895, 3381, 2148, 1468, 1317, 2648, 3838,  486, 2691,
+                           4269, 1833, 4130, 2494, 2935, 4534, 1404,  631, 2237,
+                           3119, 2408, 4857, 3452, 3551,  652, 1979,  294, 2907,
+                           4341,  963, 1166, 1723, 2311, 2016, 4067, 2454, 3108,
+                           4422,  594, 1090, 1798, 1231, 1189, 3083, 3007, 2134,
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+                           4164, 2100, 1603,  715, 4639, 1853, 3257, 1572, 4514,
+                           2943, 1003, 4748, 1038, 1012, 3061,  294]),
+       values=tensor([0.0072, 0.2895, 0.9639, 0.0057, 0.4191, 0.2094, 0.7103,
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+                      0.7981, 0.0839, 0.1133, 0.3382, 0.5864, 0.5344, 0.5684,
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+                      0.4455, 0.6775, 0.1108, 0.8486, 0.1605, 0.0632, 0.7729,
+                      0.1033, 0.7416, 0.1100, 0.7509, 0.4420, 0.1639, 0.2794,
+                      0.8260, 0.8724, 0.3230, 0.8818, 0.5434, 0.6423, 0.5673,
+                      0.7089, 0.6119, 0.9976, 0.0416, 0.2792]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0013, 0.0858, 0.8984,  ..., 0.5676, 0.8612, 0.3338])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.406220436096191 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3711, 2509, 1480, 2246, 4155, 2306,  315, 3219,  781,
+                           3895, 3381, 2148, 1468, 1317, 2648, 3838,  486, 2691,
+                           4269, 1833, 4130, 2494, 2935, 4534, 1404,  631, 2237,
+                           3119, 2408, 4857, 3452, 3551,  652, 1979,  294, 2907,
+                           4341,  963, 1166, 1723, 2311, 2016, 4067, 2454, 3108,
+                           4422,  594, 1090, 1798, 1231, 1189, 3083, 3007, 2134,
+                           3681,  526, 4251, 1258, 2420, 4062,  326, 2947,  386,
+                           3623, 4002, 1015, 2488, 2914,  344,  749, 2046, 3369,
+                           2183, 4810,  804, 4709, 4216, 4774, 3285, 1736, 1631,
+                           1116, 2085, 4390, 2715, 1633, 1339, 4203, 1468, 3776,
+                           4650, 1964, 1644, 3484,  556, 1113,  359, 2615, 4829,
+                           4748, 1322,  159, 1685, 3154, 4693, 4031, 1252, 1027,
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+                            470, 1943, 1148, 4302, 4930, 4799, 1057,  474, 2030,
+                           3336,  862, 2916, 4504, 1767, 3103, 2022, 3927, 3702,
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+                           1875,  855, 1323,  963, 2816, 4148, 4829, 4066, 4913,
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+                           2345, 1525, 4678, 2542, 2753, 3445, 3912, 2714, 1361,
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+                           4270, 3263, 3475, 3737,  446, 1786, 2984, 2510, 2736,
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+                           4839,  645, 2259, 3905, 3083, 2174, 1253, 1258, 2465,
+                           3785, 2824,   24, 1918, 2335,  918, 1175, 3575, 2352,
+                           4164, 2100, 1603,  715, 4639, 1853, 3257, 1572, 4514,
+                           2943, 1003, 4748, 1038, 1012, 3061,  294]),
+       values=tensor([0.0072, 0.2895, 0.9639, 0.0057, 0.4191, 0.2094, 0.7103,
+                      0.8218, 0.3375, 0.5039, 0.5062, 0.5584, 0.5972, 0.9352,
+                      0.8333, 0.7188, 0.6342, 0.9555, 0.9103, 0.1687, 0.2984,
+                      0.7732, 0.0449, 0.0772, 0.1352, 0.5023, 0.0443, 0.4171,
+                      0.2148, 0.7142, 0.2678, 0.2649, 0.5734, 0.2586, 0.1803,
+                      0.3367, 0.7155, 0.6815, 0.6287, 0.8390, 0.5032, 0.1992,
+                      0.5162, 0.5707, 0.0670, 0.5923, 0.5384, 0.7500, 0.0960,
+                      0.4905, 0.7846, 0.7390, 0.3348, 0.9396, 0.2679, 0.8099,
+                      0.4907, 0.0176, 0.1919, 0.5036, 0.7682, 0.7675, 0.5778,
+                      0.9394, 0.8838, 0.1647, 0.2045, 0.3204, 0.5816, 0.4877,
+                      0.4316, 0.5907, 0.3880, 0.5556, 0.6079, 0.5805, 0.9477,
+                      0.7717, 0.2301, 0.4363, 0.4192, 0.7264, 0.9246, 0.5163,
+                      0.0957, 0.1670, 0.3706, 0.2621, 0.2557, 0.7081, 0.3520,
+                      0.9207, 0.5713, 0.9991, 0.2774, 0.9953, 0.3693, 0.6174,
+                      0.8286, 0.4524, 0.9605, 0.1877, 0.9322, 0.0179, 0.6890,
+                      0.8811, 0.8437, 0.1818, 0.1680, 0.0986, 0.7979, 0.9912,
+                      0.8202, 0.1132, 0.4257, 0.5766, 0.6866, 0.1937, 0.7442,
+                      0.9210, 0.2915, 0.9278, 0.6093, 0.0128, 0.7291, 0.8036,
+                      0.5824, 0.8528, 0.6888, 0.3925, 0.4263, 0.3416, 0.9010,
+                      0.2543, 0.7049, 0.8368, 0.2533, 0.1239, 0.2556, 0.3482,
+                      0.6122, 0.3407, 0.8598, 0.6533, 0.0993, 0.8400, 0.5464,
+                      0.2659, 0.0791, 0.9360, 0.6384, 0.4202, 0.5451, 0.6770,
+                      0.9558, 0.2536, 0.5924, 0.5367, 0.4377, 0.3759, 0.9344,
+                      0.0785, 0.9178, 0.5703, 0.2621, 0.7840, 0.6650, 0.5173,
+                      0.7316, 0.8675, 0.0573, 0.5592, 0.5656, 0.1368, 0.7342,
+                      0.4891, 0.5212, 0.5980, 0.9850, 0.3144, 0.9416, 0.3586,
+                      0.5874, 0.8863, 0.8557, 0.4322, 0.3167, 0.3279, 0.7906,
+                      0.9595, 0.6426, 0.5182, 0.3380, 0.6725, 0.1898, 0.5553,
+                      0.6660, 0.7693, 0.0543, 0.1495, 0.4661, 0.0013, 0.2189,
+                      0.2756, 0.4230, 0.3033, 0.9296, 0.0600, 0.3160, 0.8967,
+                      0.7981, 0.0839, 0.1133, 0.3382, 0.5864, 0.5344, 0.5684,
+                      0.8353, 0.4735, 0.5909, 0.0547, 0.2196, 0.1029, 0.2516,
+                      0.4455, 0.6775, 0.1108, 0.8486, 0.1605, 0.0632, 0.7729,
+                      0.1033, 0.7416, 0.1100, 0.7509, 0.4420, 0.1639, 0.2794,
+                      0.8260, 0.8724, 0.3230, 0.8818, 0.5434, 0.6423, 0.5673,
+                      0.7089, 0.6119, 0.9976, 0.0416, 0.2792]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0013, 0.0858, 0.8984,  ..., 0.5676, 0.8612, 0.3338])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.406220436096191 seconds
+
+[39.5, 43.3, 38.37, 38.43, 38.66, 38.52, 38.75, 38.13, 38.31, 39.28]
+[64.13]
+13.215740442276001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 565598, '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': 10.406220436096191, 'TIME_S_1KI': 0.018398616041952396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.5254345631599, 'W': 64.13}
+[39.5, 43.3, 38.37, 38.43, 38.66, 38.52, 38.75, 38.13, 38.31, 39.28, 40.56, 38.94, 38.24, 38.25, 38.39, 38.16, 38.85, 38.41, 38.49, 38.25]
+698.9950000000001
+34.94975000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 565598, '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': 10.406220436096191, 'TIME_S_1KI': 0.018398616041952396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.5254345631599, 'W': 64.13, 'J_1KI': 1.498459037272338, 'W_1KI': 0.11338441790812556, 'W_D': 29.180249999999987, 'J_D': 385.6386100407241, 'W_D_1KI': 0.05159185499241509, 'J_D_1KI': 9.121647352433192e-05}
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 2a06eb8..027433b 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": 3646, "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": 10.427528619766235, "TIME_S_1KI": 2.8599913932436194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.8558839225768, "W": 47.94, "J_1KI": 190.03178385150215, "W_1KI": 13.14865606143719, "W_D": 31.4125, "J_D": 453.99114421606066, "W_D_1KI": 8.615606143719145, "J_D_1KI": 2.3630296609213235}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3626, "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": 10.40371823310852, "TIME_S_1KI": 2.869199733344876, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 756.9219121170044, "W": 52.54, "J_1KI": 208.74845894015564, "W_1KI": 14.489795918367347, "W_D": 36.50025, "J_D": 525.8439098353386, "W_D_1KI": 10.066257584114727, "J_D_1KI": 2.776132814151883}
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 cc7a205..8a43e29 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 +1,14 @@
 ['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', '1000', '-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": 2.879791498184204}
+{"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": 2.89510178565979}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      23,  ...,  999979,
-                             999989, 1000000]),
-       col_indices=tensor([ 5015, 13201, 16372,  ..., 56043, 65196, 77096]),
-       values=tensor([0.8877, 0.8022, 0.3967,  ..., 0.7199, 0.8399, 0.8151]),
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999968,
+                             999983, 1000000]),
+       col_indices=tensor([23348, 35658, 56723,  ..., 82423, 86979, 88187]),
+       values=tensor([0.8917, 0.1559, 0.5748,  ..., 0.5915, 0.7647, 0.8715]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6595, 0.9245, 0.4951,  ..., 0.4587, 0.0765, 0.0892])
+tensor([0.4707, 0.9474, 0.3412,  ..., 0.5588, 0.8812, 0.4153])
 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: 2.879791498184204 seconds
+Time: 2.89510178565979 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', '3646', '-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": 10.427528619766235}
+['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', '3626', '-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": 10.40371823310852}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      24,  ...,  999984,
-                             999992, 1000000]),
-       col_indices=tensor([12724, 24596, 29019,  ..., 72798, 83516, 98300]),
-       values=tensor([0.5582, 0.8508, 0.8777,  ..., 0.7164, 0.8705, 0.2253]),
+tensor(crow_indices=tensor([      0,       5,      15,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([16760, 54124, 62778,  ..., 86983, 90495, 98787]),
+       values=tensor([0.0638, 0.0650, 0.2338,  ..., 0.3776, 0.7465, 0.0262]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8849, 0.3552, 0.8045,  ..., 0.9875, 0.5127, 0.0107])
+tensor([0.7059, 0.4263, 0.8303,  ..., 0.6514, 0.5791, 0.5612])
 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: 10.427528619766235 seconds
+Time: 10.40371823310852 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      24,  ...,  999984,
-                             999992, 1000000]),
-       col_indices=tensor([12724, 24596, 29019,  ..., 72798, 83516, 98300]),
-       values=tensor([0.5582, 0.8508, 0.8777,  ..., 0.7164, 0.8705, 0.2253]),
+tensor(crow_indices=tensor([      0,       5,      15,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([16760, 54124, 62778,  ..., 86983, 90495, 98787]),
+       values=tensor([0.0638, 0.0650, 0.2338,  ..., 0.3776, 0.7465, 0.0262]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8849, 0.3552, 0.8045,  ..., 0.9875, 0.5127, 0.0107])
+tensor([0.7059, 0.4263, 0.8303,  ..., 0.6514, 0.5791, 0.5612])
 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: 10.427528619766235 seconds
+Time: 10.40371823310852 seconds
 
-[18.35, 17.89, 18.28, 18.14, 17.94, 18.1, 18.09, 18.19, 18.06, 18.09]
-[47.94]
-14.452563285827637
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3646, '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': 10.427528619766235, 'TIME_S_1KI': 2.8599913932436194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.8558839225768, 'W': 47.94}
-[18.35, 17.89, 18.28, 18.14, 17.94, 18.1, 18.09, 18.19, 18.06, 18.09, 18.01, 18.32, 17.86, 17.98, 18.22, 18.07, 19.34, 21.72, 17.99, 18.27]
-330.54999999999995
-16.527499999999996
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3646, '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': 10.427528619766235, 'TIME_S_1KI': 2.8599913932436194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.8558839225768, 'W': 47.94, 'J_1KI': 190.03178385150215, 'W_1KI': 13.14865606143719, 'W_D': 31.4125, 'J_D': 453.99114421606066, 'W_D_1KI': 8.615606143719145, 'J_D_1KI': 2.3630296609213235}
+[18.45, 17.61, 17.77, 17.55, 17.61, 17.99, 17.58, 18.4, 17.81, 17.62]
+[52.54]
+14.406583786010742
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3626, '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': 10.40371823310852, 'TIME_S_1KI': 2.869199733344876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9219121170044, 'W': 52.54}
+[18.45, 17.61, 17.77, 17.55, 17.61, 17.99, 17.58, 18.4, 17.81, 17.62, 18.49, 17.87, 17.62, 17.77, 17.72, 17.81, 18.01, 17.57, 17.69, 18.27]
+320.79499999999996
+16.039749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3626, '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': 10.40371823310852, 'TIME_S_1KI': 2.869199733344876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9219121170044, 'W': 52.54, 'J_1KI': 208.74845894015564, 'W_1KI': 14.489795918367347, 'W_D': 36.50025, 'J_D': 525.8439098353386, 'W_D_1KI': 10.066257584114727, 'J_D_1KI': 2.776132814151883}
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
new file mode 100644
index 0000000..46cf4d4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "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": 27.417505741119385, "TIME_S_1KI": 27.417505741119385, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1870.535600104332, "W": 53.17, "J_1KI": 1870.535600104332, "W_1KI": 53.17, "W_D": 36.779250000000005, "J_D": 1293.9043910125495, "W_D_1KI": 36.779250000000005, "J_D_1KI": 36.779250000000005}
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
new file mode 100644
index 0000000..523884d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 27.417505741119385}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      206,  ...,  9999814,
+                             9999907, 10000000]),
+       col_indices=tensor([  430,  1206,  1283,  ..., 96095, 96254, 99884]),
+       values=tensor([0.0855, 0.2486, 0.3160,  ..., 0.5781, 0.8085, 0.1274]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4876, 0.8099, 0.9530,  ..., 0.3051, 0.4863, 0.6986])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 27.417505741119385 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      206,  ...,  9999814,
+                             9999907, 10000000]),
+       col_indices=tensor([  430,  1206,  1283,  ..., 96095, 96254, 99884]),
+       values=tensor([0.0855, 0.2486, 0.3160,  ..., 0.5781, 0.8085, 0.1274]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4876, 0.8099, 0.9530,  ..., 0.3051, 0.4863, 0.6986])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 27.417505741119385 seconds
+
+[18.51, 17.88, 18.06, 17.74, 17.69, 18.37, 18.17, 17.77, 18.14, 17.72]
+[53.17]
+35.18028211593628
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 27.417505741119385, 'TIME_S_1KI': 27.417505741119385, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.535600104332, 'W': 53.17}
+[18.51, 17.88, 18.06, 17.74, 17.69, 18.37, 18.17, 17.77, 18.14, 17.72, 18.92, 17.72, 17.91, 22.37, 18.39, 17.62, 17.83, 17.88, 17.9, 17.6]
+327.815
+16.39075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 27.417505741119385, 'TIME_S_1KI': 27.417505741119385, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.535600104332, 'W': 53.17, 'J_1KI': 1870.535600104332, 'W_1KI': 53.17, 'W_D': 36.779250000000005, 'J_D': 1293.9043910125495, 'W_D_1KI': 36.779250000000005, 'J_D_1KI': 36.779250000000005}
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 1871367..fb45506 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": 8006, "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": 10.406643390655518, "TIME_S_1KI": 1.2998555321828025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 663.0740174865723, "W": 46.720000000000006, "J_1KI": 82.82213558413343, "W_1KI": 5.835623282538097, "W_D": 30.276250000000005, "J_D": 429.69594867140063, "W_D_1KI": 3.7816949787659264, "J_D_1KI": 0.47235760414263384}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 7957, "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": 10.378219842910767, "TIME_S_1KI": 1.3042880285171252, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 722.9498359966278, "W": 51.18, "J_1KI": 90.85708633864871, "W_1KI": 6.432072389091366, "W_D": 34.9585, "J_D": 493.81089960312846, "W_D_1KI": 4.3934271710443635, "J_D_1KI": 0.5521461821093834}
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 b29167b..f2950b1 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', '1000', '-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": 1.3114714622497559}
+{"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": 1.319572925567627}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99998,  99998,
+tensor(crow_indices=tensor([     0,      3,      4,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([15714, 63018, 47083,  ..., 95898, 11433, 73543]),
-       values=tensor([0.8298, 0.7556, 0.0451,  ..., 0.9622, 0.2125, 0.4932]),
+       col_indices=tensor([ 8050, 18600, 47626,  ..., 72573,  7071, 11396]),
+       values=tensor([0.6679, 0.8144, 0.2788,  ..., 0.2480, 0.1170, 0.9852]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8440, 0.1023, 0.7738,  ..., 0.5206, 0.7518, 0.6360])
+tensor([0.3322, 0.6851, 0.8140,  ..., 0.1719, 0.4686, 0.0560])
 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: 1.3114714622497559 seconds
+Time: 1.319572925567627 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', '8006', '-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": 10.406643390655518}
+['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', '7957', '-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": 10.378219842910767}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([38549, 23010, 96204,  ..., 15384, 78128, 94145]),
-       values=tensor([0.9276, 0.2040, 0.0329,  ..., 0.0402, 0.0179, 0.0490]),
+       col_indices=tensor([79139, 34438, 57240,  ..., 99522, 68399,  1834]),
+       values=tensor([0.8717, 0.0754, 0.3550,  ..., 0.4586, 0.3508, 0.4372]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1445, 0.8456, 0.7445,  ..., 0.5274, 0.1855, 0.5940])
+tensor([0.1033, 0.1471, 0.4199,  ..., 0.6623, 0.5752, 0.0388])
 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: 10.406643390655518 seconds
+Time: 10.378219842910767 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([38549, 23010, 96204,  ..., 15384, 78128, 94145]),
-       values=tensor([0.9276, 0.2040, 0.0329,  ..., 0.0402, 0.0179, 0.0490]),
+       col_indices=tensor([79139, 34438, 57240,  ..., 99522, 68399,  1834]),
+       values=tensor([0.8717, 0.0754, 0.3550,  ..., 0.4586, 0.3508, 0.4372]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1445, 0.8456, 0.7445,  ..., 0.5274, 0.1855, 0.5940])
+tensor([0.1033, 0.1471, 0.4199,  ..., 0.6623, 0.5752, 0.0388])
 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: 10.406643390655518 seconds
+Time: 10.378219842910767 seconds
 
-[20.45, 19.81, 17.91, 18.51, 18.24, 17.88, 18.26, 18.12, 17.9, 18.59]
-[46.72]
-14.192508935928345
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8006, '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': 10.406643390655518, 'TIME_S_1KI': 1.2998555321828025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.0740174865723, 'W': 46.720000000000006}
-[20.45, 19.81, 17.91, 18.51, 18.24, 17.88, 18.26, 18.12, 17.9, 18.59, 18.4, 18.11, 18.06, 18.12, 17.9, 18.34, 18.04, 17.94, 17.82, 18.39]
-328.875
-16.44375
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8006, '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': 10.406643390655518, 'TIME_S_1KI': 1.2998555321828025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.0740174865723, 'W': 46.720000000000006, 'J_1KI': 82.82213558413343, 'W_1KI': 5.835623282538097, 'W_D': 30.276250000000005, 'J_D': 429.69594867140063, 'W_D_1KI': 3.7816949787659264, 'J_D_1KI': 0.47235760414263384}
+[19.98, 17.81, 17.8, 18.03, 17.89, 17.86, 17.65, 18.02, 17.79, 17.59]
+[51.18]
+14.12563180923462
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7957, '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': 10.378219842910767, 'TIME_S_1KI': 1.3042880285171252, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.9498359966278, 'W': 51.18}
+[19.98, 17.81, 17.8, 18.03, 17.89, 17.86, 17.65, 18.02, 17.79, 17.59, 18.5, 18.06, 17.99, 17.58, 17.86, 17.88, 17.85, 17.69, 17.49, 22.29]
+324.43
+16.2215
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7957, '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': 10.378219842910767, 'TIME_S_1KI': 1.3042880285171252, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.9498359966278, 'W': 51.18, 'J_1KI': 90.85708633864871, 'W_1KI': 6.432072389091366, 'W_D': 34.9585, 'J_D': 493.81089960312846, 'W_D_1KI': 4.3934271710443635, 'J_D_1KI': 0.5521461821093834}
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 eaa436a..641470f 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": 85057, "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": 10.436183214187622, "TIME_S_1KI": 0.12269634732223829, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 663.123476600647, "W": 46.56, "J_1KI": 7.796224609387199, "W_1KI": 0.5473976274733414, "W_D": 8.939750000000004, "J_D": 127.32298324614769, "W_D_1KI": 0.10510304854391765, "J_D_1KI": 0.0012356778224475076}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 83764, "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": 10.409894704818726, "TIME_S_1KI": 0.12427647563175977, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 711.6688519239426, "W": 50.42, "J_1KI": 8.49611828379665, "W_1KI": 0.6019292297407001, "W_D": 34.06175, "J_D": 480.7752185049654, "W_D_1KI": 0.40663948713050957, "J_D_1KI": 0.004854585348485144}
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 0f19044..168b413 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', '1000', '-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": 0.138319730758667}
+{"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": 0.13988041877746582}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([6848, 9607, 2682,  ..., 9449, 6129, 3470]),
-       values=tensor([0.4694, 0.9529, 0.1463,  ..., 0.1268, 0.1399, 0.3765]),
+tensor(crow_indices=tensor([    0,     2,     5,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([5444, 7298, 2758,  ..., 5406,  201, 2159]),
+       values=tensor([0.2785, 0.9301, 0.1173,  ..., 0.6105, 0.0625, 0.6073]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5998, 0.7790, 0.8385,  ..., 0.1561, 0.5420, 0.2267])
+tensor([0.9117, 0.7600, 0.5676,  ..., 0.4107, 0.0296, 0.3559])
 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: 0.138319730758667 seconds
+Time: 0.13988041877746582 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', '75911', '-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": 9.370872497558594}
+['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', '75064', '-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": 9.409368753433228}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([2414, 5580, 2005,  ..., 9768,  442, 1851]),
-       values=tensor([0.7205, 0.5630, 0.0022,  ..., 0.3635, 0.2630, 0.6566]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([ 559, 1691, 3057,  ..., 6770,  161, 9445]),
+       values=tensor([0.2390, 0.7843, 0.4833,  ..., 0.8916, 0.1224, 0.1645]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5071, 0.1792, 0.6304,  ..., 0.9432, 0.9596, 0.2753])
+tensor([0.9833, 0.3493, 0.9306,  ..., 0.5004, 0.5453, 0.7909])
 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: 9.370872497558594 seconds
+Time: 9.409368753433228 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', '85057', '-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": 10.436183214187622}
+['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', '83764', '-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": 10.409894704818726}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([2255, 7580, 9802,  ..., 6433, 5292, 8461]),
-       values=tensor([0.3444, 0.5478, 0.9067,  ..., 0.7957, 0.9972, 0.7349]),
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1791, 2178, 4941,  ..., 8437, 8977, 5726]),
+       values=tensor([0.7542, 0.7473, 0.0826,  ..., 0.7863, 0.2178, 0.9123]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8453, 0.7973, 0.9010,  ..., 0.7504, 0.8828, 0.5942])
+tensor([0.0349, 0.7342, 0.7720,  ..., 0.6458, 0.8179, 0.1428])
 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: 10.436183214187622 seconds
+Time: 10.409894704818726 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([2255, 7580, 9802,  ..., 6433, 5292, 8461]),
-       values=tensor([0.3444, 0.5478, 0.9067,  ..., 0.7957, 0.9972, 0.7349]),
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1791, 2178, 4941,  ..., 8437, 8977, 5726]),
+       values=tensor([0.7542, 0.7473, 0.0826,  ..., 0.7863, 0.2178, 0.9123]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8453, 0.7973, 0.9010,  ..., 0.7504, 0.8828, 0.5942])
+tensor([0.0349, 0.7342, 0.7720,  ..., 0.6458, 0.8179, 0.1428])
 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: 10.436183214187622 seconds
+Time: 10.409894704818726 seconds
 
-[38.19, 37.44, 39.94, 42.89, 39.51, 39.81, 47.03, 47.24, 47.36, 43.53]
-[46.56]
-14.242342710494995
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 85057, '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': 10.436183214187622, 'TIME_S_1KI': 0.12269634732223829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.123476600647, 'W': 46.56}
-[38.19, 37.44, 39.94, 42.89, 39.51, 39.81, 47.03, 47.24, 47.36, 43.53, 45.01, 44.41, 42.1, 40.98, 40.92, 39.18, 41.09, 39.73, 39.61, 39.6]
-752.405
-37.62025
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 85057, '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': 10.436183214187622, 'TIME_S_1KI': 0.12269634732223829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.123476600647, 'W': 46.56, 'J_1KI': 7.796224609387199, 'W_1KI': 0.5473976274733414, 'W_D': 8.939750000000004, 'J_D': 127.32298324614769, 'W_D_1KI': 0.10510304854391765, 'J_D_1KI': 0.0012356778224475076}
+[18.39, 17.85, 18.37, 17.98, 17.9, 18.07, 21.28, 18.76, 18.12, 17.67]
+[50.42]
+14.11481261253357
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 83764, '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': 10.409894704818726, 'TIME_S_1KI': 0.12427647563175977, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 711.6688519239426, 'W': 50.42}
+[18.39, 17.85, 18.37, 17.98, 17.9, 18.07, 21.28, 18.76, 18.12, 17.67, 18.33, 17.97, 17.87, 17.66, 17.77, 17.96, 17.86, 17.77, 17.81, 17.94]
+327.16499999999996
+16.358249999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 83764, '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': 10.409894704818726, 'TIME_S_1KI': 0.12427647563175977, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 711.6688519239426, 'W': 50.42, 'J_1KI': 8.49611828379665, 'W_1KI': 0.6019292297407001, 'W_D': 34.06175, 'J_D': 480.7752185049654, 'W_D_1KI': 0.40663948713050957, 'J_D_1KI': 0.004854585348485144}
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 aaafc55..b3d9f1e 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": 34558, "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": 10.457140684127808, "TIME_S_1KI": 0.3025968135924477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 669.6752694511413, "W": 47.19, "J_1KI": 19.37829936486895, "W_1KI": 1.3655304126396204, "W_D": 30.795499999999997, "J_D": 437.020232260704, "W_D_1KI": 0.8911250651079344, "J_D_1KI": 0.02578636104832266}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 33076, "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": 10.000443935394287, "TIME_S_1KI": 0.30234744030095195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 713.14643699646, "W": 51.71, "J_1KI": 21.560842816436693, "W_1KI": 1.5633692103035435, "W_D": 35.256, "J_D": 486.2249232788086, "W_D_1KI": 1.065908816059983, "J_D_1KI": 0.03222604958459254}
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 0fdae4e..108ea7e 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', '1000', '-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": 0.3263256549835205}
+{"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": 0.31745004653930664}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     22,  ...,  99981,  99992,
+tensor(crow_indices=tensor([     0,     13,     25,  ...,  99974,  99988,
                             100000]),
-       col_indices=tensor([  85, 1274, 1422,  ..., 6599, 6784, 7278]),
-       values=tensor([0.2164, 0.2550, 1.0000,  ..., 0.9260, 0.0708, 0.0725]),
+       col_indices=tensor([ 189, 1046, 1680,  ..., 7652, 7822, 9876]),
+       values=tensor([0.3200, 0.6172, 0.8426,  ..., 0.6310, 0.2892, 0.4983]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6027, 0.7133, 0.6416,  ..., 0.5356, 0.1307, 0.5576])
+tensor([0.5979, 0.0691, 0.5787,  ..., 0.1637, 0.0173, 0.7657])
 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: 0.3263256549835205 seconds
+Time: 0.31745004653930664 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', '32176', '-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": 9.776132822036743}
+['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', '33076', '-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": 10.000443935394287}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99981,  99994,
+tensor(crow_indices=tensor([     0,      5,     16,  ...,  99975,  99984,
                             100000]),
-       col_indices=tensor([ 544,  706, 2472,  ..., 6055, 7261, 9945]),
-       values=tensor([0.4979, 0.3488, 0.7538,  ..., 0.1989, 0.3068, 0.3191]),
+       col_indices=tensor([2058, 2088, 2648,  ..., 8443, 9183, 9230]),
+       values=tensor([0.6058, 0.3120, 0.6569,  ..., 0.6120, 0.0868, 0.9498]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5709, 0.1010, 0.9044,  ..., 0.7157, 0.3275, 0.4556])
+tensor([0.4574, 0.0884, 0.9388,  ..., 0.4572, 0.8159, 0.8640])
 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: 9.776132822036743 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', '34558', '-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": 10.457140684127808}
+Time: 10.000443935394287 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99980,  99989,
+tensor(crow_indices=tensor([     0,      5,     16,  ...,  99975,  99984,
                             100000]),
-       col_indices=tensor([  44, 4326, 6855,  ..., 8487, 8731, 9188]),
-       values=tensor([0.5894, 0.7815, 0.8660,  ..., 0.0108, 0.2427, 0.5894]),
+       col_indices=tensor([2058, 2088, 2648,  ..., 8443, 9183, 9230]),
+       values=tensor([0.6058, 0.3120, 0.6569,  ..., 0.6120, 0.0868, 0.9498]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0531, 0.8679, 0.3068,  ..., 0.5318, 0.1294, 0.3589])
+tensor([0.4574, 0.0884, 0.9388,  ..., 0.4572, 0.8159, 0.8640])
 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: 10.457140684127808 seconds
+Time: 10.000443935394287 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99980,  99989,
-                            100000]),
-       col_indices=tensor([  44, 4326, 6855,  ..., 8487, 8731, 9188]),
-       values=tensor([0.5894, 0.7815, 0.8660,  ..., 0.0108, 0.2427, 0.5894]),
-       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0531, 0.8679, 0.3068,  ..., 0.5318, 0.1294, 0.3589])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 100000
-Density: 0.001
-Time: 10.457140684127808 seconds
-
-[19.5, 18.03, 18.32, 17.98, 18.23, 18.17, 18.6, 17.99, 18.33, 18.09]
-[47.19]
-14.191041946411133
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34558, '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': 10.457140684127808, 'TIME_S_1KI': 0.3025968135924477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 669.6752694511413, 'W': 47.19}
-[19.5, 18.03, 18.32, 17.98, 18.23, 18.17, 18.6, 17.99, 18.33, 18.09, 18.43, 17.95, 18.1, 18.53, 18.09, 18.56, 18.03, 17.91, 18.04, 18.04]
-327.89
-16.3945
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34558, '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': 10.457140684127808, 'TIME_S_1KI': 0.3025968135924477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 669.6752694511413, 'W': 47.19, 'J_1KI': 19.37829936486895, 'W_1KI': 1.3655304126396204, 'W_D': 30.795499999999997, 'J_D': 437.020232260704, 'W_D_1KI': 0.8911250651079344, 'J_D_1KI': 0.02578636104832266}
+[18.4, 17.89, 17.86, 18.15, 21.93, 17.6, 17.74, 17.84, 17.81, 17.8]
+[51.71]
+13.791267395019531
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33076, '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': 10.000443935394287, 'TIME_S_1KI': 0.30234744030095195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 713.14643699646, 'W': 51.71}
+[18.4, 17.89, 17.86, 18.15, 21.93, 17.6, 17.74, 17.84, 17.81, 17.8, 22.42, 17.97, 17.98, 17.67, 18.14, 18.06, 18.09, 18.36, 17.77, 17.82]
+329.08000000000004
+16.454
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33076, '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': 10.000443935394287, 'TIME_S_1KI': 0.30234744030095195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 713.14643699646, 'W': 51.71, 'J_1KI': 21.560842816436693, 'W_1KI': 1.5633692103035435, 'W_D': 35.256, 'J_D': 486.2249232788086, 'W_D_1KI': 1.065908816059983, 'J_D_1KI': 0.03222604958459254}
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 b18c025..ec86a79 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": 5537, "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": 10.417654037475586, "TIME_S_1KI": 1.8814618091882944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 687.7580058908463, "W": 47.74000000000001, "J_1KI": 124.21130682514833, "W_1KI": 8.621997471554996, "W_D": 31.48425000000001, "J_D": 453.5723711137177, "W_D_1KI": 5.686156763590393, "J_D_1KI": 1.0269381910042248}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5536, "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": 10.43858790397644, "TIME_S_1KI": 1.885583075140253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.4033546972274, "W": 52.18, "J_1KI": 136.81418979357431, "W_1KI": 9.425578034682081, "W_D": 35.855000000000004, "J_D": 520.4426462757588, "W_D_1KI": 6.476697976878613, "J_D_1KI": 1.1699237674997496}
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 42d7009..cdf759d 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', '1000', '-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": 1.8961181640625}
+{"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": 1.8966615200042725}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      96,     195,  ...,  999806,
-                             999906, 1000000]),
-       col_indices=tensor([  19,  113,  151,  ..., 9681, 9759, 9836]),
-       values=tensor([0.1144, 0.7732, 0.9749,  ..., 0.1321, 0.3947, 0.2714]),
+tensor(crow_indices=tensor([      0,      98,     196,  ...,  999798,
+                             999896, 1000000]),
+       col_indices=tensor([ 136,  346,  355,  ..., 9896, 9907, 9979]),
+       values=tensor([0.5884, 0.9037, 0.2601,  ..., 0.4944, 0.5993, 0.9598]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1354, 0.8257, 0.6569,  ..., 0.0257, 0.7874, 0.8457])
+tensor([0.5307, 0.6978, 0.6134,  ..., 0.5179, 0.0970, 0.9420])
 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: 1.8961181640625 seconds
+Time: 1.8966615200042725 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', '5537', '-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": 10.417654037475586}
+['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', '5536', '-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": 10.43858790397644}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     198,  ...,  999795,
-                             999893, 1000000]),
-       col_indices=tensor([ 194,  313,  451,  ..., 9690, 9776, 9879]),
-       values=tensor([0.2779, 0.8250, 0.2083,  ..., 0.7384, 0.0572, 0.6638]),
+tensor(crow_indices=tensor([      0,      90,     180,  ...,  999793,
+                             999892, 1000000]),
+       col_indices=tensor([  10,   80,  127,  ..., 9954, 9956, 9988]),
+       values=tensor([0.2975, 0.8577, 0.6251,  ..., 0.1783, 0.1753, 0.5886]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5010, 0.3969, 0.7780,  ..., 0.5969, 0.2345, 0.7915])
+tensor([0.4757, 0.0822, 0.0813,  ..., 0.4411, 0.1352, 0.6104])
 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: 10.417654037475586 seconds
+Time: 10.43858790397644 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     198,  ...,  999795,
-                             999893, 1000000]),
-       col_indices=tensor([ 194,  313,  451,  ..., 9690, 9776, 9879]),
-       values=tensor([0.2779, 0.8250, 0.2083,  ..., 0.7384, 0.0572, 0.6638]),
+tensor(crow_indices=tensor([      0,      90,     180,  ...,  999793,
+                             999892, 1000000]),
+       col_indices=tensor([  10,   80,  127,  ..., 9954, 9956, 9988]),
+       values=tensor([0.2975, 0.8577, 0.6251,  ..., 0.1783, 0.1753, 0.5886]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5010, 0.3969, 0.7780,  ..., 0.5969, 0.2345, 0.7915])
+tensor([0.4757, 0.0822, 0.0813,  ..., 0.4411, 0.1352, 0.6104])
 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: 10.417654037475586 seconds
+Time: 10.43858790397644 seconds
 
-[18.42, 18.05, 18.03, 18.24, 18.1, 18.01, 17.83, 17.89, 18.16, 18.06]
-[47.74]
-14.406326055526733
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5537, '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': 10.417654037475586, 'TIME_S_1KI': 1.8814618091882944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 687.7580058908463, 'W': 47.74000000000001}
-[18.42, 18.05, 18.03, 18.24, 18.1, 18.01, 17.83, 17.89, 18.16, 18.06, 18.38, 17.81, 18.07, 18.25, 18.24, 17.94, 18.02, 18.06, 18.02, 17.93]
-325.115
-16.25575
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5537, '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': 10.417654037475586, 'TIME_S_1KI': 1.8814618091882944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 687.7580058908463, 'W': 47.74000000000001, 'J_1KI': 124.21130682514833, 'W_1KI': 8.621997471554996, 'W_D': 31.48425000000001, 'J_D': 453.5723711137177, 'W_D_1KI': 5.686156763590393, 'J_D_1KI': 1.0269381910042248}
+[18.3, 18.15, 17.72, 17.62, 17.65, 18.51, 19.15, 17.59, 17.78, 18.09]
+[52.18]
+14.515204191207886
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5536, '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': 10.43858790397644, 'TIME_S_1KI': 1.885583075140253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.4033546972274, 'W': 52.18}
+[18.3, 18.15, 17.72, 17.62, 17.65, 18.51, 19.15, 17.59, 17.78, 18.09, 18.11, 18.06, 18.06, 18.01, 19.44, 18.82, 18.14, 17.87, 17.86, 17.64]
+326.5
+16.325
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5536, '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': 10.43858790397644, 'TIME_S_1KI': 1.885583075140253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.4033546972274, 'W': 52.18, 'J_1KI': 136.81418979357431, 'W_1KI': 9.425578034682081, 'W_D': 35.855000000000004, 'J_D': 520.4426462757588, 'W_D_1KI': 6.476697976878613, 'J_D_1KI': 1.1699237674997496}
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 8a616ae..4abbf3f 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": 1000, "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": 10.884310483932495, "TIME_S_1KI": 10.884310483932495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 825.0359781861306, "W": 47.81, "J_1KI": 825.0359781861306, "W_1KI": 47.81, "W_D": 31.353, "J_D": 541.0448237621785, "W_D_1KI": 31.352999999999998, "J_D_1KI": 31.352999999999998}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "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": 10.659594058990479, "TIME_S_1KI": 10.659594058990479, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 944.4377518177032, "W": 52.04, "J_1KI": 944.4377518177032, "W_1KI": 52.04, "W_D": 22.411249999999995, "J_D": 406.72618304044, "W_D_1KI": 22.411249999999995, "J_D_1KI": 22.411249999999995}
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 aad2263..e447139 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', '1000', '-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": 10.884310483932495}
+{"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": 10.659594058990479}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     518,    1026,  ..., 4999007,
-                            4999483, 5000000]),
-       col_indices=tensor([   3,   39,   78,  ..., 9968, 9975, 9994]),
-       values=tensor([0.2142, 0.4373, 0.1249,  ..., 0.9529, 0.9095, 0.5518]),
+tensor(crow_indices=tensor([      0,     491,     987,  ..., 4999032,
+                            4999549, 5000000]),
+       col_indices=tensor([   2,   62,   63,  ..., 9943, 9957, 9997]),
+       values=tensor([0.7700, 0.3306, 0.4646,  ..., 0.1296, 0.2152, 0.2390]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2404, 0.3133, 0.0015,  ..., 0.7254, 0.6117, 0.4995])
+tensor([0.3463, 0.4470, 0.4445,  ..., 0.6886, 0.1263, 0.4488])
 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: 10.884310483932495 seconds
+Time: 10.659594058990479 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     518,    1026,  ..., 4999007,
-                            4999483, 5000000]),
-       col_indices=tensor([   3,   39,   78,  ..., 9968, 9975, 9994]),
-       values=tensor([0.2142, 0.4373, 0.1249,  ..., 0.9529, 0.9095, 0.5518]),
+tensor(crow_indices=tensor([      0,     491,     987,  ..., 4999032,
+                            4999549, 5000000]),
+       col_indices=tensor([   2,   62,   63,  ..., 9943, 9957, 9997]),
+       values=tensor([0.7700, 0.3306, 0.4646,  ..., 0.1296, 0.2152, 0.2390]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2404, 0.3133, 0.0015,  ..., 0.7254, 0.6117, 0.4995])
+tensor([0.3463, 0.4470, 0.4445,  ..., 0.6886, 0.1263, 0.4488])
 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: 10.884310483932495 seconds
+Time: 10.659594058990479 seconds
 
-[18.07, 17.88, 18.1, 21.37, 18.24, 18.16, 18.18, 18.01, 18.03, 17.87]
-[47.81]
-17.256556749343872
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 10.884310483932495, 'TIME_S_1KI': 10.884310483932495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 825.0359781861306, 'W': 47.81}
-[18.07, 17.88, 18.1, 21.37, 18.24, 18.16, 18.18, 18.01, 18.03, 17.87, 18.49, 18.1, 18.43, 18.06, 18.42, 17.93, 18.03, 17.8, 18.13, 18.11]
-329.14
-16.457
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 10.884310483932495, 'TIME_S_1KI': 10.884310483932495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 825.0359781861306, 'W': 47.81, 'J_1KI': 825.0359781861306, 'W_1KI': 47.81, 'W_D': 31.353, 'J_D': 541.0448237621785, 'W_D_1KI': 31.352999999999998, 'J_D_1KI': 31.352999999999998}
+[18.53, 17.91, 17.8, 17.55, 17.82, 18.2, 17.7, 17.96, 22.04, 18.01]
+[52.04]
+18.148304224014282
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 10.659594058990479, 'TIME_S_1KI': 10.659594058990479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 944.4377518177032, 'W': 52.04}
+[18.53, 17.91, 17.8, 17.55, 17.82, 18.2, 17.7, 17.96, 22.04, 18.01, 43.17, 47.09, 51.95, 51.61, 51.61, 46.81, 50.13, 50.7, 46.45, 18.78]
+592.575
+29.628750000000004
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 10.659594058990479, 'TIME_S_1KI': 10.659594058990479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 944.4377518177032, 'W': 52.04, 'J_1KI': 944.4377518177032, 'W_1KI': 52.04, 'W_D': 22.411249999999995, 'J_D': 406.72618304044, 'W_D_1KI': 22.411249999999995, 'J_D_1KI': 22.411249999999995}
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
new file mode 100644
index 0000000..3316ce4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "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": 26.374675512313843, "TIME_S_1KI": 26.374675512313843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1870.3769276547432, "W": 52.87, "J_1KI": 1870.3769276547432, "W_1KI": 52.87, "W_D": 36.2005, "J_D": 1280.661622272849, "W_D_1KI": 36.2005, "J_D_1KI": 36.2005}
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
new file mode 100644
index 0000000..fb57672
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 26.374675512313843}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1071,     2053,  ...,  9998024,
+                             9999000, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9980, 9985, 9995]),
+       values=tensor([0.3665, 0.1961, 0.0802,  ..., 0.1951, 0.2808, 0.5332]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6172, 0.4719, 0.5685,  ..., 0.7751, 0.3390, 0.5446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 26.374675512313843 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1071,     2053,  ...,  9998024,
+                             9999000, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9980, 9985, 9995]),
+       values=tensor([0.3665, 0.1961, 0.0802,  ..., 0.1951, 0.2808, 0.5332]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6172, 0.4719, 0.5685,  ..., 0.7751, 0.3390, 0.5446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 26.374675512313843 seconds
+
+[18.71, 18.02, 18.12, 18.01, 22.7, 19.43, 18.17, 18.48, 18.54, 18.36]
+[52.87]
+35.376904249191284
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 26.374675512313843, 'TIME_S_1KI': 26.374675512313843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.3769276547432, 'W': 52.87}
+[18.71, 18.02, 18.12, 18.01, 22.7, 19.43, 18.17, 18.48, 18.54, 18.36, 18.41, 17.86, 17.86, 18.01, 17.87, 17.66, 17.87, 17.6, 18.07, 22.76]
+333.39
+16.6695
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 26.374675512313843, 'TIME_S_1KI': 26.374675512313843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.3769276547432, 'W': 52.87, 'J_1KI': 1870.3769276547432, 'W_1KI': 52.87, 'W_D': 36.2005, 'J_D': 1280.661622272849, 'W_D_1KI': 36.2005, 'J_D_1KI': 36.2005}
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 a0720a2..bb313c5 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": 225343, "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": 10.386851072311401, "TIME_S_1KI": 0.04609351553991649, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.4945677185059, "W": 46.24, "J_1KI": 2.935500848566434, "W_1KI": 0.20519829770616352, "W_D": 30.072000000000003, "J_D": 430.2003598709107, "W_D_1KI": 0.1334498963801849, "J_D_1KI": 0.0005922078625925141}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 225815, "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": 10.501307487487793, "TIME_S_1KI": 0.046504029792032386, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 725.5277880001069, "W": 50.86, "J_1KI": 3.2129300002218932, "W_1KI": 0.22522861634523836, "W_D": 34.5345, "J_D": 492.64135656094555, "W_D_1KI": 0.15293271040453468, "J_D_1KI": 0.0006772477931250567}
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 6742334..9f5c396 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
@@ -1,1131 +1,373 @@
 ['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', '1000', '-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": 0.06630802154541016}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([6116, 9123, 2230, 4007, 2708, 6506, 8700, 3316, 2761,
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-tensor([0.5197, 0.1343, 0.9407,  ..., 0.1023, 0.5237, 0.0220])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 0.06630802154541016 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', '158351', '-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": 7.378459692001343}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the 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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-                      1.2285e-01, 6.1279e-01, 7.3482e-01, 6.7204e-01]),
-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4203, 0.6136, 0.5758,  ..., 0.4091, 0.3563, 0.6125])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 7.378459692001343 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', '225343', '-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": 10.386851072311401}
+{"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": 0.06352877616882324}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0633, 0.2712, 0.1613,  ..., 0.7795, 0.8074, 0.9414])
+tensor([0.7941, 0.9355, 0.0308,  ..., 0.8188, 0.6700, 0.4642])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.386851072311401 seconds
+Time: 0.06352877616882324 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', '165279', '-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": 7.6851487159729}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      5.8933e-02, 5.3074e-01, 3.6413e-01, 2.2481e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0633, 0.2712, 0.1613,  ..., 0.7795, 0.8074, 0.9414])
+tensor([0.5425, 0.1860, 0.7804,  ..., 0.3375, 0.7494, 0.4073])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +754,554 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.386851072311401 seconds
+Time: 7.6851487159729 seconds
 
-[18.39, 17.96, 17.95, 18.06, 18.03, 17.74, 18.17, 18.27, 17.97, 17.88]
-[46.24]
-14.305678367614746
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225343, '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': 10.386851072311401, 'TIME_S_1KI': 0.04609351553991649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.4945677185059, 'W': 46.24}
-[18.39, 17.96, 17.95, 18.06, 18.03, 17.74, 18.17, 18.27, 17.97, 17.88, 18.01, 17.95, 18.03, 17.68, 17.83, 17.84, 17.98, 17.81, 17.89, 18.12]
-323.35999999999996
-16.168
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225343, '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': 10.386851072311401, 'TIME_S_1KI': 0.04609351553991649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.4945677185059, 'W': 46.24, 'J_1KI': 2.935500848566434, 'W_1KI': 0.20519829770616352, 'W_D': 30.072000000000003, 'J_D': 430.2003598709107, 'W_D_1KI': 0.1334498963801849, 'J_D_1KI': 0.0005922078625925141}
+['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', '225815', '-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": 10.501307487487793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 621, 8067, 8701, 6486, 5538, 5824,  379, 1918, 5000,
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+                      0.6725, 0.8987, 0.3589, 0.1096, 0.3360, 0.7261]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5928, 0.5923, 0.2769,  ..., 0.7868, 0.2495, 0.0989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.501307487487793 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 621, 8067, 8701, 6486, 5538, 5824,  379, 1918, 5000,
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+                      0.9111, 0.6567, 0.5216, 0.9797, 0.3080, 0.5219, 0.5582,
+                      0.3741, 0.2265, 0.1644, 0.9904, 0.9096, 0.6977, 0.7378,
+                      0.6163, 0.2973, 0.5170, 0.1559, 0.9194, 0.1883, 0.3665,
+                      0.6107, 0.4618, 0.4740, 0.0187, 0.8554, 0.8371, 0.2227,
+                      0.9554, 0.1756, 0.1396, 0.9691, 0.1329, 0.2855, 0.2852,
+                      0.5955, 0.2284, 0.7196, 0.1940, 0.7502, 0.5298, 0.9924,
+                      0.7439, 0.6637, 0.2900, 0.6522, 0.0204, 0.0933, 0.3725,
+                      0.2785, 0.1362, 0.2224, 0.2585, 0.4365, 0.6210, 0.9700,
+                      0.5368, 0.3375, 0.9444, 0.7297, 0.5092, 0.3376, 0.8591,
+                      0.9008, 0.5379, 0.9510, 0.9421, 0.0713, 0.2091, 0.6298,
+                      0.4005, 0.7776, 0.4989, 0.8706, 0.2922, 0.3070, 0.0813,
+                      0.5714, 0.2514, 0.4480, 0.5234, 0.0360, 0.0758, 0.3730,
+                      0.7634, 0.0856, 0.8934, 0.6490, 0.5291, 0.3184, 0.3037,
+                      0.3443, 0.5792, 0.5490, 0.5327, 0.4678, 0.2006, 0.8032,
+                      0.9436, 0.9189, 0.7953, 0.9640, 0.0058, 0.7983, 0.6143,
+                      0.9639, 0.7438, 0.7417, 0.1685, 0.5369, 0.7292, 0.5840,
+                      0.3088, 0.6745, 0.1144, 0.4342, 0.4148, 0.3081, 0.3141,
+                      0.7523, 0.9548, 0.1159, 0.9214, 0.7606, 0.1319, 0.0108,
+                      0.5213, 0.7386, 0.2722, 0.3665, 0.1771, 0.0556, 0.9859,
+                      0.0054, 0.2965, 0.3687, 0.8425, 0.4479, 0.3524, 0.2824,
+                      0.4727, 0.3410, 0.8501, 0.7125, 0.9448, 0.2352, 0.2937,
+                      0.6534, 0.3739, 0.6671, 0.8901, 0.3611, 0.4255, 0.2194,
+                      0.1776, 0.7356, 0.6315, 0.2672, 0.6863, 0.5095, 0.5719,
+                      0.6850, 0.4423, 0.4707, 0.8096, 0.8117, 0.4330, 0.1565,
+                      0.6528, 0.3855, 0.7960, 0.8457, 0.9509, 0.4271, 0.9874,
+                      0.2012, 0.1858, 0.4458, 0.2549, 0.0069, 0.9617, 0.8083,
+                      0.8286, 0.3440, 0.6245, 0.1685, 0.2887, 0.8351, 0.3371,
+                      0.8326, 0.2133, 0.4971, 0.1969, 0.9997, 0.0108, 0.8265,
+                      0.2603, 0.2486, 0.1123, 0.3933, 0.1972, 0.1286, 0.2410,
+                      0.9427, 0.8517, 0.1056, 0.4498, 0.3528, 0.4997, 0.1873,
+                      0.9308, 0.5270, 0.1818, 0.0192, 0.5147, 0.6995, 0.3808,
+                      0.1801, 0.1254, 0.8616, 0.9334, 0.6635, 0.6097, 0.9317,
+                      0.0639, 0.5688, 0.1321, 0.2406, 0.0702, 0.3025, 0.5470,
+                      0.2516, 0.7748, 0.5947, 0.3008, 0.0640, 0.6075, 0.7333,
+                      0.2489, 0.8087, 0.9366, 0.5301, 0.5997, 0.9710, 0.1167,
+                      0.8349, 0.0943, 0.3077, 0.1781, 0.0964, 0.5114, 0.2721,
+                      0.4485, 0.5358, 0.9249, 0.1281, 0.7787, 0.9696, 0.7641,
+                      0.9267, 0.8005, 0.2565, 0.9156, 0.0177, 0.4399, 0.6139,
+                      0.6561, 0.2921, 0.2455, 0.0677, 0.8007, 0.1673, 0.6272,
+                      0.9708, 0.7757, 0.7844, 0.4143, 0.7244, 0.3178, 0.2015,
+                      0.7092, 0.6947, 0.5693, 0.4803, 0.3248, 0.5740, 0.1234,
+                      0.7532, 0.8432, 0.5693, 0.7346, 0.8612, 0.6697, 0.0106,
+                      0.9867, 0.9456, 0.7600, 0.4214, 0.7451, 0.1923, 0.8683,
+                      0.7611, 0.2801, 0.7750, 0.6682, 0.9146, 0.0443, 0.5069,
+                      0.8481, 0.2446, 0.9950, 0.4152, 0.1745, 0.1637, 0.2428,
+                      0.6569, 0.4895, 0.4090, 0.9641, 0.2949, 0.4773, 0.4363,
+                      0.3083, 0.0374, 0.4019, 0.0579, 0.0686, 0.4139, 0.1952,
+                      0.0362, 0.0311, 0.8833, 0.6790, 0.8853, 0.7058, 0.2329,
+                      0.9566, 0.6724, 0.9699, 0.2549, 0.0950, 0.9403, 0.8375,
+                      0.7841, 0.9563, 0.7054, 0.1040, 0.8158, 0.8050, 0.1655,
+                      0.1587, 0.3559, 0.9451, 0.7518, 0.4895, 0.3845, 0.5434,
+                      0.2137, 0.1454, 0.9690, 0.7963, 0.0141, 0.6547, 0.3894,
+                      0.1222, 0.1580, 0.2599, 0.1493, 0.3838, 0.7658, 0.2010,
+                      0.8027, 0.8916, 0.5453, 0.4941, 0.7164, 0.8434, 0.4222,
+                      0.2960, 0.0732, 0.0275, 0.3155, 0.9582, 0.2006, 0.5100,
+                      0.3835, 0.7693, 0.0825, 0.5403, 0.0058, 0.5775, 0.4579,
+                      0.7231, 0.5432, 0.1385, 0.9942, 0.7281, 0.1494, 0.2026,
+                      0.0383, 0.2089, 0.1678, 0.3352, 0.3308, 0.9511, 0.9479,
+                      0.3486, 0.6349, 0.3617, 0.1790, 0.9290, 0.9003, 0.7128,
+                      0.2573, 0.5128, 0.2734, 0.8333, 0.7565, 0.2114, 0.0550,
+                      0.1806, 0.7785, 0.3657, 0.1751, 0.5105, 0.4138, 0.6648,
+                      0.0988, 0.5462, 0.5957, 0.0841, 0.7102, 0.7202, 0.1878,
+                      0.8772, 0.4902, 0.6123, 0.9058, 0.6380, 0.2081, 0.1591,
+                      0.1205, 0.1672, 0.9664, 0.6634, 0.6646, 0.2765, 0.4269,
+                      0.5829, 0.8144, 0.4256, 0.2309, 0.0615, 0.0926, 0.0573,
+                      0.0407, 0.6434, 0.2531, 0.0667, 0.6337, 0.0241, 0.7295,
+                      0.9133, 0.7637, 0.9403, 0.7968, 0.3787, 0.2873, 0.0225,
+                      0.1808, 0.3069, 0.1014, 0.5506, 0.2895, 0.7186, 0.5054,
+                      0.4710, 0.7466, 0.0956, 0.6689, 0.1987, 0.9374, 0.4713,
+                      0.8415, 0.9657, 0.9038, 0.1088, 0.8688, 0.2488, 0.6505,
+                      0.9593, 0.4610, 0.7212, 0.1418, 0.6637, 0.4566, 0.9474,
+                      0.6935, 0.5967, 0.6983, 0.4944, 0.8522, 0.4429, 0.6141,
+                      0.6725, 0.8987, 0.3589, 0.1096, 0.3360, 0.7261]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5928, 0.5923, 0.2769,  ..., 0.7868, 0.2495, 0.0989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.501307487487793 seconds
+
+[18.25, 18.14, 17.95, 17.87, 17.91, 17.96, 17.87, 17.59, 18.03, 17.97]
+[50.86]
+14.265194416046143
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225815, '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': 10.501307487487793, 'TIME_S_1KI': 0.046504029792032386, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 725.5277880001069, 'W': 50.86}
+[18.25, 18.14, 17.95, 17.87, 17.91, 17.96, 17.87, 17.59, 18.03, 17.97, 17.9, 17.6, 18.05, 17.8, 18.18, 17.66, 17.82, 21.98, 18.23, 17.62]
+326.51
+16.325499999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225815, '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': 10.501307487487793, 'TIME_S_1KI': 0.046504029792032386, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 725.5277880001069, 'W': 50.86, 'J_1KI': 3.2129300002218932, 'W_1KI': 0.22522861634523836, 'W_D': 34.5345, 'J_D': 492.64135656094555, 'W_D_1KI': 0.15293271040453468, 'J_D_1KI': 0.0006772477931250567}
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 d7d5020..f1d93dd 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": 1000, "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": 13.995913743972778, "TIME_S_1KI": 13.995913743972778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 871.8022893977165, "W": 48.71, "J_1KI": 871.8022893977165, "W_1KI": 48.71, "W_D": 32.347, "J_D": 578.9404363610745, "W_D_1KI": 32.347, "J_D_1KI": 32.347}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "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": 13.435759782791138, "TIME_S_1KI": 13.435759782791138, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 971.6288854122162, "W": 53.4, "J_1KI": 971.6288854122162, "W_1KI": 53.4, "W_D": 37.34824999999999, "J_D": 679.562519093573, "W_D_1KI": 37.34824999999999, "J_D_1KI": 37.34824999999999}
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 530a6a0..35674e7 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
@@ -1,15 +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', '1000', '-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": 13.995913743972778}
+{"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": 13.435759782791138}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
-                            2499997, 2500000]),
-       col_indices=tensor([ 17718, 235055,  35243,  ...,  14166, 348855,
-                           416543]),
-       values=tensor([0.0021, 0.9166, 0.2725,  ..., 0.7498, 0.6792, 0.5299]),
+tensor(crow_indices=tensor([      0,       3,       8,  ..., 2499995,
+                            2499996, 2500000]),
+       col_indices=tensor([ 61754, 291279, 469696,  ..., 173785, 177543,
+                           423232]),
+       values=tensor([0.5269, 0.9088, 0.4901,  ..., 0.3381, 0.9016, 0.0517]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0136, 0.8273, 0.9896,  ..., 0.5941, 0.9828, 0.6210])
+tensor([0.7575, 0.8230, 0.6656,  ..., 0.2327, 0.7437, 0.7040])
 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: 13.995913743972778 seconds
+Time: 13.435759782791138 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
-                            2499997, 2500000]),
-       col_indices=tensor([ 17718, 235055,  35243,  ...,  14166, 348855,
-                           416543]),
-       values=tensor([0.0021, 0.9166, 0.2725,  ..., 0.7498, 0.6792, 0.5299]),
+tensor(crow_indices=tensor([      0,       3,       8,  ..., 2499995,
+                            2499996, 2500000]),
+       col_indices=tensor([ 61754, 291279, 469696,  ..., 173785, 177543,
+                           423232]),
+       values=tensor([0.5269, 0.9088, 0.4901,  ..., 0.3381, 0.9016, 0.0517]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0136, 0.8273, 0.9896,  ..., 0.5941, 0.9828, 0.6210])
+tensor([0.7575, 0.8230, 0.6656,  ..., 0.2327, 0.7437, 0.7040])
 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: 13.995913743972778 seconds
+Time: 13.435759782791138 seconds
 
-[18.5, 17.83, 21.98, 17.84, 18.21, 18.0, 17.97, 17.87, 17.99, 17.99]
-[48.71]
-17.897809267044067
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 13.995913743972778, 'TIME_S_1KI': 13.995913743972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 871.8022893977165, 'W': 48.71}
-[18.5, 17.83, 21.98, 17.84, 18.21, 18.0, 17.97, 17.87, 17.99, 17.99, 18.27, 17.72, 18.53, 17.6, 18.07, 17.75, 17.78, 17.88, 17.93, 17.86]
-327.26
-16.363
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 13.995913743972778, 'TIME_S_1KI': 13.995913743972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 871.8022893977165, 'W': 48.71, 'J_1KI': 871.8022893977165, 'W_1KI': 48.71, 'W_D': 32.347, 'J_D': 578.9404363610745, 'W_D_1KI': 32.347, 'J_D_1KI': 32.347}
+[18.26, 17.5, 18.12, 17.51, 17.77, 17.68, 17.73, 17.56, 17.86, 17.52]
+[53.4]
+18.195297479629517
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 13.435759782791138, 'TIME_S_1KI': 13.435759782791138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 971.6288854122162, 'W': 53.4}
+[18.26, 17.5, 18.12, 17.51, 17.77, 17.68, 17.73, 17.56, 17.86, 17.52, 18.07, 17.37, 18.42, 19.11, 17.73, 17.65, 18.23, 17.55, 17.59, 17.46]
+321.035
+16.051750000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, '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': 13.435759782791138, 'TIME_S_1KI': 13.435759782791138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 971.6288854122162, 'W': 53.4, 'J_1KI': 971.6288854122162, 'W_1KI': 53.4, 'W_D': 37.34824999999999, 'J_D': 679.562519093573, 'W_D_1KI': 37.34824999999999, 'J_D_1KI': 37.34824999999999}
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 d6c035a..af9c51f 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": 8984, "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": 10.250720024108887, "TIME_S_1KI": 1.1409973312676855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 667.435348098278, "W": 47.13, "J_1KI": 74.29155700114404, "W_1KI": 5.245992876224399, "W_D": 30.525750000000002, "J_D": 432.29290424805885, "W_D_1KI": 3.3977905164737314, "J_D_1KI": 0.37820464341871457}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9021, "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": 10.444438695907593, "TIME_S_1KI": 1.1577916745269474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.9580717468261, "W": 51.98, "J_1KI": 81.58275931125442, "W_1KI": 5.762110630750471, "W_D": 35.674749999999996, "J_D": 505.1004274730682, "W_D_1KI": 3.9546336326349625, "J_D_1KI": 0.43838084831337576}
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 eb4a2d8..945b881 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', '1000', '-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": 1.1686315536499023}
+{"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": 1.1638367176055908}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     12,  ..., 249984, 249988,
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 249989, 249992,
                             250000]),
-       col_indices=tensor([ 9222, 11801, 17371,  ..., 41613, 43396, 49641]),
-       values=tensor([0.5050, 0.7653, 0.0671,  ..., 0.1421, 0.6855, 0.0275]),
+       col_indices=tensor([ 5085, 27218, 28258,  ..., 33170, 33475, 34242]),
+       values=tensor([0.4699, 0.9594, 0.0965,  ..., 0.7443, 0.7286, 0.0273]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2330, 0.0304, 0.5518,  ..., 0.1557, 0.6263, 0.0730])
+tensor([0.3938, 0.4910, 0.8553,  ..., 0.5913, 0.5925, 0.7936])
 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: 1.1686315536499023 seconds
+Time: 1.1638367176055908 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', '8984', '-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": 10.250720024108887}
+['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', '9021', '-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": 10.444438695907593}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249994, 249997,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 249988, 249994,
                             250000]),
-       col_indices=tensor([ 1146,  2450, 11327,  ...,   241,  2629, 25085]),
-       values=tensor([0.2696, 0.3732, 0.9366,  ..., 0.5943, 0.0784, 0.3144]),
+       col_indices=tensor([ 3969, 14280, 16197,  ..., 14337, 15782, 32993]),
+       values=tensor([0.2139, 0.2141, 0.1060,  ..., 0.9818, 0.6790, 0.2416]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7229, 0.2746, 0.7643,  ..., 0.7812, 0.8470, 0.7243])
+tensor([0.8858, 0.9490, 0.2990,  ..., 0.1473, 0.1815, 0.8776])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.250720024108887 seconds
+Time: 10.444438695907593 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249994, 249997,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 249988, 249994,
                             250000]),
-       col_indices=tensor([ 1146,  2450, 11327,  ...,   241,  2629, 25085]),
-       values=tensor([0.2696, 0.3732, 0.9366,  ..., 0.5943, 0.0784, 0.3144]),
+       col_indices=tensor([ 3969, 14280, 16197,  ..., 14337, 15782, 32993]),
+       values=tensor([0.2139, 0.2141, 0.1060,  ..., 0.9818, 0.6790, 0.2416]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7229, 0.2746, 0.7643,  ..., 0.7812, 0.8470, 0.7243])
+tensor([0.8858, 0.9490, 0.2990,  ..., 0.1473, 0.1815, 0.8776])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.250720024108887 seconds
+Time: 10.444438695907593 seconds
 
-[18.69, 18.1, 18.11, 18.0, 21.83, 17.95, 18.22, 18.09, 18.15, 18.1]
-[47.13]
-14.161581754684448
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8984, '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': 10.250720024108887, 'TIME_S_1KI': 1.1409973312676855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 667.435348098278, 'W': 47.13}
-[18.69, 18.1, 18.11, 18.0, 21.83, 17.95, 18.22, 18.09, 18.15, 18.1, 21.63, 17.85, 18.41, 18.24, 17.96, 18.09, 18.1, 17.94, 18.92, 17.83]
-332.08500000000004
-16.60425
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8984, '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': 10.250720024108887, 'TIME_S_1KI': 1.1409973312676855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 667.435348098278, 'W': 47.13, 'J_1KI': 74.29155700114404, 'W_1KI': 5.245992876224399, 'W_D': 30.525750000000002, 'J_D': 432.29290424805885, 'W_D_1KI': 3.3977905164737314, 'J_D_1KI': 0.37820464341871457}
+[18.28, 18.06, 17.98, 18.51, 17.98, 18.02, 19.12, 19.17, 17.99, 18.0]
+[51.98]
+14.158485412597656
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9021, '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': 10.444438695907593, 'TIME_S_1KI': 1.1577916745269474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.9580717468261, 'W': 51.98}
+[18.28, 18.06, 17.98, 18.51, 17.98, 18.02, 19.12, 19.17, 17.99, 18.0, 18.49, 17.73, 18.15, 17.89, 18.02, 17.91, 17.85, 17.6, 17.85, 17.78]
+326.105
+16.30525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9021, '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': 10.444438695907593, 'TIME_S_1KI': 1.1577916745269474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.9580717468261, 'W': 51.98, 'J_1KI': 81.58275931125442, 'W_1KI': 5.762110630750471, 'W_D': 35.674749999999996, 'J_D': 505.1004274730682, 'W_D_1KI': 3.9546336326349625, 'J_D_1KI': 0.43838084831337576}
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 6747ace..0738cf0 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": 1969, "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": 10.35115671157837, "TIME_S_1KI": 5.25706282964874, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.7024494171142, "W": 48.48, "J_1KI": 369.5797102169193, "W_1KI": 24.62163534789233, "W_D": 32.2385, "J_D": 483.9116216075421, "W_D_1KI": 16.373031995937023, "J_D_1KI": 8.315404771933482}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1973, "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": 10.479424238204956, "TIME_S_1KI": 5.311416238319795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 791.6620005321503, "W": 53.24, "J_1KI": 401.2478461896352, "W_1KI": 26.984287886467307, "W_D": 37.20700000000001, "J_D": 553.2563496205807, "W_D_1KI": 18.858084135833757, "J_D_1KI": 9.558076095202107}
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 edef138..3bd1995 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', '1000', '-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": 5.33142876625061}
+{"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": 5.321045160293579}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     102,  ..., 2499894,
-                            2499942, 2500000]),
-       col_indices=tensor([  362,   476,   734,  ..., 42817, 42901, 48624]),
-       values=tensor([0.1861, 0.1141, 0.9529,  ..., 0.0521, 0.7769, 0.3485]),
+tensor(crow_indices=tensor([      0,      54,      97,  ..., 2499896,
+                            2499948, 2500000]),
+       col_indices=tensor([  176,   180,   853,  ..., 47415, 47956, 49304]),
+       values=tensor([0.4358, 0.1204, 0.8362,  ..., 0.7793, 0.3332, 0.4077]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6800, 0.1652, 0.7606,  ..., 0.1973, 0.6571, 0.7552])
+tensor([0.9660, 0.1174, 0.2174,  ..., 0.0235, 0.8944, 0.4447])
 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: 5.33142876625061 seconds
+Time: 5.321045160293579 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', '1969', '-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": 10.35115671157837}
+['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', '1973', '-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": 10.479424238204956}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     106,  ..., 2499882,
-                            2499933, 2500000]),
-       col_indices=tensor([  752,  1386,  1561,  ..., 49182, 49404, 49846]),
-       values=tensor([0.0219, 0.4602, 0.8212,  ..., 0.9720, 0.3228, 0.9373]),
+tensor(crow_indices=tensor([      0,      43,     100,  ..., 2499912,
+                            2499964, 2500000]),
+       col_indices=tensor([  471,   539,  1515,  ..., 46324, 49367, 49678]),
+       values=tensor([0.0688, 0.1954, 0.6278,  ..., 0.4403, 0.6708, 0.8543]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3378, 0.8054, 0.7422,  ..., 0.6857, 0.1927, 0.4134])
+tensor([0.7713, 0.8001, 0.0882,  ..., 0.6644, 0.4702, 0.2491])
 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: 10.35115671157837 seconds
+Time: 10.479424238204956 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     106,  ..., 2499882,
-                            2499933, 2500000]),
-       col_indices=tensor([  752,  1386,  1561,  ..., 49182, 49404, 49846]),
-       values=tensor([0.0219, 0.4602, 0.8212,  ..., 0.9720, 0.3228, 0.9373]),
+tensor(crow_indices=tensor([      0,      43,     100,  ..., 2499912,
+                            2499964, 2500000]),
+       col_indices=tensor([  471,   539,  1515,  ..., 46324, 49367, 49678]),
+       values=tensor([0.0688, 0.1954, 0.6278,  ..., 0.4403, 0.6708, 0.8543]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3378, 0.8054, 0.7422,  ..., 0.6857, 0.1927, 0.4134])
+tensor([0.7713, 0.8001, 0.0882,  ..., 0.6644, 0.4702, 0.2491])
 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: 10.35115671157837 seconds
+Time: 10.479424238204956 seconds
 
-[18.43, 18.02, 18.06, 17.85, 18.04, 18.18, 18.02, 17.8, 18.02, 18.51]
-[48.48]
-15.010364055633545
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1969, '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': 10.35115671157837, 'TIME_S_1KI': 5.25706282964874, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7024494171142, 'W': 48.48}
-[18.43, 18.02, 18.06, 17.85, 18.04, 18.18, 18.02, 17.8, 18.02, 18.51, 18.08, 17.97, 18.18, 18.15, 17.97, 18.08, 18.16, 17.86, 18.05, 17.82]
-324.83
-16.2415
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1969, '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': 10.35115671157837, 'TIME_S_1KI': 5.25706282964874, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7024494171142, 'W': 48.48, 'J_1KI': 369.5797102169193, 'W_1KI': 24.62163534789233, 'W_D': 32.2385, 'J_D': 483.9116216075421, 'W_D_1KI': 16.373031995937023, 'J_D_1KI': 8.315404771933482}
+[18.09, 17.68, 17.78, 17.6, 17.88, 17.97, 17.49, 17.67, 17.75, 17.8]
+[53.24]
+14.86968445777893
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1973, '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': 10.479424238204956, 'TIME_S_1KI': 5.311416238319795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.6620005321503, 'W': 53.24}
+[18.09, 17.68, 17.78, 17.6, 17.88, 17.97, 17.49, 17.67, 17.75, 17.8, 18.63, 17.66, 18.2, 17.66, 17.95, 17.62, 18.05, 17.65, 17.58, 18.42]
+320.65999999999997
+16.032999999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1973, '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': 10.479424238204956, 'TIME_S_1KI': 5.311416238319795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.6620005321503, 'W': 53.24, 'J_1KI': 401.2478461896352, 'W_1KI': 26.984287886467307, 'W_D': 37.20700000000001, 'J_D': 553.2563496205807, 'W_D_1KI': 18.858084135833757, 'J_D_1KI': 9.558076095202107}
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 91b7ae2..0990f32 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": 21352, "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": 10.102294206619263, "TIME_S_1KI": 0.47313105126542065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 644.554582464695, "W": 46.45, "J_1KI": 30.187082355971103, "W_1KI": 2.1754402397901837, "W_D": 30.18325, "J_D": 418.83212273794413, "W_D_1KI": 1.4136029411764706, "J_D_1KI": 0.066204708747493}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21464, "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": 10.162778615951538, "TIME_S_1KI": 0.47348018151097365, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 708.2274965262413, "W": 50.99, "J_1KI": 32.99606301370859, "W_1KI": 2.3756056653000375, "W_D": 25.345, "J_D": 352.0303176987171, "W_D_1KI": 1.1808143868803578, "J_D_1KI": 0.055013715378324536}
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 c1ed6fd..eca4709 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 +1,13 @@
 ['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', '1000', '-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": 0.49173688888549805}
+{"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": 0.4891834259033203}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([  851, 39596,  1204,  ...,  6262, 34652, 46359]),
-       values=tensor([0.1009, 0.2308, 0.6894,  ..., 0.4766, 0.7010, 0.2687]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16409, 39665, 45486,  ..., 40216, 44015, 30698]),
+       values=tensor([0.3828, 0.2137, 0.3194,  ..., 0.5609, 0.6557, 0.9594]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2088, 0.1405, 0.6063,  ..., 0.1063, 0.3954, 0.8044])
+tensor([0.1367, 0.4150, 0.8251,  ..., 0.6451, 0.2178, 0.9645])
 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: 0.49173688888549805 seconds
+Time: 0.4891834259033203 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', '21352', '-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": 10.102294206619263}
+['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', '21464', '-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": 10.162778615951538}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 24998, 25000]),
-       col_indices=tensor([15528, 30130, 16433,  ..., 30917, 35420, 44166]),
-       values=tensor([0.6196, 0.0183, 0.2015,  ..., 0.9265, 0.2661, 0.3216]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24999, 25000]),
+       col_indices=tensor([27591, 28713, 10997,  ...,  3373, 26495, 43984]),
+       values=tensor([0.0595, 0.2219, 0.9508,  ..., 0.7420, 0.6896, 0.8252]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3139, 0.2113, 0.1225,  ..., 0.3436, 0.4255, 0.1892])
+tensor([0.4890, 0.2230, 0.0247,  ..., 0.5863, 0.9029, 0.3113])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,15 +34,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.102294206619263 seconds
+Time: 10.162778615951538 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 24998, 25000]),
-       col_indices=tensor([15528, 30130, 16433,  ..., 30917, 35420, 44166]),
-       values=tensor([0.6196, 0.0183, 0.2015,  ..., 0.9265, 0.2661, 0.3216]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24999, 25000]),
+       col_indices=tensor([27591, 28713, 10997,  ...,  3373, 26495, 43984]),
+       values=tensor([0.0595, 0.2219, 0.9508,  ..., 0.7420, 0.6896, 0.8252]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3139, 0.2113, 0.1225,  ..., 0.3436, 0.4255, 0.1892])
+tensor([0.4890, 0.2230, 0.0247,  ..., 0.5863, 0.9029, 0.3113])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -50,13 +50,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.102294206619263 seconds
+Time: 10.162778615951538 seconds
 
-[18.39, 18.05, 18.35, 17.95, 18.07, 17.91, 18.01, 17.84, 17.86, 18.12]
-[46.45]
-13.876309633255005
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21352, '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': 10.102294206619263, 'TIME_S_1KI': 0.47313105126542065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.554582464695, 'W': 46.45}
-[18.39, 18.05, 18.35, 17.95, 18.07, 17.91, 18.01, 17.84, 17.86, 18.12, 18.35, 17.9, 17.93, 17.87, 17.99, 17.82, 18.04, 18.01, 19.31, 17.99]
-325.33500000000004
-16.266750000000002
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21352, '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': 10.102294206619263, 'TIME_S_1KI': 0.47313105126542065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.554582464695, 'W': 46.45, 'J_1KI': 30.187082355971103, 'W_1KI': 2.1754402397901837, 'W_D': 30.18325, 'J_D': 418.83212273794413, 'W_D_1KI': 1.4136029411764706, 'J_D_1KI': 0.066204708747493}
+[22.1, 18.96, 18.21, 17.89, 17.76, 17.62, 17.96, 18.44, 17.92, 17.8]
+[50.99]
+13.88953709602356
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21464, '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': 10.162778615951538, 'TIME_S_1KI': 0.47348018151097365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.2274965262413, 'W': 50.99}
+[22.1, 18.96, 18.21, 17.89, 17.76, 17.62, 17.96, 18.44, 17.92, 17.8, 27.02, 48.47, 52.31, 52.11, 52.93, 45.58, 32.74, 23.06, 18.26, 18.44]
+512.9000000000001
+25.645000000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21464, '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': 10.162778615951538, 'TIME_S_1KI': 0.47348018151097365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.2274965262413, 'W': 50.99, 'J_1KI': 32.99606301370859, 'W_1KI': 2.3756056653000375, 'W_D': 25.345, 'J_D': 352.0303176987171, 'W_D_1KI': 1.1808143868803578, 'J_D_1KI': 0.055013715378324536}
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
new file mode 100644
index 0000000..cdd4446
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220548, "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": 10.518115997314453, "TIME_S_1KI": 0.04769082466091034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 712.8332035136224, "W": 50.77000000000001, "J_1KI": 3.2321000576456025, "W_1KI": 0.23019932168960958, "W_D": 34.49475000000001, "J_D": 484.32151165848984, "W_D_1KI": 0.15640472822242782, "J_D_1KI": 0.0007091641194770654}
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
new file mode 100644
index 0000000..0444e69
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 0.06373429298400879}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2496, 2500, 2500]),
+       col_indices=tensor([ 225,  423, 3600,  ..., 1030, 3468, 3660]),
+       values=tensor([0.7007, 0.4494, 0.9248,  ..., 0.2922, 0.0433, 0.9500]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8445, 0.6906, 0.6660,  ..., 0.8648, 0.6232, 0.6893])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.06373429298400879 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', '164746', '-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": 7.8433122634887695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3043, 3415, 2314,  ..., 4144,   83, 2442]),
+       values=tensor([0.9885, 0.5870, 0.9255,  ..., 0.0554, 0.8705, 0.0319]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7612, 0.3828, 0.3624,  ..., 0.7209, 0.0836, 0.1248])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 7.8433122634887695 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', '220548', '-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": 10.518115997314453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1110, 1648, 1178,  ..., 3403,  882, 3863]),
+       values=tensor([0.7053, 0.9818, 0.3657,  ..., 0.7070, 0.0906, 0.0064]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7696, 0.8663, 0.2054,  ..., 0.2110, 0.6343, 0.9754])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.518115997314453 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1110, 1648, 1178,  ..., 3403,  882, 3863]),
+       values=tensor([0.7053, 0.9818, 0.3657,  ..., 0.7070, 0.0906, 0.0064]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7696, 0.8663, 0.2054,  ..., 0.2110, 0.6343, 0.9754])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.518115997314453 seconds
+
+[18.38, 17.72, 18.11, 17.9, 17.72, 18.84, 18.26, 17.75, 18.1, 19.21]
+[50.77]
+14.040441274642944
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220548, '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': 10.518115997314453, 'TIME_S_1KI': 0.04769082466091034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.8332035136224, 'W': 50.77000000000001}
+[18.38, 17.72, 18.11, 17.9, 17.72, 18.84, 18.26, 17.75, 18.1, 19.21, 18.75, 19.27, 17.62, 17.61, 18.13, 17.64, 17.82, 17.78, 18.21, 17.71]
+325.505
+16.27525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220548, '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': 10.518115997314453, 'TIME_S_1KI': 0.04769082466091034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.8332035136224, 'W': 50.77000000000001, 'J_1KI': 3.2321000576456025, 'W_1KI': 0.23019932168960958, 'W_D': 34.49475000000001, 'J_D': 484.32151165848984, 'W_D_1KI': 0.15640472822242782, 'J_D_1KI': 0.0007091641194770654}
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
new file mode 100644
index 0000000..8603573
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 110820, "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": 10.466707706451416, "TIME_S_1KI": 0.0944478226534147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.0924870371819, "W": 51.05, "J_1KI": 6.606140471369625, "W_1KI": 0.46065692113336937, "W_D": 34.78399999999999, "J_D": 498.82673984527577, "W_D_1KI": 0.3138783613066233, "J_D_1KI": 0.0028323259457374416}
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
new file mode 100644
index 0000000..d9f1f9f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 0.10903120040893555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([ 238, 1233, 1853,  ..., 2176, 2430, 4262]),
+       values=tensor([0.6643, 0.7436, 0.3106,  ..., 0.6873, 0.4400, 0.9022]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1554, 0.8998, 0.5501,  ..., 0.9645, 0.8024, 0.0587])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.10903120040893555 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', '96302', '-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": 9.124423503875732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([ 172,  514, 1428,  ..., 3067, 4065, 4821]),
+       values=tensor([0.3942, 0.3525, 0.9893,  ..., 0.1091, 0.2236, 0.5194]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3072, 0.9146, 0.5714,  ..., 0.0055, 0.2166, 0.7033])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.124423503875732 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', '110820', '-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": 10.466707706451416}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 254, 2428, 3765,  ..., 2763, 3021, 4452]),
+       values=tensor([0.4991, 0.2229, 0.1709,  ..., 0.9765, 0.1191, 0.1560]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0044, 0.7640, 0.5767,  ..., 0.5512, 0.1474, 0.2527])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.466707706451416 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 254, 2428, 3765,  ..., 2763, 3021, 4452]),
+       values=tensor([0.4991, 0.2229, 0.1709,  ..., 0.9765, 0.1191, 0.1560]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0044, 0.7640, 0.5767,  ..., 0.5512, 0.1474, 0.2527])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.466707706451416 seconds
+
+[18.71, 18.12, 17.8, 18.17, 17.79, 18.66, 18.24, 17.82, 17.96, 18.33]
+[51.05]
+14.340695142745972
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 110820, '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': 10.466707706451416, 'TIME_S_1KI': 0.0944478226534147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.0924870371819, 'W': 51.05}
+[18.71, 18.12, 17.8, 18.17, 17.79, 18.66, 18.24, 17.82, 17.96, 18.33, 18.24, 17.71, 17.88, 18.05, 17.89, 18.02, 18.2, 18.01, 17.96, 18.8]
+325.32000000000005
+16.266000000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 110820, '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': 10.466707706451416, 'TIME_S_1KI': 0.0944478226534147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.0924870371819, 'W': 51.05, 'J_1KI': 6.606140471369625, 'W_1KI': 0.46065692113336937, 'W_D': 34.78399999999999, 'J_D': 498.82673984527577, 'W_D_1KI': 0.3138783613066233, 'J_D_1KI': 0.0028323259457374416}
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
new file mode 100644
index 0000000..480b88c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20672, "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": 10.150692462921143, "TIME_S_1KI": 0.4910358196072534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 730.3927840781212, "W": 52.39, "J_1KI": 35.332468270032955, "W_1KI": 2.534345975232198, "W_D": 36.085, "J_D": 503.07737380146983, "W_D_1KI": 1.7455979102167183, "J_D_1KI": 0.08444262336574683}
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
new file mode 100644
index 0000000..a5c980d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.5079245567321777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     95,  ..., 249890, 249948,
+                            250000]),
+       col_indices=tensor([  55,   65,  142,  ..., 4926, 4940, 4998]),
+       values=tensor([0.9119, 0.0018, 0.8572,  ..., 0.6690, 0.1772, 0.9395]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5150, 0.8940, 0.4191,  ..., 0.2946, 0.8617, 0.5629])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.5079245567321777 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', '20672', '-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": 10.150692462921143}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     43,     97,  ..., 249889, 249944,
+                            250000]),
+       col_indices=tensor([   6,   85,  316,  ..., 4939, 4964, 4997]),
+       values=tensor([0.9982, 0.1843, 0.4498,  ..., 0.0146, 0.5221, 0.3769]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3609, 0.3004, 0.4171,  ..., 0.6127, 0.3616, 0.7085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.150692462921143 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     43,     97,  ..., 249889, 249944,
+                            250000]),
+       col_indices=tensor([   6,   85,  316,  ..., 4939, 4964, 4997]),
+       values=tensor([0.9982, 0.1843, 0.4498,  ..., 0.0146, 0.5221, 0.3769]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3609, 0.3004, 0.4171,  ..., 0.6127, 0.3616, 0.7085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.150692462921143 seconds
+
+[18.49, 17.73, 17.92, 17.82, 17.93, 17.75, 17.92, 17.66, 18.2, 17.78]
+[52.39]
+13.9414541721344
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20672, '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': 10.150692462921143, 'TIME_S_1KI': 0.4910358196072534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 730.3927840781212, 'W': 52.39}
+[18.49, 17.73, 17.92, 17.82, 17.93, 17.75, 17.92, 17.66, 18.2, 17.78, 18.3, 17.44, 17.89, 17.79, 17.89, 17.6, 18.39, 22.34, 17.77, 17.55]
+326.1
+16.305
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20672, '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': 10.150692462921143, 'TIME_S_1KI': 0.4910358196072534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 730.3927840781212, 'W': 52.39, 'J_1KI': 35.332468270032955, 'W_1KI': 2.534345975232198, 'W_D': 36.085, 'J_D': 503.07737380146983, 'W_D_1KI': 1.7455979102167183, 'J_D_1KI': 0.08444262336574683}
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
new file mode 100644
index 0000000..e52e6e6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"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": 10.511178016662598, "TIME_S_1KI": 2.332189486723452, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.7805129575729, "W": 51.93, "J_1KI": 168.13412756990746, "W_1KI": 11.522076769469715, "W_D": 35.70625, "J_D": 521.0379441708326, "W_D_1KI": 7.922398491235855, "J_D_1KI": 1.7577986446052485}
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
new file mode 100644
index 0000000..a885cd8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 2.3295679092407227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     254,     504,  ..., 1249528,
+                            1249756, 1250000]),
+       col_indices=tensor([   6,   36,   59,  ..., 4952, 4989, 4991]),
+       values=tensor([0.0659, 0.7749, 0.0668,  ..., 0.7589, 0.1810, 0.5312]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3067, 0.0072, 0.7740,  ..., 0.2122, 0.3107, 0.3197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 2.3295679092407227 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', '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": 10.511178016662598}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     219,     483,  ..., 1249530,
+                            1249766, 1250000]),
+       col_indices=tensor([  20,   32,  102,  ..., 4974, 4977, 4994]),
+       values=tensor([0.6920, 0.8171, 0.6223,  ..., 0.4625, 0.9983, 0.7249]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2561, 0.5591, 0.5400,  ..., 0.8818, 0.4529, 0.6860])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511178016662598 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     219,     483,  ..., 1249530,
+                            1249766, 1250000]),
+       col_indices=tensor([  20,   32,  102,  ..., 4974, 4977, 4994]),
+       values=tensor([0.6920, 0.8171, 0.6223,  ..., 0.4625, 0.9983, 0.7249]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2561, 0.5591, 0.5400,  ..., 0.8818, 0.4529, 0.6860])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511178016662598 seconds
+
+[18.39, 17.88, 18.12, 17.92, 17.75, 18.25, 17.78, 17.64, 17.79, 18.46]
+[51.93]
+14.592345714569092
+{'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': 10.511178016662598, 'TIME_S_1KI': 2.332189486723452, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.7805129575729, 'W': 51.93}
+[18.39, 17.88, 18.12, 17.92, 17.75, 18.25, 17.78, 17.64, 17.79, 18.46, 18.69, 17.8, 17.97, 17.87, 17.86, 17.58, 17.99, 19.45, 18.2, 17.71]
+324.47499999999997
+16.22375
+{'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': 10.511178016662598, 'TIME_S_1KI': 2.332189486723452, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.7805129575729, 'W': 51.93, 'J_1KI': 168.13412756990746, 'W_1KI': 11.522076769469715, 'W_D': 35.70625, 'J_D': 521.0379441708326, 'W_D_1KI': 7.922398491235855, 'J_D_1KI': 1.7577986446052485}
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
new file mode 100644
index 0000000..635a552
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2058, "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": 10.45784878730774, "TIME_S_1KI": 5.08155917750619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 785.1421909189224, "W": 52.34, "J_1KI": 381.5073813988933, "W_1KI": 25.432458697764822, "W_D": 35.778000000000006, "J_D": 536.6988404030801, "W_D_1KI": 17.384839650145775, "J_D_1KI": 8.44744395050815}
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
new file mode 100644
index 0000000..4a3b3d8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_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-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 5.10130500793457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     995,  ..., 2499020,
+                            2499527, 2500000]),
+       col_indices=tensor([  19,   49,   51,  ..., 4986, 4987, 4995]),
+       values=tensor([0.7936, 0.5375, 0.7301,  ..., 0.7605, 0.2307, 0.9856]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5487, 0.7747, 0.8035,  ..., 0.5625, 0.3730, 0.5706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 5.10130500793457 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', '2058', '-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": 10.45784878730774}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1017,  ..., 2498984,
+                            2499497, 2500000]),
+       col_indices=tensor([   6,   22,   24,  ..., 4979, 4998, 4999]),
+       values=tensor([0.4917, 0.1142, 0.9293,  ..., 0.2344, 0.9124, 0.9917]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7101, 0.7759, 0.4138,  ..., 0.4795, 0.2601, 0.9117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.45784878730774 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1017,  ..., 2498984,
+                            2499497, 2500000]),
+       col_indices=tensor([   6,   22,   24,  ..., 4979, 4998, 4999]),
+       values=tensor([0.4917, 0.1142, 0.9293,  ..., 0.2344, 0.9124, 0.9917]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7101, 0.7759, 0.4138,  ..., 0.4795, 0.2601, 0.9117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.45784878730774 seconds
+
+[17.93, 17.71, 18.06, 17.99, 17.73, 17.68, 22.42, 18.22, 17.93, 17.72]
+[52.34]
+15.000806093215942
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2058, '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': 10.45784878730774, 'TIME_S_1KI': 5.08155917750619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 785.1421909189224, 'W': 52.34}
+[17.93, 17.71, 18.06, 17.99, 17.73, 17.68, 22.42, 18.22, 17.93, 17.72, 17.97, 22.23, 18.27, 18.03, 17.94, 17.65, 17.66, 17.82, 18.34, 17.5]
+331.24
+16.562
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2058, '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': 10.45784878730774, 'TIME_S_1KI': 5.08155917750619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 785.1421909189224, 'W': 52.34, 'J_1KI': 381.5073813988933, 'W_1KI': 25.432458697764822, 'W_D': 35.778000000000006, 'J_D': 536.6988404030801, 'W_D_1KI': 17.384839650145775, 'J_D_1KI': 8.44744395050815}
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
new file mode 100644
index 0000000..6c5cd21
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 359075, "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": 10.526627540588379, "TIME_S_1KI": 0.029315957782046587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.3910086989404, "W": 50.77, "J_1KI": 2.0257355947892233, "W_1KI": 0.14139107428810138, "W_D": 34.40475000000001, "J_D": 492.92310038477194, "W_D_1KI": 0.09581494116827963, "J_D_1KI": 0.00026683824039066943}
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
new file mode 100644
index 0000000..f806f78
--- /dev/null
+++ b/pytorch/output_synthetic_1core/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', '1000', '-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": 0.11398792266845703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 241, 1973,  126, 4921, 4422, 2653, 3082, 2201,  909,
+                            773, 2476, 1101, 4124, 1149, 4932, 4150,  708, 3916,
+                           3901, 3756, 2285, 2145, 2412, 4449, 1421, 1959,  273,
+                            295,  438, 3557, 1406, 2159, 1555, 1352, 2308,  123,
+                            422,  816, 1668, 2887,  824, 2337,  308, 3497,  990,
+                            532, 4077,  543, 4572, 3537, 2814,  363, 2178,  459,
+                            194, 3590, 3027, 4470, 4045, 3521, 3600, 3448, 3378,
+                           3735, 2740, 4248, 3124, 1351, 4670,  655,   21, 1574,
+                           1992, 4925, 3906, 2630, 1378, 3476, 2249, 1157,  791,
+                           4242,  829, 3492,  751, 3125,  155, 1550, 3503, 3772,
+                           4314, 2771, 3009,  651,  454, 3292, 4403, 3040, 3507,
+                           2608, 4119, 1826, 4717, 3363,  464, 3190, 2566, 1334,
+                           3602, 3134, 4282, 4686, 3398,  415, 1914, 1278, 3697,
+                           3496,  579, 3955, 1068, 4099,  763, 3707, 3389, 1217,
+                           1044, 4869, 1375, 3824, 2384, 1580, 1119, 2286, 4182,
+                            194, 4854, 2427, 3130, 4857, 3962, 2164, 2297, 3429,
+                           4738, 1374, 1526, 1469,  698, 2341, 4993, 1945, 4526,
+                           2645, 2777, 3401,  889, 4389,  444, 1509, 4747, 2279,
+                           4668,   72,  129, 1221, 3493,  378,  595,   67, 1157,
+                           1657, 2497, 2001, 1078, 4882, 3030, 2378,  193, 2365,
+                           3970, 4956, 3547,  158, 4478, 3594, 3986, 4843, 4633,
+                           1401, 3655,  934, 1838, 4467, 1935, 2294,  329, 1885,
+                           2444, 2560, 3870, 4475,  843, 2939, 3686, 4333, 3066,
+                           1183,  367, 3706, 3954, 4842, 1757, 4835, 4167, 4982,
+                           1096, 3863, 1904, 2261, 4656, 4688, 3811, 4079, 2898,
+                            525, 3689,   59, 2698,  369, 2440, 1363, 4533, 2450,
+                           3223, 1033, 4049, 3368, 2542, 4831, 3226, 3742, 4496,
+                            434, 1015, 2564, 1295, 3848, 4039,  804]),
+       values=tensor([0.2184, 0.5485, 0.5631, 0.7186, 0.3971, 0.9050, 0.7143,
+                      0.7288, 0.3895, 0.9734, 0.7253, 0.3854, 0.7553, 0.4272,
+                      0.9870, 0.8470, 0.2594, 0.4864, 0.4236, 0.8391, 0.1976,
+                      0.0203, 0.1892, 0.3198, 0.2335, 0.4485, 0.4766, 0.2460,
+                      0.8756, 0.2717, 0.6013, 0.3920, 0.2318, 0.2314, 0.6325,
+                      0.7402, 0.4011, 0.6801, 0.0374, 0.5386, 0.8760, 0.4919,
+                      0.9099, 0.6426, 0.0752, 0.2458, 0.7495, 0.4949, 0.4717,
+                      0.8587, 0.9263, 0.5756, 0.1987, 0.1048, 0.8736, 0.4765,
+                      0.2414, 0.4379, 0.9381, 0.5720, 0.7831, 0.1225, 0.0871,
+                      0.1953, 0.0019, 0.7763, 0.7548, 0.3103, 0.4088, 0.9386,
+                      0.6409, 0.3915, 0.4398, 0.8886, 0.6326, 0.8708, 0.6836,
+                      0.2686, 0.0291, 0.4089, 0.8430, 0.7311, 0.2220, 0.0973,
+                      0.4335, 0.3659, 0.1254, 0.1858, 0.2947, 0.6441, 0.6573,
+                      0.8939, 0.8485, 0.7258, 0.8542, 0.3356, 0.6753, 0.2728,
+                      0.1795, 0.8246, 0.2224, 0.2674, 0.8957, 0.1897, 0.5785,
+                      0.0612, 0.0570, 0.6450, 0.0772, 0.5313, 0.3238, 0.7938,
+                      0.9961, 0.4101, 0.7007, 0.3996, 0.0865, 0.3609, 0.3202,
+                      0.4978, 0.4886, 0.2294, 0.1102, 0.5506, 0.2172, 0.1849,
+                      0.3574, 0.0197, 0.0592, 0.3653, 0.9739, 0.5626, 0.3629,
+                      0.5946, 0.5286, 0.9497, 0.4607, 0.1036, 0.7227, 0.1313,
+                      0.2695, 0.1429, 0.5049, 0.5045, 0.0131, 0.8291, 0.1488,
+                      0.2606, 0.8600, 0.2356, 0.5905, 0.8817, 0.3417, 0.2576,
+                      0.1052, 0.2996, 0.2243, 0.4829, 0.2637, 0.4923, 0.6774,
+                      0.3415, 0.2189, 0.4198, 0.9822, 0.0220, 0.9119, 0.7410,
+                      0.2466, 0.2072, 0.8839, 0.7516, 0.8153, 0.2575, 0.8303,
+                      0.9406, 0.0281, 0.0637, 0.8256, 0.0137, 0.8551, 0.6904,
+                      0.7955, 0.7126, 0.4854, 0.7077, 0.7877, 0.2703, 0.2627,
+                      0.1225, 0.6814, 0.1981, 0.0012, 0.1101, 0.2261, 0.0650,
+                      0.7540, 0.2474, 0.6597, 0.2387, 0.2473, 0.3505, 0.4892,
+                      0.1885, 0.9295, 0.0390, 0.0947, 0.3171, 0.4778, 0.2438,
+                      0.6996, 0.4455, 0.6953, 0.9830, 0.4988, 0.5386, 0.2650,
+                      0.2674, 0.7866, 0.9811, 0.0823, 0.0951, 0.2368, 0.8950,
+                      0.6075, 0.7359, 0.6430, 0.6470, 0.0664, 0.2765, 0.1109,
+                      0.1504, 0.4845, 0.0431, 0.3770, 0.2384, 0.0687, 0.8824,
+                      0.9446, 0.8249, 0.8327, 0.3623, 0.1484, 0.9592, 0.8566,
+                      0.3466, 0.6434, 0.1142, 0.1855, 0.2031]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1137, 0.5017, 0.2439,  ..., 0.6384, 0.0681, 0.9585])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.11398792266845703 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', '92115', '-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": 2.6936018466949463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 137, 3972, 2939, 2536, 3585, 3536, 4694, 4081, 1091,
+                           3547, 2158, 1560, 4654, 3916, 1298, 1826,  148, 3363,
+                           2515,  695, 1436, 2549, 3112, 1426, 4349, 4876, 3863,
+                           2266,   79, 3433, 3354, 3087, 4915, 1126, 3703, 2213,
+                           4969, 2103, 3978, 4220, 3833, 3752, 4926, 1827, 2953,
+                           4810,  372, 1434,  633, 4328, 3235, 2981, 1886, 1672,
+                           4865, 3611, 2035, 3841, 2469, 1487, 1861, 3293, 4642,
+                           1604, 4933, 4004, 2061, 3358, 3726, 2632,  960,  126,
+                           2232, 2877,  895,  621, 3810, 4400, 2844, 3004, 2625,
+                           1260, 1779,  776, 2146, 1667, 3230,  539, 2113, 1737,
+                           4402,  465, 2922, 3985,  142, 4315, 2921, 2750,  885,
+                            710, 4008, 1590, 1261, 4292, 3623, 3503, 1672, 3336,
+                           2572, 3267, 2993,   70, 1995,  836, 1449, 4056, 4774,
+                           1934, 3439, 2960, 4562, 3889, 2634, 1182, 2896, 3385,
+                            205,  905, 4516, 1281,  169, 4524,  563,  927, 1718,
+                           3751, 3566, 1379, 2664,  985, 2775, 4965, 4796,  483,
+                           2960, 2505, 3939, 4782, 2656, 1648, 2553,  588, 2612,
+                           4485, 4017, 1943, 4451, 4661, 1851, 2653, 4614,  956,
+                           1822, 2814, 2160, 1989, 3032,  922,  291, 1256, 4491,
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+                           3608, 3776,  669, 4280, 4958, 4125, 2468, 2256, 2146,
+                           4901, 2841, 3736,  283,  190, 3398, 1922]),
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+                      0.7143, 0.7824, 0.2108, 0.1572, 0.2915, 0.4564, 0.4382,
+                      0.0848, 0.7623, 0.7257, 0.3674, 0.7093]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1760, 0.3447, 0.5672,  ..., 0.4540, 0.2179, 0.2738])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 2.6936018466949463 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', '359075', '-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": 10.526627540588379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3778, 4984, 4122, 2676, 3957, 4059, 4909, 4911, 2572,
+                           1267, 1150, 3364, 3576, 4257, 4803, 3469, 2315, 1996,
+                           1589, 4554, 3627,  222,  735, 2019, 1196, 3402,  918,
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+                           4933, 2676, 1892, 1253, 2562, 3303,   93, 1367, 4037,
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+                           3071, 4886, 2701, 3617, 3981, 2453, 1634,  906, 2460,
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+                            908, 1015, 1680,  677,  202, 1047, 1747, 4308, 1250,
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+                           2652, 2501, 1137, 3069, 4789,  548, 1908,  709, 3367,
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+                           3732,  288,  642, 4326, 2761, 4086, 1629,  946, 4083,
+                           1089, 2210, 3114, 3172,  376, 4660, 3852, 3198, 3613,
+                            592, 1388, 3114, 4183, 4318, 1850, 4771,  843, 2522,
+                           2774, 2939, 3529, 1857, 2895, 2137, 4447]),
+       values=tensor([0.4475, 0.8812, 0.1292, 0.7293, 0.6267, 0.0108, 0.5387,
+                      0.9156, 0.4928, 0.6543, 0.3448, 0.7375, 0.4487, 0.3828,
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+                      0.3564, 0.1539, 0.3683, 0.3338, 0.2500, 0.3763, 0.4479,
+                      0.2028, 0.8079, 0.0187, 0.3962, 0.2530, 0.6932, 0.4307,
+                      0.2510, 0.2498, 0.5817, 0.8657, 0.8402]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7545, 0.7162, 0.2861,  ..., 0.9381, 0.3630, 0.3493])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.526627540588379 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([3778, 4984, 4122, 2676, 3957, 4059, 4909, 4911, 2572,
+                           1267, 1150, 3364, 3576, 4257, 4803, 3469, 2315, 1996,
+                           1589, 4554, 3627,  222,  735, 2019, 1196, 3402,  918,
+                            508, 1833, 3932, 3749, 3244, 4451, 1193, 3387, 2934,
+                           4933, 2676, 1892, 1253, 2562, 3303,   93, 1367, 4037,
+                            388, 4569, 3905, 2205,  438, 2955, 2830, 2546, 3603,
+                           3071, 4886, 2701, 3617, 3981, 2453, 1634,  906, 2460,
+                           4767, 4482, 2328, 3968, 2373,  709, 1470, 1396, 1265,
+                            427, 2495,   18, 4172, 3266, 4196,  702,  133, 2624,
+                           2942, 4262, 4579, 1940, 2403,   42, 1771,  590, 3624,
+                           3674, 2977, 3577, 3648,  673, 1388, 4388,   17,  194,
+                            155,  552, 2075, 1300, 4736,  849, 2848, 3737, 3431,
+                           4900, 4636,  211, 2218,  935,  599, 2948, 4874,  369,
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+                           1089, 2210, 3114, 3172,  376, 4660, 3852, 3198, 3613,
+                            592, 1388, 3114, 4183, 4318, 1850, 4771,  843, 2522,
+                           2774, 2939, 3529, 1857, 2895, 2137, 4447]),
+       values=tensor([0.4475, 0.8812, 0.1292, 0.7293, 0.6267, 0.0108, 0.5387,
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+                      0.4274, 0.7485, 0.9571, 0.9768, 0.5673, 0.4241, 0.5508,
+                      0.4033, 0.2950, 0.2855, 0.8415, 0.9844, 0.7770, 0.3923,
+                      0.5787, 0.9241, 0.3429, 0.2388, 0.7432, 0.5287, 0.4894,
+                      0.3564, 0.1539, 0.3683, 0.3338, 0.2500, 0.3763, 0.4479,
+                      0.2028, 0.8079, 0.0187, 0.3962, 0.2530, 0.6932, 0.4307,
+                      0.2510, 0.2498, 0.5817, 0.8657, 0.8402]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7545, 0.7162, 0.2861,  ..., 0.9381, 0.3630, 0.3493])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.526627540588379 seconds
+
+[18.43, 17.7, 18.24, 17.84, 17.84, 17.8, 18.19, 17.69, 17.91, 17.97]
+[50.77]
+14.327181577682495
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 359075, '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': 10.526627540588379, 'TIME_S_1KI': 0.029315957782046587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.3910086989404, 'W': 50.77}
+[18.43, 17.7, 18.24, 17.84, 17.84, 17.8, 18.19, 17.69, 17.91, 17.97, 18.65, 17.88, 17.9, 17.81, 18.36, 17.89, 17.8, 17.9, 21.76, 18.54]
+327.305
+16.36525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 359075, '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': 10.526627540588379, 'TIME_S_1KI': 0.029315957782046587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.3910086989404, 'W': 50.77, 'J_1KI': 2.0257355947892233, 'W_1KI': 0.14139107428810138, 'W_D': 34.40475000000001, 'J_D': 492.92310038477194, 'W_D_1KI': 0.09581494116827963, 'J_D_1KI': 0.00026683824039066943}
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 5cee3aa..41d6bb0 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": 1000, "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": 22.577640295028687, "TIME_S_1KI": 22.577640295028687, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.5292521381377, "W": 64.81606725703797, "J_1KI": 1196.5292521381377, "W_1KI": 64.81606725703797, "W_D": 45.67406725703797, "J_D": 843.1606521334647, "W_D_1KI": 45.67406725703797, "J_D_1KI": 45.67406725703797}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 17.146100997924805, "TIME_S_1KI": 17.146100997924805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1342.6130161285403, "W": 65.43610945887401, "J_1KI": 1342.6130161285403, "W_1KI": 65.43610945887401, "W_D": 46.466109458874016, "J_D": 953.388028173447, "W_D_1KI": 46.466109458874016, "J_D_1KI": 46.466109458874016}
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 047a391..451f542 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 1000 -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": 22.577640295028687}
+{"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": 17.146100997924805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      13,      26,  ...,  999978,
-                             999989, 1000000]),
-       col_indices=tensor([16134, 16354, 24327,  ..., 64689, 79970, 99510]),
-       values=tensor([0.0032, 0.4253, 0.4412,  ..., 0.5357, 0.1333, 0.2349]),
+tensor(crow_indices=tensor([      0,       9,      17,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([13952, 31113, 48803,  ..., 72766, 82982, 86351]),
+       values=tensor([0.4430, 0.0507, 0.5237,  ..., 0.5341, 0.7602, 0.3481]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8413, 0.1731, 0.9001,  ..., 0.4021, 0.4850, 0.1983])
+tensor([0.1669, 0.1860, 0.1675,  ..., 0.5137, 0.0308, 0.0638])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 22.577640295028687 seconds
+Time: 17.146100997924805 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      13,      26,  ...,  999978,
-                             999989, 1000000]),
-       col_indices=tensor([16134, 16354, 24327,  ..., 64689, 79970, 99510]),
-       values=tensor([0.0032, 0.4253, 0.4412,  ..., 0.5357, 0.1333, 0.2349]),
+tensor(crow_indices=tensor([      0,       9,      17,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([13952, 31113, 48803,  ..., 72766, 82982, 86351]),
+       values=tensor([0.4430, 0.0507, 0.5237,  ..., 0.5341, 0.7602, 0.3481]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8413, 0.1731, 0.9001,  ..., 0.4021, 0.4850, 0.1983])
+tensor([0.1669, 0.1860, 0.1675,  ..., 0.5137, 0.0308, 0.0638])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 22.577640295028687 seconds
+Time: 17.146100997924805 seconds
 
-[21.64, 21.64, 21.48, 21.36, 21.64, 21.36, 21.36, 21.28, 21.44, 21.36]
-[21.12, 21.24, 21.32, 22.2, 24.84, 39.96, 57.72, 73.16, 89.8, 95.92, 95.4, 95.4, 93.32, 91.16, 90.92, 93.4, 92.2, 91.64]
-18.46038031578064
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 22.577640295028687, 'TIME_S_1KI': 22.577640295028687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.5292521381377, 'W': 64.81606725703797}
-[21.64, 21.64, 21.48, 21.36, 21.64, 21.36, 21.36, 21.28, 21.44, 21.36, 21.08, 20.96, 20.96, 21.0, 21.28, 21.08, 21.28, 21.12, 21.12, 20.88]
-382.84000000000003
-19.142000000000003
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 22.577640295028687, 'TIME_S_1KI': 22.577640295028687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.5292521381377, 'W': 64.81606725703797, 'J_1KI': 1196.5292521381377, 'W_1KI': 64.81606725703797, 'W_D': 45.67406725703797, 'J_D': 843.1606521334647, 'W_D_1KI': 45.67406725703797, 'J_D_1KI': 45.67406725703797}
+[21.44, 21.28, 21.16, 21.16, 20.96, 20.96, 20.96, 21.0, 21.24, 21.4]
+[21.44, 21.28, 21.92, 23.96, 24.84, 38.64, 55.84, 70.04, 84.52, 91.04, 91.04, 91.56, 90.84, 89.12, 88.88, 89.24, 89.64, 88.8, 89.08, 90.0]
+20.517922401428223
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 17.146100997924805, 'TIME_S_1KI': 17.146100997924805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1342.6130161285403, 'W': 65.43610945887401}
+[21.44, 21.28, 21.16, 21.16, 20.96, 20.96, 20.96, 21.0, 21.24, 21.4, 21.16, 21.04, 21.08, 20.92, 20.88, 20.88, 21.04, 20.96, 21.2, 21.36]
+379.4
+18.97
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 17.146100997924805, 'TIME_S_1KI': 17.146100997924805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1342.6130161285403, 'W': 65.43610945887401, 'J_1KI': 1342.6130161285403, 'W_1KI': 65.43610945887401, 'W_D': 46.466109458874016, 'J_D': 953.388028173447, 'W_D_1KI': 46.466109458874016, 'J_D_1KI': 46.466109458874016}
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
new file mode 100644
index 0000000..c78bc8a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 159.18061113357544, "TIME_S_1KI": 159.18061113357544, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 13305.551260681154, "W": 81.94491850885544, "J_1KI": 13305.551260681154, "W_1KI": 81.94491850885544, "W_D": 61.991918508855434, "J_D": 10065.7449476676, "W_D_1KI": 61.991918508855434, "J_D_1KI": 61.991918508855434}
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
new file mode 100644
index 0000000..a21bcbb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -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": 159.18061113357544}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      182,  ...,  9999785,
+                             9999891, 10000000]),
+       col_indices=tensor([ 2375,  2397,  2562,  ..., 95994, 97725, 99229]),
+       values=tensor([3.2988e-01, 7.8520e-04, 8.6482e-01,  ...,
+                      9.5198e-01, 4.5600e-01, 9.5863e-01]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4135, 0.2091, 0.0976,  ..., 0.1293, 0.9759, 0.9614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 159.18061113357544 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      182,  ...,  9999785,
+                             9999891, 10000000]),
+       col_indices=tensor([ 2375,  2397,  2562,  ..., 95994, 97725, 99229]),
+       values=tensor([3.2988e-01, 7.8520e-04, 8.6482e-01,  ...,
+                      9.5198e-01, 4.5600e-01, 9.5863e-01]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4135, 0.2091, 0.0976,  ..., 0.1293, 0.9759, 0.9614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 159.18061113357544 seconds
+
+[22.04, 21.88, 21.96, 22.12, 21.96, 21.92, 22.08, 22.08, 22.24, 22.08]
+[22.12, 22.0, 22.4, 24.16, 25.0, 26.76, 35.48, 36.76, 45.64, 60.64, 68.76, 79.68, 90.72, 91.28, 91.28, 90.96, 89.36, 89.36, 87.68, 88.24, 87.52, 88.44, 91.68, 90.56, 91.8, 92.44, 92.04, 93.12, 92.52, 92.52, 92.88, 91.88, 91.24, 91.12, 90.44, 89.64, 89.48, 89.16, 87.68, 85.84, 85.0, 86.96, 88.04, 88.04, 88.0, 89.08, 87.48, 87.52, 86.2, 84.8, 84.64, 85.48, 85.76, 86.32, 87.44, 87.44, 88.64, 89.72, 89.72, 89.4, 87.64, 88.72, 89.0, 89.12, 89.76, 91.04, 88.36, 87.88, 86.68, 87.88, 86.48, 86.8, 86.68, 87.68, 87.68, 86.8, 87.56, 86.76, 84.72, 85.2, 85.08, 85.44, 86.48, 85.92, 86.4, 86.84, 84.56, 83.28, 84.6, 84.6, 85.76, 88.64, 88.68, 89.48, 90.88, 87.96, 88.04, 89.64, 89.6, 88.16, 88.6, 87.04, 86.96, 86.24, 86.24, 87.56, 87.32, 88.48, 89.36, 88.68, 89.56, 88.2, 85.8, 85.8, 86.36, 86.36, 88.2, 88.52, 91.48, 91.48, 91.08, 90.04, 89.24, 88.08, 88.36, 89.92, 89.76, 90.6, 89.4, 87.04, 84.72, 85.04, 83.32, 84.92, 84.92, 84.2, 85.16, 84.0, 84.2, 83.92, 84.84, 84.84, 87.04, 88.68, 91.04, 91.52, 89.88, 89.96, 89.44, 89.44, 88.36, 88.12, 90.24, 89.76, 88.8, 88.0, 86.72, 86.4]
+162.37188959121704
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 159.18061113357544, 'TIME_S_1KI': 159.18061113357544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13305.551260681154, 'W': 81.94491850885544}
+[22.04, 21.88, 21.96, 22.12, 21.96, 21.92, 22.08, 22.08, 22.24, 22.08, 21.88, 22.04, 22.48, 22.64, 22.36, 22.6, 22.6, 22.44, 21.76, 21.8]
+399.06000000000006
+19.953000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 159.18061113357544, 'TIME_S_1KI': 159.18061113357544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13305.551260681154, 'W': 81.94491850885544, 'J_1KI': 13305.551260681154, 'W_1KI': 81.94491850885544, 'W_D': 61.991918508855434, 'J_D': 10065.7449476676, 'W_D_1KI': 61.991918508855434, 'J_D_1KI': 61.991918508855434}
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 15d76ee..dc43877 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": 5444, "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": 15.66837453842163, "TIME_S_1KI": 2.878099658049528, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.8989781379698, "W": 61.815457006610345, "J_1KI": 222.24448533026631, "W_1KI": 11.354786371530189, "W_D": 42.416457006610344, "J_D": 830.2070464842318, "W_D_1KI": 7.791413851324457, "J_D_1KI": 1.4311928455776004}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3301, "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": 12.605360507965088, "TIME_S_1KI": 3.8186490481566455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 881.8972729587555, "W": 57.01281240597429, "J_1KI": 267.1606400965633, "W_1KI": 17.27137606966807, "W_D": 37.77781240597429, "J_D": 584.3625026237966, "W_D_1KI": 11.444353955157311, "J_D_1KI": 3.466935460514181}
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 529e04c..842e972 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 1000 -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": 2.735213041305542}
+{"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": 3.180464267730713}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      3,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99997,  99997,
                             100000]),
-       col_indices=tensor([47108, 85356, 39968,  ..., 81528, 26483, 51109]),
-       values=tensor([0.3148, 0.6992, 0.6314,  ..., 0.5894, 0.0851, 0.0670]),
+       col_indices=tensor([ 3926, 50379, 15277,  ..., 29136, 40772, 68436]),
+       values=tensor([0.5699, 0.5366, 0.1661,  ..., 0.2141, 0.3018, 0.3946]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4890, 0.3896, 0.3852,  ..., 0.6786, 0.1828, 0.3984])
+tensor([0.8865, 0.6102, 0.2945,  ..., 0.5701, 0.8700, 0.6634])
 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: 2.735213041305542 seconds
+Time: 3.180464267730713 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3838 -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": 7.402097463607788}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3301 -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": 12.605360507965088}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      3,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([ 1694, 16648, 92396,  ..., 98787, 30932, 62089]),
-       values=tensor([0.4689, 0.5529, 0.8985,  ..., 0.1212, 0.7499, 0.9985]),
+       col_indices=tensor([33916, 32242, 16140,  ..., 45457, 58350, 84955]),
+       values=tensor([0.9718, 0.7827, 0.4187,  ..., 0.1750, 0.8602, 0.7313]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8040, 0.7540, 0.7072,  ..., 0.4394, 0.3265, 0.7941])
+tensor([0.7754, 0.6786, 0.3605,  ..., 0.9739, 0.1301, 0.4075])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 7.402097463607788 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5444 -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": 15.66837453842163}
+Time: 12.605360507965088 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([ 8956,  7966, 63353,  ..., 28673, 30724, 93829]),
-       values=tensor([0.9652, 0.8395, 0.8363,  ..., 0.6704, 0.2134, 0.9962]),
+       col_indices=tensor([33916, 32242, 16140,  ..., 45457, 58350, 84955]),
+       values=tensor([0.9718, 0.7827, 0.4187,  ..., 0.1750, 0.8602, 0.7313]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1215, 0.6198, 0.6986,  ..., 0.9502, 0.5989, 0.9473])
+tensor([0.7754, 0.6786, 0.3605,  ..., 0.9739, 0.1301, 0.4075])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,30 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 15.66837453842163 seconds
+Time: 12.605360507965088 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([ 8956,  7966, 63353,  ..., 28673, 30724, 93829]),
-       values=tensor([0.9652, 0.8395, 0.8363,  ..., 0.6704, 0.2134, 0.9962]),
-       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1215, 0.6198, 0.6986,  ..., 0.9502, 0.5989, 0.9473])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 100000
-Density: 1e-05
-Time: 15.66837453842163 seconds
-
-[21.92, 21.92, 22.08, 21.96, 21.68, 21.6, 21.44, 21.8, 21.6, 21.76]
-[21.64, 21.56, 21.96, 22.76, 26.44, 43.68, 43.68, 58.28, 74.48, 87.48, 92.36, 91.24, 90.2, 88.56, 87.2, 86.36, 85.72, 85.72, 85.2]
-19.572757959365845
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5444, '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': 15.66837453842163, 'TIME_S_1KI': 2.878099658049528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.8989781379698, 'W': 61.815457006610345}
-[21.92, 21.92, 22.08, 21.96, 21.68, 21.6, 21.44, 21.8, 21.6, 21.76, 21.4, 21.32, 21.48, 21.48, 21.32, 21.52, 21.24, 21.16, 21.2, 21.28]
-387.98
-19.399
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5444, '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': 15.66837453842163, 'TIME_S_1KI': 2.878099658049528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.8989781379698, 'W': 61.815457006610345, 'J_1KI': 222.24448533026631, 'W_1KI': 11.354786371530189, 'W_D': 42.416457006610344, 'J_D': 830.2070464842318, 'W_D_1KI': 7.791413851324457, 'J_D_1KI': 1.4311928455776004}
+[21.24, 21.24, 21.36, 21.4, 21.52, 21.72, 21.56, 21.64, 21.48, 21.48]
+[21.4, 21.4, 21.8, 22.88, 23.96, 35.92, 52.84, 66.08, 81.2, 91.72, 90.6, 91.36, 91.84, 91.24, 91.24]
+15.46840500831604
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3301, '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': 12.605360507965088, 'TIME_S_1KI': 3.8186490481566455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 881.8972729587555, 'W': 57.01281240597429}
+[21.24, 21.24, 21.36, 21.4, 21.52, 21.72, 21.56, 21.64, 21.48, 21.48, 21.28, 21.28, 21.32, 21.48, 21.6, 21.48, 21.28, 21.12, 20.84, 20.76]
+384.69999999999993
+19.234999999999996
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3301, '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': 12.605360507965088, 'TIME_S_1KI': 3.8186490481566455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 881.8972729587555, 'W': 57.01281240597429, 'J_1KI': 267.1606400965633, 'W_1KI': 17.27137606966807, 'W_D': 37.77781240597429, 'J_D': 584.3625026237966, 'W_D_1KI': 11.444353955157311, 'J_D_1KI': 3.466935460514181}
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 b31b4ad..b901590 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": 31990, "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": 10.162655591964722, "TIME_S_1KI": 0.3176822629560713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 281.0764434432984, "W": 21.267196136821532, "J_1KI": 8.786384602791447, "W_1KI": 0.6648076316605669, "W_D": 2.733196136821533, "J_D": 36.12309984016423, "W_D_1KI": 0.08543907898785662, "J_D_1KI": 0.0026708058451971432}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 32089, "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": 10.109246492385864, "TIME_S_1KI": 0.3150377541333748, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.60778367996215, "W": 22.396548812340328, "J_1KI": 9.92887854654125, "W_1KI": 0.6979509742385342, "W_D": 4.033548812340328, "J_D": 57.38027131915093, "W_D_1KI": 0.1256988005964763, "J_D_1KI": 0.003917192826092315}
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 3e5c97c..8b8a1a7 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 1000 -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": 0.32822322845458984}
+{"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": 0.3272056579589844}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9997,  9998, 10000]),
-       col_indices=tensor([2721, 4826, 6729,  ..., 6567,  802, 8084]),
-       values=tensor([0.9788, 0.8960, 0.9515,  ..., 0.3823, 0.9672, 0.4403]),
+tensor(crow_indices=tensor([    0,     3,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([ 654, 4587, 9013,  ..., 1787, 1854, 8773]),
+       values=tensor([0.1124, 0.2109, 0.1818,  ..., 0.9520, 0.5472, 0.0091]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1442, 0.5021, 0.5745,  ..., 0.9716, 0.6255, 0.3521])
+tensor([0.1189, 0.4488, 0.9345,  ..., 0.0324, 0.3464, 0.4030])
 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: 0.32822322845458984 seconds
+Time: 0.3272056579589844 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 31990 -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": 10.162655591964722}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32089 -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": 10.109246492385864}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9997, 10000, 10000]),
-       col_indices=tensor([1219, 6055, 1582,  ..., 3506, 4664, 5684]),
-       values=tensor([0.3475, 0.3226, 0.1217,  ..., 0.8742, 0.3097, 0.9052]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([6261, 1350, 3983,  ..., 9586, 2579, 6781]),
+       values=tensor([0.3771, 0.7405, 0.3284,  ..., 0.1626, 0.7239, 0.9996]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3839, 0.3550, 0.5972,  ..., 0.2550, 0.5835, 0.6125])
+tensor([0.1818, 0.1444, 0.2139,  ..., 0.0964, 0.7255, 0.0411])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,15 +34,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.162655591964722 seconds
+Time: 10.109246492385864 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ...,  9997, 10000, 10000]),
-       col_indices=tensor([1219, 6055, 1582,  ..., 3506, 4664, 5684]),
-       values=tensor([0.3475, 0.3226, 0.1217,  ..., 0.8742, 0.3097, 0.9052]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([6261, 1350, 3983,  ..., 9586, 2579, 6781]),
+       values=tensor([0.3771, 0.7405, 0.3284,  ..., 0.1626, 0.7239, 0.9996]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3839, 0.3550, 0.5972,  ..., 0.2550, 0.5835, 0.6125])
+tensor([0.1818, 0.1444, 0.2139,  ..., 0.0964, 0.7255, 0.0411])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -50,13 +50,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.162655591964722 seconds
+Time: 10.109246492385864 seconds
 
-[20.68, 20.44, 20.44, 20.76, 20.52, 20.44, 20.48, 20.32, 20.48, 20.48]
-[20.68, 20.88, 21.64, 22.32, 23.4, 23.4, 23.8, 24.24, 23.6, 23.6, 23.44, 23.48, 23.64]
-13.216431617736816
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 31990, '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': 10.162655591964722, 'TIME_S_1KI': 0.3176822629560713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 281.0764434432984, 'W': 21.267196136821532}
-[20.68, 20.44, 20.44, 20.76, 20.52, 20.44, 20.48, 20.32, 20.48, 20.48, 20.56, 20.64, 20.48, 20.52, 20.48, 20.72, 20.72, 20.8, 20.92, 21.32]
-370.67999999999995
-18.534
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 31990, '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': 10.162655591964722, 'TIME_S_1KI': 0.3176822629560713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 281.0764434432984, 'W': 21.267196136821532, 'J_1KI': 8.786384602791447, 'W_1KI': 0.6648076316605669, 'W_D': 2.733196136821533, 'J_D': 36.12309984016423, 'W_D_1KI': 0.08543907898785662, 'J_D_1KI': 0.0026708058451971432}
+[20.52, 20.4, 20.52, 20.52, 20.52, 20.32, 20.32, 20.2, 20.36, 20.24]
+[20.32, 20.48, 20.88, 25.52, 26.52, 27.24, 27.6, 24.68, 23.88, 23.88, 23.36, 23.6, 23.68, 23.6]
+14.225753545761108
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32089, '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': 10.109246492385864, 'TIME_S_1KI': 0.3150377541333748, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.60778367996215, 'W': 22.396548812340328}
+[20.52, 20.4, 20.52, 20.52, 20.52, 20.32, 20.32, 20.2, 20.36, 20.24, 20.72, 20.52, 20.6, 20.4, 20.32, 20.52, 20.44, 20.32, 20.16, 20.16]
+367.26
+18.363
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32089, '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': 10.109246492385864, 'TIME_S_1KI': 0.3150377541333748, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.60778367996215, 'W': 22.396548812340328, 'J_1KI': 9.92887854654125, 'W_1KI': 0.6979509742385342, 'W_D': 4.033548812340328, 'J_D': 57.38027131915093, 'W_D_1KI': 0.1256988005964763, 'J_D_1KI': 0.003917192826092315}
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 44e0916..28a5b99 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": 4642, "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": 10.39481520652771, "TIME_S_1KI": 2.2392966838706827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.1584589004516, "W": 23.611594488388015, "J_1KI": 72.20130523490987, "W_1KI": 5.086513246098236, "W_D": 5.040594488388017, "J_D": 71.54950427007671, "W_D_1KI": 1.0858669729401156, "J_D_1KI": 0.23392222596728038}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4566, "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": 10.211250305175781, "TIME_S_1KI": 2.236366689701222, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.4135251998902, "W": 22.432139051903395, "J_1KI": 64.91754822599435, "W_1KI": 4.912864444131274, "W_D": 4.068139051903394, "J_D": 53.75552614879615, "W_D_1KI": 0.8909634366849307, "J_D_1KI": 0.19512996861255602}
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 12aea87..f081110 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 1000 -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": 2.2614803314208984}
+{"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": 2.2992472648620605}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     19,  ...,  99975,  99988,
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99980,  99989,
                             100000]),
-       col_indices=tensor([ 662,  710, 3445,  ..., 9576, 9602, 9965]),
-       values=tensor([0.0517, 0.2381, 0.9401,  ..., 0.3987, 0.7682, 0.4070]),
+       col_indices=tensor([ 655, 1592, 1705,  ..., 9238, 9783, 9811]),
+       values=tensor([0.0624, 0.8226, 0.1738,  ..., 0.6448, 0.8074, 0.7220]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1766, 0.1636, 0.7477,  ..., 0.1192, 0.5625, 0.2605])
+tensor([0.5841, 0.1855, 0.2176,  ..., 0.5967, 0.9561, 0.0240])
 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: 2.2614803314208984 seconds
+Time: 2.2992472648620605 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4642 -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": 10.39481520652771}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4566 -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": 10.211250305175781}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     19,  ...,  99983,  99997,
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99989,  99992,
                             100000]),
-       col_indices=tensor([  82, 3146, 3840,  ..., 8041, 8695, 8893]),
-       values=tensor([0.8450, 0.6541, 0.7727,  ..., 0.8034, 0.8111, 0.1952]),
+       col_indices=tensor([ 778, 1147, 3454,  ..., 4854, 5919, 8867]),
+       values=tensor([0.6002, 0.4939, 0.0259,  ..., 0.9282, 0.0584, 0.5342]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0486, 0.3621, 0.6684,  ..., 0.7127, 0.4964, 0.1751])
+tensor([0.8806, 0.9663, 0.5124,  ..., 0.2617, 0.2277, 0.6355])
 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: 10.39481520652771 seconds
+Time: 10.211250305175781 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     19,  ...,  99983,  99997,
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99989,  99992,
                             100000]),
-       col_indices=tensor([  82, 3146, 3840,  ..., 8041, 8695, 8893]),
-       values=tensor([0.8450, 0.6541, 0.7727,  ..., 0.8034, 0.8111, 0.1952]),
+       col_indices=tensor([ 778, 1147, 3454,  ..., 4854, 5919, 8867]),
+       values=tensor([0.6002, 0.4939, 0.0259,  ..., 0.9282, 0.0584, 0.5342]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0486, 0.3621, 0.6684,  ..., 0.7127, 0.4964, 0.1751])
+tensor([0.8806, 0.9663, 0.5124,  ..., 0.2617, 0.2277, 0.6355])
 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: 10.39481520652771 seconds
+Time: 10.211250305175781 seconds
 
-[20.6, 20.72, 20.92, 21.0, 20.88, 20.88, 20.64, 20.36, 20.08, 19.92]
-[20.16, 20.2, 20.48, 21.88, 25.12, 28.64, 29.48, 29.64, 29.64, 29.56, 24.6, 24.56, 24.44, 24.24]
-14.194655895233154
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4642, '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': 10.39481520652771, 'TIME_S_1KI': 2.2392966838706827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.1584589004516, 'W': 23.611594488388015}
-[20.6, 20.72, 20.92, 21.0, 20.88, 20.88, 20.64, 20.36, 20.08, 19.92, 20.36, 20.52, 20.6, 20.84, 20.8, 20.56, 20.64, 20.64, 20.64, 20.52]
-371.41999999999996
-18.570999999999998
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4642, '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': 10.39481520652771, 'TIME_S_1KI': 2.2392966838706827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.1584589004516, 'W': 23.611594488388015, 'J_1KI': 72.20130523490987, 'W_1KI': 5.086513246098236, 'W_D': 5.040594488388017, 'J_D': 71.54950427007671, 'W_D_1KI': 1.0858669729401156, 'J_D_1KI': 0.23392222596728038}
+[20.52, 20.28, 20.28, 20.24, 20.2, 20.2, 20.16, 20.44, 20.56, 20.64]
+[20.68, 20.52, 20.84, 21.8, 22.68, 26.2, 26.72, 26.88, 26.88, 26.92, 24.56, 24.6, 24.48]
+13.21378779411316
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4566, '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': 10.211250305175781, 'TIME_S_1KI': 2.236366689701222, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.4135251998902, 'W': 22.432139051903395}
+[20.52, 20.28, 20.28, 20.24, 20.2, 20.2, 20.16, 20.44, 20.56, 20.64, 20.56, 20.52, 20.32, 20.28, 20.24, 20.4, 20.6, 20.68, 20.68, 20.68]
+367.28000000000003
+18.364
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4566, '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': 10.211250305175781, 'TIME_S_1KI': 2.236366689701222, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.4135251998902, 'W': 22.432139051903395, 'J_1KI': 64.91754822599435, 'W_1KI': 4.912864444131274, 'W_D': 4.068139051903394, 'J_D': 53.75552614879615, 'W_D_1KI': 0.8909634366849307, 'J_D_1KI': 0.19512996861255602}
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 6da6654..123a4ab 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": 1000, "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": 21.402220964431763, "TIME_S_1KI": 21.402220964431763, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 598.721107492447, "W": 23.65441632405048, "J_1KI": 598.721107492447, "W_1KI": 23.65441632405048, "W_D": 5.107416324050483, "J_D": 129.27471623349203, "W_D_1KI": 5.107416324050483, "J_D_1KI": 5.107416324050483}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 21.205244779586792, "TIME_S_1KI": 21.205244779586792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 620.3200452423096, "W": 24.48389957916532, "J_1KI": 620.3200452423096, "W_1KI": 24.48389957916532, "W_D": 6.114899579165321, "J_D": 154.92608811497686, "W_D_1KI": 6.114899579165321, "J_D_1KI": 6.114899579165321}
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 5b10379..9e8858d 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 1000 -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": 21.402220964431763}
+{"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": 21.205244779586792}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     109,     232,  ...,  999786,
-                             999885, 1000000]),
-       col_indices=tensor([  48,  108,  238,  ..., 9836, 9911, 9942]),
-       values=tensor([0.7065, 0.8335, 0.4165,  ..., 0.0617, 0.0653, 0.1993]),
+tensor(crow_indices=tensor([      0,      87,     190,  ...,  999787,
+                             999893, 1000000]),
+       col_indices=tensor([  40,  232,  261,  ..., 9741, 9779, 9904]),
+       values=tensor([0.6083, 0.3635, 0.2569,  ..., 0.1971, 0.1171, 0.3174]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6170, 0.2022, 0.1812,  ..., 0.2173, 0.9754, 0.3705])
+tensor([0.7546, 0.0325, 0.8716,  ..., 0.3834, 0.9539, 0.7452])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.402220964431763 seconds
+Time: 21.205244779586792 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     109,     232,  ...,  999786,
-                             999885, 1000000]),
-       col_indices=tensor([  48,  108,  238,  ..., 9836, 9911, 9942]),
-       values=tensor([0.7065, 0.8335, 0.4165,  ..., 0.0617, 0.0653, 0.1993]),
+tensor(crow_indices=tensor([      0,      87,     190,  ...,  999787,
+                             999893, 1000000]),
+       col_indices=tensor([  40,  232,  261,  ..., 9741, 9779, 9904]),
+       values=tensor([0.6083, 0.3635, 0.2569,  ..., 0.1971, 0.1171, 0.3174]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6170, 0.2022, 0.1812,  ..., 0.2173, 0.9754, 0.3705])
+tensor([0.7546, 0.0325, 0.8716,  ..., 0.3834, 0.9539, 0.7452])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.402220964431763 seconds
+Time: 21.205244779586792 seconds
 
-[20.56, 20.56, 20.76, 20.64, 20.84, 20.76, 21.0, 20.84, 20.84, 20.8]
-[20.64, 20.52, 20.44, 21.52, 22.84, 29.04, 29.96, 30.44, 29.96, 27.0, 24.4, 24.4, 24.24, 24.08, 24.0, 24.16, 24.04, 24.2, 24.28, 24.16, 24.12, 23.92, 23.72, 23.72, 23.72]
-25.311176538467407
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 21.402220964431763, 'TIME_S_1KI': 21.402220964431763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.721107492447, 'W': 23.65441632405048}
-[20.56, 20.56, 20.76, 20.64, 20.84, 20.76, 21.0, 20.84, 20.84, 20.8, 20.76, 20.56, 20.4, 20.28, 20.24, 20.28, 20.36, 20.6, 20.6, 20.64]
-370.93999999999994
-18.546999999999997
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 21.402220964431763, 'TIME_S_1KI': 21.402220964431763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.721107492447, 'W': 23.65441632405048, 'J_1KI': 598.721107492447, 'W_1KI': 23.65441632405048, 'W_D': 5.107416324050483, 'J_D': 129.27471623349203, 'W_D_1KI': 5.107416324050483, 'J_D_1KI': 5.107416324050483}
+[20.12, 20.4, 20.4, 20.36, 20.16, 20.24, 20.32, 20.32, 20.32, 20.52]
+[20.52, 20.76, 21.12, 22.84, 24.76, 33.76, 34.6, 34.52, 33.84, 26.88, 24.24, 24.24, 24.12, 24.12, 24.04, 24.0, 24.0, 24.04, 24.12, 23.96, 24.0, 24.12, 23.96, 23.96, 24.0]
+25.335835218429565
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 21.205244779586792, 'TIME_S_1KI': 21.205244779586792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 620.3200452423096, 'W': 24.48389957916532}
+[20.12, 20.4, 20.4, 20.36, 20.16, 20.24, 20.32, 20.32, 20.32, 20.52, 20.52, 20.56, 20.6, 20.44, 20.44, 20.32, 20.28, 20.64, 20.68, 20.64]
+367.38
+18.369
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 21.205244779586792, 'TIME_S_1KI': 21.205244779586792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 620.3200452423096, 'W': 24.48389957916532, 'J_1KI': 620.3200452423096, 'W_1KI': 24.48389957916532, 'W_D': 6.114899579165321, 'J_D': 154.92608811497686, 'W_D_1KI': 6.114899579165321, 'J_D_1KI': 6.114899579165321}
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 23a9329..054792b 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": 1000, "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": 108.30107378959656, "TIME_S_1KI": 108.30107378959656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2693.844181451797, "W": 24.185192869557547, "J_1KI": 2693.844181451797, "W_1KI": 24.185192869557547, "W_D": 5.823192869557545, "J_D": 648.6106732220641, "W_D_1KI": 5.823192869557545, "J_D_1KI": 5.823192869557545}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 106.56615614891052, "TIME_S_1KI": 106.56615614891052, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2781.6071025085453, "W": 24.542113905929394, "J_1KI": 2781.6071025085453, "W_1KI": 24.542113905929394, "W_D": 6.099113905929396, "J_D": 691.2745423955923, "W_D_1KI": 6.099113905929396, "J_D_1KI": 6.099113905929396}
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 f20f6e0..1064fac 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 1000 -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": 108.30107378959656}
+{"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": 106.56615614891052}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     484,    1003,  ..., 4999033,
-                            4999518, 5000000]),
-       col_indices=tensor([  10,   43,   51,  ..., 9955, 9982, 9992]),
-       values=tensor([0.0167, 0.2062, 0.3972,  ..., 0.2194, 0.0680, 0.6916]),
+tensor(crow_indices=tensor([      0,     509,    1020,  ..., 4998992,
+                            4999488, 5000000]),
+       col_indices=tensor([   3,   11,   31,  ..., 9971, 9976, 9990]),
+       values=tensor([0.8435, 0.0304, 0.5451,  ..., 0.3255, 0.3710, 0.6386]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1565, 0.8667, 0.6742,  ..., 0.1248, 0.3395, 0.1639])
+tensor([0.1386, 0.0671, 0.1165,  ..., 0.0400, 0.5375, 0.5366])
 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: 108.30107378959656 seconds
+Time: 106.56615614891052 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     484,    1003,  ..., 4999033,
-                            4999518, 5000000]),
-       col_indices=tensor([  10,   43,   51,  ..., 9955, 9982, 9992]),
-       values=tensor([0.0167, 0.2062, 0.3972,  ..., 0.2194, 0.0680, 0.6916]),
+tensor(crow_indices=tensor([      0,     509,    1020,  ..., 4998992,
+                            4999488, 5000000]),
+       col_indices=tensor([   3,   11,   31,  ..., 9971, 9976, 9990]),
+       values=tensor([0.8435, 0.0304, 0.5451,  ..., 0.3255, 0.3710, 0.6386]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1565, 0.8667, 0.6742,  ..., 0.1248, 0.3395, 0.1639])
+tensor([0.1386, 0.0671, 0.1165,  ..., 0.0400, 0.5375, 0.5366])
 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: 108.30107378959656 seconds
+Time: 106.56615614891052 seconds
 
-[20.52, 20.36, 20.4, 20.4, 20.48, 20.48, 20.48, 20.12, 20.12, 20.2]
-[20.12, 20.52, 21.16, 22.2, 23.84, 29.72, 33.04, 33.04, 32.72, 32.68, 29.16, 24.72, 24.68, 24.72, 24.52, 24.44, 24.24, 24.24, 24.12, 24.08, 24.08, 24.28, 24.12, 24.28, 24.44, 24.44, 24.64, 24.6, 24.64, 24.52, 24.56, 24.4, 24.4, 24.28, 24.48, 24.36, 24.44, 24.48, 24.36, 24.24, 24.28, 24.12, 24.28, 24.32, 24.32, 24.28, 24.28, 24.4, 24.44, 24.36, 24.16, 24.0, 23.88, 23.92, 24.0, 24.08, 24.2, 24.2, 24.24, 24.16, 24.28, 24.36, 24.24, 24.36, 24.44, 24.48, 24.76, 24.56, 24.4, 24.4, 24.32, 24.28, 24.12, 24.4, 24.8, 24.76, 24.84, 24.84, 24.4, 24.4, 24.4, 24.2, 24.2, 24.16, 24.16, 23.84, 23.92, 24.12, 24.52, 24.52, 24.68, 24.52, 24.48, 24.24, 24.24, 24.28, 24.28, 24.52, 24.64, 24.48, 24.32, 24.16, 24.16, 24.0, 23.96, 23.96, 24.08, 24.08, 23.96, 24.28]
-111.38402724266052
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 108.30107378959656, 'TIME_S_1KI': 108.30107378959656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2693.844181451797, 'W': 24.185192869557547}
-[20.52, 20.36, 20.4, 20.4, 20.48, 20.48, 20.48, 20.12, 20.12, 20.2, 20.64, 20.72, 20.68, 20.72, 20.36, 20.32, 20.24, 20.16, 20.24, 20.56]
-367.24
-18.362000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 108.30107378959656, 'TIME_S_1KI': 108.30107378959656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2693.844181451797, 'W': 24.185192869557547, 'J_1KI': 2693.844181451797, 'W_1KI': 24.185192869557547, 'W_D': 5.823192869557545, 'J_D': 648.6106732220641, 'W_D_1KI': 5.823192869557545, 'J_D_1KI': 5.823192869557545}
+[20.4, 20.64, 20.52, 20.56, 20.6, 20.52, 20.56, 20.52, 20.2, 20.56]
+[20.56, 20.68, 20.68, 24.36, 26.16, 32.64, 39.96, 40.52, 36.88, 36.0, 27.96, 24.36, 24.12, 24.28, 24.28, 24.44, 24.64, 24.72, 24.6, 24.36, 24.2, 23.96, 24.12, 24.08, 24.12, 24.24, 24.24, 24.28, 24.68, 24.56, 24.72, 24.84, 24.88, 24.56, 24.48, 24.32, 24.44, 24.52, 24.52, 24.72, 24.68, 24.68, 24.28, 24.0, 23.92, 23.84, 24.0, 24.24, 24.44, 24.48, 24.44, 24.44, 24.64, 24.68, 24.76, 24.72, 24.68, 24.52, 24.56, 24.64, 24.52, 24.68, 24.44, 24.44, 24.4, 24.64, 24.8, 24.68, 24.76, 24.64, 24.4, 24.12, 24.48, 24.56, 24.72, 24.72, 24.8, 24.96, 24.52, 24.32, 24.36, 24.24, 24.08, 24.04, 23.96, 24.08, 24.4, 24.48, 24.48, 24.64, 24.76, 24.4, 24.36, 24.4, 24.56, 24.68, 24.68, 24.48, 24.36, 24.2, 24.2, 24.56, 24.48, 24.4, 24.36, 24.44, 24.36, 24.44, 24.36, 24.64, 24.52, 24.48]
+113.34015941619873
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 106.56615614891052, 'TIME_S_1KI': 106.56615614891052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2781.6071025085453, 'W': 24.542113905929394}
+[20.4, 20.64, 20.52, 20.56, 20.6, 20.52, 20.56, 20.52, 20.2, 20.56, 20.52, 20.48, 20.48, 20.56, 20.48, 20.36, 20.48, 20.52, 20.44, 20.4]
+368.85999999999996
+18.442999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 106.56615614891052, 'TIME_S_1KI': 106.56615614891052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2781.6071025085453, 'W': 24.542113905929394, 'J_1KI': 2781.6071025085453, 'W_1KI': 24.542113905929394, 'W_D': 6.099113905929396, 'J_D': 691.2745423955923, 'W_D_1KI': 6.099113905929396, 'J_D_1KI': 6.099113905929396}
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
new file mode 100644
index 0000000..175168f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 210.99842429161072, "TIME_S_1KI": 210.99842429161072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5320.685762977602, "W": 24.45864835325204, "J_1KI": 5320.685762977602, "W_1KI": 24.45864835325204, "W_D": 6.0876483532520425, "J_D": 1324.2949264960312, "W_D_1KI": 6.0876483532520425, "J_D_1KI": 6.0876483532520425}
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
new file mode 100644
index 0000000..1928ba2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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 1000 -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": 210.99842429161072}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1065,     2071,  ...,  9998045,
+                             9999047, 10000000]),
+       col_indices=tensor([   6,   19,   22,  ..., 9974, 9992, 9993]),
+       values=tensor([0.1921, 0.6014, 0.9806,  ..., 0.7679, 0.7737, 0.6028]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1676, 0.2617, 0.2303,  ..., 0.3636, 0.4445, 0.4181])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.99842429161072 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1065,     2071,  ...,  9998045,
+                             9999047, 10000000]),
+       col_indices=tensor([   6,   19,   22,  ..., 9974, 9992, 9993]),
+       values=tensor([0.1921, 0.6014, 0.9806,  ..., 0.7679, 0.7737, 0.6028]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1676, 0.2617, 0.2303,  ..., 0.3636, 0.4445, 0.4181])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.99842429161072 seconds
+
+[20.48, 20.52, 20.52, 20.68, 20.64, 20.64, 20.6, 20.56, 20.36, 20.48]
+[20.52, 20.6, 21.44, 23.16, 24.32, 34.12, 34.12, 35.68, 38.2, 37.44, 37.12, 27.84, 27.04, 24.4, 24.44, 24.4, 24.32, 24.48, 24.48, 24.36, 24.44, 24.72, 25.0, 24.92, 24.96, 24.96, 24.52, 24.4, 24.32, 24.24, 24.36, 24.36, 24.64, 24.56, 24.44, 24.52, 24.4, 24.6, 24.68, 24.84, 24.88, 24.64, 24.52, 24.52, 24.6, 24.48, 24.4, 24.28, 24.28, 24.32, 24.44, 24.44, 24.56, 24.52, 24.2, 24.16, 24.16, 24.12, 24.32, 24.36, 24.28, 24.28, 24.0, 24.12, 24.24, 24.4, 24.56, 25.04, 25.04, 24.84, 24.8, 24.76, 24.48, 24.44, 24.56, 24.48, 24.48, 24.4, 24.48, 24.36, 24.48, 24.48, 24.6, 24.68, 25.0, 25.0, 24.84, 24.76, 24.68, 24.44, 24.52, 24.52, 24.6, 24.6, 24.76, 24.48, 24.52, 24.4, 24.16, 24.24, 24.0, 24.24, 24.12, 24.24, 24.44, 24.44, 24.8, 24.8, 24.72, 24.64, 24.52, 24.28, 24.24, 24.16, 24.2, 24.32, 24.48, 24.32, 24.32, 24.28, 24.28, 24.32, 24.52, 24.56, 24.56, 24.6, 24.48, 24.4, 24.28, 24.24, 24.24, 24.24, 24.32, 24.48, 24.4, 24.4, 24.2, 24.08, 24.24, 24.4, 24.64, 24.68, 24.64, 24.64, 24.8, 24.6, 24.72, 24.8, 24.76, 24.76, 24.92, 25.08, 24.92, 24.88, 24.68, 24.68, 24.48, 24.32, 24.64, 24.68, 24.92, 24.92, 24.8, 24.68, 24.64, 24.44, 24.6, 24.6, 24.68, 24.52, 24.4, 24.44, 24.36, 24.12, 24.32, 24.24, 24.16, 24.24, 24.0, 24.24, 24.24, 24.44, 24.44, 24.6, 24.64, 24.44, 24.36, 24.48, 24.4, 24.64, 24.44, 24.64, 24.64, 24.6, 24.44, 24.64, 24.64, 24.32, 24.36, 24.24, 24.08, 24.36, 24.4, 24.48, 24.56, 24.56, 24.44, 24.32, 24.2, 24.36, 24.56, 24.68, 24.76, 24.92, 24.88]
+217.5380129814148
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 210.99842429161072, 'TIME_S_1KI': 210.99842429161072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5320.685762977602, 'W': 24.45864835325204}
+[20.48, 20.52, 20.52, 20.68, 20.64, 20.64, 20.6, 20.56, 20.36, 20.48, 20.4, 20.36, 20.32, 20.32, 20.2, 20.2, 20.28, 20.16, 20.24, 20.28]
+367.41999999999996
+18.371
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 210.99842429161072, 'TIME_S_1KI': 210.99842429161072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5320.685762977602, 'W': 24.45864835325204, 'J_1KI': 5320.685762977602, 'W_1KI': 24.45864835325204, 'W_D': 6.0876483532520425, 'J_D': 1324.2949264960312, 'W_D_1KI': 6.0876483532520425, 'J_D_1KI': 6.0876483532520425}
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 c8342c5..40d44d2 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": 141816, "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": 10.164389848709106, "TIME_S_1KI": 0.07167308236524163, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 285.17019953727726, "W": 21.625492932171987, "J_1KI": 2.010846445656888, "W_1KI": 0.1524897961596152, "W_D": 3.365492932171989, "J_D": 44.37994981288918, "W_D_1KI": 0.023731405004879483, "J_D_1KI": 0.00016733940461499043}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 142926, "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": 10.514646053314209, "TIME_S_1KI": 0.07356706304880994, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 341.9344939994813, "W": 24.04792007204229, "J_1KI": 2.3923883268228403, "W_1KI": 0.1682543419114947, "W_D": 4.089920072042293, "J_D": 58.15408343601235, "W_D_1KI": 0.02861564776207473, "J_D_1KI": 0.00020021303165326625}
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 c5a4de0..e3d4653 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
@@ -1,373 +1,266 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -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": 0.07915711402893066}
+{"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": 0.08297038078308105}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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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+                      0.1200, 0.6279, 0.3021, 0.5054, 0.7498, 0.9300]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.9198, 0.2486, 0.6139,  ..., 0.7346, 0.8053, 0.7353])
+tensor([0.3956, 0.7164, 0.0973,  ..., 0.3827, 0.2591, 0.9120])
 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: 0.07915711402893066 seconds
+Time: 0.08297038078308105 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 132647 -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": 9.821086883544922}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126551 -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": 9.296976089477539}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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-                           6777, 6449, 1983, 8864, 9572, 9119,   62, 8989, 7326,
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-                           4606, 1985, 8551, 3694, 5878, 8651, 3978, 4944, 7842,
-                           3461, 1926, 1851, 8100, 6746, 7543, 1835, 7605, 3542,
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 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,378 +647,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 9.821086883544922 seconds
+Time: 9.296976089477539 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141816 -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": 10.164389848709106}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 142926 -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": 10.514646053314209}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+                      3.0564e-01, 8.8046e-01, 1.5537e-01, 2.9485e-01,
+                      4.3426e-01, 1.5200e-01, 2.3927e-01, 4.1251e-02,
+                      2.6271e-01, 5.8274e-01, 5.6945e-02, 1.4017e-01,
+                      3.0720e-01, 3.6401e-01, 1.6795e-01, 6.9631e-02,
+                      6.9758e-01, 5.9161e-01, 8.3040e-01, 9.4507e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7176, 0.7209, 0.4421,  ..., 0.7861, 0.0630, 0.0040])
+tensor([0.7781, 0.3926, 0.9305,  ..., 0.7246, 0.2621, 0.2068])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +1026,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.164389848709106 seconds
+Time: 10.514646053314209 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+                      1.6678e-01, 7.3058e-01, 1.4131e-01, 3.2380e-01,
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+                      6.6954e-02, 4.9328e-01, 7.2243e-01, 8.3534e-01,
+                      9.9053e-01, 2.3837e-01, 1.9357e-01, 5.5049e-01,
+                      6.7173e-01, 6.2973e-03, 2.5163e-01, 4.1258e-01,
+                      6.6680e-01, 5.0225e-01, 6.5658e-01, 7.9015e-01,
+                      4.6985e-01, 9.5341e-01, 1.8942e-01, 9.6505e-01,
+                      5.9884e-01, 7.6761e-01, 9.9066e-01, 1.7115e-01,
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+                      1.4245e-01, 7.4121e-01, 4.5033e-01, 7.0448e-01,
+                      8.4109e-01, 9.6275e-01, 5.6967e-01, 4.6629e-01,
+                      4.6346e-01, 8.8916e-01, 5.7855e-01, 5.9218e-01,
+                      9.7444e-01, 5.1624e-01, 5.3685e-01, 3.2910e-01,
+                      4.5445e-03, 3.8540e-01, 9.5888e-01, 5.3370e-01,
+                      5.2745e-01, 7.0115e-01, 1.7582e-02, 7.0961e-01,
+                      4.8579e-01, 4.4872e-01, 1.8650e-01, 9.1635e-01,
+                      7.1700e-01, 7.1226e-01, 3.2373e-01, 9.7173e-01,
+                      9.5299e-01, 8.4083e-01, 2.5068e-01, 3.3961e-01,
+                      2.8141e-01, 1.6843e-01, 7.2851e-01, 3.5589e-01,
+                      4.7044e-01, 3.3914e-01, 9.9225e-01, 8.3744e-01,
+                      6.3322e-01, 2.6791e-01, 1.4790e-01, 8.5573e-01,
+                      8.6051e-03, 6.8401e-01, 8.5940e-01, 3.1405e-01,
+                      8.0239e-01, 5.3587e-01, 7.6761e-02, 1.9059e-01,
+                      5.5597e-01, 2.2680e-01, 5.0511e-01, 7.4720e-01,
+                      5.6044e-01, 1.2904e-01, 2.6998e-02, 2.6815e-02,
+                      1.4811e-01, 5.7710e-01, 3.8720e-01, 3.1164e-01,
+                      5.0377e-01, 6.6323e-01, 3.0251e-01, 7.6729e-01,
+                      4.0920e-01, 2.6721e-01, 4.7283e-01, 7.9623e-01,
+                      3.3513e-01, 9.2036e-01, 3.7500e-01, 1.7646e-01,
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+                      5.2796e-02, 7.3738e-01, 3.3700e-02, 5.8595e-01,
+                      7.1338e-01, 1.9473e-01, 7.7258e-01, 8.6642e-01,
+                      3.1804e-01, 9.2864e-01, 3.4002e-01, 5.8527e-01,
+                      3.2231e-01, 2.5907e-01, 2.4104e-02, 9.6474e-01,
+                      7.6358e-01, 2.8793e-01, 2.4963e-01, 3.2547e-03,
+                      3.7889e-01, 7.2848e-01, 7.8352e-01, 6.5869e-01,
+                      6.7556e-01, 9.9946e-01, 7.1099e-01, 7.8559e-01,
+                      2.4594e-01, 7.5866e-01, 2.1889e-01, 8.6192e-01,
+                      1.0492e-01, 6.6974e-01, 1.9110e-01, 4.2178e-01,
+                      5.2211e-01, 5.7968e-01, 4.3791e-01, 1.6162e-01,
+                      1.8223e-01, 4.0645e-01, 8.0597e-01, 1.8599e-01,
+                      8.5659e-01, 8.8819e-01, 4.3915e-01, 2.0250e-01,
+                      7.8476e-02, 6.6803e-01, 7.7438e-01, 2.0798e-02,
+                      6.5758e-01, 3.2100e-01, 3.5731e-04, 2.1289e-01,
+                      2.5481e-01, 6.2077e-01, 2.4535e-01, 8.8808e-01,
+                      3.0564e-01, 8.8046e-01, 1.5537e-01, 2.9485e-01,
+                      4.3426e-01, 1.5200e-01, 2.3927e-01, 4.1251e-02,
+                      2.6271e-01, 5.8274e-01, 5.6945e-02, 1.4017e-01,
+                      3.0720e-01, 3.6401e-01, 1.6795e-01, 6.9631e-02,
+                      6.9758e-01, 5.9161e-01, 8.3040e-01, 9.4507e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7176, 0.7209, 0.4421,  ..., 0.7861, 0.0630, 0.0040])
+tensor([0.7781, 0.3926, 0.9305,  ..., 0.7246, 0.2621, 0.2068])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +1402,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.164389848709106 seconds
+Time: 10.514646053314209 seconds
 
-[20.48, 20.52, 20.52, 20.52, 20.2, 20.28, 20.28, 20.24, 20.08, 20.24]
-[20.36, 20.52, 21.96, 22.92, 24.08, 24.08, 24.4, 25.08, 24.4, 23.76, 24.0, 23.72, 23.4]
-13.186760663986206
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141816, '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': 10.164389848709106, 'TIME_S_1KI': 0.07167308236524163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 285.17019953727726, 'W': 21.625492932171987}
-[20.48, 20.52, 20.52, 20.52, 20.2, 20.28, 20.28, 20.24, 20.08, 20.24, 20.16, 20.28, 20.28, 20.28, 20.12, 20.12, 20.0, 20.12, 20.52, 20.8]
-365.2
-18.259999999999998
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141816, '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': 10.164389848709106, 'TIME_S_1KI': 0.07167308236524163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 285.17019953727726, 'W': 21.625492932171987, 'J_1KI': 2.010846445656888, 'W_1KI': 0.1524897961596152, 'W_D': 3.365492932171989, 'J_D': 44.37994981288918, 'W_D_1KI': 0.023731405004879483, 'J_D_1KI': 0.00016733940461499043}
+[21.04, 21.72, 22.28, 22.76, 23.44, 23.84, 24.4, 25.56, 26.08, 25.56]
+[25.56, 25.8, 25.36, 25.76, 26.12, 26.96, 27.2, 26.52, 26.28, 25.72, 25.56, 25.04, 24.92, 24.92]
+14.21888017654419
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 142926, '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': 10.514646053314209, 'TIME_S_1KI': 0.07356706304880994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.9344939994813, 'W': 24.04792007204229}
+[21.04, 21.72, 22.28, 22.76, 23.44, 23.84, 24.4, 25.56, 26.08, 25.56, 20.64, 20.76, 20.84, 20.68, 20.84, 20.92, 20.48, 20.28, 20.36, 20.6]
+399.15999999999997
+19.958
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 142926, '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': 10.514646053314209, 'TIME_S_1KI': 0.07356706304880994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.9344939994813, 'W': 24.04792007204229, 'J_1KI': 2.3923883268228403, 'W_1KI': 0.1682543419114947, 'W_D': 4.089920072042293, 'J_D': 58.15408343601235, 'W_D_1KI': 0.02861564776207473, 'J_D_1KI': 0.00020021303165326625}
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 cb5e414..510a9ba 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": 1000, "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": 39.37885141372681, "TIME_S_1KI": 39.37885141372681, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3668.398367080688, "W": 77.07751960335096, "J_1KI": 3668.398367080688, "W_1KI": 77.07751960335096, "W_D": 57.18851960335097, "J_D": 2721.8088102509973, "W_D_1KI": 57.18851960335097, "J_D_1KI": 57.18851960335097}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 37.050822496414185, "TIME_S_1KI": 37.050822496414185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3160.1361892318723, "W": 76.88509213042873, "J_1KI": 3160.1361892318723, "W_1KI": 76.88509213042873, "W_D": 56.91509213042873, "J_D": 2339.3279161286355, "W_D_1KI": 56.91509213042873, "J_D_1KI": 56.91509213042873}
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 56c2844..259a25f 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 1000 -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": 39.37885141372681}
+{"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": 37.050822496414185}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 2499992,
-                            2499996, 2500000]),
-       col_indices=tensor([  4222, 120413, 177881,  ..., 234997, 318812,
-                           370543]),
-       values=tensor([0.6429, 0.8175, 0.9231,  ..., 0.8720, 0.9829, 0.6195]),
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499993,
+                            2499998, 2500000]),
+       col_indices=tensor([159663, 166958, 205263,  ..., 483859,  36662,
+                           138241]),
+       values=tensor([0.8479, 0.2779, 0.6227,  ..., 0.0012, 0.5209, 0.2466]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7836, 0.9661, 0.9943,  ..., 0.1995, 0.6325, 0.8613])
+tensor([0.9479, 0.6643, 0.5077,  ..., 0.6908, 0.0479, 0.6658])
 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: 39.37885141372681 seconds
+Time: 37.050822496414185 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      10,  ..., 2499992,
-                            2499996, 2500000]),
-       col_indices=tensor([  4222, 120413, 177881,  ..., 234997, 318812,
-                           370543]),
-       values=tensor([0.6429, 0.8175, 0.9231,  ..., 0.8720, 0.9829, 0.6195]),
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499993,
+                            2499998, 2500000]),
+       col_indices=tensor([159663, 166958, 205263,  ..., 483859,  36662,
+                           138241]),
+       values=tensor([0.8479, 0.2779, 0.6227,  ..., 0.0012, 0.5209, 0.2466]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7836, 0.9661, 0.9943,  ..., 0.1995, 0.6325, 0.8613])
+tensor([0.9479, 0.6643, 0.5077,  ..., 0.6908, 0.0479, 0.6658])
 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: 39.37885141372681 seconds
+Time: 37.050822496414185 seconds
 
-[21.56, 21.8, 21.92, 21.8, 21.8, 21.72, 21.76, 21.96, 22.0, 22.0]
-[22.12, 21.92, 21.96, 23.24, 24.64, 30.88, 42.28, 55.2, 68.48, 80.52, 87.0, 88.04, 90.88, 91.16, 92.4, 93.96, 93.96, 94.68, 93.52, 94.04, 94.08, 94.16, 93.04, 90.92, 89.84, 90.08, 89.76, 89.56, 91.4, 89.32, 89.44, 89.84, 89.84, 90.68, 89.76, 89.96, 89.52, 87.76, 87.84, 90.36, 91.92, 93.44, 92.0, 91.96, 92.36, 91.2]
-47.59362244606018
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 39.37885141372681, 'TIME_S_1KI': 39.37885141372681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3668.398367080688, 'W': 77.07751960335096}
-[21.56, 21.8, 21.92, 21.8, 21.8, 21.72, 21.76, 21.96, 22.0, 22.0, 23.68, 23.6, 22.52, 21.8, 21.8, 21.84, 22.16, 22.32, 22.24, 22.24]
-397.78
-19.889
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 39.37885141372681, 'TIME_S_1KI': 39.37885141372681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3668.398367080688, 'W': 77.07751960335096, 'J_1KI': 3668.398367080688, 'W_1KI': 77.07751960335096, 'W_D': 57.18851960335097, 'J_D': 2721.8088102509973, 'W_D_1KI': 57.18851960335097, 'J_D_1KI': 57.18851960335097}
+[22.44, 22.28, 22.0, 22.08, 22.44, 22.44, 22.24, 22.28, 22.28, 21.92]
+[21.84, 21.84, 22.08, 23.32, 24.04, 37.0, 51.0, 68.72, 84.04, 93.2, 93.2, 96.84, 97.16, 96.0, 93.56, 93.56, 93.4, 92.48, 94.2, 94.4, 93.76, 94.28, 92.52, 92.4, 93.48, 93.48, 95.4, 93.6, 93.04, 91.68, 87.68, 87.08, 87.96, 88.4, 87.72, 87.2, 88.56, 88.0, 89.28, 89.12]
+41.1020667552948
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 37.050822496414185, 'TIME_S_1KI': 37.050822496414185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3160.1361892318723, 'W': 76.88509213042873}
+[22.44, 22.28, 22.0, 22.08, 22.44, 22.44, 22.24, 22.28, 22.28, 21.92, 21.72, 21.72, 21.8, 22.04, 22.2, 22.32, 22.48, 22.44, 22.24, 22.16]
+399.4
+19.97
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 37.050822496414185, 'TIME_S_1KI': 37.050822496414185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3160.1361892318723, 'W': 76.88509213042873, 'J_1KI': 3160.1361892318723, 'W_1KI': 76.88509213042873, 'W_D': 56.91509213042873, 'J_D': 2339.3279161286355, 'W_D_1KI': 56.91509213042873, 'J_D_1KI': 56.91509213042873}
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 47ee4ee..372bd49 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": 1525, "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": 10.375513792037964, "TIME_S_1KI": 6.80361560133637, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1039.7556144714354, "W": 59.41096095809724, "J_1KI": 681.8069603091379, "W_1KI": 38.958007185637534, "W_D": 40.714960958097244, "J_D": 712.5555380096434, "W_D_1KI": 26.698335054489995, "J_D_1KI": 17.507104953763932}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 10.036840677261353, "TIME_S_1KI": 10.036840677261353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.6639336013795, "W": 51.991207713568286, "J_1KI": 692.6639336013795, "W_1KI": 51.991207713568286, "W_D": 33.03220771356828, "J_D": 440.0786197633745, "W_D_1KI": 33.03220771356828, "J_D_1KI": 33.03220771356828}
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 b2aa093..338ad01 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 1000 -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": 6.884527921676636}
+{"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": 10.036840677261353}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     12,  ..., 249988, 249997,
+tensor(crow_indices=tensor([     0,     11,     16,  ..., 249987, 249992,
                             250000]),
-       col_indices=tensor([ 1848, 28763, 31705,  ...,  4981, 22506, 45960]),
-       values=tensor([0.8493, 0.0534, 0.5342,  ..., 0.4299, 0.9704, 0.1142]),
+       col_indices=tensor([ 4880, 10510, 11344,  ..., 23863, 34979, 45750]),
+       values=tensor([0.1743, 0.6413, 0.1893,  ..., 0.8376, 0.7119, 0.1905]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1630, 0.3141, 0.8980,  ..., 0.6818, 0.2617, 0.8646])
+tensor([0.5173, 0.4884, 0.4084,  ..., 0.9473, 0.2501, 0.4146])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 6.884527921676636 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1525 -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": 10.375513792037964}
+Time: 10.036840677261353 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     12,  ..., 249992, 249994,
+tensor(crow_indices=tensor([     0,     11,     16,  ..., 249987, 249992,
                             250000]),
-       col_indices=tensor([ 3205, 25770, 28303,  ..., 16579, 33459, 36956]),
-       values=tensor([0.6871, 0.0301, 0.1880,  ..., 0.0850, 0.6966, 0.8839]),
+       col_indices=tensor([ 4880, 10510, 11344,  ..., 23863, 34979, 45750]),
+       values=tensor([0.1743, 0.6413, 0.1893,  ..., 0.8376, 0.7119, 0.1905]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3970, 0.9447, 0.7491,  ..., 0.5145, 0.9554, 0.9707])
+tensor([0.5173, 0.4884, 0.4084,  ..., 0.9473, 0.2501, 0.4146])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,30 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.375513792037964 seconds
+Time: 10.036840677261353 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     12,  ..., 249992, 249994,
-                            250000]),
-       col_indices=tensor([ 3205, 25770, 28303,  ..., 16579, 33459, 36956]),
-       values=tensor([0.6871, 0.0301, 0.1880,  ..., 0.0850, 0.6966, 0.8839]),
-       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3970, 0.9447, 0.7491,  ..., 0.5145, 0.9554, 0.9707])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 250000
-Density: 0.0001
-Time: 10.375513792037964 seconds
-
-[20.84, 20.72, 20.68, 20.6, 20.64, 20.4, 20.52, 20.48, 20.44, 20.84]
-[21.0, 20.96, 21.88, 23.12, 23.12, 32.08, 50.16, 64.0, 76.16, 91.92, 90.6, 89.16, 89.36, 89.52, 88.8, 89.28, 89.04]
-17.501073837280273
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1525, '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': 10.375513792037964, 'TIME_S_1KI': 6.80361560133637, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1039.7556144714354, 'W': 59.41096095809724}
-[20.84, 20.72, 20.68, 20.6, 20.64, 20.4, 20.52, 20.48, 20.44, 20.84, 20.92, 20.84, 20.84, 20.72, 21.0, 21.0, 21.28, 21.24, 20.92, 20.6]
-373.91999999999996
-18.695999999999998
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1525, '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': 10.375513792037964, 'TIME_S_1KI': 6.80361560133637, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1039.7556144714354, 'W': 59.41096095809724, 'J_1KI': 681.8069603091379, 'W_1KI': 38.958007185637534, 'W_D': 40.714960958097244, 'J_D': 712.5555380096434, 'W_D_1KI': 26.698335054489995, 'J_D_1KI': 17.507104953763932}
+[21.04, 21.04, 21.36, 21.48, 21.32, 21.52, 21.4, 21.16, 21.08, 21.12]
+[21.08, 21.08, 21.08, 22.4, 23.64, 33.72, 51.68, 65.48, 81.88, 95.88, 94.44, 94.2, 93.16]
+13.322712898254395
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 10.036840677261353, 'TIME_S_1KI': 10.036840677261353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6639336013795, 'W': 51.991207713568286}
+[21.04, 21.04, 21.36, 21.48, 21.32, 21.52, 21.4, 21.16, 21.08, 21.12, 21.08, 21.04, 20.96, 20.96, 20.76, 20.68, 20.64, 20.84, 20.92, 20.8]
+379.18
+18.959
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 10.036840677261353, 'TIME_S_1KI': 10.036840677261353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6639336013795, 'W': 51.991207713568286, 'J_1KI': 692.6639336013795, 'W_1KI': 51.991207713568286, 'W_D': 33.03220771356828, 'J_D': 440.0786197633745, 'W_D_1KI': 33.03220771356828, 'J_D_1KI': 33.03220771356828}
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 c30df0a..aeffedd 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": 1000, "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": 60.445369720458984, "TIME_S_1KI": 60.445369720458984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5178.136594352722, "W": 75.0095899661512, "J_1KI": 5178.136594352722, "W_1KI": 75.0095899661512, "W_D": 55.72458996615119, "J_D": 3846.835299849509, "W_D_1KI": 55.72458996615119, "J_D_1KI": 55.72458996615119}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 64.77457070350647, "TIME_S_1KI": 64.77457070350647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4994.562112493517, "W": 73.50821663676467, "J_1KI": 4994.562112493517, "W_1KI": 73.50821663676467, "W_D": 54.114216636764674, "J_D": 3676.8245582232494, "W_D_1KI": 54.114216636764674, "J_D_1KI": 54.114216636764674}
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 1aeb456..56ca1f5 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 1000 -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": 60.445369720458984}
+{"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": 64.77457070350647}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      42,      88,  ..., 2499911,
-                            2499959, 2500000]),
-       col_indices=tensor([  784,  2104,  3070,  ..., 44692, 45478, 45799]),
-       values=tensor([0.0569, 0.3731, 0.2156,  ..., 0.1856, 0.5823, 0.7517]),
+tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499908,
+                            2499960, 2500000]),
+       col_indices=tensor([  291,  1039,  1041,  ..., 49096, 49434, 49928]),
+       values=tensor([0.5586, 0.2987, 0.2608,  ..., 0.4587, 0.5222, 0.8471]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7969, 0.4843, 0.4078,  ..., 0.5644, 0.6126, 0.7864])
+tensor([0.1693, 0.3191, 0.9556,  ..., 0.1736, 0.4599, 0.2505])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 60.445369720458984 seconds
+Time: 64.77457070350647 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      42,      88,  ..., 2499911,
-                            2499959, 2500000]),
-       col_indices=tensor([  784,  2104,  3070,  ..., 44692, 45478, 45799]),
-       values=tensor([0.0569, 0.3731, 0.2156,  ..., 0.1856, 0.5823, 0.7517]),
+tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499908,
+                            2499960, 2500000]),
+       col_indices=tensor([  291,  1039,  1041,  ..., 49096, 49434, 49928]),
+       values=tensor([0.5586, 0.2987, 0.2608,  ..., 0.4587, 0.5222, 0.8471]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7969, 0.4843, 0.4078,  ..., 0.5644, 0.6126, 0.7864])
+tensor([0.1693, 0.3191, 0.9556,  ..., 0.1736, 0.4599, 0.2505])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 60.445369720458984 seconds
+Time: 64.77457070350647 seconds
 
-[21.52, 21.76, 21.6, 21.68, 21.68, 21.48, 21.52, 21.52, 21.72, 22.04]
-[22.08, 22.0, 22.16, 23.24, 24.16, 35.0, 49.88, 62.44, 76.6, 76.6, 84.16, 86.2, 86.12, 85.64, 84.84, 83.12, 82.96, 81.88, 81.44, 81.36, 81.2, 82.36, 83.24, 83.28, 84.08, 84.08, 84.48, 84.24, 83.64, 83.96, 83.6, 83.68, 83.56, 84.48, 83.84, 84.08, 84.36, 84.48, 84.04, 84.4, 85.08, 85.08, 84.88, 84.76, 84.72, 84.08, 83.2, 83.08, 83.4, 83.56, 83.6, 83.56, 83.72, 83.4, 84.96, 85.84, 86.72, 86.72, 87.6, 88.08, 87.2, 87.32, 87.24, 86.88, 86.4, 85.96, 84.76]
-69.0329942703247
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 60.445369720458984, 'TIME_S_1KI': 60.445369720458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5178.136594352722, 'W': 75.0095899661512}
-[21.52, 21.76, 21.6, 21.68, 21.68, 21.48, 21.52, 21.52, 21.72, 22.04, 21.4, 21.44, 21.4, 21.2, 21.2, 21.2, 21.2, 21.04, 20.96, 21.24]
-385.70000000000005
-19.285000000000004
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 60.445369720458984, 'TIME_S_1KI': 60.445369720458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5178.136594352722, 'W': 75.0095899661512, 'J_1KI': 5178.136594352722, 'W_1KI': 75.0095899661512, 'W_D': 55.72458996615119, 'J_D': 3846.835299849509, 'W_D_1KI': 55.72458996615119, 'J_D_1KI': 55.72458996615119}
+[21.48, 21.56, 21.32, 21.36, 21.52, 21.32, 21.32, 21.4, 21.44, 21.56]
+[21.64, 21.6, 22.08, 23.76, 24.8, 35.36, 47.08, 60.56, 71.04, 80.24, 82.48, 82.48, 82.88, 83.68, 82.36, 82.16, 82.04, 80.88, 80.72, 80.88, 80.44, 80.6, 80.44, 80.8, 80.56, 79.4, 78.92, 78.92, 78.4, 78.64, 80.36, 81.28, 82.52, 82.68, 81.64, 81.52, 81.32, 81.2, 80.48, 80.32, 82.04, 81.64, 81.64, 82.44, 82.48, 81.2, 81.08, 81.04, 81.44, 81.2, 81.68, 81.32, 81.0, 81.28, 81.36, 81.24, 81.36, 81.36, 81.88, 82.48, 83.6, 83.4, 83.64, 82.8, 82.56, 82.36]
+67.94563031196594
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 64.77457070350647, 'TIME_S_1KI': 64.77457070350647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4994.562112493517, 'W': 73.50821663676467}
+[21.48, 21.56, 21.32, 21.36, 21.52, 21.32, 21.32, 21.4, 21.44, 21.56, 21.88, 21.92, 21.8, 21.52, 21.68, 21.52, 21.68, 21.68, 21.6, 21.56]
+387.88
+19.394
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 64.77457070350647, 'TIME_S_1KI': 64.77457070350647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4994.562112493517, 'W': 73.50821663676467, 'J_1KI': 4994.562112493517, 'W_1KI': 73.50821663676467, 'W_D': 54.114216636764674, 'J_D': 3676.8245582232494, 'W_D_1KI': 54.114216636764674, 'J_D_1KI': 54.114216636764674}
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 0a48909..2428814 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": 8439, "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": 16.30658531188965, "TIME_S_1KI": 1.9322888152493953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 993.3751641559601, "W": 60.56268692312065, "J_1KI": 117.71242613531936, "W_1KI": 7.176524105121538, "W_D": 41.768686923120654, "J_D": 685.1079160590172, "W_D_1KI": 4.949482986505588, "J_D_1KI": 0.5865011241267435}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 6367, "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": 14.819957971572876, "TIME_S_1KI": 2.3276202248426068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 977.0843494701387, "W": 58.720628093786544, "J_1KI": 153.46071139785437, "W_1KI": 9.222652441304625, "W_D": 39.857628093786545, "J_D": 663.2126712820532, "W_D_1KI": 6.260032683176778, "J_D_1KI": 0.9831997303560197}
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 ee322a5..c94062e 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 1000 -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": 3.2654190063476562}
+{"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": 2.0054540634155273}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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, 25000, 25000]),
-       col_indices=tensor([ 6514, 22496, 11789,  ..., 40007,  5149, 28458]),
-       values=tensor([0.4327, 0.6473, 0.1491,  ..., 0.8954, 0.9190, 0.6593]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([29300, 37118, 28917,  ..., 16725, 28059, 47397]),
+       values=tensor([0.1773, 0.7310, 0.0095,  ..., 0.4568, 0.7722, 0.2574]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4414, 0.3955, 0.1417,  ..., 0.3292, 0.0955, 0.0474])
+tensor([0.0745, 0.0507, 0.1628,  ..., 0.0663, 0.8219, 0.2626])
 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: 3.2654190063476562 seconds
+Time: 2.0054540634155273 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3215 -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": 4.000005722045898}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5235 -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": 8.632020473480225}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([27536, 25934, 37963,  ...,  3997, 32688, 28318]),
-       values=tensor([0.1759, 0.2893, 0.0177,  ..., 0.2344, 0.0283, 0.5475]),
+       col_indices=tensor([ 6005,  4214, 13465,  ..., 35902,  7875,  2053]),
+       values=tensor([0.3591, 0.3792, 0.0771,  ..., 0.2893, 0.2529, 0.4673]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4720, 0.7633, 0.9347,  ..., 0.8863, 0.6224, 0.2346])
+tensor([0.1098, 0.6338, 0.4539,  ..., 0.7586, 0.0998, 0.7821])
 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: 4.000005722045898 seconds
+Time: 8.632020473480225 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8439 -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": 16.30658531188965}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6367 -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": 14.819957971572876}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([22959,  5139, 40799,  ..., 46493,  8579,  7673]),
-       values=tensor([0.4149, 0.3641, 0.9895,  ..., 0.4042, 0.1062, 0.3479]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([30839, 39998,  2326,  ..., 30652, 20576,  5061]),
+       values=tensor([0.3250, 0.6882, 0.6966,  ..., 0.0105, 0.7219, 0.0367]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8723, 0.6408, 0.2457,  ..., 0.3733, 0.2625, 0.6379])
+tensor([0.0417, 0.5559, 0.6322,  ..., 0.5652, 0.2111, 0.1243])
 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: 16.30658531188965 seconds
+Time: 14.819957971572876 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([22959,  5139, 40799,  ..., 46493,  8579,  7673]),
-       values=tensor([0.4149, 0.3641, 0.9895,  ..., 0.4042, 0.1062, 0.3479]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([30839, 39998,  2326,  ..., 30652, 20576,  5061]),
+       values=tensor([0.3250, 0.6882, 0.6966,  ..., 0.0105, 0.7219, 0.0367]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8723, 0.6408, 0.2457,  ..., 0.3733, 0.2625, 0.6379])
+tensor([0.0417, 0.5559, 0.6322,  ..., 0.5652, 0.2111, 0.1243])
 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: 16.30658531188965 seconds
+Time: 14.819957971572876 seconds
 
-[21.24, 20.92, 20.92, 21.08, 20.72, 20.88, 21.0, 21.0, 20.68, 20.92]
-[21.0, 20.72, 20.88, 25.2, 27.52, 41.48, 58.76, 70.32, 84.16, 91.04, 91.68, 91.84, 91.84, 92.08, 92.44, 91.88]
-16.40242886543274
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8439, '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': 16.30658531188965, 'TIME_S_1KI': 1.9322888152493953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 993.3751641559601, 'W': 60.56268692312065}
-[21.24, 20.92, 20.92, 21.08, 20.72, 20.88, 21.0, 21.0, 20.68, 20.92, 20.8, 20.96, 20.92, 20.92, 20.76, 20.88, 20.8, 20.92, 20.76, 20.56]
-375.88
-18.794
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8439, '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': 16.30658531188965, 'TIME_S_1KI': 1.9322888152493953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 993.3751641559601, 'W': 60.56268692312065, 'J_1KI': 117.71242613531936, 'W_1KI': 7.176524105121538, 'W_D': 41.768686923120654, 'J_D': 685.1079160590172, 'W_D_1KI': 4.949482986505588, 'J_D_1KI': 0.5865011241267435}
+[20.72, 20.6, 20.92, 21.12, 21.0, 21.04, 21.08, 20.8, 20.88, 21.08]
+[20.96, 21.0, 21.0, 24.32, 26.04, 35.64, 50.52, 67.6, 77.24, 93.48, 91.28, 89.6, 88.96, 88.4, 88.92, 90.04]
+16.63954186439514
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 6367, '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': 14.819957971572876, 'TIME_S_1KI': 2.3276202248426068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 977.0843494701387, 'W': 58.720628093786544}
+[20.72, 20.6, 20.92, 21.12, 21.0, 21.04, 21.08, 20.8, 20.88, 21.08, 20.88, 20.92, 20.68, 20.84, 21.0, 21.0, 20.92, 21.32, 21.28, 21.04]
+377.26
+18.863
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 6367, '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': 14.819957971572876, 'TIME_S_1KI': 2.3276202248426068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 977.0843494701387, 'W': 58.720628093786544, 'J_1KI': 153.46071139785437, 'W_1KI': 9.222652441304625, 'W_D': 39.857628093786545, 'J_D': 663.2126712820532, 'W_D_1KI': 6.260032683176778, 'J_D_1KI': 0.9831997303560197}
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
new file mode 100644
index 0000000..fef2205
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 97519, "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": 10.570974826812744, "TIME_S_1KI": 0.10839913070081465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.3015551567077, "W": 22.190891521159134, "J_1KI": 3.233232038440793, "W_1KI": 0.22755454343419368, "W_D": 3.682891521159135, "J_D": 52.32874141120907, "W_D_1KI": 0.037765886864704674, "J_D_1KI": 0.00038726696197361203}
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
new file mode 100644
index 0000000..c6cbfe2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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 1000 -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": 0.11602067947387695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([ 613, 2610, 3896,  ..., 2268, 1349, 1721]),
+       values=tensor([0.3594, 0.2050, 0.8766,  ..., 0.2511, 0.4340, 0.6606]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7862, 0.0116, 0.6512,  ..., 0.0192, 0.3599, 0.4463])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.11602067947387695 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 90501 -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": 9.744299173355103}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([3869,  881, 2923,  ..., 3064, 1070, 3092]),
+       values=tensor([0.3867, 0.1123, 0.7736,  ..., 0.1665, 0.3688, 0.6121]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7562, 0.4892, 0.9144,  ..., 0.6968, 0.8474, 0.7157])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 9.744299173355103 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97519 -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": 10.570974826812744}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 2497, 2499, 2500]),
+       col_indices=tensor([4211, 3231, 4340,  ..., 2446, 2540,  154]),
+       values=tensor([0.2167, 0.9555, 0.6550,  ..., 0.2361, 0.5850, 0.4084]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3692, 0.1411, 0.4138,  ..., 0.1913, 0.1315, 0.0581])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.570974826812744 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 2497, 2499, 2500]),
+       col_indices=tensor([4211, 3231, 4340,  ..., 2446, 2540,  154]),
+       values=tensor([0.2167, 0.9555, 0.6550,  ..., 0.2361, 0.5850, 0.4084]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3692, 0.1411, 0.4138,  ..., 0.1913, 0.1315, 0.0581])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.570974826812744 seconds
+
+[20.44, 20.44, 20.48, 20.4, 20.48, 20.68, 20.76, 20.76, 20.72, 20.88]
+[20.64, 20.56, 21.92, 23.68, 23.68, 25.04, 25.48, 26.12, 24.52, 24.44, 23.92, 24.04, 24.32, 24.36]
+14.20860242843628
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97519, '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': 10.570974826812744, 'TIME_S_1KI': 0.10839913070081465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.3015551567077, 'W': 22.190891521159134}
+[20.44, 20.44, 20.48, 20.4, 20.48, 20.68, 20.76, 20.76, 20.72, 20.88, 20.4, 20.52, 20.56, 20.56, 20.56, 20.6, 20.52, 20.4, 20.6, 20.52]
+370.15999999999997
+18.508
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97519, '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': 10.570974826812744, 'TIME_S_1KI': 0.10839913070081465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.3015551567077, 'W': 22.190891521159134, 'J_1KI': 3.233232038440793, 'W_1KI': 0.22755454343419368, 'W_D': 3.682891521159135, 'J_D': 52.32874141120907, 'W_D_1KI': 0.037765886864704674, 'J_D_1KI': 0.00038726696197361203}
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
new file mode 100644
index 0000000..6041c9a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17764, "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": 10.691047191619873, "TIME_S_1KI": 0.6018378288459735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.0331690597534, "W": 23.15708952749249, "J_1KI": 18.522470674383776, "W_1KI": 1.3035965732657337, "W_D": 4.568089527492493, "J_D": 64.90681706762312, "W_D_1KI": 0.2571543305276116, "J_D_1KI": 0.01447615010851225}
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
new file mode 100644
index 0000000..626c252
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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 1000 -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": 0.6273210048675537}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,  ..., 24988, 24996, 25000]),
+       col_indices=tensor([1892, 2918, 4655,  ..., 2029, 2603, 3010]),
+       values=tensor([0.8283, 0.5273, 0.2909,  ..., 0.5828, 0.6477, 0.7502]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8412, 0.7891, 0.2404,  ..., 0.8503, 0.9914, 0.6212])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.6273210048675537 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 16737 -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": 9.892534494400024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    10,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([4752,  479, 2068,  ..., 1338, 4478, 4539]),
+       values=tensor([0.3996, 0.8763, 0.4834,  ..., 0.3300, 0.4860, 0.9993]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9991, 0.1904, 0.1090,  ..., 0.8295, 0.4248, 0.2043])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.892534494400024 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17764 -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": 10.691047191619873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    15,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([1098, 1490, 1639,  ..., 3549, 3645, 4602]),
+       values=tensor([0.2763, 0.7775, 0.9451,  ..., 0.5590, 0.3508, 0.3085]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1047, 0.7118, 0.3308,  ..., 0.3344, 0.9893, 0.0200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.691047191619873 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,    15,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([1098, 1490, 1639,  ..., 3549, 3645, 4602]),
+       values=tensor([0.2763, 0.7775, 0.9451,  ..., 0.5590, 0.3508, 0.3085]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1047, 0.7118, 0.3308,  ..., 0.3344, 0.9893, 0.0200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.691047191619873 seconds
+
+[20.44, 20.2, 20.44, 20.64, 20.6, 20.72, 21.04, 21.04, 21.04, 20.88]
+[20.72, 20.68, 20.52, 21.28, 23.28, 29.52, 30.36, 30.6, 30.4, 23.8, 23.72, 23.72, 23.84, 23.92]
+14.208744525909424
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17764, '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': 10.691047191619873, 'TIME_S_1KI': 0.6018378288459735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.0331690597534, 'W': 23.15708952749249}
+[20.44, 20.2, 20.44, 20.64, 20.6, 20.72, 21.04, 21.04, 21.04, 20.88, 20.68, 20.8, 20.72, 20.4, 20.32, 20.52, 20.64, 20.6, 20.72, 20.68]
+371.78
+18.589
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17764, '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': 10.691047191619873, 'TIME_S_1KI': 0.6018378288459735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.0331690597534, 'W': 23.15708952749249, 'J_1KI': 18.522470674383776, 'W_1KI': 1.3035965732657337, 'W_D': 4.568089527492493, 'J_D': 64.90681706762312, 'W_D_1KI': 0.2571543305276116, 'J_D_1KI': 0.01447615010851225}
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
new file mode 100644
index 0000000..5558ea5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1959, "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": 11.120546340942383, "TIME_S_1KI": 5.676644380266659, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.91848049163815, "W": 23.94849862540507, "J_1KI": 173.51632490640029, "W_1KI": 12.224858920574308, "W_D": 5.3214986254050665, "J_D": 75.53190515112868, "W_D_1KI": 2.716436255949498, "J_D_1KI": 1.3866443368808055}
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
new file mode 100644
index 0000000..c4f4108
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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 1000 -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": 5.658592224121094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     40,     84,  ..., 249908, 249949,
+                            250000]),
+       col_indices=tensor([ 330,  398,  412,  ..., 4758, 4825, 4990]),
+       values=tensor([0.1241, 0.3411, 0.2552,  ..., 0.9324, 0.8443, 0.4144]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9270, 0.0262, 0.1807,  ..., 0.7250, 0.9803, 0.9114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 5.658592224121094 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1855 -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": 9.939346075057983}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     42,     99,  ..., 249900, 249944,
+                            250000]),
+       col_indices=tensor([  71,   83,  134,  ..., 4502, 4510, 4544]),
+       values=tensor([0.1222, 0.9313, 0.0593,  ..., 0.6337, 0.4012, 0.6808]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6318, 0.1040, 0.0347,  ..., 0.9714, 0.1743, 0.3337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 9.939346075057983 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1959 -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": 11.120546340942383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     94,  ..., 249896, 249940,
+                            250000]),
+       col_indices=tensor([  62,  114,  171,  ..., 4675, 4821, 4860]),
+       values=tensor([0.5686, 0.1100, 0.2304,  ..., 0.6863, 0.4817, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8036, 0.6564, 0.9943,  ..., 0.3026, 0.0525, 0.3398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 11.120546340942383 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     94,  ..., 249896, 249940,
+                            250000]),
+       col_indices=tensor([  62,  114,  171,  ..., 4675, 4821, 4860]),
+       values=tensor([0.5686, 0.1100, 0.2304,  ..., 0.6863, 0.4817, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8036, 0.6564, 0.9943,  ..., 0.3026, 0.0525, 0.3398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 11.120546340942383 seconds
+
+[20.4, 20.56, 20.84, 20.88, 21.08, 21.16, 21.16, 20.92, 20.72, 20.76]
+[20.2, 20.2, 20.96, 22.4, 23.64, 30.44, 31.56, 31.08, 30.2, 30.2, 24.44, 24.12, 24.04, 23.84]
+14.19372820854187
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1959, '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': 11.120546340942383, 'TIME_S_1KI': 5.676644380266659, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.91848049163815, 'W': 23.94849862540507}
+[20.4, 20.56, 20.84, 20.88, 21.08, 21.16, 21.16, 20.92, 20.72, 20.76, 20.32, 20.52, 20.64, 20.6, 20.44, 20.28, 20.44, 20.6, 20.64, 20.64]
+372.54
+18.627000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1959, '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': 11.120546340942383, 'TIME_S_1KI': 5.676644380266659, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.91848049163815, 'W': 23.94849862540507, 'J_1KI': 173.51632490640029, 'W_1KI': 12.224858920574308, 'W_D': 5.3214986254050665, 'J_D': 75.53190515112868, 'W_D_1KI': 2.716436255949498, 'J_D_1KI': 1.3866443368808055}
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
new file mode 100644
index 0000000..88b3edd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 26.64292335510254, "TIME_S_1KI": 26.64292335510254, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.6764411926268, "W": 24.280702381341985, "J_1KI": 738.6764411926268, "W_1KI": 24.280702381341985, "W_D": 5.882702381341982, "J_D": 178.96573136138895, "W_D_1KI": 5.882702381341982, "J_D_1KI": 5.882702381341982}
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
new file mode 100644
index 0000000..d050d33
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -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": 26.64292335510254}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     258,     500,  ..., 1249494,
+                            1249753, 1250000]),
+       col_indices=tensor([  51,   83,   92,  ..., 4940, 4981, 4997]),
+       values=tensor([0.9970, 0.1345, 0.9294,  ..., 0.5035, 0.6973, 0.4629]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6657, 0.7944, 0.8404,  ..., 0.5502, 0.2324, 0.9138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.64292335510254 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     258,     500,  ..., 1249494,
+                            1249753, 1250000]),
+       col_indices=tensor([  51,   83,   92,  ..., 4940, 4981, 4997]),
+       values=tensor([0.9970, 0.1345, 0.9294,  ..., 0.5035, 0.6973, 0.4629]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6657, 0.7944, 0.8404,  ..., 0.5502, 0.2324, 0.9138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.64292335510254 seconds
+
+[20.76, 20.56, 20.76, 20.24, 20.0, 20.0, 19.96, 19.88, 20.0, 20.0]
+[20.2, 20.56, 20.84, 23.48, 25.48, 31.96, 32.48, 32.96, 29.96, 29.16, 23.84, 23.8, 23.88, 23.88, 24.28, 24.2, 24.36, 24.32, 23.8, 24.12, 24.2, 24.36, 24.28, 24.24, 24.24, 24.24, 24.32, 24.24, 24.48, 24.52]
+30.422367095947266
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 26.64292335510254, 'TIME_S_1KI': 26.64292335510254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.6764411926268, 'W': 24.280702381341985}
+[20.76, 20.56, 20.76, 20.24, 20.0, 20.0, 19.96, 19.88, 20.0, 20.0, 20.52, 20.44, 20.56, 20.64, 20.52, 20.76, 20.64, 20.92, 20.96, 20.96]
+367.96000000000004
+18.398000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 26.64292335510254, 'TIME_S_1KI': 26.64292335510254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.6764411926268, 'W': 24.280702381341985, 'J_1KI': 738.6764411926268, 'W_1KI': 24.280702381341985, 'W_D': 5.882702381341982, 'J_D': 178.96573136138895, 'W_D_1KI': 5.882702381341982, 'J_D_1KI': 5.882702381341982}
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
new file mode 100644
index 0000000..70fec2c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "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": 53.09800863265991, "TIME_S_1KI": 53.09800863265991, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1383.3292028236385, "W": 24.387974690726086, "J_1KI": 1383.3292028236385, "W_1KI": 24.387974690726086, "W_D": 5.857974690726085, "J_D": 332.2747198915477, "W_D_1KI": 5.857974690726085, "J_D_1KI": 5.857974690726085}
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
new file mode 100644
index 0000000..20994df
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -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": 53.09800863265991}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,     997,  ..., 2498998,
+                            2499500, 2500000]),
+       col_indices=tensor([   3,   13,   35,  ..., 4966, 4993, 4997]),
+       values=tensor([0.0178, 0.5574, 0.8921,  ..., 0.2131, 0.1882, 0.3495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5571, 0.9138, 0.4400,  ..., 0.1682, 0.6225, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.09800863265991 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,     997,  ..., 2498998,
+                            2499500, 2500000]),
+       col_indices=tensor([   3,   13,   35,  ..., 4966, 4993, 4997]),
+       values=tensor([0.0178, 0.5574, 0.8921,  ..., 0.2131, 0.1882, 0.3495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5571, 0.9138, 0.4400,  ..., 0.1682, 0.6225, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.09800863265991 seconds
+
+[20.6, 20.6, 20.72, 20.68, 20.72, 20.8, 20.84, 20.8, 20.92, 20.52]
+[20.6, 20.4, 23.56, 24.32, 24.32, 28.84, 34.48, 35.48, 33.04, 32.72, 26.72, 24.2, 24.24, 24.24, 24.0, 24.0, 23.96, 23.96, 23.88, 23.96, 23.96, 23.92, 23.92, 23.68, 23.64, 23.76, 24.08, 24.12, 24.08, 24.08, 24.32, 24.08, 23.92, 24.04, 24.0, 23.96, 24.12, 24.24, 24.28, 24.24, 24.2, 23.92, 23.92, 24.0, 24.2, 24.24, 24.4, 24.2, 24.16, 24.0, 24.16, 24.32, 24.36, 24.36, 24.36, 24.12]
+56.72177457809448
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 53.09800863265991, 'TIME_S_1KI': 53.09800863265991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.3292028236385, 'W': 24.387974690726086}
+[20.6, 20.6, 20.72, 20.68, 20.72, 20.8, 20.84, 20.8, 20.92, 20.52, 20.32, 20.36, 20.4, 20.48, 20.44, 20.64, 20.48, 20.36, 20.48, 20.32]
+370.6
+18.53
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, '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': 53.09800863265991, 'TIME_S_1KI': 53.09800863265991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.3292028236385, 'W': 24.387974690726086, 'J_1KI': 1383.3292028236385, 'W_1KI': 24.387974690726086, 'W_D': 5.857974690726085, 'J_D': 332.2747198915477, 'W_D_1KI': 5.857974690726085, 'J_D_1KI': 5.857974690726085}
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
new file mode 100644
index 0000000..7d0783d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 275920, "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": 10.428882360458374, "TIME_S_1KI": 0.037796761236801875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.0905113220215, "W": 22.127187461407587, "J_1KI": 1.141963291251165, "W_1KI": 0.08019421376271234, "W_D": 3.664187461407586, "J_D": 52.177923778533895, "W_D_1KI": 0.013279890770540686, "J_D_1KI": 4.812949684887172e-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
new file mode 100644
index 0000000..a254447
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,383 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -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": 0.04542350769042969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1381, 3398, 2478, 1052,  529,  491, 2775, 3229, 1279,
+                           3454,  296, 3084, 4650, 2467,  784,  568,  918,  741,
+                           4819, 1730,  837,  408, 1523,  948, 4825, 1342,  952,
+                           2524, 3378, 2774,  370, 2319, 3980, 4108,  276, 4067,
+                           3823, 3153, 3158,  540, 2360, 1999, 1044, 1298, 4540,
+                            533, 3507, 1489, 2361, 1008,  555, 3416,  305, 3290,
+                           1136, 3809, 4448, 2408, 3611, 2892, 2540, 3779, 2041,
+                           4793, 4839,  534, 3664, 2180, 4711, 4601, 1136, 3681,
+                            165, 2858, 2937, 1364, 4737, 4916, 4412, 4772, 4253,
+                            200, 1254, 2702, 3949, 1138, 3253, 4523, 3563, 4932,
+                            724, 1152, 3157, 1713, 4323, 2340,  951, 3022, 1343,
+                           2260, 3881, 2605,  161, 4434, 3331, 1742, 2563, 4238,
+                            127, 3937,  396, 2283, 1557, 1554, 3292, 4855, 4197,
+                           4720,  716,   85, 4379, 3823, 2263, 2186, 2869, 1787,
+                           1168, 2429, 3045, 2919, 2350, 3479, 2094, 1065,  340,
+                           1288, 1877, 3764, 3457,  509, 1055, 3089,  605, 1110,
+                           3765, 3334, 3358,  602, 1278, 2312, 2279, 3749, 3299,
+                           4530,  804, 4261,  418, 4624,  585, 3050, 3236,  596,
+                           2133,  933, 4209, 3895,  174,  765, 3980,  381, 2181,
+                           2969,   46, 3997, 2920, 1083, 1216, 4056,  126,  248,
+                           1696,  352, 2821,  625, 3058, 4954, 4557,  865, 2010,
+                           2268, 2460, 1542,  329, 4649, 4740, 2546, 1491, 1783,
+                           2436, 2269, 2383,  734, 4372, 4876, 3373,  210, 4004,
+                           4560, 1501, 3320, 2378, 1630,  757, 3013, 4961, 4950,
+                           3415, 2145, 1401, 3711, 4355,  611, 1420, 3710, 4405,
+                           2508, 3816,    3, 3115, 4093, 2712, 1642, 4784, 2945,
+                           3902, 1255, 2147, 1010, 3088, 1205, 4589,  714, 2492,
+                           1954, 4006, 3877,  588,  962,   61, 4470]),
+       values=tensor([6.2379e-01, 5.1445e-01, 5.2888e-01, 6.4643e-01,
+                      3.6807e-01, 4.6260e-01, 2.5238e-01, 5.8157e-01,
+                      8.8267e-01, 2.6474e-01, 2.8446e-01, 9.5475e-01,
+                      4.8999e-01, 6.6621e-01, 3.2615e-02, 2.5044e-01,
+                      4.5496e-01, 3.7415e-01, 2.9199e-01, 2.8386e-01,
+                      7.1383e-01, 3.1109e-01, 1.1332e-01, 2.2089e-01,
+                      2.1912e-01, 5.6452e-01, 4.7190e-01, 5.8604e-01,
+                      7.8763e-01, 9.5122e-01, 1.1018e-01, 1.3969e-01,
+                      7.2800e-01, 6.6977e-01, 2.9413e-01, 6.1351e-01,
+                      4.9889e-01, 3.4691e-01, 3.9756e-01, 7.5031e-01,
+                      1.4612e-01, 6.6037e-01, 2.5630e-01, 9.1057e-02,
+                      8.2140e-01, 9.9620e-01, 5.5939e-01, 1.0762e-01,
+                      7.8811e-01, 5.4825e-01, 1.0084e-01, 8.9423e-01,
+                      7.7729e-01, 2.7164e-01, 7.0220e-01, 1.6836e-01,
+                      5.3765e-01, 2.0228e-01, 1.5568e-02, 8.3985e-01,
+                      2.3206e-01, 6.7022e-01, 4.7791e-01, 6.4798e-01,
+                      6.7036e-01, 1.6005e-01, 7.3101e-01, 9.4913e-01,
+                      2.2292e-01, 4.6540e-01, 7.6590e-01, 2.9344e-01,
+                      5.6223e-01, 8.4355e-01, 8.4945e-01, 1.4869e-01,
+                      2.8265e-01, 3.2754e-01, 5.8549e-01, 9.8812e-01,
+                      5.4427e-01, 9.3814e-01, 8.4516e-01, 1.7512e-01,
+                      1.2307e-02, 2.2939e-01, 7.7071e-01, 1.9977e-01,
+                      6.3831e-01, 1.4402e-01, 3.9596e-02, 8.3780e-01,
+                      6.9744e-01, 5.2304e-02, 1.7853e-01, 2.9282e-01,
+                      5.7428e-01, 3.6008e-01, 1.5117e-01, 8.0683e-01,
+                      6.9041e-02, 5.8242e-01, 9.0514e-01, 7.4588e-01,
+                      7.5412e-01, 9.1699e-01, 3.8286e-01, 9.6918e-01,
+                      7.4727e-01, 1.2312e-01, 4.8375e-01, 3.6856e-01,
+                      5.6299e-01, 5.3561e-01, 1.4061e-01, 8.9669e-01,
+                      2.2440e-02, 2.9850e-01, 1.9549e-01, 5.4525e-01,
+                      3.6535e-01, 4.3468e-01, 5.3884e-01, 4.1129e-01,
+                      3.4185e-02, 4.7048e-01, 7.9007e-01, 7.4755e-01,
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+                      3.8728e-01, 2.4384e-01, 9.0271e-01, 7.7139e-01,
+                      4.5138e-01, 3.9539e-01, 2.1438e-01, 3.5791e-01,
+                      1.8080e-01, 7.7421e-01, 4.8385e-01, 4.9788e-02,
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+                      9.7435e-01, 1.3732e-04, 3.9675e-01, 1.5987e-01,
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+                      4.6135e-02, 7.5784e-01, 9.8773e-01, 1.4273e-01,
+                      6.0358e-01, 8.2895e-01, 4.6077e-01, 8.1063e-01,
+                      7.6600e-01, 4.0656e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.9879, 0.6356, 0.7019,  ..., 0.7112, 0.3671, 0.2365])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04542350769042969 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 231157 -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": 8.796557664871216}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([3111, 1505, 3032,  732, 1363, 1458, 3691, 3479, 1828,
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+                           3635,  259,  715, 4710, 2651, 3054,  874, 3683, 2173,
+                           4229, 1021, 1554, 2109, 4700, 2191,  703]),
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+                      2.3487e-01, 5.2578e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.6897, 0.5913, 0.2174,  ..., 0.0385, 0.9360, 0.0281])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 8.796557664871216 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 275920 -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": 10.428882360458374}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1877, 1947, 3773, 1993, 3968, 2885, 3290, 1001, 3173,
+                           4910, 3638, 1123,  363, 1623, 2284, 1294, 3337, 1282,
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+                           3986, 4768, 1832,  171,  533,  127, 3370, 4616, 3556,
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+                      0.2829, 0.0867, 0.8730, 0.1781, 0.6966]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7582, 0.3275, 0.7400,  ..., 0.8955, 0.3174, 0.3280])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.428882360458374 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([1877, 1947, 3773, 1993, 3968, 2885, 3290, 1001, 3173,
+                           4910, 3638, 1123,  363, 1623, 2284, 1294, 3337, 1282,
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+                           3986, 4768, 1832,  171,  533,  127, 3370, 4616, 3556,
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+                      0.0171, 0.1702, 0.0571, 0.1251, 0.4789, 0.2100, 0.4597,
+                      0.8236, 0.2093, 0.3392, 0.8809, 0.8206, 0.6653, 0.7105,
+                      0.9427, 0.4744, 0.2605, 0.1657, 0.1195, 0.1792, 0.5307,
+                      0.1174, 0.6758, 0.8184, 0.0607, 0.0558, 0.3782, 0.8926,
+                      0.6897, 0.9924, 0.7956, 0.0060, 0.2666, 0.9269, 0.6602,
+                      0.5276, 0.2277, 0.4849, 0.8321, 0.2135, 0.2296, 0.7282,
+                      0.5446, 0.1493, 0.5845, 0.2697, 0.2635, 0.0055, 0.3342,
+                      0.6531, 0.8835, 0.6970, 0.3925, 0.6332, 0.2833, 0.7464,
+                      0.9403, 0.9564, 0.8529, 0.8534, 0.4902, 0.3672, 0.4884,
+                      0.3826, 0.8277, 0.2524, 0.5006, 0.8262, 0.8556, 0.5518,
+                      0.9345, 0.1818, 0.7419, 0.5510, 0.7359, 0.2338, 0.5242,
+                      0.8847, 0.7894, 0.5148, 0.5220, 0.3152, 0.5588, 0.6758,
+                      0.0222, 0.8094, 0.8800, 0.5482, 0.7029, 0.4511, 0.5521,
+                      0.1426, 0.5819, 0.4684, 0.3203, 0.4558, 0.0605, 0.4645,
+                      0.6967, 0.5420, 0.5383, 0.3399, 0.6017, 0.2217, 0.2779,
+                      0.6034, 0.6186, 0.5877, 0.7226, 0.4771, 0.2736, 0.9442,
+                      0.4016, 0.5813, 0.3926, 0.6636, 0.2000, 0.5234, 0.8594,
+                      0.4283, 0.8253, 0.1300, 0.3810, 0.0496, 0.8722, 0.5976,
+                      0.0028, 0.5374, 0.0379, 0.0610, 0.9205, 0.9022, 0.6780,
+                      0.7337, 0.3928, 0.7007, 0.0730, 0.0899, 0.4352, 0.2480,
+                      0.7721, 0.6286, 0.0462, 0.5434, 0.2214, 0.2005, 0.5352,
+                      0.2866, 0.1634, 0.3716, 0.1574, 0.2559, 0.6104, 0.9417,
+                      0.5436, 0.9351, 0.6446, 0.8506, 0.6360, 0.5124, 0.9341,
+                      0.9751, 0.4728, 0.6908, 0.5778, 0.2603, 0.9571, 0.5985,
+                      0.0453, 0.2921, 0.4748, 0.9573, 0.6189, 0.2369, 0.4918,
+                      0.2829, 0.0867, 0.8730, 0.1781, 0.6966]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7582, 0.3275, 0.7400,  ..., 0.8955, 0.3174, 0.3280])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.428882360458374 seconds
+
+[20.56, 20.68, 20.64, 20.44, 20.64, 20.48, 20.48, 20.64, 20.68, 20.72]
+[20.84, 20.84, 21.12, 24.24, 25.12, 25.84, 26.16, 24.96, 23.68, 23.68, 23.56, 23.8, 24.0, 23.88]
+14.239971160888672
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 275920, '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': 10.428882360458374, 'TIME_S_1KI': 0.037796761236801875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.0905113220215, 'W': 22.127187461407587}
+[20.56, 20.68, 20.64, 20.44, 20.64, 20.48, 20.48, 20.64, 20.68, 20.72, 20.56, 20.72, 20.56, 20.64, 20.56, 20.24, 20.2, 20.44, 20.2, 20.2]
+369.26
+18.463
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 275920, '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': 10.428882360458374, 'TIME_S_1KI': 0.037796761236801875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.0905113220215, 'W': 22.127187461407587, 'J_1KI': 1.141963291251165, 'W_1KI': 0.08019421376271234, 'W_D': 3.664187461407586, 'J_D': 52.177923778533895, 'W_D_1KI': 0.013279890770540686, 'J_D_1KI': 4.812949684887172e-05}
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 87aabb6..4a28d6c 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": 66395, "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": 10.71402621269226, "TIME_S_1KI": 0.16136796765859268, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1809.3928852605818, "W": 143.23, "J_1KI": 27.251944954598716, "W_1KI": 2.15724075608103, "W_D": 107.452, "J_D": 1357.417330915451, "W_D_1KI": 1.6183748776263271, "J_D_1KI": 0.02437495109008701}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 70787, "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": 11.243863582611084, "TIME_S_1KI": 0.15884079820604183, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.8814449405672, "W": 142.52, "J_1KI": 29.184475185282146, "W_1KI": 2.01336403576928, "W_D": 106.74100000000001, "J_D": 1547.2512722032072, "W_D_1KI": 1.5079181205588599, "J_D_1KI": 0.02130218995802704}
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 bc4163e..320d8b0 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', '1000', '-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": 0.2295377254486084}
+{"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": 0.22568225860595703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999980,
-                             999992, 1000000]),
-       col_indices=tensor([ 2595, 16687, 29551,  ..., 82666, 84305, 92330]),
-       values=tensor([0.2399, 0.6496, 0.1067,  ..., 0.4780, 0.9034, 0.0304]),
+tensor(crow_indices=tensor([      0,       9,      21,  ...,  999976,
+                             999987, 1000000]),
+       col_indices=tensor([66167, 77335, 80388,  ..., 91843, 96961, 99110]),
+       values=tensor([0.4269, 0.3181, 0.3880,  ..., 0.8858, 0.0510, 0.2541]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9784, 0.5709, 0.3671,  ..., 0.6067, 0.7821, 0.8363])
+tensor([0.0143, 0.7097, 0.7299,  ..., 0.1191, 0.1743, 0.7741])
 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: 0.2295377254486084 seconds
+Time: 0.22568225860595703 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45744', '-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": 7.2341063022613525}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46525', '-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": 6.901124477386475}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999977,
-                             999994, 1000000]),
-       col_indices=tensor([  464, 33291, 41816,  ..., 39255, 78479, 83666]),
-       values=tensor([0.4695, 0.4859, 0.9230,  ..., 0.6746, 0.1683, 0.8174]),
+tensor(crow_indices=tensor([      0,       9,      22,  ...,  999980,
+                             999991, 1000000]),
+       col_indices=tensor([ 6899, 15825, 20330,  ..., 53773, 69034, 81991]),
+       values=tensor([0.2590, 0.4256, 0.8626,  ..., 0.0809, 0.7182, 0.1540]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0937, 0.3379, 0.3499,  ..., 0.6520, 0.3862, 0.7030])
+tensor([0.5717, 0.8218, 0.0250,  ..., 0.8733, 0.0737, 0.0088])
 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: 7.2341063022613525 seconds
+Time: 6.901124477386475 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66395', '-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": 10.71402621269226}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70787', '-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": 11.243863582611084}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      21,  ...,  999982,
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999983,
                              999990, 1000000]),
-       col_indices=tensor([ 7090, 12502, 14648,  ..., 47720, 74306, 81506]),
-       values=tensor([0.0325, 0.8127, 0.1017,  ..., 0.2993, 0.6676, 0.4101]),
+       col_indices=tensor([  361,  1115,  8788,  ..., 71181, 76543, 91304]),
+       values=tensor([0.4904, 0.2440, 0.4094,  ..., 0.6184, 0.1804, 0.3924]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9849, 0.0117, 0.6257,  ..., 0.6699, 0.0244, 0.0988])
+tensor([0.2770, 0.4028, 0.6616,  ..., 0.6682, 0.3245, 0.3679])
 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: 10.71402621269226 seconds
+Time: 11.243863582611084 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      21,  ...,  999982,
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999983,
                              999990, 1000000]),
-       col_indices=tensor([ 7090, 12502, 14648,  ..., 47720, 74306, 81506]),
-       values=tensor([0.0325, 0.8127, 0.1017,  ..., 0.2993, 0.6676, 0.4101]),
+       col_indices=tensor([  361,  1115,  8788,  ..., 71181, 76543, 91304]),
+       values=tensor([0.4904, 0.2440, 0.4094,  ..., 0.6184, 0.1804, 0.3924]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9849, 0.0117, 0.6257,  ..., 0.6699, 0.0244, 0.0988])
+tensor([0.2770, 0.4028, 0.6616,  ..., 0.6682, 0.3245, 0.3679])
 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: 10.71402621269226 seconds
+Time: 11.243863582611084 seconds
 
-[41.74, 39.92, 40.13, 39.36, 40.44, 39.29, 39.29, 39.23, 40.06, 40.03]
-[143.23]
-12.632778644561768
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66395, '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': 10.71402621269226, 'TIME_S_1KI': 0.16136796765859268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1809.3928852605818, 'W': 143.23}
-[41.74, 39.92, 40.13, 39.36, 40.44, 39.29, 39.29, 39.23, 40.06, 40.03, 39.88, 40.13, 39.21, 40.07, 39.16, 39.33, 39.33, 40.09, 39.55, 40.29]
-715.56
-35.778
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66395, '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': 10.71402621269226, 'TIME_S_1KI': 0.16136796765859268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1809.3928852605818, 'W': 143.23, 'J_1KI': 27.251944954598716, 'W_1KI': 2.15724075608103, 'W_D': 107.452, 'J_D': 1357.417330915451, 'W_D_1KI': 1.6183748776263271, 'J_D_1KI': 0.02437495109008701}
+[40.71, 39.67, 39.62, 40.0, 39.39, 40.7, 39.22, 40.0, 39.3, 39.88]
+[142.52]
+14.495379209518433
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70787, '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': 11.243863582611084, 'TIME_S_1KI': 0.15884079820604183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.8814449405672, 'W': 142.52}
+[40.71, 39.67, 39.62, 40.0, 39.39, 40.7, 39.22, 40.0, 39.3, 39.88, 39.86, 40.15, 39.35, 40.1, 39.35, 40.22, 39.27, 39.7, 39.16, 40.31]
+715.5799999999999
+35.778999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70787, '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': 11.243863582611084, 'TIME_S_1KI': 0.15884079820604183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.8814449405672, 'W': 142.52, 'J_1KI': 29.184475185282146, 'W_1KI': 2.01336403576928, 'W_D': 106.74100000000001, 'J_D': 1547.2512722032072, 'W_D_1KI': 1.5079181205588599, 'J_D_1KI': 0.02130218995802704}
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
new file mode 100644
index 0000000..0f55c41
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4257, "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": 11.433454513549805, "TIME_S_1KI": 2.685800919321072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1901.6043726658822, "W": 126.17, "J_1KI": 446.7005808470477, "W_1KI": 29.638242894056848, "W_D": 90.424, "J_D": 1362.8491225643158, "W_D_1KI": 21.241249706365988, "J_D_1KI": 4.989722740513504}
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
new file mode 100644
index 0000000..dc2dadc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 2.4663901329040527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      118,      225,  ...,  9999805,
+                             9999897, 10000000]),
+       col_indices=tensor([ 1682,  1744,  2076,  ..., 96929, 97254, 99780]),
+       values=tensor([0.4019, 0.5057, 0.8739,  ..., 0.0479, 0.2913, 0.6813]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2475, 0.7795, 0.3565,  ..., 0.8481, 0.6371, 0.4321])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 2.4663901329040527 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4257', '-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": 11.433454513549805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       96,      213,  ...,  9999811,
+                             9999904, 10000000]),
+       col_indices=tensor([  561,   663,  1931,  ..., 97741, 99513, 99851]),
+       values=tensor([0.7974, 0.7905, 0.8203,  ..., 0.5966, 0.6231, 0.2009]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4967, 0.7208, 0.9275,  ..., 0.8267, 0.3582, 0.8531])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.433454513549805 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       96,      213,  ...,  9999811,
+                             9999904, 10000000]),
+       col_indices=tensor([  561,   663,  1931,  ..., 97741, 99513, 99851]),
+       values=tensor([0.7974, 0.7905, 0.8203,  ..., 0.5966, 0.6231, 0.2009]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4967, 0.7208, 0.9275,  ..., 0.8267, 0.3582, 0.8531])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.433454513549805 seconds
+
+[41.19, 39.18, 40.43, 39.7, 39.84, 39.19, 40.16, 39.17, 40.24, 39.08]
+[126.17]
+15.071763277053833
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4257, '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': 11.433454513549805, 'TIME_S_1KI': 2.685800919321072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.6043726658822, 'W': 126.17}
+[41.19, 39.18, 40.43, 39.7, 39.84, 39.19, 40.16, 39.17, 40.24, 39.08, 41.18, 39.54, 39.33, 39.89, 39.75, 39.57, 39.78, 39.56, 39.24, 39.25]
+714.9200000000001
+35.746
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4257, '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': 11.433454513549805, 'TIME_S_1KI': 2.685800919321072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.6043726658822, 'W': 126.17, 'J_1KI': 446.7005808470477, 'W_1KI': 29.638242894056848, 'W_D': 90.424, 'J_D': 1362.8491225643158, 'W_D_1KI': 21.241249706365988, 'J_D_1KI': 4.989722740513504}
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 9f14449..382c4a4 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": 102925, "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": 11.207890748977661, "TIME_S_1KI": 0.10889376486740501, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1545.566165971756, "W": 113.81, "J_1KI": 15.016431051462288, "W_1KI": 1.1057566188972554, "W_D": 78.15, "J_D": 1061.2951047420502, "W_D_1KI": 0.7592907456886082, "J_D_1KI": 0.007377126506568941}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 103292, "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": 10.412234544754028, "TIME_S_1KI": 0.10080388166318813, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1494.9555576324462, "W": 114.72, "J_1KI": 14.473101088491328, "W_1KI": 1.1106378035085003, "W_D": 77.68374999999999, "J_D": 1012.3235163897275, "W_D_1KI": 0.7520790574294233, "J_D_1KI": 0.0072810968654825475}
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 73316f3..a8441fc 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', '1000', '-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": 0.1461803913116455}
+{"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": 0.13699960708618164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99996,  99998,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([53462, 64739,  8211,  ..., 77032, 12066, 66338]),
-       values=tensor([0.7526, 0.8412, 0.0484,  ..., 0.1652, 0.9362, 0.7970]),
+       col_indices=tensor([ 8916, 68486, 49297,  ..., 83214, 51117, 46502]),
+       values=tensor([0.0565, 0.4187, 0.1663,  ..., 0.8089, 0.3832, 0.9501]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2578, 0.3705, 0.8367,  ..., 0.6623, 0.7950, 0.3656])
+tensor([0.6605, 0.5566, 0.3055,  ..., 0.1791, 0.1309, 0.6380])
 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: 0.1461803913116455 seconds
+Time: 0.13699960708618164 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71829', '-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": 7.327654123306274}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '76642', '-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": 7.790924310684204}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([53445, 61427, 55256,  ..., 99710, 79743, 76910]),
-       values=tensor([0.2043, 0.7921, 0.3637,  ..., 0.3183, 0.9272, 0.3273]),
+       col_indices=tensor([17249, 94297, 21433,  ..., 88389, 79911, 81112]),
+       values=tensor([0.0934, 0.2541, 0.4263,  ..., 0.3405, 0.2702, 0.1947]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6989, 0.9157, 0.2952,  ..., 0.1186, 0.5845, 0.8882])
+tensor([0.1521, 0.7703, 0.8999,  ..., 0.0235, 0.4756, 0.0049])
 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: 7.327654123306274 seconds
+Time: 7.790924310684204 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102925', '-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": 11.207890748977661}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '103292', '-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": 10.412234544754028}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([13249, 39443, 49972,  ..., 18781, 78628, 93775]),
-       values=tensor([0.7488, 0.1329, 0.0380,  ..., 0.8918, 0.6119, 0.7720]),
+       col_indices=tensor([40816, 84426, 84611,  ..., 44515, 10095, 58427]),
+       values=tensor([0.3036, 0.7331, 0.5691,  ..., 0.0050, 0.0920, 0.5982]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5554, 0.6245, 0.8914,  ..., 0.6605, 0.7651, 0.7091])
+tensor([0.8398, 0.7355, 0.2034,  ..., 0.0172, 0.0859, 0.7739])
 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: 11.207890748977661 seconds
+Time: 10.412234544754028 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([13249, 39443, 49972,  ..., 18781, 78628, 93775]),
-       values=tensor([0.7488, 0.1329, 0.0380,  ..., 0.8918, 0.6119, 0.7720]),
+       col_indices=tensor([40816, 84426, 84611,  ..., 44515, 10095, 58427]),
+       values=tensor([0.3036, 0.7331, 0.5691,  ..., 0.0050, 0.0920, 0.5982]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5554, 0.6245, 0.8914,  ..., 0.6605, 0.7651, 0.7091])
+tensor([0.8398, 0.7355, 0.2034,  ..., 0.0172, 0.0859, 0.7739])
 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: 11.207890748977661 seconds
+Time: 10.412234544754028 seconds
 
-[41.24, 39.24, 40.12, 39.1, 40.04, 39.21, 39.63, 39.07, 40.14, 39.14]
-[113.81]
-13.580231666564941
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102925, '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': 11.207890748977661, 'TIME_S_1KI': 0.10889376486740501, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.566165971756, 'W': 113.81}
-[41.24, 39.24, 40.12, 39.1, 40.04, 39.21, 39.63, 39.07, 40.14, 39.14, 41.44, 39.12, 40.01, 39.13, 39.64, 39.12, 40.27, 39.05, 39.93, 38.94]
-713.2
-35.660000000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102925, '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': 11.207890748977661, 'TIME_S_1KI': 0.10889376486740501, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.566165971756, 'W': 113.81, 'J_1KI': 15.016431051462288, 'W_1KI': 1.1057566188972554, 'W_D': 78.15, 'J_D': 1061.2951047420502, 'W_D_1KI': 0.7592907456886082, 'J_D_1KI': 0.007377126506568941}
+[39.92, 39.29, 39.24, 40.21, 39.35, 45.54, 40.38, 40.14, 39.43, 39.27]
+[114.72]
+13.031342029571533
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103292, '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': 10.412234544754028, 'TIME_S_1KI': 0.10080388166318813, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.9555576324462, 'W': 114.72}
+[39.92, 39.29, 39.24, 40.21, 39.35, 45.54, 40.38, 40.14, 39.43, 39.27, 39.96, 39.14, 45.44, 43.38, 51.52, 39.13, 40.45, 39.57, 39.41, 39.06]
+740.7250000000001
+37.03625000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103292, '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': 10.412234544754028, 'TIME_S_1KI': 0.10080388166318813, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.9555576324462, 'W': 114.72, 'J_1KI': 14.473101088491328, 'W_1KI': 1.1106378035085003, 'W_D': 77.68374999999999, 'J_D': 1012.3235163897275, 'W_D_1KI': 0.7520790574294233, 'J_D_1KI': 0.0072810968654825475}
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 5c9115a..2ba417a 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": 289350, "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": 10.562561988830566, "TIME_S_1KI": 0.036504447861864756, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1275.0872325897217, "W": 96.48, "J_1KI": 4.406729678900023, "W_1KI": 0.3334370139968896, "W_D": 61.3225, "J_D": 810.4429604113102, "W_D_1KI": 0.21193191636426473, "J_D_1KI": 0.0007324413905797986}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 289765, "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": 10.56649661064148, "TIME_S_1KI": 0.03646574503698334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1246.5325114130974, "W": 97.77, "J_1KI": 4.3018739717118955, "W_1KI": 0.33741135057719185, "W_D": 62.23799999999999, "J_D": 793.5122271180152, "W_D_1KI": 0.21478784532293407, "J_D_1KI": 0.0007412484093073148}
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 1a5d3e8..4b8f527 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', '1000', '-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": 0.052317142486572266}
+{"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": 0.053604841232299805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([1023, 5622, 6334,  ..., 8476, 7727, 1588]),
-       values=tensor([0.9992, 0.3273, 0.0949,  ..., 0.9070, 0.7782, 0.9129]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([4181, 1858, 2276,  ..., 2485, 7240, 8510]),
+       values=tensor([0.9106, 0.2407, 0.2677,  ..., 0.1883, 0.5204, 0.9919]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3589, 0.4614, 0.1782,  ..., 0.3543, 0.5532, 0.1489])
+tensor([0.4673, 0.8867, 0.2183,  ..., 0.9392, 0.5032, 0.8250])
 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: 0.052317142486572266 seconds
+Time: 0.053604841232299805 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '200699', '-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": 7.282996416091919}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '195877', '-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": 7.097846031188965}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9998, 10000, 10000]),
-       col_indices=tensor([5654, 2010, 6092,  ..., 8357, 4618, 8765]),
-       values=tensor([0.6548, 0.7548, 0.4241,  ..., 0.2252, 0.7987, 0.4358]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([6113, 1564,  232,  ..., 3255, 2043, 9640]),
+       values=tensor([0.7859, 0.4083, 0.3727,  ..., 0.9664, 0.2618, 0.1646]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1881, 0.6615, 0.7402,  ..., 0.4130, 0.3712, 0.1085])
+tensor([0.1848, 0.2081, 0.2382,  ..., 0.7788, 0.6054, 0.6678])
 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: 7.282996416091919 seconds
+Time: 7.097846031188965 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289350', '-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": 10.562561988830566}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289765', '-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": 10.56649661064148}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     3,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([  41, 4057, 4525,  ...,  395, 6429, 4913]),
-       values=tensor([0.6795, 0.3093, 0.3215,  ..., 0.9868, 0.7022, 0.9945]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([4848, 1770,   22,  ..., 2903,  374, 1123]),
+       values=tensor([0.4832, 0.4922, 0.1673,  ..., 0.2881, 0.3225, 0.2417]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0893, 0.5810, 0.8251,  ..., 0.0535, 0.5355, 0.1364])
+tensor([0.7933, 0.2380, 0.0639,  ..., 0.5554, 0.1913, 0.9685])
 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: 10.562561988830566 seconds
+Time: 10.56649661064148 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     3,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([  41, 4057, 4525,  ...,  395, 6429, 4913]),
-       values=tensor([0.6795, 0.3093, 0.3215,  ..., 0.9868, 0.7022, 0.9945]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([4848, 1770,   22,  ..., 2903,  374, 1123]),
+       values=tensor([0.4832, 0.4922, 0.1673,  ..., 0.2881, 0.3225, 0.2417]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0893, 0.5810, 0.8251,  ..., 0.0535, 0.5355, 0.1364])
+tensor([0.7933, 0.2380, 0.0639,  ..., 0.5554, 0.1913, 0.9685])
 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: 10.562561988830566 seconds
+Time: 10.56649661064148 seconds
 
-[40.18, 38.78, 39.39, 38.64, 39.47, 38.59, 40.17, 38.45, 39.45, 38.53]
-[96.48]
-13.216078281402588
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289350, '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': 10.562561988830566, 'TIME_S_1KI': 0.036504447861864756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1275.0872325897217, 'W': 96.48}
-[40.18, 38.78, 39.39, 38.64, 39.47, 38.59, 40.17, 38.45, 39.45, 38.53, 39.33, 38.72, 38.74, 39.53, 38.66, 39.45, 38.52, 39.57, 38.65, 38.7]
-703.1500000000001
-35.157500000000006
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289350, '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': 10.562561988830566, 'TIME_S_1KI': 0.036504447861864756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1275.0872325897217, 'W': 96.48, 'J_1KI': 4.406729678900023, 'W_1KI': 0.3334370139968896, 'W_D': 61.3225, 'J_D': 810.4429604113102, 'W_D_1KI': 0.21193191636426473, 'J_D_1KI': 0.0007324413905797986}
+[39.58, 39.81, 39.03, 39.86, 38.88, 39.9, 39.07, 38.97, 41.59, 39.84]
+[97.77]
+12.749642133712769
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289765, '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': 10.56649661064148, 'TIME_S_1KI': 0.03646574503698334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5325114130974, 'W': 97.77}
+[39.58, 39.81, 39.03, 39.86, 38.88, 39.9, 39.07, 38.97, 41.59, 39.84, 40.25, 39.66, 38.91, 39.84, 38.8, 39.81, 38.94, 39.0, 38.89, 39.69]
+710.6400000000001
+35.532000000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289765, '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': 10.56649661064148, 'TIME_S_1KI': 0.03646574503698334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5325114130974, 'W': 97.77, 'J_1KI': 4.3018739717118955, 'W_1KI': 0.33741135057719185, 'W_D': 62.23799999999999, 'J_D': 793.5122271180152, 'W_D_1KI': 0.21478784532293407, 'J_D_1KI': 0.0007412484093073148}
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 b5102bb..463b3c7 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": 187965, "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": 10.065882205963135, "TIME_S_1KI": 0.053551896395409436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1344.2036644935608, "W": 106.8, "J_1KI": 7.1513508604982885, "W_1KI": 0.568190886601229, "W_D": 70.53074999999998, "J_D": 887.7124776168464, "W_D_1KI": 0.3752334211156331, "J_D_1KI": 0.001996294103240673}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 132694, "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": 10.224570512771606, "TIME_S_1KI": 0.07705375158463537, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.35663690567, "W": 103.59999999999998, "J_1KI": 8.043744531822615, "W_1KI": 0.7807436658778842, "W_D": 68.22474999999997, "J_D": 702.8971014838812, "W_D_1KI": 0.5141509789440364, "J_D_1KI": 0.003874711584126158}
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 81bf5e6..f18d35d 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', '1000', '-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": 0.07673120498657227}
+{"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": 0.07912898063659668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     16,  ...,  99979,  99991,
+tensor(crow_indices=tensor([     0,      9,     15,  ...,  99979,  99988,
                             100000]),
-       col_indices=tensor([ 168,  470, 1159,  ..., 7824, 8386, 8755]),
-       values=tensor([0.2770, 0.4979, 0.7971,  ..., 0.1786, 0.3153, 0.6794]),
+       col_indices=tensor([ 430,  646,  878,  ..., 7983, 8028, 8773]),
+       values=tensor([0.1249, 0.1009, 0.6404,  ..., 0.8347, 0.6604, 0.7086]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8782, 0.5630, 0.5978,  ..., 0.9864, 0.4940, 0.0083])
+tensor([0.6668, 0.6238, 0.5068,  ..., 0.0173, 0.0134, 0.2844])
 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: 0.07673120498657227 seconds
+Time: 0.07912898063659668 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '136841', '-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": 7.644104957580566}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '132694', '-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": 10.224570512771606}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     23,  ...,  99978,  99990,
+tensor(crow_indices=tensor([     0,     11,     19,  ...,  99972,  99988,
                             100000]),
-       col_indices=tensor([1562, 4109, 4242,  ..., 5789, 5816, 7878]),
-       values=tensor([0.3397, 0.5295, 0.0107,  ..., 0.2250, 0.1834, 0.1775]),
+       col_indices=tensor([ 681, 2736, 3433,  ..., 9108, 9366, 9692]),
+       values=tensor([0.5511, 0.6516, 0.1231,  ..., 0.0939, 0.8699, 0.6381]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8704, 0.9073, 0.5102,  ..., 0.5120, 0.6818, 0.6416])
+tensor([0.2594, 0.0089, 0.5427,  ..., 0.9106, 0.5838, 0.6290])
 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: 7.644104957580566 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '187965', '-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": 10.065882205963135}
+Time: 10.224570512771606 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     25,  ...,  99981,  99991,
+tensor(crow_indices=tensor([     0,     11,     19,  ...,  99972,  99988,
                             100000]),
-       col_indices=tensor([ 564, 1289, 1589,  ..., 8514, 9743, 9976]),
-       values=tensor([0.9535, 0.4673, 0.4047,  ..., 0.1356, 0.2907, 0.4698]),
+       col_indices=tensor([ 681, 2736, 3433,  ..., 9108, 9366, 9692]),
+       values=tensor([0.5511, 0.6516, 0.1231,  ..., 0.0939, 0.8699, 0.6381]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6755, 0.5642, 0.0135,  ..., 0.9982, 0.6342, 0.7704])
+tensor([0.2594, 0.0089, 0.5427,  ..., 0.9106, 0.5838, 0.6290])
 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: 10.065882205963135 seconds
+Time: 10.224570512771606 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     25,  ...,  99981,  99991,
-                            100000]),
-       col_indices=tensor([ 564, 1289, 1589,  ..., 8514, 9743, 9976]),
-       values=tensor([0.9535, 0.4673, 0.4047,  ..., 0.1356, 0.2907, 0.4698]),
-       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6755, 0.5642, 0.0135,  ..., 0.9982, 0.6342, 0.7704])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 100000
-Density: 0.001
-Time: 10.065882205963135 seconds
-
-[45.4, 39.59, 38.94, 38.84, 38.95, 40.73, 38.79, 39.87, 38.85, 39.58]
-[106.8]
-12.586176633834839
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 187965, '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': 10.065882205963135, 'TIME_S_1KI': 0.053551896395409436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.2036644935608, 'W': 106.8}
-[45.4, 39.59, 38.94, 38.84, 38.95, 40.73, 38.79, 39.87, 38.85, 39.58, 39.31, 39.68, 39.37, 39.04, 45.6, 48.05, 38.82, 39.58, 38.6, 39.88]
-725.3850000000001
-36.26925000000001
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 187965, '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': 10.065882205963135, 'TIME_S_1KI': 0.053551896395409436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.2036644935608, 'W': 106.8, 'J_1KI': 7.1513508604982885, 'W_1KI': 0.568190886601229, 'W_D': 70.53074999999998, 'J_D': 887.7124776168464, 'W_D_1KI': 0.3752334211156331, 'J_D_1KI': 0.001996294103240673}
+[39.52, 39.72, 39.2, 38.94, 38.87, 40.37, 39.0, 39.66, 38.95, 39.86]
+[103.6]
+10.302670240402222
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132694, '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': 10.224570512771606, 'TIME_S_1KI': 0.07705375158463537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.35663690567, 'W': 103.59999999999998}
+[39.52, 39.72, 39.2, 38.94, 38.87, 40.37, 39.0, 39.66, 38.95, 39.86, 39.41, 39.62, 39.01, 39.67, 38.86, 38.84, 38.84, 39.8, 38.92, 39.68]
+707.505
+35.37525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132694, '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': 10.224570512771606, 'TIME_S_1KI': 0.07705375158463537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.35663690567, 'W': 103.59999999999998, 'J_1KI': 8.043744531822615, 'W_1KI': 0.7807436658778842, 'W_D': 68.22474999999997, 'J_D': 702.8971014838812, 'W_D_1KI': 0.5141509789440364, 'J_D_1KI': 0.003874711584126158}
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 be0819d..995b9fb 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": 105478, "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": 10.5971040725708, "TIME_S_1KI": 0.10046743465529115, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1740.769259557724, "W": 131.98, "J_1KI": 16.503624069073396, "W_1KI": 1.2512561861241205, "W_D": 96.29974999999999, "J_D": 1270.159452213168, "W_D_1KI": 0.912984224198411, "J_D_1KI": 0.008655683879087687}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 107069, "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": 11.18355131149292, "TIME_S_1KI": 0.10445181435796468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1763.49504935503, "W": 132.69, "J_1KI": 16.470640889099833, "W_1KI": 1.2392942868617434, "W_D": 96.9815, "J_D": 1288.9169879344702, "W_D_1KI": 0.905785054497567, "J_D_1KI": 0.008459825481675993}
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 1731635..afaa491 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', '1000', '-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": 0.16547083854675293}
+{"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": 0.1338520050048828}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      96,     207,  ...,  999810,
-                             999906, 1000000]),
-       col_indices=tensor([  26,   37,   76,  ..., 9653, 9723, 9999]),
-       values=tensor([0.3241, 0.3803, 0.4811,  ..., 0.7106, 0.6386, 0.1440]),
+tensor(crow_indices=tensor([      0,      91,     190,  ...,  999794,
+                             999887, 1000000]),
+       col_indices=tensor([  40,  344,  548,  ..., 9830, 9841, 9960]),
+       values=tensor([0.4008, 0.1162, 0.8586,  ..., 0.0804, 0.9517, 0.8982]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9687, 0.4748, 0.5344,  ..., 0.6395, 0.7779, 0.2708])
+tensor([0.6204, 0.8036, 0.5749,  ..., 0.0150, 0.4782, 0.5342])
 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: 0.16547083854675293 seconds
+Time: 0.1338520050048828 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63455', '-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": 6.316709756851196}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78444', '-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": 8.085982084274292}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     176,  ...,  999794,
-                             999890, 1000000]),
-       col_indices=tensor([  23,  147,  291,  ..., 9810, 9851, 9893]),
-       values=tensor([0.8158, 0.9343, 0.8649,  ..., 0.9539, 0.1935, 0.2240]),
+tensor(crow_indices=tensor([      0,      93,     187,  ...,  999812,
+                             999901, 1000000]),
+       col_indices=tensor([ 276,  302,  470,  ..., 9539, 9540, 9930]),
+       values=tensor([0.4664, 0.1616, 0.7456,  ..., 0.5929, 0.0487, 0.3579]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7787, 0.2300, 0.4854,  ..., 0.5355, 0.5696, 0.8377])
+tensor([0.7338, 0.4039, 0.6812,  ..., 0.4093, 0.7174, 0.1386])
 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: 6.316709756851196 seconds
+Time: 8.085982084274292 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '105478', '-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": 10.5971040725708}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '101862', '-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": 9.98931097984314}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     112,     216,  ...,  999816,
-                             999921, 1000000]),
-       col_indices=tensor([  50,   64,  228,  ..., 9846, 9935, 9998]),
-       values=tensor([0.2081, 0.8355, 0.6203,  ..., 0.0415, 0.1924, 0.6602]),
+tensor(crow_indices=tensor([      0,      97,     208,  ...,  999782,
+                             999887, 1000000]),
+       col_indices=tensor([ 113,  292,  413,  ..., 9756, 9814, 9863]),
+       values=tensor([0.7037, 0.4902, 0.2249,  ..., 0.1343, 0.1681, 0.3653]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3579, 0.4434, 0.7372,  ..., 0.2272, 0.7887, 0.7519])
+tensor([0.0898, 0.3365, 0.9954,  ..., 0.9623, 0.9055, 0.9870])
 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: 10.5971040725708 seconds
+Time: 9.98931097984314 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '107069', '-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": 11.18355131149292}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     112,     216,  ...,  999816,
-                             999921, 1000000]),
-       col_indices=tensor([  50,   64,  228,  ..., 9846, 9935, 9998]),
-       values=tensor([0.2081, 0.8355, 0.6203,  ..., 0.0415, 0.1924, 0.6602]),
+tensor(crow_indices=tensor([      0,     101,     199,  ...,  999771,
+                             999895, 1000000]),
+       col_indices=tensor([  30,   45,   94,  ..., 9508, 9668, 9839]),
+       values=tensor([0.9351, 0.0667, 0.7279,  ..., 0.8651, 0.3266, 0.8240]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3579, 0.4434, 0.7372,  ..., 0.2272, 0.7887, 0.7519])
+tensor([0.5009, 0.1141, 0.1222,  ..., 0.6365, 0.9492, 0.1421])
 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: 10.5971040725708 seconds
+Time: 11.18355131149292 seconds
 
-[41.21, 38.88, 39.8, 38.81, 39.92, 39.04, 39.53, 38.73, 39.74, 38.85]
-[131.98]
-13.18964433670044
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105478, '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': 10.5971040725708, 'TIME_S_1KI': 0.10046743465529115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1740.769259557724, 'W': 131.98}
-[41.21, 38.88, 39.8, 38.81, 39.92, 39.04, 39.53, 38.73, 39.74, 38.85, 39.66, 39.98, 38.99, 40.02, 39.22, 39.28, 39.07, 41.17, 38.93, 45.27]
-713.6050000000001
-35.68025000000001
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105478, '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': 10.5971040725708, 'TIME_S_1KI': 0.10046743465529115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1740.769259557724, 'W': 131.98, 'J_1KI': 16.503624069073396, 'W_1KI': 1.2512561861241205, 'W_D': 96.29974999999999, 'J_D': 1270.159452213168, 'W_D_1KI': 0.912984224198411, 'J_D_1KI': 0.008655683879087687}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     199,  ...,  999771,
+                             999895, 1000000]),
+       col_indices=tensor([  30,   45,   94,  ..., 9508, 9668, 9839]),
+       values=tensor([0.9351, 0.0667, 0.7279,  ..., 0.8651, 0.3266, 0.8240]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5009, 0.1141, 0.1222,  ..., 0.6365, 0.9492, 0.1421])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 11.18355131149292 seconds
+
+[39.9, 40.21, 39.53, 40.02, 39.22, 40.03, 39.19, 40.06, 39.89, 39.6]
+[132.69]
+13.29033875465393
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107069, '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': 11.18355131149292, 'TIME_S_1KI': 0.10445181435796468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1763.49504935503, 'W': 132.69}
+[39.9, 40.21, 39.53, 40.02, 39.22, 40.03, 39.19, 40.06, 39.89, 39.6, 40.49, 39.14, 39.91, 39.61, 39.91, 39.69, 39.58, 39.1, 39.07, 40.03]
+714.1700000000001
+35.7085
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107069, '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': 11.18355131149292, 'TIME_S_1KI': 0.10445181435796468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1763.49504935503, 'W': 132.69, 'J_1KI': 16.470640889099833, 'W_1KI': 1.2392942868617434, 'W_D': 96.9815, 'J_D': 1288.9169879344702, 'W_D_1KI': 0.905785054497567, 'J_D_1KI': 0.008459825481675993}
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 2c99a83..05bc7cf 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": 28261, "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": 10.545162916183472, "TIME_S_1KI": 0.37313481179659147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2163.6921325206754, "W": 147.39, "J_1KI": 76.56106056122131, "W_1KI": 5.2153143908566575, "W_D": 111.76774999999999, "J_D": 1640.7558270204065, "W_D_1KI": 3.9548405930434165, "J_D_1KI": 0.13993986741599437}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28163, "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": 10.459697484970093, "TIME_S_1KI": 0.371398554307783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2122.055966448784, "W": 150.9, "J_1KI": 75.34907383619587, "W_1KI": 5.358093953058979, "W_D": 115.1085, "J_D": 1618.7321352814438, "W_D_1KI": 4.087224372403509, "J_D_1KI": 0.1451274499308848}
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 d58d64d..e823d57 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', '1000', '-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": 0.4614067077636719}
+{"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": 0.4528634548187256}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1040,  ..., 4999055,
-                            4999519, 5000000]),
-       col_indices=tensor([   1,    5,   26,  ..., 9948, 9962, 9996]),
-       values=tensor([0.6869, 0.8475, 0.6936,  ..., 0.3132, 0.2618, 0.7215]),
+tensor(crow_indices=tensor([      0,     492,     984,  ..., 4999007,
+                            4999498, 5000000]),
+       col_indices=tensor([  17,   26,   49,  ..., 9943, 9965, 9968]),
+       values=tensor([0.3785, 0.7951, 0.2972,  ..., 0.3720, 0.7853, 0.1204]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5929, 0.6414, 0.0366,  ..., 0.9216, 0.5044, 0.3359])
+tensor([0.5665, 0.1637, 0.5801,  ..., 0.5211, 0.8646, 0.6970])
 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: 0.4614067077636719 seconds
+Time: 0.4528634548187256 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22756', '-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": 8.454672574996948}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23185', '-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": 8.643981695175171}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     518,     995,  ..., 4998951,
-                            4999482, 5000000]),
-       col_indices=tensor([   3,    5,   12,  ..., 9960, 9985, 9990]),
-       values=tensor([0.0194, 0.0116, 0.2988,  ..., 0.0510, 0.2477, 0.0241]),
+tensor(crow_indices=tensor([      0,     462,     943,  ..., 4999021,
+                            4999500, 5000000]),
+       col_indices=tensor([   4,   33,   72,  ..., 9956, 9968, 9998]),
+       values=tensor([0.9717, 0.2077, 0.4481,  ..., 0.1268, 0.5535, 0.1753]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8184, 0.3974, 0.7641,  ..., 0.0303, 0.5906, 0.4265])
+tensor([0.9761, 0.2557, 0.3900,  ..., 0.3250, 0.2223, 0.7021])
 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: 8.454672574996948 seconds
+Time: 8.643981695175171 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28261', '-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": 10.545162916183472}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28163', '-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": 10.459697484970093}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     472,     967,  ..., 4998984,
-                            4999479, 5000000]),
-       col_indices=tensor([  28,   36,   55,  ..., 9923, 9953, 9987]),
-       values=tensor([0.3537, 0.0932, 0.3681,  ..., 0.2268, 0.3044, 0.8997]),
+tensor(crow_indices=tensor([      0,     510,    1022,  ..., 4999000,
+                            4999485, 5000000]),
+       col_indices=tensor([  31,   34,   40,  ..., 9926, 9941, 9984]),
+       values=tensor([0.9067, 0.8635, 0.5661,  ..., 0.0254, 0.7052, 0.7869]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5459, 0.4301, 0.8105,  ..., 0.9349, 0.4459, 0.6946])
+tensor([0.2490, 0.1590, 0.1294,  ..., 0.2235, 0.7822, 0.7952])
 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: 10.545162916183472 seconds
+Time: 10.459697484970093 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     472,     967,  ..., 4998984,
-                            4999479, 5000000]),
-       col_indices=tensor([  28,   36,   55,  ..., 9923, 9953, 9987]),
-       values=tensor([0.3537, 0.0932, 0.3681,  ..., 0.2268, 0.3044, 0.8997]),
+tensor(crow_indices=tensor([      0,     510,    1022,  ..., 4999000,
+                            4999485, 5000000]),
+       col_indices=tensor([  31,   34,   40,  ..., 9926, 9941, 9984]),
+       values=tensor([0.9067, 0.8635, 0.5661,  ..., 0.0254, 0.7052, 0.7869]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5459, 0.4301, 0.8105,  ..., 0.9349, 0.4459, 0.6946])
+tensor([0.2490, 0.1590, 0.1294,  ..., 0.2235, 0.7822, 0.7952])
 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: 10.545162916183472 seconds
+Time: 10.459697484970093 seconds
 
-[39.85, 40.19, 39.24, 39.5, 39.23, 40.01, 39.26, 40.01, 39.09, 39.94]
-[147.39]
-14.680047035217285
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28261, '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': 10.545162916183472, 'TIME_S_1KI': 0.37313481179659147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2163.6921325206754, 'W': 147.39}
-[39.85, 40.19, 39.24, 39.5, 39.23, 40.01, 39.26, 40.01, 39.09, 39.94, 39.9, 39.27, 40.14, 39.15, 40.0, 39.07, 39.99, 39.51, 39.37, 39.14]
-712.4449999999999
-35.622249999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28261, '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': 10.545162916183472, 'TIME_S_1KI': 0.37313481179659147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2163.6921325206754, 'W': 147.39, 'J_1KI': 76.56106056122131, 'W_1KI': 5.2153143908566575, 'W_D': 111.76774999999999, 'J_D': 1640.7558270204065, 'W_D_1KI': 3.9548405930434165, 'J_D_1KI': 0.13993986741599437}
+[40.87, 39.6, 40.38, 39.19, 40.47, 39.26, 39.48, 39.2, 40.2, 39.44]
+[150.9]
+14.062663793563843
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28163, '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': 10.459697484970093, 'TIME_S_1KI': 0.371398554307783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2122.055966448784, 'W': 150.9}
+[40.87, 39.6, 40.38, 39.19, 40.47, 39.26, 39.48, 39.2, 40.2, 39.44, 41.45, 39.66, 39.99, 39.31, 39.56, 39.24, 40.15, 39.52, 39.84, 39.8]
+715.8299999999999
+35.7915
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28163, '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': 10.459697484970093, 'TIME_S_1KI': 0.371398554307783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2122.055966448784, 'W': 150.9, 'J_1KI': 75.34907383619587, 'W_1KI': 5.358093953058979, 'W_D': 115.1085, 'J_D': 1618.7321352814438, 'W_D_1KI': 4.087224372403509, 'J_D_1KI': 0.1451274499308848}
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
new file mode 100644
index 0000000..8638631
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 5238, "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": 11.192334175109863, "TIME_S_1KI": 2.1367571926517495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2038.619791316986, "W": 124.02, "J_1KI": 389.1981273991955, "W_1KI": 23.676975945017183, "W_D": 88.21424999999999, "J_D": 1450.0509266746044, "W_D_1KI": 16.841208476517753, "J_D_1KI": 3.2151982582126295}
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
new file mode 100644
index 0000000..6292d32
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 2.209188461303711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     2007,  ...,  9997952,
+                             9998968, 10000000]),
+       col_indices=tensor([  14,   18,   26,  ..., 9968, 9972, 9997]),
+       values=tensor([0.9669, 0.3653, 0.3089,  ..., 0.5289, 0.5202, 0.9028]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8016, 0.0222, 0.4456,  ..., 0.4115, 0.6943, 0.5313])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 2.209188461303711 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4752', '-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": 9.52530813217163}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      954,     1940,  ...,  9998038,
+                             9998994, 10000000]),
+       col_indices=tensor([   0,    3,    4,  ..., 9964, 9979, 9998]),
+       values=tensor([0.5875, 0.0019, 0.5119,  ..., 0.4152, 0.5002, 0.2921]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8144, 0.0248, 0.0526,  ..., 0.0067, 0.4287, 0.2758])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 9.52530813217163 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5238', '-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": 11.192334175109863}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1001,     2002,  ...,  9997918,
+                             9998966, 10000000]),
+       col_indices=tensor([   9,   21,   97,  ..., 9973, 9981, 9990]),
+       values=tensor([0.6111, 0.6801, 0.6895,  ..., 0.1092, 0.3002, 0.2815]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0277, 0.0823, 0.3111,  ..., 0.6513, 0.2238, 0.0558])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.192334175109863 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1001,     2002,  ...,  9997918,
+                             9998966, 10000000]),
+       col_indices=tensor([   9,   21,   97,  ..., 9973, 9981, 9990]),
+       values=tensor([0.6111, 0.6801, 0.6895,  ..., 0.1092, 0.3002, 0.2815]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0277, 0.0823, 0.3111,  ..., 0.6513, 0.2238, 0.0558])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.192334175109863 seconds
+
+[41.1, 40.26, 39.38, 39.47, 39.45, 40.18, 39.58, 40.31, 39.46, 40.14]
+[124.02]
+16.437830924987793
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5238, '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': 11.192334175109863, 'TIME_S_1KI': 2.1367571926517495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2038.619791316986, 'W': 124.02}
+[41.1, 40.26, 39.38, 39.47, 39.45, 40.18, 39.58, 40.31, 39.46, 40.14, 40.09, 40.24, 39.54, 40.08, 39.32, 40.12, 39.35, 39.35, 39.19, 40.34]
+716.115
+35.80575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5238, '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': 11.192334175109863, 'TIME_S_1KI': 2.1367571926517495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2038.619791316986, 'W': 124.02, 'J_1KI': 389.1981273991955, 'W_1KI': 23.676975945017183, 'W_D': 88.21424999999999, 'J_D': 1450.0509266746044, 'W_D_1KI': 16.841208476517753, 'J_D_1KI': 3.2151982582126295}
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 e9f145d..4449f80 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": 352057, "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": 10.363084554672241, "TIME_S_1KI": 0.029435814526262056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.9154741740226, "W": 94.69, "J_1KI": 3.4764696460346554, "W_1KI": 0.2689621282917255, "W_D": 59.05925, "J_D": 763.3702605144381, "W_D_1KI": 0.1677547953882468, "J_D_1KI": 0.0004764989629186376}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 362169, "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": 10.681929588317871, "TIME_S_1KI": 0.029494323336116207, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.685609395504, "W": 96.13, "J_1KI": 3.4864541399056903, "W_1KI": 0.2654285706396737, "W_D": 60.50875, "J_D": 794.7937986841798, "W_D_1KI": 0.1670732448111241, "J_D_1KI": 0.0004613129362566208}
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 f1c1f4f..f681b74 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
@@ -1,266 +1,373 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.05632638931274414}
+{"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": 0.044791460037231445}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([6812, 5345, 3814, 3851, 1180, 2370, 9747, 9157, 9309,
-                           1844,  451, 6602, 4443, 8006, 5413, 9948, 6902, 4781,
-                           5273, 3102, 9193, 6090, 9660, 8598, 9786, 3453, 7823,
-                           8095, 5864, 1933, 5014, 3401, 1663, 8599, 9714, 5815,
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+                      9.2290e-01, 3.5271e-01, 4.6772e-01, 3.3679e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8769, 0.8902, 0.3074,  ..., 0.5063, 0.6820, 0.8149])
+tensor([0.4545, 0.4841, 0.5462,  ..., 0.7193, 0.8873, 0.8657])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,378 +375,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.05632638931274414 seconds
+Time: 0.044791460037231445 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '186413', '-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": 5.5597083568573}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '234419', '-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": 6.79626727104187}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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([0.5451, 0.9325, 0.7234,  ..., 0.9278, 0.0652, 0.2905])
+tensor([0.8987, 0.7248, 0.0383,  ..., 0.6918, 0.0447, 0.2254])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -647,378 +647,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 5.5597083568573 seconds
+Time: 6.79626727104187 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '352057', '-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": 10.363084554672241}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '362169', '-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": 10.681929588317871}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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-                           5945]),
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+                      8.6251e-01, 4.5483e-01, 7.5964e-01, 2.8131e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3721, 0.5043, 0.5568,  ..., 0.8647, 0.9880, 0.8941])
+tensor([0.4226, 0.0556, 0.1398,  ..., 0.5751, 0.9814, 0.4838])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,375 +1026,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.363084554672241 seconds
+Time: 10.681929588317871 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+                      8.0438e-01, 2.4075e-01, 5.0321e-01, 2.8248e-01,
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+                      8.6251e-01, 4.5483e-01, 7.5964e-01, 2.8131e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3721, 0.5043, 0.5568,  ..., 0.8647, 0.9880, 0.8941])
+tensor([0.4226, 0.0556, 0.1398,  ..., 0.5751, 0.9814, 0.4838])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1402,13 +1402,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.363084554672241 seconds
+Time: 10.681929588317871 seconds
 
-[39.17, 38.6, 39.23, 38.35, 39.37, 38.46, 39.25, 43.56, 39.01, 38.43]
-[94.69]
-12.925498723983765
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 352057, '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': 10.363084554672241, 'TIME_S_1KI': 0.029435814526262056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.9154741740226, 'W': 94.69}
-[39.17, 38.6, 39.23, 38.35, 39.37, 38.46, 39.25, 43.56, 39.01, 38.43, 39.13, 39.47, 39.53, 39.29, 44.98, 39.37, 38.9, 38.56, 38.54, 39.56]
-712.615
-35.63075
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 352057, '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': 10.363084554672241, 'TIME_S_1KI': 0.029435814526262056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.9154741740226, 'W': 94.69, 'J_1KI': 3.4764696460346554, 'W_1KI': 0.2689621282917255, 'W_D': 59.05925, 'J_D': 763.3702605144381, 'W_D_1KI': 0.1677547953882468, 'J_D_1KI': 0.0004764989629186376}
+[41.15, 38.98, 39.75, 38.85, 39.88, 39.03, 39.18, 38.82, 39.8, 38.85]
+[96.13]
+13.135187864303589
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 362169, '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': 10.681929588317871, 'TIME_S_1KI': 0.029494323336116207, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.685609395504, 'W': 96.13}
+[41.15, 38.98, 39.75, 38.85, 39.88, 39.03, 39.18, 38.82, 39.8, 38.85, 40.18, 39.93, 38.85, 39.83, 38.86, 39.3, 38.99, 39.61, 39.19, 46.97]
+712.425
+35.621249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 362169, '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': 10.681929588317871, 'TIME_S_1KI': 0.029494323336116207, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.685609395504, 'W': 96.13, 'J_1KI': 3.4864541399056903, 'W_1KI': 0.2654285706396737, 'W_D': 60.50875, 'J_D': 794.7937986841798, 'W_D_1KI': 0.1670732448111241, 'J_D_1KI': 0.0004613129362566208}
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 9ddd17e..c41b46b 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": 21395, "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": 10.323282241821289, "TIME_S_1KI": 0.482509102211792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2021.2716293263436, "W": 152.47, "J_1KI": 94.47401866447038, "W_1KI": 7.1264314092077585, "W_D": 115.044, "J_D": 1525.1208324537276, "W_D_1KI": 5.377144192568356, "J_D_1KI": 0.2513271415082195}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21272, "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": 10.296250820159912, "TIME_S_1KI": 0.4840283386686683, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2004.567332353592, "W": 151.77, "J_1KI": 94.23501938480594, "W_1KI": 7.134731101918015, "W_D": 115.36950000000002, "J_D": 1523.7921252551082, "W_D_1KI": 5.423537984204589, "J_D_1KI": 0.2549613569107084}
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 50f6332..ff821c9 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', '1000', '-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": 0.532757043838501}
+{"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": 0.5370402336120605}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,
+tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499988,
                             2499995, 2500000]),
-       col_indices=tensor([298854, 299868, 317882,  ..., 208197, 239895,
-                           321556]),
-       values=tensor([0.0947, 0.1899, 0.7776,  ..., 0.8480, 0.0740, 0.2913]),
+       col_indices=tensor([   667,  84326, 231414,  ..., 445492, 452435,
+                           478533]),
+       values=tensor([0.3723, 0.9059, 0.5582,  ..., 0.5128, 0.0660, 0.1881]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2732, 0.7262, 0.3001,  ..., 0.8229, 0.3388, 0.7233])
+tensor([0.0315, 0.2189, 0.8055,  ..., 0.9902, 0.0196, 0.5860])
 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: 0.532757043838501 seconds
+Time: 0.5370402336120605 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19708', '-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": 9.672011375427246}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19551', '-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": 9.650388717651367}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499994,
-                            2500000, 2500000]),
-       col_indices=tensor([ 49185, 277910, 351023,  ..., 230263, 378248,
-                           487183]),
-       values=tensor([0.7966, 0.8451, 0.5460,  ..., 0.3570, 0.2848, 0.9857]),
+tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([ 11262,  76750, 152870,  ..., 221537, 283064,
+                           452441]),
+       values=tensor([0.8111, 0.5495, 0.0260,  ..., 0.8118, 0.4893, 0.3789]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8196, 0.2368, 0.8865,  ..., 0.6520, 0.2281, 0.7931])
+tensor([0.5436, 0.8281, 0.7063,  ..., 0.1699, 0.2640, 0.5110])
 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: 9.672011375427246 seconds
+Time: 9.650388717651367 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21395', '-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": 10.323282241821289}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21272', '-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": 10.296250820159912}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499995,
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499993,
                             2499998, 2500000]),
-       col_indices=tensor([ 50735,  77236, 160897,  ..., 492852, 393041,
-                           457835]),
-       values=tensor([0.2461, 0.0110, 0.8932,  ..., 0.0580, 0.2778, 0.4102]),
+       col_indices=tensor([ 13054, 157067, 258216,  ..., 445117, 194165,
+                           431781]),
+       values=tensor([0.8472, 0.4724, 0.5562,  ..., 0.8941, 0.8667, 0.3682]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4625, 0.6924, 0.9316,  ..., 0.4127, 0.3248, 0.5422])
+tensor([0.2043, 0.9144, 0.3718,  ..., 0.9024, 0.4544, 0.2083])
 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: 10.323282241821289 seconds
+Time: 10.296250820159912 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499995,
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499993,
                             2499998, 2500000]),
-       col_indices=tensor([ 50735,  77236, 160897,  ..., 492852, 393041,
-                           457835]),
-       values=tensor([0.2461, 0.0110, 0.8932,  ..., 0.0580, 0.2778, 0.4102]),
+       col_indices=tensor([ 13054, 157067, 258216,  ..., 445117, 194165,
+                           431781]),
+       values=tensor([0.8472, 0.4724, 0.5562,  ..., 0.8941, 0.8667, 0.3682]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4625, 0.6924, 0.9316,  ..., 0.4127, 0.3248, 0.5422])
+tensor([0.2043, 0.9144, 0.3718,  ..., 0.9024, 0.4544, 0.2083])
 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: 10.323282241821289 seconds
+Time: 10.296250820159912 seconds
 
-[41.04, 39.37, 40.41, 40.48, 45.25, 40.28, 40.5, 39.5, 40.46, 39.64]
-[152.47]
-13.256848096847534
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21395, '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': 10.323282241821289, 'TIME_S_1KI': 0.482509102211792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2021.2716293263436, 'W': 152.47}
-[41.04, 39.37, 40.41, 40.48, 45.25, 40.28, 40.5, 39.5, 40.46, 39.64, 42.68, 45.88, 39.43, 39.56, 39.31, 40.35, 46.4, 49.8, 39.76, 40.2]
-748.52
-37.426
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21395, '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': 10.323282241821289, 'TIME_S_1KI': 0.482509102211792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2021.2716293263436, 'W': 152.47, 'J_1KI': 94.47401866447038, 'W_1KI': 7.1264314092077585, 'W_D': 115.044, 'J_D': 1525.1208324537276, 'W_D_1KI': 5.377144192568356, 'J_D_1KI': 0.2513271415082195}
+[40.04, 40.36, 39.54, 40.31, 47.07, 40.26, 39.56, 39.95, 39.57, 41.2]
+[151.77]
+13.207928657531738
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21272, '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': 10.296250820159912, 'TIME_S_1KI': 0.4840283386686683, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2004.567332353592, 'W': 151.77}
+[40.04, 40.36, 39.54, 40.31, 47.07, 40.26, 39.56, 39.95, 39.57, 41.2, 46.29, 39.48, 40.21, 39.42, 40.34, 39.68, 39.47, 39.18, 40.2, 39.29]
+728.01
+36.4005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21272, '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': 10.296250820159912, 'TIME_S_1KI': 0.4840283386686683, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2004.567332353592, 'W': 151.77, 'J_1KI': 94.23501938480594, 'W_1KI': 7.134731101918015, 'W_D': 115.36950000000002, 'J_D': 1523.7921252551082, 'W_D_1KI': 5.423537984204589, 'J_D_1KI': 0.2549613569107084}
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 f0cbadb..8fb8b3c 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": 97887, "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": 10.967289686203003, "TIME_S_1KI": 0.11204030858237563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1548.6584901952745, "W": 117.34, "J_1KI": 15.820880098432626, "W_1KI": 1.1987291468734356, "W_D": 81.498, "J_D": 1075.6141949372293, "W_D_1KI": 0.8325722516779552, "J_D_1KI": 0.008505442517167297}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91738, "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": 10.729677200317383, "TIME_S_1KI": 0.1169600078518976, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1421.8676947784425, "W": 116.68, "J_1KI": 15.49922272971334, "W_1KI": 1.2718829710697859, "W_D": 81.037, "J_D": 987.5205037860871, "W_D_1KI": 0.883352591074582, "J_D_1KI": 0.009629080545407377}
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 a9ce226..e8b4fe3 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', '1000', '-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": 0.1396017074584961}
+{"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": 0.13608026504516602}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     11,  ..., 249990, 249993,
+tensor(crow_indices=tensor([     0,      3,     12,  ..., 249989, 249998,
                             250000]),
-       col_indices=tensor([ 1901, 17696, 37644,  ..., 22666, 31352, 38471]),
-       values=tensor([0.6079, 0.0811, 0.7282,  ..., 0.2667, 0.3886, 0.6657]),
+       col_indices=tensor([17323, 35611, 42973,  ..., 47252,  2994, 12259]),
+       values=tensor([0.7287, 0.3464, 0.0193,  ..., 0.7636, 0.2298, 0.3699]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5204, 0.6126, 0.8277,  ..., 0.7159, 0.4461, 0.9246])
+tensor([0.4030, 0.5063, 0.1399,  ..., 0.2219, 0.6631, 0.1030])
 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: 0.1396017074584961 seconds
+Time: 0.13608026504516602 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75213', '-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": 8.06783390045166}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77160', '-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": 9.564647197723389}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249992, 249996,
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 249992, 249997,
                             250000]),
-       col_indices=tensor([ 3649, 15078, 16220,  ..., 32895, 36388, 49599]),
-       values=tensor([0.6393, 0.2992, 0.9532,  ..., 0.0270, 0.3430, 0.6378]),
+       col_indices=tensor([ 7731,  9587, 38710,  ..., 32177, 32664, 36235]),
+       values=tensor([0.0671, 0.3654, 0.2011,  ..., 0.4377, 0.9797, 0.5456]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0844, 0.1224, 0.7905,  ..., 0.3661, 0.3101, 0.4173])
+tensor([0.4354, 0.6450, 0.5949,  ..., 0.4585, 0.1162, 0.0017])
 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: 8.06783390045166 seconds
+Time: 9.564647197723389 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '97887', '-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": 10.967289686203003}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84705', '-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": 9.694962739944458}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249992, 249996,
+tensor(crow_indices=tensor([     0,      4,     11,  ..., 249991, 249993,
                             250000]),
-       col_indices=tensor([ 9116, 23500, 25241,  ...,  7305, 15035, 46474]),
-       values=tensor([0.8636, 0.6633, 0.2645,  ..., 0.7208, 0.8992, 0.1134]),
+       col_indices=tensor([19445, 22750, 27321,  ..., 31731, 39710, 46259]),
+       values=tensor([0.4009, 0.2006, 0.6920,  ..., 0.2884, 0.6470, 0.2171]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3603, 0.4772, 0.1653,  ..., 0.3951, 0.3400, 0.6722])
+tensor([0.3109, 0.8999, 0.0558,  ..., 0.1822, 0.8563, 0.0744])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.967289686203003 seconds
+Time: 9.694962739944458 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91738', '-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": 10.729677200317383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249992, 249996,
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249990, 249995,
                             250000]),
-       col_indices=tensor([ 9116, 23500, 25241,  ...,  7305, 15035, 46474]),
-       values=tensor([0.8636, 0.6633, 0.2645,  ..., 0.7208, 0.8992, 0.1134]),
+       col_indices=tensor([20378, 29361, 44885,  ..., 25194, 39048, 45113]),
+       values=tensor([0.6839, 0.7204, 0.3118,  ..., 0.2854, 0.8671, 0.0496]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3603, 0.4772, 0.1653,  ..., 0.3951, 0.3400, 0.6722])
+tensor([0.2159, 0.7026, 0.3184,  ..., 0.1135, 0.4559, 0.6374])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +76,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.967289686203003 seconds
+Time: 10.729677200317383 seconds
 
-[40.69, 39.7, 40.28, 39.02, 40.08, 39.07, 39.32, 38.93, 40.06, 39.15]
-[117.34]
-13.198044061660767
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 97887, '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': 10.967289686203003, 'TIME_S_1KI': 0.11204030858237563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1548.6584901952745, 'W': 117.34}
-[40.69, 39.7, 40.28, 39.02, 40.08, 39.07, 39.32, 38.93, 40.06, 39.15, 49.08, 39.91, 39.13, 39.81, 39.05, 39.67, 39.05, 40.19, 39.16, 39.9]
-716.84
-35.842
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 97887, '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': 10.967289686203003, 'TIME_S_1KI': 0.11204030858237563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1548.6584901952745, 'W': 117.34, 'J_1KI': 15.820880098432626, 'W_1KI': 1.1987291468734356, 'W_D': 81.498, 'J_D': 1075.6141949372293, 'W_D_1KI': 0.8325722516779552, 'J_D_1KI': 0.008505442517167297}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([20378, 29361, 44885,  ..., 25194, 39048, 45113]),
+       values=tensor([0.6839, 0.7204, 0.3118,  ..., 0.2854, 0.8671, 0.0496]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2159, 0.7026, 0.3184,  ..., 0.1135, 0.4559, 0.6374])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.729677200317383 seconds
+
+[40.36, 39.52, 40.11, 39.22, 40.19, 39.14, 40.18, 39.47, 39.42, 39.16]
+[116.68]
+12.186044692993164
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91738, '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': 10.729677200317383, 'TIME_S_1KI': 0.1169600078518976, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.8676947784425, 'W': 116.68}
+[40.36, 39.52, 40.11, 39.22, 40.19, 39.14, 40.18, 39.47, 39.42, 39.16, 39.82, 39.18, 40.07, 39.1, 40.14, 39.1, 40.11, 39.11, 39.63, 39.0]
+712.86
+35.643
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91738, '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': 10.729677200317383, 'TIME_S_1KI': 0.1169600078518976, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.8676947784425, 'W': 116.68, 'J_1KI': 15.49922272971334, 'W_1KI': 1.2718829710697859, 'W_D': 81.037, 'J_D': 987.5205037860871, 'W_D_1KI': 0.883352591074582, 'J_D_1KI': 0.009629080545407377}
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 dcf99f8..18b313c 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": 47277, "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": 10.432827234268188, "TIME_S_1KI": 0.2206744766856651, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1956.9828476619723, "W": 146.36, "J_1KI": 41.393972706854754, "W_1KI": 3.095797110645769, "W_D": 110.02925000000002, "J_D": 1471.2035733199718, "W_D_1KI": 2.3273314719631117, "J_D_1KI": 0.04922756249260976}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46932, "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": 10.4467294216156, "TIME_S_1KI": 0.22259288804260632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1943.0940554380418, "W": 146.55, "J_1KI": 41.40232795188873, "W_1KI": 3.122602914855536, "W_D": 110.75150000000002, "J_D": 1468.4447716195587, "W_D_1KI": 2.3598291144634795, "J_D_1KI": 0.05028187834448734}
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 c946bac..c54c9fb 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', '1000', '-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": 0.2981231212615967}
+{"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": 0.2965991497039795}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      43,     103,  ..., 2499901,
-                            2499951, 2500000]),
-       col_indices=tensor([  154,  1105,  2164,  ..., 43048, 45641, 46786]),
-       values=tensor([0.5353, 0.9585, 0.2831,  ..., 0.0513, 0.1909, 0.0614]),
+tensor(crow_indices=tensor([      0,      49,     105,  ..., 2499896,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1888,  3456,  5299,  ..., 45108, 48153, 49689]),
+       values=tensor([0.2133, 0.4832, 0.5162,  ..., 0.1550, 0.2104, 0.0398]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3993, 0.6905, 0.7348,  ..., 0.6851, 0.9182, 0.5409])
+tensor([0.8558, 0.3690, 0.3196,  ..., 0.7609, 0.2901, 0.1393])
 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: 0.2981231212615967 seconds
+Time: 0.2965991497039795 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35220', '-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": 7.822157621383667}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35401', '-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": 7.9200310707092285}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     100,  ..., 2499912,
-                            2499953, 2500000]),
-       col_indices=tensor([  120,   161,   363,  ..., 47642, 48044, 49939]),
-       values=tensor([0.7949, 0.8676, 0.3054,  ..., 0.9459, 0.0848, 0.8977]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4844, 0.7866, 0.3385,  ..., 0.0837, 0.3382, 0.6328])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 7.822157621383667 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '47277', '-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": 10.432827234268188}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     109,  ..., 2499897,
+tensor(crow_indices=tensor([      0,      50,      98,  ..., 2499887,
                             2499942, 2500000]),
-       col_indices=tensor([ 2040,  2609,  3779,  ..., 46933, 47654, 47998]),
-       values=tensor([0.9101, 0.3119, 0.8580,  ..., 0.1192, 0.4361, 0.9803]),
+       col_indices=tensor([ 1341,  6881,  6901,  ..., 49243, 49539, 49603]),
+       values=tensor([0.6621, 0.7599, 0.1509,  ..., 0.9636, 0.0388, 0.7851]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1061, 0.6227, 0.1589,  ..., 0.5507, 0.9975, 0.5119])
+tensor([0.4875, 0.8207, 0.8190,  ..., 0.4243, 0.1238, 0.4257])
 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: 10.432827234268188 seconds
+Time: 7.9200310707092285 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46932', '-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": 10.4467294216156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     109,  ..., 2499897,
+tensor(crow_indices=tensor([      0,      37,      82,  ..., 2499888,
                             2499942, 2500000]),
-       col_indices=tensor([ 2040,  2609,  3779,  ..., 46933, 47654, 47998]),
-       values=tensor([0.9101, 0.3119, 0.8580,  ..., 0.1192, 0.4361, 0.9803]),
+       col_indices=tensor([ 2117,  2189,  2263,  ..., 47568, 48115, 49415]),
+       values=tensor([0.8006, 0.3321, 0.7026,  ..., 0.2322, 0.3552, 0.1894]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1061, 0.6227, 0.1589,  ..., 0.5507, 0.9975, 0.5119])
+tensor([0.9529, 0.8532, 0.0899,  ..., 0.0711, 0.7399, 0.8898])
 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: 10.432827234268188 seconds
+Time: 10.4467294216156 seconds
 
-[40.03, 40.19, 39.31, 40.07, 40.41, 39.74, 44.15, 41.27, 39.36, 40.06]
-[146.36]
-13.371022462844849
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 47277, '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': 10.432827234268188, 'TIME_S_1KI': 0.2206744766856651, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.9828476619723, 'W': 146.36}
-[40.03, 40.19, 39.31, 40.07, 40.41, 39.74, 44.15, 41.27, 39.36, 40.06, 40.74, 39.25, 45.03, 39.17, 39.46, 39.16, 40.2, 39.66, 39.96, 39.62]
-726.615
-36.33075
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 47277, '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': 10.432827234268188, 'TIME_S_1KI': 0.2206744766856651, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.9828476619723, 'W': 146.36, 'J_1KI': 41.393972706854754, 'W_1KI': 3.095797110645769, 'W_D': 110.02925000000002, 'J_D': 1471.2035733199718, 'W_D_1KI': 2.3273314719631117, 'J_D_1KI': 0.04922756249260976}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      37,      82,  ..., 2499888,
+                            2499942, 2500000]),
+       col_indices=tensor([ 2117,  2189,  2263,  ..., 47568, 48115, 49415]),
+       values=tensor([0.8006, 0.3321, 0.7026,  ..., 0.2322, 0.3552, 0.1894]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9529, 0.8532, 0.0899,  ..., 0.0711, 0.7399, 0.8898])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.4467294216156 seconds
+
+[40.61, 39.27, 40.33, 39.32, 40.41, 39.18, 40.21, 39.47, 39.59, 40.71]
+[146.55]
+13.258915424346924
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46932, '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': 10.4467294216156, 'TIME_S_1KI': 0.22259288804260632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.0940554380418, 'W': 146.55}
+[40.61, 39.27, 40.33, 39.32, 40.41, 39.18, 40.21, 39.47, 39.59, 40.71, 40.22, 40.14, 39.29, 40.17, 39.21, 40.37, 39.38, 39.54, 39.32, 40.0]
+715.9699999999999
+35.7985
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46932, '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': 10.4467294216156, 'TIME_S_1KI': 0.22259288804260632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.0940554380418, 'W': 146.55, 'J_1KI': 41.40232795188873, 'W_1KI': 3.122602914855536, 'W_D': 110.75150000000002, 'J_D': 1468.4447716195587, 'W_D_1KI': 2.3598291144634795, 'J_D_1KI': 0.05028187834448734}
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 a40aca5..a53c562 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": 129830, "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": 10.498366355895996, "TIME_S_1KI": 0.08086240742429328, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1343.8986691188811, "W": 102.66, "J_1KI": 10.351218278663492, "W_1KI": 0.7907263344373412, "W_D": 67.04849999999999, "J_D": 877.7166366298197, "W_D_1KI": 0.5164330278055919, "J_D_1KI": 0.003977763443006947}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 132622, "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": 10.695917844772339, "TIME_S_1KI": 0.08064964971703291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1381.936604347229, "W": 102.52, "J_1KI": 10.420115850667528, "W_1KI": 0.7730240834853945, "W_D": 66.90350000000001, "J_D": 901.8376473755837, "W_D_1KI": 0.5044675845636472, "J_D_1KI": 0.0038038001580706607}
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 7b7017b..89e9f79 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
@@ -1,13 +1,32 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.11773824691772461}
+{"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": 0.13474559783935547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([43476,  3093, 41733,  ..., 42921, 16006, 37299]),
+       values=tensor([0.8834, 0.6775, 0.5620,  ..., 0.7889, 0.3307, 0.4663]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1655, 0.9515, 0.3152,  ..., 0.5133, 0.8067, 0.9282])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.13474559783935547 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77924', '-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": 6.767163991928101}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([16845,  2751, 33930,  ..., 33536, 38018, 30474]),
-       values=tensor([0.6858, 0.5470, 0.3190,  ..., 0.3110, 0.3011, 0.6040]),
+       col_indices=tensor([35071, 44060, 31911,  ..., 37021, 35082, 17458]),
+       values=tensor([0.6370, 0.7388, 0.5924,  ..., 0.3636, 0.5677, 0.2522]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7348, 0.5937, 0.8612,  ..., 0.8920, 0.9109, 0.1161])
+tensor([0.8033, 0.0482, 0.8958,  ..., 0.4016, 0.2560, 0.2344])
 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: 0.11773824691772461 seconds
+Time: 6.767163991928101 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89180', '-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": 7.942249059677124}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '120907', '-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": 9.572461605072021}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 5133, 25494,  8495,  ..., 18153, 14682, 27268]),
-       values=tensor([0.7177, 0.6433, 0.0497,  ..., 0.6766, 0.5365, 0.3286]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 3082, 46101, 46713,  ..., 40768, 36655, 17054]),
+       values=tensor([0.2693, 0.1416, 0.6603,  ..., 0.5561, 0.2474, 0.5454]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6426, 0.1118, 0.3197,  ..., 0.9296, 0.1873, 0.3702])
+tensor([0.5277, 0.5906, 0.6144,  ..., 0.6636, 0.4334, 0.5688])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,19 +53,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 7.942249059677124 seconds
+Time: 9.572461605072021 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '117899', '-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": 9.535074234008789}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '132622', '-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": 10.695917844772339}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 24998, 25000]),
-       col_indices=tensor([ 1468,  1704, 43281,  ...,  3197, 24132, 30286]),
-       values=tensor([1.4228e-01, 5.9740e-01, 9.5210e-06,  ...,
-                      2.4125e-01, 6.2955e-01, 4.9169e-01]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([ 1978, 29423,  7022,  ..., 46456, 14629, 46564]),
+       values=tensor([0.3729, 0.4306, 0.6677,  ..., 0.7805, 0.6392, 0.2909]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8788, 0.8743, 0.0964,  ..., 0.0391, 0.4204, 0.2909])
+tensor([0.9195, 0.7845, 0.1112,  ..., 0.9886, 0.0043, 0.8706])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -54,18 +72,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.535074234008789 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '129830', '-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": 10.498366355895996}
+Time: 10.695917844772339 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([16477, 45779, 22583,  ..., 30055, 21515, 45820]),
-       values=tensor([0.8893, 0.7790, 0.5329,  ..., 0.5529, 0.2667, 0.0404]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([ 1978, 29423,  7022,  ..., 46456, 14629, 46564]),
+       values=tensor([0.3729, 0.4306, 0.6677,  ..., 0.7805, 0.6392, 0.2909]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0469, 0.9963, 0.7558,  ..., 0.9652, 0.6676, 0.7778])
+tensor([0.9195, 0.7845, 0.1112,  ..., 0.9886, 0.0043, 0.8706])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,29 +88,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.498366355895996 seconds
+Time: 10.695917844772339 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([16477, 45779, 22583,  ..., 30055, 21515, 45820]),
-       values=tensor([0.8893, 0.7790, 0.5329,  ..., 0.5529, 0.2667, 0.0404]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0469, 0.9963, 0.7558,  ..., 0.9652, 0.6676, 0.7778])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 10.498366355895996 seconds
-
-[41.64, 39.36, 40.21, 39.07, 40.05, 39.18, 39.19, 39.18, 40.04, 39.2]
-[102.66]
-13.090772151947021
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129830, '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': 10.498366355895996, 'TIME_S_1KI': 0.08086240742429328, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1343.8986691188811, 'W': 102.66}
-[41.64, 39.36, 40.21, 39.07, 40.05, 39.18, 39.19, 39.18, 40.04, 39.2, 39.71, 39.93, 38.98, 39.86, 39.33, 39.67, 39.03, 39.93, 39.08, 39.73]
-712.23
-35.6115
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129830, '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': 10.498366355895996, 'TIME_S_1KI': 0.08086240742429328, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1343.8986691188811, 'W': 102.66, 'J_1KI': 10.351218278663492, 'W_1KI': 0.7907263344373412, 'W_D': 67.04849999999999, 'J_D': 877.7166366298197, 'W_D_1KI': 0.5164330278055919, 'J_D_1KI': 0.003977763443006947}
+[40.91, 39.21, 40.28, 39.06, 40.18, 39.24, 39.39, 39.11, 40.12, 39.03]
+[102.52]
+13.4796781539917
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132622, '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': 10.695917844772339, 'TIME_S_1KI': 0.08064964971703291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.936604347229, 'W': 102.52}
+[40.91, 39.21, 40.28, 39.06, 40.18, 39.24, 39.39, 39.11, 40.12, 39.03, 40.67, 39.4, 40.19, 38.91, 39.61, 38.93, 39.87, 39.08, 40.01, 38.87]
+712.3299999999999
+35.616499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132622, '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': 10.695917844772339, 'TIME_S_1KI': 0.08064964971703291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.936604347229, 'W': 102.52, 'J_1KI': 10.420115850667528, 'W_1KI': 0.7730240834853945, 'W_D': 66.90350000000001, 'J_D': 901.8376473755837, 'W_D_1KI': 0.5044675845636472, 'J_D_1KI': 0.0038038001580706607}
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
new file mode 100644
index 0000000..c740a6d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 450692, "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": 10.661210536956787, "TIME_S_1KI": 0.023655202526241394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.7642656326295, "W": 94.48, "J_1KI": 2.82845993634817, "W_1KI": 0.2096331863001784, "W_D": 59.36250000000001, "J_D": 800.9440486729146, "W_D_1KI": 0.13171411962049473, "J_D_1KI": 0.00029224863015206556}
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
new file mode 100644
index 0000000..eed9694
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.05054283142089844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 483, 2169,  757,  ...,  173, 4439, 4656]),
+       values=tensor([0.9876, 0.6258, 0.5982,  ..., 0.3562, 0.6626, 0.2988]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5486, 0.1022, 0.5660,  ..., 0.0025, 0.4692, 0.8005])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.05054283142089844 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '207744', '-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": 4.839913845062256}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1064,  259,  704,  ..., 2037, 4830,  899]),
+       values=tensor([0.7873, 0.2357, 0.4656,  ..., 0.3402, 0.5396, 0.7236]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3390, 0.6218, 0.4185,  ..., 0.9245, 0.2892, 0.5586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 4.839913845062256 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '450692', '-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": 10.661210536956787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 2499, 2500]),
+       col_indices=tensor([2769, 4978, 3269,  ..., 2907, 4470, 1850]),
+       values=tensor([0.1814, 0.5969, 0.2629,  ..., 0.3883, 0.1478, 0.5451]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9019, 0.2172, 0.0888,  ..., 0.3698, 0.8940, 0.4050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.661210536956787 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 2499, 2500]),
+       col_indices=tensor([2769, 4978, 3269,  ..., 2907, 4470, 1850]),
+       values=tensor([0.1814, 0.5969, 0.2629,  ..., 0.3883, 0.1478, 0.5451]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9019, 0.2172, 0.0888,  ..., 0.3698, 0.8940, 0.4050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.661210536956787 seconds
+
+[39.19, 39.19, 38.63, 39.33, 38.89, 39.39, 38.57, 39.43, 38.51, 39.19]
+[94.48]
+13.492424488067627
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 450692, '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': 10.661210536956787, 'TIME_S_1KI': 0.023655202526241394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.7642656326295, 'W': 94.48}
+[39.19, 39.19, 38.63, 39.33, 38.89, 39.39, 38.57, 39.43, 38.51, 39.19, 39.32, 38.88, 39.47, 38.52, 39.48, 38.52, 39.58, 38.58, 39.31, 38.44]
+702.3499999999999
+35.11749999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 450692, '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': 10.661210536956787, 'TIME_S_1KI': 0.023655202526241394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.7642656326295, 'W': 94.48, 'J_1KI': 2.82845993634817, 'W_1KI': 0.2096331863001784, 'W_D': 59.36250000000001, 'J_D': 800.9440486729146, 'W_D_1KI': 0.13171411962049473, 'J_D_1KI': 0.00029224863015206556}
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
new file mode 100644
index 0000000..5a45f3d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 249519, "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": 10.236942052841187, "TIME_S_1KI": 0.04102670358907012, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1222.1109838342666, "W": 97.13999999999999, "J_1KI": 4.897867432276767, "W_1KI": 0.3893090305748259, "W_D": 61.76424999999999, "J_D": 777.0513520000576, "W_D_1KI": 0.24753325398065876, "J_D_1KI": 0.0009920417041614415}
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
new file mode 100644
index 0000000..bf3e9ad
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.05724024772644043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24983, 24992, 25000]),
+       col_indices=tensor([ 471, 1370, 1845,  ..., 3191, 3518, 3659]),
+       values=tensor([0.0299, 0.9557, 0.6054,  ..., 0.0635, 0.2604, 0.4528]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0205, 0.7752, 0.1498,  ..., 0.2089, 0.1619, 0.7193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05724024772644043 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '183437', '-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": 7.719191074371338}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24996, 24997, 25000]),
+       col_indices=tensor([1493, 2121, 2213,  ...,  623, 2347, 4713]),
+       values=tensor([0.6456, 0.4495, 0.4360,  ..., 0.5144, 0.5794, 0.1984]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2703, 0.0672, 0.3072,  ..., 0.2566, 0.5122, 0.5785])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.719191074371338 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '249519', '-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": 10.236942052841187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([ 752,  886,  972,  ...,  802, 1974, 3630]),
+       values=tensor([0.4437, 0.0647, 0.4607,  ..., 0.1209, 0.0125, 0.5794]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9393, 0.0560, 0.5479,  ..., 0.4533, 0.0776, 0.5900])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.236942052841187 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([ 752,  886,  972,  ...,  802, 1974, 3630]),
+       values=tensor([0.4437, 0.0647, 0.4607,  ..., 0.1209, 0.0125, 0.5794]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9393, 0.0560, 0.5479,  ..., 0.4533, 0.0776, 0.5900])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.236942052841187 seconds
+
+[40.22, 39.73, 39.42, 38.78, 39.93, 38.71, 40.68, 38.8, 39.45, 38.55]
+[97.14]
+12.580924272537231
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 249519, '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': 10.236942052841187, 'TIME_S_1KI': 0.04102670358907012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1222.1109838342666, 'W': 97.13999999999999}
+[40.22, 39.73, 39.42, 38.78, 39.93, 38.71, 40.68, 38.8, 39.45, 38.55, 40.16, 39.21, 38.74, 39.25, 39.41, 39.27, 38.96, 39.33, 38.96, 38.84]
+707.515
+35.37575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 249519, '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': 10.236942052841187, 'TIME_S_1KI': 0.04102670358907012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1222.1109838342666, 'W': 97.13999999999999, 'J_1KI': 4.897867432276767, 'W_1KI': 0.3893090305748259, 'W_D': 61.76424999999999, 'J_D': 777.0513520000576, 'W_D_1KI': 0.24753325398065876, 'J_D_1KI': 0.0009920417041614415}
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
new file mode 100644
index 0000000..e6c8fdb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 146173, "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": 10.55378007888794, "TIME_S_1KI": 0.07220061214374707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1340.7953616142272, "W": 116.1, "J_1KI": 9.17266089916898, "W_1KI": 0.7942643306219342, "W_D": 80.21499999999999, "J_D": 926.372953763008, "W_D_1KI": 0.5487675562518385, "J_D_1KI": 0.0037542333827166336}
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
new file mode 100644
index 0000000..021f115
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.09166121482849121}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     94,  ..., 249894, 249942,
+                            250000]),
+       col_indices=tensor([ 119,  293,  345,  ..., 4744, 4847, 4998]),
+       values=tensor([0.2600, 0.0492, 0.0782,  ..., 0.6942, 0.7814, 0.7527]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8315, 0.0983, 0.7447,  ..., 0.4668, 0.9945, 0.1855])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.09166121482849121 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '114552', '-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": 8.2285475730896}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    113,  ..., 249897, 249950,
+                            250000]),
+       col_indices=tensor([  60,   61,   88,  ..., 4754, 4809, 4933]),
+       values=tensor([0.8655, 0.3309, 0.5749,  ..., 0.8443, 0.2705, 0.0665]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6570, 0.9775, 0.7976,  ..., 0.2365, 0.6987, 0.3821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.2285475730896 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '146173', '-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": 10.55378007888794}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    109,  ..., 249914, 249951,
+                            250000]),
+       col_indices=tensor([  51,   99,  229,  ..., 4435, 4821, 4904]),
+       values=tensor([0.7585, 0.4725, 0.0422,  ..., 0.0029, 0.1086, 0.7072]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3786, 0.1898, 0.9439,  ..., 0.4562, 0.4771, 0.6918])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.55378007888794 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    109,  ..., 249914, 249951,
+                            250000]),
+       col_indices=tensor([  51,   99,  229,  ..., 4435, 4821, 4904]),
+       values=tensor([0.7585, 0.4725, 0.0422,  ..., 0.0029, 0.1086, 0.7072]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3786, 0.1898, 0.9439,  ..., 0.4562, 0.4771, 0.6918])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.55378007888794 seconds
+
+[39.38, 39.69, 39.36, 39.25, 38.8, 39.79, 38.95, 39.94, 38.86, 45.03]
+[116.1]
+11.548624992370605
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 146173, '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': 10.55378007888794, 'TIME_S_1KI': 0.07220061214374707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.7953616142272, 'W': 116.1}
+[39.38, 39.69, 39.36, 39.25, 38.8, 39.79, 38.95, 39.94, 38.86, 45.03, 39.59, 39.72, 39.05, 38.89, 38.89, 39.88, 38.77, 40.8, 45.19, 39.74]
+717.7
+35.885000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 146173, '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': 10.55378007888794, 'TIME_S_1KI': 0.07220061214374707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.7953616142272, 'W': 116.1, 'J_1KI': 9.17266089916898, 'W_1KI': 0.7942643306219342, 'W_D': 80.21499999999999, 'J_D': 926.372953763008, 'W_D_1KI': 0.5487675562518385, 'J_D_1KI': 0.0037542333827166336}
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
new file mode 100644
index 0000000..4f9e011
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 92778, "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": 10.476318120956421, "TIME_S_1KI": 0.11291812844592922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1732.4749909281732, "W": 131.59, "J_1KI": 18.6733384091937, "W_1KI": 1.4183319321390848, "W_D": 96.0545, "J_D": 1264.6251160126926, "W_D_1KI": 1.0353154842742893, "J_D_1KI": 0.011159062323765217}
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
new file mode 100644
index 0000000..85eae8d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.1557161808013916}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     231,     495,  ..., 1249487,
+                            1249744, 1250000]),
+       col_indices=tensor([   9,   30,   58,  ..., 4828, 4865, 4971]),
+       values=tensor([0.7438, 0.5258, 0.4698,  ..., 0.4344, 0.2594, 0.0033]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.1880, 0.8169, 0.5226,  ..., 0.2752, 0.9006, 0.0611])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.1557161808013916 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67430', '-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": 7.631251096725464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     518,  ..., 1249509,
+                            1249753, 1250000]),
+       col_indices=tensor([  31,   45,  102,  ..., 4944, 4977, 4981]),
+       values=tensor([0.8150, 0.4433, 0.0676,  ..., 0.5361, 0.0056, 0.9882]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0156, 0.0219, 0.6064,  ..., 0.7934, 0.6259, 0.0204])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 7.631251096725464 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '92778', '-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": 10.476318120956421}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     520,  ..., 1249470,
+                            1249738, 1250000]),
+       col_indices=tensor([  32,   37,   46,  ..., 4950, 4963, 4989]),
+       values=tensor([0.4206, 0.9091, 0.7478,  ..., 0.6711, 0.2779, 0.9141]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6953, 0.1111, 0.6307,  ..., 0.1029, 0.6511, 0.8226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.476318120956421 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     520,  ..., 1249470,
+                            1249738, 1250000]),
+       col_indices=tensor([  32,   37,   46,  ..., 4950, 4963, 4989]),
+       values=tensor([0.4206, 0.9091, 0.7478,  ..., 0.6711, 0.2779, 0.9141]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6953, 0.1111, 0.6307,  ..., 0.1029, 0.6511, 0.8226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.476318120956421 seconds
+
+[40.71, 39.95, 38.97, 39.83, 39.79, 39.14, 38.93, 39.78, 39.42, 39.71]
+[131.59]
+13.165704011917114
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92778, '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': 10.476318120956421, 'TIME_S_1KI': 0.11291812844592922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1732.4749909281732, 'W': 131.59}
+[40.71, 39.95, 38.97, 39.83, 39.79, 39.14, 38.93, 39.78, 39.42, 39.71, 40.83, 39.04, 39.95, 38.93, 39.14, 39.24, 39.86, 38.92, 39.75, 38.89]
+710.71
+35.5355
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92778, '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': 10.476318120956421, 'TIME_S_1KI': 0.11291812844592922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1732.4749909281732, 'W': 131.59, 'J_1KI': 18.6733384091937, 'W_1KI': 1.4183319321390848, 'W_D': 96.0545, 'J_D': 1264.6251160126926, 'W_D_1KI': 1.0353154842742893, 'J_D_1KI': 0.011159062323765217}
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
new file mode 100644
index 0000000..d3bf1fe
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52513, "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": 10.26560354232788, "TIME_S_1KI": 0.19548689928832635, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1794.3579230117798, "W": 136.24, "J_1KI": 34.16978506297069, "W_1KI": 2.594405194904119, "W_D": 100.32050000000001, "J_D": 1321.2777746293546, "W_D_1KI": 1.9103936168186928, "J_D_1KI": 0.036379441601483306}
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
new file mode 100644
index 0000000..ca278ce
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.2491617202758789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1030,  ..., 2499018,
+                            2499503, 2500000]),
+       col_indices=tensor([   5,    7,    9,  ..., 4974, 4988, 4992]),
+       values=tensor([0.9314, 0.8722, 0.2786,  ..., 0.3461, 0.5001, 0.4531]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5860, 0.7303, 0.0322,  ..., 0.3067, 0.0639, 0.6907])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.2491617202758789 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42141', '-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": 8.425995349884033}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     480,     969,  ..., 2498991,
+                            2499495, 2500000]),
+       col_indices=tensor([   1,    8,   15,  ..., 4990, 4995, 4997]),
+       values=tensor([0.6450, 0.7913, 0.7669,  ..., 0.2675, 0.7315, 0.7922]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8872, 0.3458, 0.7222,  ..., 0.3185, 0.9459, 0.1327])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 8.425995349884033 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52513', '-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": 10.26560354232788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     496,    1000,  ..., 2499050,
+                            2499547, 2500000]),
+       col_indices=tensor([   1,    8,   12,  ..., 4944, 4951, 4977]),
+       values=tensor([0.2566, 0.4868, 0.9344,  ..., 0.5912, 0.8684, 0.6618]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5960, 0.0213, 0.1088,  ..., 0.8621, 0.3601, 0.4544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.26560354232788 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     496,    1000,  ..., 2499050,
+                            2499547, 2500000]),
+       col_indices=tensor([   1,    8,   12,  ..., 4944, 4951, 4977]),
+       values=tensor([0.2566, 0.4868, 0.9344,  ..., 0.5912, 0.8684, 0.6618]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5960, 0.0213, 0.1088,  ..., 0.8621, 0.3601, 0.4544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.26560354232788 seconds
+
+[47.36, 40.59, 40.08, 39.29, 39.8, 40.2, 40.01, 39.07, 40.08, 38.96]
+[136.24]
+13.170566082000732
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52513, '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': 10.26560354232788, 'TIME_S_1KI': 0.19548689928832635, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1794.3579230117798, 'W': 136.24}
+[47.36, 40.59, 40.08, 39.29, 39.8, 40.2, 40.01, 39.07, 40.08, 38.96, 40.34, 40.06, 39.51, 39.55, 39.19, 39.83, 39.16, 39.75, 38.95, 39.88]
+718.3900000000001
+35.919500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52513, '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': 10.26560354232788, 'TIME_S_1KI': 0.19548689928832635, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1794.3579230117798, 'W': 136.24, 'J_1KI': 34.16978506297069, 'W_1KI': 2.594405194904119, 'W_D': 100.32050000000001, 'J_D': 1321.2777746293546, 'W_D_1KI': 1.9103936168186928, 'J_D_1KI': 0.036379441601483306}
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
new file mode 100644
index 0000000..b172c0c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 470922, "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": 10.104466915130615, "TIME_S_1KI": 0.021456773977708867, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1145.4606072449685, "W": 92.79, "J_1KI": 2.4323786258551703, "W_1KI": 0.19703900008918676, "W_D": 57.138000000000005, "J_D": 705.3489403681756, "W_D_1KI": 0.12133219514059655, "J_D_1KI": 0.0002576481777037313}
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
new file mode 100644
index 0000000..263466b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,356 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.06183266639709473}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([2604,  880,   70, 3579, 4688, 1415, 4052, 2136, 2789,
+                           1920, 1039, 1558, 2117, 2959,  828,  201, 2786, 2764,
+                           2257,  277, 2288,  309, 1119, 4553,  992, 4344, 1852,
+                           1654, 3440, 2337, 4465, 3747,  865, 1053,  722, 4388,
+                           1118, 2434, 2479, 2179, 2623, 1327, 1850, 4354, 1080,
+                            294, 3733, 2629, 4844, 2052,  338, 3690, 2779, 4781,
+                            442,  500, 2501, 2111, 2134, 4050, 4965, 2490, 1539,
+                           1728, 3791, 2480,  429,   85, 2238, 4139, 1911, 2702,
+                           1667,  623,  834,  958, 2640,  639, 3527, 4275, 2167,
+                           2457,  991,  806, 4483,  513, 3720, 1136, 1176, 1064,
+                            771,  912, 1234, 1122, 4461, 4277, 1464,  345, 1997,
+                           2256, 2917,   38, 2975,  472, 2189, 2640,  491,  245,
+                            718, 3839, 2523,  240, 4832, 1434, 3727, 2402, 3795,
+                            977, 2914, 3289, 1194, 1229, 3616, 4441, 1900, 4483,
+                           4227, 4209, 4021, 4316,  794, 1149, 4287, 2054, 4565,
+                           4842,   69,   93, 2768, 2785, 2781, 1662, 4565, 3083,
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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.06183266639709473 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '169813', '-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": 3.7862648963928223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+                      3.8840e-01, 1.4761e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.8065, 0.5790, 0.9005,  ..., 0.0135, 0.6788, 0.2076])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.7862648963928223 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '470922', '-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": 10.104466915130615}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 286, 2098, 2957,   31, 4770, 3649, 4063, 3564, 3211,
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+                      0.5950, 0.5479, 0.1406, 0.5404, 0.7323]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4539, 0.8865, 0.6514,  ..., 0.0864, 0.1789, 0.3670])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.104466915130615 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 286, 2098, 2957,   31, 4770, 3649, 4063, 3564, 3211,
+                            519,  372, 1653, 2583, 2464, 2987, 1744, 4556, 3480,
+                           4025,  965, 3221, 3480, 1237,  638, 2731, 2204, 3903,
+                            337, 4640, 3708, 4928,  846, 4069, 3241, 2342, 3155,
+                           3904, 4645, 1110, 3262,  206, 2707,  291, 1906, 3653,
+                           4410, 4931, 1727, 4173, 1383, 3385, 3838, 1305, 3168,
+                           1375, 4057, 2761, 2787, 2307,    6, 2503, 1872, 3680,
+                           2234, 1597, 3084, 1758,  491, 3779, 4890, 3184,  831,
+                            331, 2968, 3525, 1971,  454,  168, 2971, 2622, 1099,
+                           3321, 3822, 4888, 2660, 4331, 3839,  847,  453, 3854,
+                            958, 4865, 2336,  403, 4990,  684,  801, 4446,  671,
+                           4256, 4579, 3616,  522, 3560, 4436, 4875, 4839, 4252,
+                           2678, 3408,  277, 1706, 3353, 4272,  200, 4495, 1971,
+                           1057, 2080, 4776, 2636, 1840, 1457, 1455, 3267,  879,
+                           4146, 2502, 4940, 2313,   21, 1504,  535, 3781,  367,
+                           2250,  357, 4188,  146, 2230, 1761, 1304, 1785,  442,
+                           2853, 3699,   79, 4930, 2598, 3595, 2987,  205,  247,
+                           2873, 2237, 1134, 2086, 3420, 2896, 4246, 2080, 1618,
+                            978, 1465, 2116, 4506, 3634, 1205, 3062,  601, 2140,
+                            765, 3494, 3345,  738, 3535, 3354, 3147, 4390,  602,
+                           4817, 1923, 2074,   44, 1678, 4913, 1057, 4051, 3685,
+                           2781, 3899, 4448, 4692, 1277,  259, 2144, 2798, 4087,
+                           2596, 4771, 4479,  733, 3005, 1161, 3811, 3147, 4464,
+                           4683,  773, 3834, 3088, 1039, 3766, 2820, 3923, 3718,
+                           3049, 1976,  990, 3587, 2696, 4263, 2139, 3191, 1101,
+                           4701, 4465,  551, 3012, 2514, 2260, 1927, 3611, 4115,
+                           4664,  772, 3814, 2744, 2328,  560, 3629, 3666, 4110,
+                           1272,  515, 3230, 2775, 3191, 4516, 1702]),
+       values=tensor([0.4950, 0.4387, 0.7062, 0.8184, 0.9685, 0.9491, 0.6387,
+                      0.3930, 0.4627, 0.2264, 0.4673, 0.2803, 0.8352, 0.7116,
+                      0.3144, 0.9721, 0.1277, 0.9601, 0.0123, 0.3968, 0.9183,
+                      0.0517, 0.5676, 0.9009, 0.4901, 0.3378, 0.4750, 0.6307,
+                      0.7160, 0.7754, 0.8317, 0.5508, 0.6443, 0.1719, 0.1190,
+                      0.2292, 0.9505, 0.2302, 0.5965, 0.4343, 0.9706, 0.9472,
+                      0.7071, 0.4120, 0.5080, 0.6133, 0.5804, 0.7848, 0.1131,
+                      0.7398, 0.2113, 0.5136, 0.9362, 0.4868, 0.7307, 0.9542,
+                      0.1907, 0.7842, 0.0075, 0.1654, 0.1604, 0.5554, 0.9265,
+                      0.9594, 0.1847, 0.0412, 0.1458, 0.3185, 0.9474, 0.7262,
+                      0.9867, 0.9175, 0.8563, 0.0555, 0.5865, 0.1402, 0.0777,
+                      0.1693, 0.3284, 0.8041, 0.3119, 0.6054, 0.1208, 0.1474,
+                      0.6411, 0.6397, 0.9233, 0.0205, 0.1838, 0.9985, 0.4716,
+                      0.4977, 0.8331, 0.9916, 0.5989, 0.7640, 0.9210, 0.4278,
+                      0.0911, 0.8508, 0.2547, 0.5851, 0.9233, 0.2665, 0.1213,
+                      0.8754, 0.6206, 0.7311, 0.2194, 0.9834, 0.8122, 0.4946,
+                      0.7260, 0.9509, 0.7893, 0.0815, 0.9968, 0.5027, 0.3558,
+                      0.7001, 0.1542, 0.3964, 0.0402, 0.9298, 0.1070, 0.4902,
+                      0.8333, 0.6213, 0.7680, 0.5975, 0.2149, 0.9396, 0.8765,
+                      0.8836, 0.3422, 0.3496, 0.7499, 0.8855, 0.3598, 0.7125,
+                      0.1563, 0.2571, 0.2028, 0.2313, 0.3287, 0.3989, 0.4172,
+                      0.9776, 0.9673, 0.6099, 0.3489, 0.5171, 0.3263, 0.3550,
+                      0.8206, 0.1824, 0.1805, 0.0479, 0.6241, 0.3393, 0.7730,
+                      0.0623, 0.4418, 0.3306, 0.0692, 0.1691, 0.9139, 0.9289,
+                      0.1653, 0.5991, 0.0793, 0.6308, 0.8611, 0.1878, 0.5735,
+                      0.8923, 0.1845, 0.1387, 0.3446, 0.0333, 0.5909, 0.0051,
+                      0.6730, 0.2001, 0.7864, 0.3596, 0.6702, 0.7444, 0.5210,
+                      0.7057, 0.5369, 0.0193, 0.2647, 0.1729, 0.2634, 0.6010,
+                      0.4976, 0.7177, 0.7966, 0.8166, 0.9702, 0.2066, 0.9091,
+                      0.4739, 0.8346, 0.6718, 0.2794, 0.6249, 0.0434, 0.4190,
+                      0.9938, 0.9770, 0.8053, 0.5102, 0.4949, 0.5149, 0.3290,
+                      0.8346, 0.3511, 0.4625, 0.1176, 0.9732, 0.6568, 0.0814,
+                      0.1466, 0.9735, 0.9996, 0.5023, 0.0806, 0.6393, 0.9851,
+                      0.9968, 0.7168, 0.8555, 0.4797, 0.5400, 0.6489, 0.3087,
+                      0.4955, 0.2041, 0.9406, 0.8471, 0.5173, 0.1622, 0.0921,
+                      0.5950, 0.5479, 0.1406, 0.5404, 0.7323]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4539, 0.8865, 0.6514,  ..., 0.0864, 0.1789, 0.3670])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.104466915130615 seconds
+
+[39.77, 39.06, 39.0, 43.22, 38.95, 38.87, 39.0, 38.96, 40.09, 38.55]
+[92.79]
+12.344655752182007
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 470922, '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': 10.104466915130615, 'TIME_S_1KI': 0.021456773977708867, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1145.4606072449685, 'W': 92.79}
+[39.77, 39.06, 39.0, 43.22, 38.95, 38.87, 39.0, 38.96, 40.09, 38.55, 44.05, 41.96, 39.49, 38.5, 38.79, 39.12, 39.88, 38.31, 39.38, 38.55]
+713.04
+35.652
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 470922, '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': 10.104466915130615, 'TIME_S_1KI': 0.021456773977708867, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1145.4606072449685, 'W': 92.79, 'J_1KI': 2.4323786258551703, 'W_1KI': 0.19703900008918676, 'W_D': 57.138000000000005, 'J_D': 705.3489403681756, 'W_D_1KI': 0.12133219514059655, 'J_D_1KI': 0.0002576481777037313}
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 1cb7a6a..648e77d 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": 33560, "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": 10.490610837936401, "TIME_S_1KI": 0.3125926948133612, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1237.3838691329956, "W": 88.17, "J_1KI": 36.87079467023229, "W_1KI": 2.6272348033373065, "W_D": 71.61225, "J_D": 1005.0112621335984, "W_D_1KI": 2.133857270560191, "J_D_1KI": 0.06358335132777686}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33926, "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": 10.632460117340088, "TIME_S_1KI": 0.31340152441608465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1251.6098043680192, "W": 88.42000000000002, "J_1KI": 36.892348180393185, "W_1KI": 2.606260685020339, "W_D": 71.92675000000001, "J_D": 1018.1432424375416, "W_D_1KI": 2.120106997582975, "J_D_1KI": 0.062492100382685115}
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 14d045d..aff18b0 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', '1000', '-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": 0.3128688335418701}
+{"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": 0.309490442276001}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      17,      23,  ...,  999976,
-                             999990, 1000000]),
-       col_indices=tensor([  283,   794, 12077,  ..., 88041, 96002, 98956]),
-       values=tensor([0.6667, 0.7061, 0.4936,  ..., 0.0020, 0.2226, 0.8107]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3746, 0.1787, 0.3326,  ..., 0.2981, 0.5262, 0.4171])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 0.3128688335418701 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33560', '-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": 10.490610837936401}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      24,  ...,  999975,
+tensor(crow_indices=tensor([      0,       8,      15,  ...,  999979,
                              999989, 1000000]),
-       col_indices=tensor([  291,  3246,  3703,  ..., 78390, 83116, 86469]),
-       values=tensor([0.7026, 0.5046, 0.5818,  ..., 0.3671, 0.4061, 0.2873]),
+       col_indices=tensor([ 8594, 29009, 41843,  ..., 77886, 78317, 95347]),
+       values=tensor([0.9328, 0.5746, 0.1196,  ..., 0.5058, 0.9583, 0.4434]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3720, 0.0968, 0.4099,  ..., 0.6733, 0.7032, 0.3728])
+tensor([0.8206, 0.6612, 0.6620,  ..., 0.9270, 0.4872, 0.3406])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.490610837936401 seconds
+Time: 0.309490442276001 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33926', '-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": 10.632460117340088}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      24,  ...,  999975,
-                             999989, 1000000]),
-       col_indices=tensor([  291,  3246,  3703,  ..., 78390, 83116, 86469]),
-       values=tensor([0.7026, 0.5046, 0.5818,  ..., 0.3671, 0.4061, 0.2873]),
+tensor(crow_indices=tensor([      0,       7,      18,  ...,  999986,
+                             999991, 1000000]),
+       col_indices=tensor([ 9555, 32072, 52846,  ..., 78086, 80072, 96075]),
+       values=tensor([0.9751, 0.3269, 0.5720,  ..., 0.0320, 0.6071, 0.6982]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3720, 0.0968, 0.4099,  ..., 0.6733, 0.7032, 0.3728])
+tensor([0.5445, 0.0121, 0.5604,  ..., 0.3280, 0.5430, 0.6322])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.490610837936401 seconds
+Time: 10.632460117340088 seconds
 
-[18.34, 17.84, 18.15, 18.01, 17.99, 21.31, 18.71, 18.25, 18.1, 17.94]
-[88.17]
-14.034069061279297
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33560, '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': 10.490610837936401, 'TIME_S_1KI': 0.3125926948133612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.3838691329956, 'W': 88.17}
-[18.34, 17.84, 18.15, 18.01, 17.99, 21.31, 18.71, 18.25, 18.1, 17.94, 18.37, 20.96, 17.82, 18.03, 18.16, 17.77, 17.89, 17.87, 17.71, 18.52]
-331.155
-16.55775
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33560, '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': 10.490610837936401, 'TIME_S_1KI': 0.3125926948133612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.3838691329956, 'W': 88.17, 'J_1KI': 36.87079467023229, 'W_1KI': 2.6272348033373065, 'W_D': 71.61225, 'J_D': 1005.0112621335984, 'W_D_1KI': 2.133857270560191, 'J_D_1KI': 0.06358335132777686}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999986,
+                             999991, 1000000]),
+       col_indices=tensor([ 9555, 32072, 52846,  ..., 78086, 80072, 96075]),
+       values=tensor([0.9751, 0.3269, 0.5720,  ..., 0.0320, 0.6071, 0.6982]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5445, 0.0121, 0.5604,  ..., 0.3280, 0.5430, 0.6322])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.632460117340088 seconds
+
+[18.53, 17.97, 18.07, 18.07, 17.99, 18.09, 21.33, 17.98, 18.39, 17.84]
+[88.42]
+14.155279397964478
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33926, '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': 10.632460117340088, 'TIME_S_1KI': 0.31340152441608465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.6098043680192, 'W': 88.42000000000002}
+[18.53, 17.97, 18.07, 18.07, 17.99, 18.09, 21.33, 17.98, 18.39, 17.84, 18.71, 17.91, 17.99, 18.02, 18.68, 18.08, 17.92, 18.59, 18.24, 18.01]
+329.865
+16.49325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33926, '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': 10.632460117340088, 'TIME_S_1KI': 0.31340152441608465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.6098043680192, 'W': 88.42000000000002, 'J_1KI': 36.892348180393185, 'W_1KI': 2.606260685020339, 'W_D': 71.92675000000001, 'J_D': 1018.1432424375416, 'W_D_1KI': 2.120106997582975, 'J_D_1KI': 0.062492100382685115}
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
new file mode 100644
index 0000000..b1b6585
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2890, "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": 10.78333592414856, "TIME_S_1KI": 3.731258105241716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1463.5743112421035, "W": 81.86, "J_1KI": 506.4270973156068, "W_1KI": 28.325259515570934, "W_D": 65.62225000000001, "J_D": 1173.259703712523, "W_D_1KI": 22.706660899653983, "J_D_1KI": 7.856976089845669}
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
new file mode 100644
index 0000000..1352df1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 3.6327288150787354}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      182,  ...,  9999803,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1164,  1511,  2606,  ..., 97059, 99366, 99637]),
+       values=tensor([0.1789, 0.4314, 0.0466,  ..., 0.4339, 0.7049, 0.9540]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5756, 0.3189, 0.9065,  ..., 0.6359, 0.4482, 0.1651])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 3.6327288150787354 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2890', '-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": 10.78333592414856}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      118,      207,  ...,  9999808,
+                             9999910, 10000000]),
+       col_indices=tensor([  712,   968,  1059,  ..., 96997, 98856, 99104]),
+       values=tensor([0.5177, 0.6712, 0.5343,  ..., 0.8226, 0.3425, 0.6939]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8858, 0.8376, 0.8837,  ..., 0.6861, 0.2657, 0.8920])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.78333592414856 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      118,      207,  ...,  9999808,
+                             9999910, 10000000]),
+       col_indices=tensor([  712,   968,  1059,  ..., 96997, 98856, 99104]),
+       values=tensor([0.5177, 0.6712, 0.5343,  ..., 0.8226, 0.3425, 0.6939]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8858, 0.8376, 0.8837,  ..., 0.6861, 0.2657, 0.8920])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.78333592414856 seconds
+
+[18.4, 18.1, 18.05, 18.1, 18.29, 18.11, 18.1, 17.9, 17.96, 17.98]
+[81.86]
+17.878992319107056
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2890, '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': 10.78333592414856, 'TIME_S_1KI': 3.731258105241716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1463.5743112421035, 'W': 81.86}
+[18.4, 18.1, 18.05, 18.1, 18.29, 18.11, 18.1, 17.9, 17.96, 17.98, 18.26, 17.88, 17.92, 17.96, 18.1, 17.9, 17.98, 18.14, 18.06, 17.77]
+324.755
+16.23775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2890, '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': 10.78333592414856, 'TIME_S_1KI': 3.731258105241716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1463.5743112421035, 'W': 81.86, 'J_1KI': 506.4270973156068, 'W_1KI': 28.325259515570934, 'W_D': 65.62225000000001, 'J_D': 1173.259703712523, 'W_D_1KI': 22.706660899653983, 'J_D_1KI': 7.856976089845669}
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 0f4c2a6..c182694 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": 65588, "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": 10.838059663772583, "TIME_S_1KI": 0.16524455180479025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1179.1915578985215, "W": 82.78, "J_1KI": 17.97876986489177, "W_1KI": 1.2621211197170215, "W_D": 66.50475, "J_D": 947.3524977065921, "W_D_1KI": 1.0139774044032446, "J_D_1KI": 0.015459800640410512}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64311, "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": 10.418502807617188, "TIME_S_1KI": 0.16200187849072767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1158.3267517518998, "W": 82.98, "J_1KI": 18.011331681234932, "W_1KI": 1.2902924849559174, "W_D": 66.6565, "J_D": 930.4652582327127, "W_D_1KI": 1.036471210212872, "J_D_1KI": 0.01611654631731542}
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 7b64a62..055aab1 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', '1000', '-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": 0.17682647705078125}
+{"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": 0.17745423316955566}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      3,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([ 3198, 22722, 88522,  ..., 47695, 53177, 56584]),
-       values=tensor([0.0931, 0.9110, 0.9063,  ..., 0.1473, 0.7899, 0.0419]),
+       col_indices=tensor([42546, 58983, 86183,  ..., 98460, 14991, 73616]),
+       values=tensor([0.4174, 0.2060, 0.0899,  ..., 0.6212, 0.4971, 0.7481]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4850, 0.3145, 0.7013,  ..., 0.1298, 0.2149, 0.6470])
+tensor([0.8074, 0.4851, 0.0283,  ..., 0.2070, 0.7576, 0.4733])
 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: 0.17682647705078125 seconds
+Time: 0.17745423316955566 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '59380', '-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": 9.506051540374756}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '59170', '-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": 9.660528182983398}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([45126, 76716, 27115,  ..., 82599, 76675, 53817]),
-       values=tensor([0.5870, 0.5895, 0.9992,  ..., 0.5279, 0.4372, 0.6677]),
+       col_indices=tensor([96712,  9860, 17593,  ..., 59712, 70511, 99970]),
+       values=tensor([0.7958, 0.9740, 0.0109,  ..., 0.7243, 0.7214, 0.8821]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8372, 0.3480, 0.3478,  ..., 0.9164, 0.0517, 0.0932])
+tensor([0.4741, 0.0741, 0.4151,  ..., 0.2722, 0.2577, 0.9729])
 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: 9.506051540374756 seconds
+Time: 9.660528182983398 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '65588', '-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": 10.838059663772583}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64311', '-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": 10.418502807617188}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([69179, 69629, 89362,  ..., 28216, 37414, 39020]),
-       values=tensor([0.6325, 0.8110, 0.8083,  ..., 0.4927, 0.7217, 0.7562]),
+       col_indices=tensor([60832, 83948,   658,  ..., 83631, 80017, 34658]),
+       values=tensor([0.5224, 0.7895, 0.2144,  ..., 0.4897, 0.2214, 0.9534]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6752, 0.8314, 0.5534,  ..., 0.1964, 0.0025, 0.5959])
+tensor([0.6371, 0.8407, 0.9472,  ..., 0.9476, 0.5347, 0.4303])
 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: 10.838059663772583 seconds
+Time: 10.418502807617188 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([69179, 69629, 89362,  ..., 28216, 37414, 39020]),
-       values=tensor([0.6325, 0.8110, 0.8083,  ..., 0.4927, 0.7217, 0.7562]),
+       col_indices=tensor([60832, 83948,   658,  ..., 83631, 80017, 34658]),
+       values=tensor([0.5224, 0.7895, 0.2144,  ..., 0.4897, 0.2214, 0.9534]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6752, 0.8314, 0.5534,  ..., 0.1964, 0.0025, 0.5959])
+tensor([0.6371, 0.8407, 0.9472,  ..., 0.9476, 0.5347, 0.4303])
 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: 10.838059663772583 seconds
+Time: 10.418502807617188 seconds
 
-[18.44, 17.92, 18.39, 18.06, 17.93, 17.86, 18.18, 18.13, 18.19, 17.89]
-[82.78]
-14.244884729385376
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65588, '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': 10.838059663772583, 'TIME_S_1KI': 0.16524455180479025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1179.1915578985215, 'W': 82.78}
-[18.44, 17.92, 18.39, 18.06, 17.93, 17.86, 18.18, 18.13, 18.19, 17.89, 18.4, 17.89, 17.79, 17.88, 18.16, 18.32, 18.28, 17.67, 18.23, 18.52]
-325.505
-16.27525
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65588, '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': 10.838059663772583, 'TIME_S_1KI': 0.16524455180479025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1179.1915578985215, 'W': 82.78, 'J_1KI': 17.97876986489177, 'W_1KI': 1.2621211197170215, 'W_D': 66.50475, 'J_D': 947.3524977065921, 'W_D_1KI': 1.0139774044032446, 'J_D_1KI': 0.015459800640410512}
+[18.9, 18.01, 18.86, 18.3, 17.96, 18.02, 18.19, 17.91, 18.92, 17.86]
+[82.98]
+13.959107637405396
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64311, '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': 10.418502807617188, 'TIME_S_1KI': 0.16200187849072767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.3267517518998, 'W': 82.98}
+[18.9, 18.01, 18.86, 18.3, 17.96, 18.02, 18.19, 17.91, 18.92, 17.86, 18.32, 17.96, 18.01, 17.83, 18.19, 17.85, 17.88, 18.01, 18.1, 17.86]
+326.47
+16.323500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64311, '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': 10.418502807617188, 'TIME_S_1KI': 0.16200187849072767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.3267517518998, 'W': 82.98, 'J_1KI': 18.011331681234932, 'W_1KI': 1.2902924849559174, 'W_D': 66.6565, 'J_D': 930.4652582327127, 'W_D_1KI': 1.036471210212872, 'J_D_1KI': 0.01611654631731542}
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 ff48609..c677488 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": 240931, "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": 10.228987216949463, "TIME_S_1KI": 0.04245608583764423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 972.0923814868927, "W": 74.28, "J_1KI": 4.034733519085932, "W_1KI": 0.30830403725547983, "W_D": 58.167500000000004, "J_D": 761.2302584832908, "W_D_1KI": 0.2414280437137604, "J_D_1KI": 0.0010020630127038877}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253635, "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": 10.510948419570923, "TIME_S_1KI": 0.04144123807664921, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.5495847654342, "W": 74.66, "J_1KI": 4.1380313630430905, "W_1KI": 0.29436000551974295, "W_D": 58.32449999999999, "J_D": 819.9096538528203, "W_D_1KI": 0.22995446212076406, "J_D_1KI": 0.0009066353702003433}
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 36a267f..d7cd9dc 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', '1000', '-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": 0.062392234802246094}
+{"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": 0.057019948959350586}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([7179, 9532, 8081,  ..., 4031, 8581, 2872]),
-       values=tensor([0.3998, 0.4929, 0.1773,  ..., 0.2243, 0.6349, 0.5923]),
+tensor(crow_indices=tensor([    0,     3,     5,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([5511, 5632, 9392,  ..., 1424, 5807, 9708]),
+       values=tensor([0.8862, 0.8794, 0.5579,  ..., 0.8535, 0.8536, 0.3017]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2436, 0.6971, 0.0487,  ..., 0.2986, 0.9140, 0.9941])
+tensor([0.8843, 0.1620, 0.1106,  ..., 0.3314, 0.8529, 0.5084])
 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: 0.062392234802246094 seconds
+Time: 0.057019948959350586 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '168290', '-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": 7.3342225551605225}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '184146', '-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": 7.623284816741943}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([7117, 7845, 2903,  ...,  807, 7859, 5458]),
-       values=tensor([0.8544, 0.9061, 0.0037,  ..., 0.6594, 0.1915, 0.6916]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([5228, 7612, 8334,  ..., 8947, 2750, 8241]),
+       values=tensor([0.5331, 0.8440, 0.9594,  ..., 0.6439, 0.5967, 0.7449]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0592, 0.4192, 0.0774,  ..., 0.7897, 0.5835, 0.6060])
+tensor([0.9017, 0.2905, 0.1618,  ..., 0.3745, 0.4560, 0.4176])
 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: 7.3342225551605225 seconds
+Time: 7.623284816741943 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '240931', '-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": 10.228987216949463}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253635', '-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": 10.510948419570923}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 10000, 10000]),
-       col_indices=tensor([1962,  399, 6914,  ..., 7707, 7379, 8204]),
-       values=tensor([0.6427, 0.2940, 0.2788,  ..., 0.7421, 0.9158, 0.7396]),
+tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([ 773, 7277, 5799,  ..., 6666, 7394, 1954]),
+       values=tensor([0.1024, 0.0437, 0.8987,  ..., 0.7237, 0.2930, 0.3597]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3387, 0.7040, 0.3501,  ..., 0.4098, 0.3396, 0.7875])
+tensor([0.1275, 0.0118, 0.1480,  ..., 0.4560, 0.1036, 0.8618])
 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: 10.228987216949463 seconds
+Time: 10.510948419570923 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 10000, 10000]),
-       col_indices=tensor([1962,  399, 6914,  ..., 7707, 7379, 8204]),
-       values=tensor([0.6427, 0.2940, 0.2788,  ..., 0.7421, 0.9158, 0.7396]),
+tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([ 773, 7277, 5799,  ..., 6666, 7394, 1954]),
+       values=tensor([0.1024, 0.0437, 0.8987,  ..., 0.7237, 0.2930, 0.3597]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3387, 0.7040, 0.3501,  ..., 0.4098, 0.3396, 0.7875])
+tensor([0.1275, 0.0118, 0.1480,  ..., 0.4560, 0.1036, 0.8618])
 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: 10.228987216949463 seconds
+Time: 10.510948419570923 seconds
 
-[18.32, 17.9, 17.75, 17.66, 17.81, 17.98, 17.81, 17.63, 17.93, 18.05]
-[74.28]
-13.086865663528442
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240931, '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': 10.228987216949463, 'TIME_S_1KI': 0.04245608583764423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 972.0923814868927, 'W': 74.28}
-[18.32, 17.9, 17.75, 17.66, 17.81, 17.98, 17.81, 17.63, 17.93, 18.05, 18.35, 17.85, 17.96, 17.83, 18.33, 17.85, 17.85, 17.82, 18.0, 17.86]
-322.25
-16.1125
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240931, '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': 10.228987216949463, 'TIME_S_1KI': 0.04245608583764423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 972.0923814868927, 'W': 74.28, 'J_1KI': 4.034733519085932, 'W_1KI': 0.30830403725547983, 'W_D': 58.167500000000004, 'J_D': 761.2302584832908, 'W_D_1KI': 0.2414280437137604, 'J_D_1KI': 0.0010020630127038877}
+[18.3, 17.87, 18.03, 17.87, 18.05, 17.86, 19.1, 17.97, 18.04, 17.74]
+[74.66]
+14.057722806930542
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253635, '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': 10.510948419570923, 'TIME_S_1KI': 0.04144123807664921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.5495847654342, 'W': 74.66}
+[18.3, 17.87, 18.03, 17.87, 18.05, 17.86, 19.1, 17.97, 18.04, 17.74, 18.05, 17.87, 18.1, 17.95, 17.96, 18.0, 19.85, 17.84, 18.3, 18.01]
+326.71000000000004
+16.335500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253635, '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': 10.510948419570923, 'TIME_S_1KI': 0.04144123807664921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.5495847654342, 'W': 74.66, 'J_1KI': 4.1380313630430905, 'W_1KI': 0.29436000551974295, 'W_D': 58.32449999999999, 'J_D': 819.9096538528203, 'W_D_1KI': 0.22995446212076406, 'J_D_1KI': 0.0009066353702003433}
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 4c2a534..8f37619 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": 201421, "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": 10.7230703830719, "TIME_S_1KI": 0.053237102303493176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1117.283116903305, "W": 79.97, "J_1KI": 5.547004120242204, "W_1KI": 0.3970291081863361, "W_D": 63.60725, "J_D": 888.6745846898556, "W_D_1KI": 0.31579254397505724, "J_D_1KI": 0.001567823335079546}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 197679, "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": 10.670233726501465, "TIME_S_1KI": 0.053977578430189674, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1114.4275546693802, "W": 79.73, "J_1KI": 5.637561676603889, "W_1KI": 0.40333065221900155, "W_D": 63.12950000000001, "J_D": 882.3937578389646, "W_D_1KI": 0.31935359851071693, "J_D_1KI": 0.001615516056387967}
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 1cf380c..4a4c171 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', '1000', '-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": 0.06886577606201172}
+{"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": 0.06928658485412598}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     19,  ...,  99984,  99991,
+tensor(crow_indices=tensor([     0,      8,     25,  ...,  99976,  99987,
                             100000]),
-       col_indices=tensor([1627, 2251, 2667,  ..., 7083, 9414, 9995]),
-       values=tensor([0.7763, 0.8562, 0.0227,  ..., 0.7081, 0.0734, 0.4206]),
+       col_indices=tensor([ 333,  360, 7030,  ..., 7825, 8274, 9549]),
+       values=tensor([0.8393, 0.7372, 0.2908,  ..., 0.1152, 0.3448, 0.5520]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6749, 0.4550, 0.5239,  ..., 0.7938, 0.7493, 0.7052])
+tensor([0.6596, 0.1551, 0.2351,  ..., 0.2147, 0.9669, 0.0099])
 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: 0.06886577606201172 seconds
+Time: 0.06928658485412598 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '152470', '-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": 7.948191404342651}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '151544', '-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": 8.049443006515503}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     22,  ...,  99977,  99992,
+tensor(crow_indices=tensor([     0,     10,     20,  ...,  99982,  99989,
                             100000]),
-       col_indices=tensor([ 579, 1179, 1463,  ..., 6326, 6539, 6627]),
-       values=tensor([0.4661, 0.6191, 0.1376,  ..., 0.4152, 0.1640, 0.4813]),
+       col_indices=tensor([ 534,  848, 1028,  ..., 7528, 7587, 7919]),
+       values=tensor([0.8744, 0.7231, 0.5055,  ..., 0.6485, 0.2326, 0.7897]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0160, 0.8279, 0.2510,  ..., 0.4302, 0.2870, 0.5452])
+tensor([0.6730, 0.3279, 0.8164,  ..., 0.2443, 0.5036, 0.1429])
 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: 7.948191404342651 seconds
+Time: 8.049443006515503 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '201421', '-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": 10.7230703830719}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '197679', '-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": 10.670233726501465}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     17,  ...,  99977,  99988,
+tensor(crow_indices=tensor([     0,     11,     18,  ...,  99979,  99990,
                             100000]),
-       col_indices=tensor([ 243, 1001, 2007,  ..., 7428, 8081, 8733]),
-       values=tensor([0.5597, 0.5588, 0.7631,  ..., 0.2707, 0.4657, 0.9680]),
+       col_indices=tensor([ 654,  920, 2120,  ..., 5173, 5860, 7868]),
+       values=tensor([0.9786, 0.8942, 0.8907,  ..., 0.0590, 0.7963, 0.5333]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1756, 0.9887, 0.2623,  ..., 0.3846, 0.9664, 0.0716])
+tensor([0.8342, 0.1347, 0.2067,  ..., 0.1241, 0.4408, 0.8118])
 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: 10.7230703830719 seconds
+Time: 10.670233726501465 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     17,  ...,  99977,  99988,
+tensor(crow_indices=tensor([     0,     11,     18,  ...,  99979,  99990,
                             100000]),
-       col_indices=tensor([ 243, 1001, 2007,  ..., 7428, 8081, 8733]),
-       values=tensor([0.5597, 0.5588, 0.7631,  ..., 0.2707, 0.4657, 0.9680]),
+       col_indices=tensor([ 654,  920, 2120,  ..., 5173, 5860, 7868]),
+       values=tensor([0.9786, 0.8942, 0.8907,  ..., 0.0590, 0.7963, 0.5333]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1756, 0.9887, 0.2623,  ..., 0.3846, 0.9664, 0.0716])
+tensor([0.8342, 0.1347, 0.2067,  ..., 0.1241, 0.4408, 0.8118])
 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: 10.7230703830719 seconds
+Time: 10.670233726501465 seconds
 
-[20.0, 17.95, 18.01, 18.57, 18.05, 17.91, 18.47, 18.3, 18.35, 18.45]
-[79.97]
-13.971278190612793
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 201421, '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': 10.7230703830719, 'TIME_S_1KI': 0.053237102303493176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1117.283116903305, 'W': 79.97}
-[20.0, 17.95, 18.01, 18.57, 18.05, 17.91, 18.47, 18.3, 18.35, 18.45, 18.1, 18.11, 18.32, 17.8, 18.31, 17.97, 17.94, 17.86, 17.95, 18.22]
-327.255
-16.36275
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 201421, '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': 10.7230703830719, 'TIME_S_1KI': 0.053237102303493176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1117.283116903305, 'W': 79.97, 'J_1KI': 5.547004120242204, 'W_1KI': 0.3970291081863361, 'W_D': 63.60725, 'J_D': 888.6745846898556, 'W_D_1KI': 0.31579254397505724, 'J_D_1KI': 0.001567823335079546}
+[18.34, 17.98, 18.08, 17.94, 18.13, 18.09, 21.2, 18.15, 17.85, 18.53]
+[79.73]
+13.977518558502197
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 197679, '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': 10.670233726501465, 'TIME_S_1KI': 0.053977578430189674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.4275546693802, 'W': 79.73}
+[18.34, 17.98, 18.08, 17.94, 18.13, 18.09, 21.2, 18.15, 17.85, 18.53, 18.16, 18.1, 18.61, 18.87, 18.17, 17.77, 20.46, 17.84, 18.3, 17.91]
+332.01
+16.6005
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 197679, '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': 10.670233726501465, 'TIME_S_1KI': 0.053977578430189674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.4275546693802, 'W': 79.73, 'J_1KI': 5.637561676603889, 'W_1KI': 0.40333065221900155, 'W_D': 63.12950000000001, 'J_D': 882.3937578389646, 'W_D_1KI': 0.31935359851071693, 'J_D_1KI': 0.001615516056387967}
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 7e50353..23a3327 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": 58758, "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": 10.521214962005615, "TIME_S_1KI": 0.1790601273359477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1248.3679735660553, "W": 87.25, "J_1KI": 21.245923509412425, "W_1KI": 1.484904183260152, "W_D": 70.41275, "J_D": 1007.4615705525875, "W_D_1KI": 1.1983517138091835, "J_D_1KI": 0.020394698829251906}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58160, "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": 10.467525959014893, "TIME_S_1KI": 0.17997809420589567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.377723479271, "W": 87.15, "J_1KI": 21.10346842295858, "W_1KI": 1.4984525447042643, "W_D": 70.98275000000001, "J_D": 999.6861285289527, "W_D_1KI": 1.2204736932599725, "J_D_1KI": 0.020984760888238866}
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 560bbb8..35e5d90 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', '1000', '-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": 0.19649839401245117}
+{"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": 0.19839882850646973}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     190,  ...,  999800,
-                             999902, 1000000]),
-       col_indices=tensor([  52,   87,  188,  ..., 9706, 9893, 9952]),
-       values=tensor([0.1675, 0.8959, 0.7675,  ..., 0.1378, 0.1178, 0.3486]),
+tensor(crow_indices=tensor([      0,      97,     186,  ...,  999796,
+                             999897, 1000000]),
+       col_indices=tensor([ 169,  359,  528,  ..., 9765, 9789, 9792]),
+       values=tensor([0.6521, 0.9085, 0.4727,  ..., 0.8814, 0.1698, 0.8627]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9304, 0.9814, 0.5110,  ..., 0.0040, 0.2898, 0.8662])
+tensor([0.7127, 0.9881, 0.6892,  ..., 0.7113, 0.3734, 0.9813])
 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: 0.19649839401245117 seconds
+Time: 0.19839882850646973 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53435', '-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": 9.548681497573853}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52923', '-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": 9.554424524307251}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      94,     197,  ...,  999809,
-                             999893, 1000000]),
-       col_indices=tensor([  61,  165,  222,  ..., 9905, 9907, 9919]),
-       values=tensor([0.6376, 0.5545, 0.9458,  ..., 0.6333, 0.2848, 0.3343]),
+tensor(crow_indices=tensor([      0,     112,     189,  ...,  999798,
+                             999899, 1000000]),
+       col_indices=tensor([ 113,  156,  184,  ..., 9769, 9838, 9941]),
+       values=tensor([0.0187, 0.7839, 0.6319,  ..., 0.9818, 0.7594, 0.0765]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2834, 0.7754, 0.6738,  ..., 0.4578, 0.3713, 0.7996])
+tensor([0.4252, 0.8416, 0.9146,  ..., 0.0970, 0.6595, 0.8304])
 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: 9.548681497573853 seconds
+Time: 9.554424524307251 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58758', '-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": 10.521214962005615}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58160', '-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": 10.467525959014893}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     219,  ...,  999802,
-                             999904, 1000000]),
-       col_indices=tensor([  63,  137,  260,  ..., 9828, 9873, 9905]),
-       values=tensor([0.1449, 0.8321, 0.3255,  ..., 0.3929, 0.1108, 0.3040]),
+tensor(crow_indices=tensor([      0,      93,     191,  ...,  999802,
+                             999899, 1000000]),
+       col_indices=tensor([  46,   78,  103,  ..., 9585, 9899, 9954]),
+       values=tensor([0.1947, 0.9409, 0.0413,  ..., 0.0261, 0.0318, 0.5135]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9048, 0.1055, 0.1608,  ..., 0.3713, 0.7919, 0.0232])
+tensor([0.1045, 0.5937, 0.6366,  ..., 0.8712, 0.6092, 0.3132])
 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: 10.521214962005615 seconds
+Time: 10.467525959014893 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     219,  ...,  999802,
-                             999904, 1000000]),
-       col_indices=tensor([  63,  137,  260,  ..., 9828, 9873, 9905]),
-       values=tensor([0.1449, 0.8321, 0.3255,  ..., 0.3929, 0.1108, 0.3040]),
+tensor(crow_indices=tensor([      0,      93,     191,  ...,  999802,
+                             999899, 1000000]),
+       col_indices=tensor([  46,   78,  103,  ..., 9585, 9899, 9954]),
+       values=tensor([0.1947, 0.9409, 0.0413,  ..., 0.0261, 0.0318, 0.5135]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9048, 0.1055, 0.1608,  ..., 0.3713, 0.7919, 0.0232])
+tensor([0.1045, 0.5937, 0.6366,  ..., 0.8712, 0.6092, 0.3132])
 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: 10.521214962005615 seconds
+Time: 10.467525959014893 seconds
 
-[21.83, 18.04, 18.3, 17.99, 18.07, 21.44, 19.02, 18.2, 18.32, 17.87]
-[87.25]
-14.307942390441895
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58758, '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': 10.521214962005615, 'TIME_S_1KI': 0.1790601273359477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.3679735660553, 'W': 87.25}
-[21.83, 18.04, 18.3, 17.99, 18.07, 21.44, 19.02, 18.2, 18.32, 17.87, 18.33, 21.51, 17.89, 18.53, 18.27, 17.81, 18.21, 18.47, 18.07, 19.18]
-336.745
-16.83725
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58758, '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': 10.521214962005615, 'TIME_S_1KI': 0.1790601273359477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.3679735660553, 'W': 87.25, 'J_1KI': 21.245923509412425, 'W_1KI': 1.484904183260152, 'W_D': 70.41275, 'J_D': 1007.4615705525875, 'W_D_1KI': 1.1983517138091835, 'J_D_1KI': 0.020394698829251906}
+[18.42, 18.1, 18.09, 17.94, 17.96, 18.13, 17.89, 17.87, 18.11, 18.12]
+[87.15]
+14.083508014678955
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58160, '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': 10.467525959014893, 'TIME_S_1KI': 0.17997809420589567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.377723479271, 'W': 87.15}
+[18.42, 18.1, 18.09, 17.94, 17.96, 18.13, 17.89, 17.87, 18.11, 18.12, 18.39, 17.75, 17.9, 17.92, 17.94, 17.89, 17.89, 17.89, 17.75, 17.72]
+323.34499999999997
+16.16725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58160, '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': 10.467525959014893, 'TIME_S_1KI': 0.17997809420589567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.377723479271, 'W': 87.15, 'J_1KI': 21.10346842295858, 'W_1KI': 1.4984525447042643, 'W_D': 70.98275000000001, 'J_D': 999.6861285289527, 'W_D_1KI': 1.2204736932599725, 'J_D_1KI': 0.020984760888238866}
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 c7df7b8..4e517f7 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": 8801, "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": 10.496035814285278, "TIME_S_1KI": 1.1925958202801135, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1351.109428257942, "W": 82.21, "J_1KI": 153.5177171069131, "W_1KI": 9.340983979093284, "W_D": 65.94874999999999, "J_D": 1083.858142644763, "W_D_1KI": 7.493324622202022, "J_D_1KI": 0.8514174096354984}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8810, "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": 10.41358470916748, "TIME_S_1KI": 1.18201869570573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1329.5588334417344, "W": 83.19, "J_1KI": 150.91473705354534, "W_1KI": 9.442678774120317, "W_D": 66.8505, "J_D": 1068.4177520735263, "W_D_1KI": 7.588024971623155, "J_D_1KI": 0.8612968185724353}
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 7df5a22..8074157 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', '1000', '-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": 1.1929755210876465}
+{"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": 1.1917307376861572}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     983,  ..., 4998996,
-                            4999523, 5000000]),
-       col_indices=tensor([  11,   47,  113,  ..., 9897, 9981, 9996]),
-       values=tensor([0.8953, 0.8081, 0.2668,  ..., 0.4279, 0.4927, 0.2076]),
+tensor(crow_indices=tensor([      0,     493,     986,  ..., 4999011,
+                            4999486, 5000000]),
+       col_indices=tensor([   9,   19,   72,  ..., 9981, 9987, 9993]),
+       values=tensor([0.5847, 0.5648, 0.9368,  ..., 0.4963, 0.0551, 0.2254]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3301, 0.9128, 0.0218,  ..., 0.3705, 0.4449, 0.9102])
+tensor([0.1357, 0.6996, 0.1280,  ..., 0.8014, 0.9186, 0.9128])
 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: 1.1929755210876465 seconds
+Time: 1.1917307376861572 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8801', '-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": 10.496035814285278}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8810', '-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": 10.41358470916748}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1022,  ..., 4998954,
-                            4999475, 5000000]),
-       col_indices=tensor([  16,   27,   72,  ..., 9970, 9971, 9996]),
-       values=tensor([0.8982, 0.6195, 0.1567,  ..., 0.8636, 0.4059, 0.3830]),
+tensor(crow_indices=tensor([      0,     520,    1021,  ..., 4999015,
+                            4999518, 5000000]),
+       col_indices=tensor([   2,   21,   23,  ..., 9856, 9947, 9960]),
+       values=tensor([0.9436, 0.1483, 0.1830,  ..., 0.0068, 0.4770, 0.7006]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3042, 0.6883, 0.8193,  ..., 0.9178, 0.9438, 0.4311])
+tensor([0.0991, 0.7135, 0.2277,  ..., 0.9430, 0.0011, 0.3680])
 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: 10.496035814285278 seconds
+Time: 10.41358470916748 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1022,  ..., 4998954,
-                            4999475, 5000000]),
-       col_indices=tensor([  16,   27,   72,  ..., 9970, 9971, 9996]),
-       values=tensor([0.8982, 0.6195, 0.1567,  ..., 0.8636, 0.4059, 0.3830]),
+tensor(crow_indices=tensor([      0,     520,    1021,  ..., 4999015,
+                            4999518, 5000000]),
+       col_indices=tensor([   2,   21,   23,  ..., 9856, 9947, 9960]),
+       values=tensor([0.9436, 0.1483, 0.1830,  ..., 0.0068, 0.4770, 0.7006]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3042, 0.6883, 0.8193,  ..., 0.9178, 0.9438, 0.4311])
+tensor([0.0991, 0.7135, 0.2277,  ..., 0.9430, 0.0011, 0.3680])
 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: 10.496035814285278 seconds
+Time: 10.41358470916748 seconds
 
-[18.39, 18.45, 17.88, 18.05, 18.11, 18.03, 17.97, 17.99, 18.04, 18.19]
-[82.21]
-16.434854984283447
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8801, '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': 10.496035814285278, 'TIME_S_1KI': 1.1925958202801135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.109428257942, 'W': 82.21}
-[18.39, 18.45, 17.88, 18.05, 18.11, 18.03, 17.97, 17.99, 18.04, 18.19, 18.32, 17.88, 17.95, 17.82, 18.37, 18.15, 18.04, 17.93, 18.22, 17.79]
-325.225
-16.26125
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8801, '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': 10.496035814285278, 'TIME_S_1KI': 1.1925958202801135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.109428257942, 'W': 82.21, 'J_1KI': 153.5177171069131, 'W_1KI': 9.340983979093284, 'W_D': 65.94874999999999, 'J_D': 1083.858142644763, 'W_D_1KI': 7.493324622202022, 'J_D_1KI': 0.8514174096354984}
+[18.42, 18.1, 18.34, 18.2, 17.91, 17.99, 18.73, 17.98, 18.05, 17.95]
+[83.19]
+15.982195377349854
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8810, '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': 10.41358470916748, 'TIME_S_1KI': 1.18201869570573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1329.5588334417344, 'W': 83.19}
+[18.42, 18.1, 18.34, 18.2, 17.91, 17.99, 18.73, 17.98, 18.05, 17.95, 18.53, 18.25, 18.01, 18.36, 18.13, 18.03, 18.01, 18.04, 18.22, 17.98]
+326.79
+16.3395
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8810, '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': 10.41358470916748, 'TIME_S_1KI': 1.18201869570573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1329.5588334417344, 'W': 83.19, 'J_1KI': 150.91473705354534, 'W_1KI': 9.442678774120317, 'W_D': 66.8505, 'J_D': 1068.4177520735263, 'W_D_1KI': 7.588024971623155, 'J_D_1KI': 0.8612968185724353}
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
new file mode 100644
index 0000000..3f0bb83
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2918, "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": 10.42000699043274, "TIME_S_1KI": 3.5709413949392523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1397.7840516352655, "W": 79.43, "J_1KI": 479.0212651251767, "W_1KI": 27.220699108978753, "W_D": 63.01475000000001, "J_D": 1108.913666974485, "W_D_1KI": 21.595185058259084, "J_D_1KI": 7.400680280417781}
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
new file mode 100644
index 0000000..099bdad
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 3.597771644592285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      977,     1956,  ...,  9997922,
+                             9998976, 10000000]),
+       col_indices=tensor([   2,    3,    9,  ..., 9970, 9977, 9979]),
+       values=tensor([0.1332, 0.2138, 0.7669,  ..., 0.0474, 0.1604, 0.1097]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2601, 0.5133, 0.4344,  ..., 0.1772, 0.3859, 0.7315])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 3.597771644592285 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2918', '-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": 10.42000699043274}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1029,     2018,  ...,  9998096,
+                             9999045, 10000000]),
+       col_indices=tensor([   7,   14,   18,  ..., 9941, 9949, 9980]),
+       values=tensor([0.9805, 0.4931, 0.0315,  ..., 0.9071, 0.5605, 0.7269]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0123, 0.2996, 0.0215,  ..., 0.5909, 0.6219, 0.0073])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.42000699043274 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1029,     2018,  ...,  9998096,
+                             9999045, 10000000]),
+       col_indices=tensor([   7,   14,   18,  ..., 9941, 9949, 9980]),
+       values=tensor([0.9805, 0.4931, 0.0315,  ..., 0.9071, 0.5605, 0.7269]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0123, 0.2996, 0.0215,  ..., 0.5909, 0.6219, 0.0073])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.42000699043274 seconds
+
+[21.55, 18.44, 17.92, 18.49, 18.01, 17.89, 18.03, 17.94, 18.06, 18.03]
+[79.43]
+17.597684144973755
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2918, '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': 10.42000699043274, 'TIME_S_1KI': 3.5709413949392523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.7840516352655, 'W': 79.43}
+[21.55, 18.44, 17.92, 18.49, 18.01, 17.89, 18.03, 17.94, 18.06, 18.03, 19.32, 18.84, 17.98, 18.09, 18.0, 17.91, 18.09, 18.07, 18.13, 17.93]
+328.30499999999995
+16.415249999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2918, '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': 10.42000699043274, 'TIME_S_1KI': 3.5709413949392523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.7840516352655, 'W': 79.43, 'J_1KI': 479.0212651251767, 'W_1KI': 27.220699108978753, 'W_D': 63.01475000000001, 'J_D': 1108.913666974485, 'W_D_1KI': 21.595185058259084, 'J_D_1KI': 7.400680280417781}
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 e2fe041..114ef88 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": 282031, "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": 10.372447967529297, "TIME_S_1KI": 0.036777687444037345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1003.1455927085877, "W": 73.48, "J_1KI": 3.5568628721969846, "W_1KI": 0.26053873510358794, "W_D": 57.203, "J_D": 780.9327346177101, "W_D_1KI": 0.20282522134091643, "J_D_1KI": 0.0007191593170286829}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 286411, "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": 10.634347915649414, "TIME_S_1KI": 0.03712967698743908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1001.8400530457496, "W": 73.11, "J_1KI": 3.4979105308306933, "W_1KI": 0.25526254229062434, "W_D": 56.78875, "J_D": 778.186900730431, "W_D_1KI": 0.19827712622769378, "J_D_1KI": 0.0006922818125969107}
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 e246539..b127831 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
@@ -1,373 +1,373 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.05941200256347656}
+{"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": 0.05389976501464844}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      6.5525e-02, 4.2302e-01, 1.5246e-01, 4.1570e-01,
+                      7.6132e-01, 6.3810e-01, 3.4472e-01, 9.2114e-01,
+                      8.9617e-01, 1.1069e-01, 1.8827e-01, 3.5004e-01,
+                      4.2144e-01, 7.2994e-01, 2.8003e-01, 9.9526e-01,
+                      1.3122e-01, 7.8430e-01, 8.2154e-01, 8.5472e-01,
+                      9.9915e-01, 1.0175e-01, 9.5443e-01, 2.0256e-01,
+                      9.9243e-01, 2.9673e-01, 1.4011e-01, 6.3467e-01,
+                      6.9318e-01, 9.8979e-01, 3.2336e-01, 9.6873e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.9856, 0.2722, 0.4021,  ..., 0.8237, 0.4550, 0.5626])
+tensor([0.7118, 0.5445, 0.7766,  ..., 0.3375, 0.1794, 0.0442])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,271 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.05941200256347656 seconds
+Time: 0.05389976501464844 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '176731', '-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": 6.579680919647217}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '194806', '-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": 7.141697645187378}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+                      8.2203e-01, 4.5287e-01, 5.9015e-01, 6.0898e-01,
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+                      6.7460e-01, 3.0153e-01, 5.3121e-01, 2.6773e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.6331, 0.4592, 0.8230,  ..., 0.6920, 0.8755, 0.3375])
+tensor([0.1721, 0.8059, 0.1299,  ..., 0.7732, 0.0077, 0.2449])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -647,378 +754,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 6.579680919647217 seconds
+Time: 7.141697645187378 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '282031', '-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": 10.372447967529297}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '286411', '-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": 10.634347915649414}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 641, 1820, 5085, 7616, 6287, 2843, 3768,  139,  557,
-                           8943, 8505, 7281,   43, 6135, 7303, 4893,  489,  615,
-                           6714, 3680,  855, 4855,  479, 9230, 4436, 9603, 1635,
-                           9190, 9268, 3004, 1125, 8864,  107, 6184, 8970, 8700,
-                           7610, 2464, 2526, 7595, 3071, 5215, 1177, 6775, 4184,
-                           7851, 6577, 5571, 7909, 9344,  735, 6183, 9381, 8186,
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+                      2.0056e-01, 9.8477e-01, 3.5288e-01, 1.5954e-01,
+                      9.1449e-01, 9.5312e-01, 6.0952e-01, 7.7001e-01,
+                      6.5414e-01, 3.7977e-01, 5.5246e-01, 8.1022e-01,
+                      4.6688e-01, 8.6118e-01, 7.9898e-01, 6.4956e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3038, 0.6445, 0.5741,  ..., 0.8215, 0.9151, 0.6540])
+tensor([0.7123, 0.9016, 0.3604,  ..., 0.7264, 0.3786, 0.3585])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,375 +1133,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.372447967529297 seconds
+Time: 10.634347915649414 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+                      9.0083e-01, 7.3734e-01, 3.2326e-02, 8.2624e-01,
+                      9.9258e-01, 9.0930e-01, 6.7614e-01, 5.4796e-02,
+                      8.5340e-01, 9.2531e-01, 7.6230e-01, 6.7267e-01,
+                      7.9031e-01, 4.7174e-01, 4.1194e-01, 1.8776e-01,
+                      6.0910e-01, 5.2252e-01, 2.1673e-01, 1.8473e-01,
+                      3.5143e-01, 7.0776e-01, 1.2603e-01, 5.0802e-01,
+                      4.5209e-01, 4.3706e-01, 7.6797e-02, 6.1164e-01,
+                      7.8143e-01, 1.3925e-01, 5.7531e-02, 8.7507e-01,
+                      5.7001e-01, 4.3906e-01, 9.2820e-01, 7.6700e-01,
+                      2.9968e-01, 5.9688e-01, 3.1468e-01, 4.2212e-01,
+                      7.9895e-02, 2.9011e-01, 1.8494e-01, 9.2888e-01,
+                      8.9319e-01, 8.2784e-01, 1.7488e-02, 8.1506e-01,
+                      5.8942e-01, 2.6648e-01, 7.2461e-01, 4.3524e-01,
+                      3.3783e-02, 3.0604e-01, 5.7524e-01, 1.2624e-01,
+                      1.8255e-01, 3.5454e-01, 3.6503e-01, 4.9452e-01,
+                      4.3380e-01, 1.0044e-01, 1.5676e-01, 7.4210e-01,
+                      7.7909e-01, 1.5953e-01, 9.7173e-01, 9.8386e-01,
+                      6.0732e-01, 4.4764e-01, 9.7740e-02, 2.5615e-01,
+                      4.6915e-01, 2.7954e-01, 5.9480e-01, 5.5372e-01,
+                      4.4142e-01, 7.4870e-01, 7.8726e-01, 5.3797e-01,
+                      7.5394e-01, 3.8676e-01, 9.0069e-01, 7.9215e-02,
+                      2.6986e-01, 7.4047e-01, 6.0732e-01, 3.5085e-01,
+                      9.1826e-01, 9.4896e-01, 6.8926e-03, 1.0309e-01,
+                      2.7614e-01, 5.7639e-01, 6.1056e-01, 6.5885e-01,
+                      7.8603e-01, 4.6450e-01, 7.5119e-01, 7.6419e-01,
+                      5.8530e-02, 5.5102e-01, 4.6821e-01, 4.6438e-01,
+                      3.5602e-01, 8.8841e-01, 5.1596e-01, 9.1532e-01,
+                      6.0969e-01, 4.8093e-01, 4.6162e-01, 9.1442e-03,
+                      8.7271e-01, 3.3897e-01, 1.0331e-01, 7.9221e-01,
+                      5.8692e-01, 6.9774e-01, 3.0172e-01, 1.0667e-01,
+                      1.0951e-02, 1.3612e-01, 7.8502e-02, 4.6719e-01,
+                      8.6153e-01, 2.2920e-01, 8.7337e-01, 2.1362e-02,
+                      8.6720e-01, 9.6225e-01, 3.2705e-01, 7.9640e-01,
+                      3.6238e-01, 3.5585e-01, 1.3643e-01, 3.7646e-01,
+                      5.3997e-01, 7.8911e-02, 9.4754e-01, 5.9488e-01,
+                      4.3521e-02, 6.4602e-01, 3.7163e-02, 1.4246e-01,
+                      9.2557e-01, 3.9489e-01, 9.4390e-01, 1.2061e-01,
+                      7.9882e-01, 5.0438e-01, 5.2005e-01, 9.3905e-01,
+                      4.3100e-01, 8.5312e-01, 7.6276e-01, 8.8805e-01,
+                      6.9230e-02, 7.5638e-01, 2.7686e-02, 6.4170e-01,
+                      6.8542e-01, 9.3072e-01, 9.1971e-02, 9.7074e-01,
+                      4.9244e-01, 9.7479e-01, 3.9805e-01, 6.5312e-01,
+                      2.7671e-01, 7.9289e-01, 4.7310e-01, 7.6491e-01,
+                      2.0056e-01, 9.8477e-01, 3.5288e-01, 1.5954e-01,
+                      9.1449e-01, 9.5312e-01, 6.0952e-01, 7.7001e-01,
+                      6.5414e-01, 3.7977e-01, 5.5246e-01, 8.1022e-01,
+                      4.6688e-01, 8.6118e-01, 7.9898e-01, 6.4956e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3038, 0.6445, 0.5741,  ..., 0.8215, 0.9151, 0.6540])
+tensor([0.7123, 0.9016, 0.3604,  ..., 0.7264, 0.3786, 0.3585])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1402,13 +1509,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.372447967529297 seconds
+Time: 10.634347915649414 seconds
 
-[18.31, 17.96, 18.02, 17.94, 18.39, 17.81, 17.97, 18.58, 17.92, 17.98]
-[73.48]
-13.651954174041748
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 282031, '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': 10.372447967529297, 'TIME_S_1KI': 0.036777687444037345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1003.1455927085877, 'W': 73.48}
-[18.31, 17.96, 18.02, 17.94, 18.39, 17.81, 17.97, 18.58, 17.92, 17.98, 18.3, 18.16, 17.88, 17.97, 17.91, 17.97, 18.81, 17.78, 18.15, 18.05]
-325.53999999999996
-16.276999999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 282031, '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': 10.372447967529297, 'TIME_S_1KI': 0.036777687444037345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1003.1455927085877, 'W': 73.48, 'J_1KI': 3.5568628721969846, 'W_1KI': 0.26053873510358794, 'W_D': 57.203, 'J_D': 780.9327346177101, 'W_D_1KI': 0.20282522134091643, 'J_D_1KI': 0.0007191593170286829}
+[18.41, 17.8, 19.45, 17.82, 18.17, 17.94, 18.34, 18.13, 18.06, 17.93]
+[73.11]
+13.703187704086304
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286411, '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': 10.634347915649414, 'TIME_S_1KI': 0.03712967698743908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1001.8400530457496, 'W': 73.11}
+[18.41, 17.8, 19.45, 17.82, 18.17, 17.94, 18.34, 18.13, 18.06, 17.93, 18.35, 18.13, 18.05, 17.73, 17.88, 18.02, 18.72, 17.91, 17.93, 18.0]
+326.42499999999995
+16.32125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286411, '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': 10.634347915649414, 'TIME_S_1KI': 0.03712967698743908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1001.8400530457496, 'W': 73.11, 'J_1KI': 3.4979105308306933, 'W_1KI': 0.25526254229062434, 'W_D': 56.78875, 'J_D': 778.186900730431, 'W_D_1KI': 0.19827712622769378, 'J_D_1KI': 0.0006922818125969107}
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 49601d0..df20cd2 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": 8372, "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": 10.861924409866333, "TIME_S_1KI": 1.297410942411172, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1310.9924754476547, "W": 87.74, "J_1KI": 156.5925078174456, "W_1KI": 10.480172001911132, "W_D": 71.52799999999999, "J_D": 1068.7562090702056, "W_D_1KI": 8.543717152412803, "J_D_1KI": 1.0205108877702822}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8417, "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": 10.80616807937622, "TIME_S_1KI": 1.2838503123887632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1304.2690537071228, "W": 87.74, "J_1KI": 154.95652295439263, "W_1KI": 10.424141618153737, "W_D": 71.23675, "J_D": 1058.9456178672315, "W_D_1KI": 8.463437091600332, "J_D_1KI": 1.005517059712526}
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 5b5d205..2e9f271 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
@@ -1,36 +1,15 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 1.2540500164031982}
+{"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": 1.2474722862243652}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499994,
-                            2499996, 2500000]),
-       col_indices=tensor([ 37595, 347043, 415637,  ..., 145391, 181131,
-                           323148]),
-       values=tensor([0.9304, 0.5156, 0.8153,  ..., 0.0582, 0.6116, 0.3872]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4551, 0.3395, 0.9990,  ..., 0.2154, 0.7020, 0.1344])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 1.2540500164031982 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8372', '-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": 10.861924409866333}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
+tensor(crow_indices=tensor([      0,       6,      15,  ..., 2499985,
                             2499994, 2500000]),
-       col_indices=tensor([140767, 212572, 418184,  ..., 257460, 329048,
-                           350732]),
-       values=tensor([0.1302, 0.7593, 0.7287,  ..., 0.1348, 0.8551, 0.2122]),
+       col_indices=tensor([131168, 178693, 230148,  ..., 341937, 350836,
+                           404119]),
+       values=tensor([0.5017, 0.1065, 0.8260,  ..., 0.9970, 0.9497, 0.3007]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3362, 0.7821, 0.5665,  ..., 0.5113, 0.4644, 0.7174])
+tensor([0.0502, 0.1581, 0.5974,  ..., 0.5502, 0.6695, 0.7013])
 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: 10.861924409866333 seconds
+Time: 1.2474722862243652 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8417', '-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": 10.80616807937622}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
-                            2499994, 2500000]),
-       col_indices=tensor([140767, 212572, 418184,  ..., 257460, 329048,
-                           350732]),
-       values=tensor([0.1302, 0.7593, 0.7287,  ..., 0.1348, 0.8551, 0.2122]),
+tensor(crow_indices=tensor([      0,       8,      12,  ..., 2499995,
+                            2499999, 2500000]),
+       col_indices=tensor([108465, 113027, 118372,  ..., 354925, 391668,
+                            96483]),
+       values=tensor([0.8038, 0.4194, 0.3623,  ..., 0.9532, 0.5964, 0.0297]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3362, 0.7821, 0.5665,  ..., 0.5113, 0.4644, 0.7174])
+tensor([0.4181, 0.0420, 0.6704,  ..., 0.4969, 0.1289, 0.9173])
 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: 10.861924409866333 seconds
+Time: 10.80616807937622 seconds
 
-[18.33, 17.76, 18.07, 18.0, 18.02, 17.78, 17.96, 17.97, 18.01, 17.77]
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499995,
+                            2499999, 2500000]),
+       col_indices=tensor([108465, 113027, 118372,  ..., 354925, 391668,
+                            96483]),
+       values=tensor([0.8038, 0.4194, 0.3623,  ..., 0.9532, 0.5964, 0.0297]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4181, 0.0420, 0.6704,  ..., 0.4969, 0.1289, 0.9173])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.80616807937622 seconds
+
+[18.27, 18.7, 18.19, 18.06, 20.48, 17.92, 18.42, 18.1, 18.5, 17.88]
 [87.74]
-14.941787958145142
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8372, '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': 10.861924409866333, 'TIME_S_1KI': 1.297410942411172, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1310.9924754476547, 'W': 87.74}
-[18.33, 17.76, 18.07, 18.0, 18.02, 17.78, 17.96, 17.97, 18.01, 17.77, 18.44, 18.07, 17.99, 17.91, 18.26, 18.15, 17.93, 17.77, 18.22, 18.2]
-324.24
-16.212
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8372, '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': 10.861924409866333, 'TIME_S_1KI': 1.297410942411172, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1310.9924754476547, 'W': 87.74, 'J_1KI': 156.5925078174456, 'W_1KI': 10.480172001911132, 'W_D': 71.52799999999999, 'J_D': 1068.7562090702056, 'W_D_1KI': 8.543717152412803, 'J_D_1KI': 1.0205108877702822}
+14.865159034729004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8417, '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': 10.80616807937622, 'TIME_S_1KI': 1.2838503123887632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1304.2690537071228, 'W': 87.74}
+[18.27, 18.7, 18.19, 18.06, 20.48, 17.92, 18.42, 18.1, 18.5, 17.88, 18.52, 18.21, 18.12, 18.29, 18.48, 18.28, 17.96, 17.98, 18.11, 17.86]
+330.06500000000005
+16.50325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8417, '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': 10.80616807937622, 'TIME_S_1KI': 1.2838503123887632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1304.2690537071228, 'W': 87.74, 'J_1KI': 154.95652295439263, 'W_1KI': 10.424141618153737, 'W_D': 71.23675, 'J_D': 1058.9456178672315, 'W_D_1KI': 8.463437091600332, 'J_D_1KI': 1.005517059712526}
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 bafcc4c..3576579 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": 78280, "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": 10.048285484313965, "TIME_S_1KI": 0.12836338125081712, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1146.701253862381, "W": 82.44, "J_1KI": 14.648713002840841, "W_1KI": 1.053142565150741, "W_D": 66.134, "J_D": 919.8925366683006, "W_D_1KI": 0.8448390393459376, "J_D_1KI": 0.010792527329406458}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 79200, "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": 10.557747602462769, "TIME_S_1KI": 0.1333048939704895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1194.9571797966958, "W": 76.09, "J_1KI": 15.087843179251209, "W_1KI": 0.9607323232323233, "W_D": 59.703, "J_D": 937.6071560704709, "W_D_1KI": 0.7538257575757576, "J_D_1KI": 0.009518001989592899}
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 d22c5fa..9da61ce 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', '1000', '-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": 0.1482532024383545}
+{"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": 0.14916634559631348}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      8,  ..., 249991, 249997,
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 249992, 249995,
                             250000]),
-       col_indices=tensor([11188, 48325,  9835,  ..., 16403, 16442, 24121]),
-       values=tensor([0.5273, 0.3289, 0.0892,  ..., 0.0153, 0.8132, 0.4919]),
+       col_indices=tensor([14210, 18192, 24309,  ..., 18863, 37423, 45495]),
+       values=tensor([0.9647, 0.6185, 0.9345,  ..., 0.6478, 0.4104, 0.2751]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4620, 0.4376, 0.8938,  ..., 0.9801, 0.7388, 0.7080])
+tensor([0.7636, 0.2305, 0.9236,  ..., 0.5850, 0.9097, 0.3088])
 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: 0.1482532024383545 seconds
+Time: 0.14916634559631348 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70824', '-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": 9.499845743179321}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70391', '-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": 9.332123041152954}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     12,  ..., 249991, 249995,
+tensor(crow_indices=tensor([     0,      9,     13,  ..., 249990, 249995,
                             250000]),
-       col_indices=tensor([ 9700, 17110, 17880,  ..., 40636, 42079, 45237]),
-       values=tensor([0.5791, 0.9394, 0.7161,  ..., 0.4792, 0.4698, 0.8140]),
+       col_indices=tensor([ 8823, 10157, 22008,  ..., 15217, 25723, 27383]),
+       values=tensor([0.1165, 0.9082, 0.4420,  ..., 0.1019, 0.9218, 0.7818]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2158, 0.6632, 0.3616,  ..., 0.9096, 0.8324, 0.6259])
+tensor([0.6996, 0.2341, 0.0689,  ..., 0.7606, 0.0770, 0.0289])
 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: 9.499845743179321 seconds
+Time: 9.332123041152954 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78280', '-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": 10.048285484313965}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '79200', '-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": 10.557747602462769}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249995, 249997,
                             250000]),
-       col_indices=tensor([ 1806, 10529, 23120,  ..., 17166, 35800, 40447]),
-       values=tensor([0.3161, 0.7150, 0.6424,  ..., 0.5169, 0.8858, 0.3422]),
+       col_indices=tensor([ 4540,  7121,  8304,  ...,  4489, 19051, 41158]),
+       values=tensor([0.2192, 0.6581, 0.9045,  ..., 0.0804, 0.2632, 0.1591]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5244, 0.0456, 0.6715,  ..., 0.9006, 0.5240, 0.6616])
+tensor([0.8844, 0.7148, 0.4526,  ..., 0.9882, 0.2475, 0.5582])
 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: 10.048285484313965 seconds
+Time: 10.557747602462769 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249995, 249997,
                             250000]),
-       col_indices=tensor([ 1806, 10529, 23120,  ..., 17166, 35800, 40447]),
-       values=tensor([0.3161, 0.7150, 0.6424,  ..., 0.5169, 0.8858, 0.3422]),
+       col_indices=tensor([ 4540,  7121,  8304,  ...,  4489, 19051, 41158]),
+       values=tensor([0.2192, 0.6581, 0.9045,  ..., 0.0804, 0.2632, 0.1591]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5244, 0.0456, 0.6715,  ..., 0.9006, 0.5240, 0.6616])
+tensor([0.8844, 0.7148, 0.4526,  ..., 0.9882, 0.2475, 0.5582])
 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: 10.048285484313965 seconds
+Time: 10.557747602462769 seconds
 
-[18.31, 18.07, 18.04, 17.95, 17.89, 18.22, 18.08, 17.87, 18.03, 18.16]
-[82.44]
-13.909525156021118
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78280, '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': 10.048285484313965, 'TIME_S_1KI': 0.12836338125081712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1146.701253862381, 'W': 82.44}
-[18.31, 18.07, 18.04, 17.95, 17.89, 18.22, 18.08, 17.87, 18.03, 18.16, 18.33, 19.12, 18.1, 18.07, 17.96, 17.95, 18.08, 18.08, 18.21, 18.0]
-326.12
-16.306
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78280, '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': 10.048285484313965, 'TIME_S_1KI': 0.12836338125081712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1146.701253862381, 'W': 82.44, 'J_1KI': 14.648713002840841, 'W_1KI': 1.053142565150741, 'W_D': 66.134, 'J_D': 919.8925366683006, 'W_D_1KI': 0.8448390393459376, 'J_D_1KI': 0.010792527329406458}
+[18.33, 17.85, 17.91, 18.15, 17.99, 17.93, 17.79, 17.99, 18.26, 17.87]
+[76.09]
+15.70452332496643
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 79200, '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': 10.557747602462769, 'TIME_S_1KI': 0.1333048939704895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1194.9571797966958, 'W': 76.09}
+[18.33, 17.85, 17.91, 18.15, 17.99, 17.93, 17.79, 17.99, 18.26, 17.87, 18.24, 18.68, 17.78, 17.91, 20.54, 18.31, 17.97, 18.31, 18.26, 17.78]
+327.74
+16.387
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 79200, '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': 10.557747602462769, 'TIME_S_1KI': 0.1333048939704895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1194.9571797966958, 'W': 76.09, 'J_1KI': 15.087843179251209, 'W_1KI': 0.9607323232323233, 'W_D': 59.703, 'J_D': 937.6071560704709, 'W_D_1KI': 0.7538257575757576, 'J_D_1KI': 0.009518001989592899}
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 9c50222..fcc4d9e 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": 17475, "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": 10.743166208267212, "TIME_S_1KI": 0.6147734597005557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.2754010248184, "W": 87.46, "J_1KI": 74.12162523747172, "W_1KI": 5.004864091559369, "W_D": 70.91274999999999, "J_D": 1050.2119905559418, "W_D_1KI": 4.057954220314734, "J_D_1KI": 0.2322148337805284}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17543, "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": 10.699259996414185, "TIME_S_1KI": 0.6098877042931189, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.7915307188034, "W": 87.71, "J_1KI": 74.14875053974825, "W_1KI": 4.9997149860343155, "W_D": 71.40625, "J_D": 1058.997209444642, "W_D_1KI": 4.070355697429174, "J_D_1KI": 0.2320216438140098}
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 95a3055..4d40449 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
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.6008265018463135}
+{"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": 0.5985264778137207}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      90,  ..., 2499902,
-                            2499947, 2500000]),
-       col_indices=tensor([ 1236,  2335,  2455,  ..., 44227, 44372, 44789]),
-       values=tensor([0.4453, 0.9405, 0.8001,  ..., 0.3243, 0.3638, 0.0708]),
+tensor(crow_indices=tensor([      0,      56,     105,  ..., 2499904,
+                            2499950, 2500000]),
+       col_indices=tensor([  106,  3863,  5117,  ..., 48831, 49457, 49843]),
+       values=tensor([0.6065, 0.7453, 0.1054,  ..., 0.0788, 0.7875, 0.5947]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5116, 0.1335, 0.5143,  ..., 0.8718, 0.6117, 0.3765])
+tensor([0.1569, 0.4932, 0.6676,  ..., 0.2477, 0.5860, 0.5432])
 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: 0.6008265018463135 seconds
+Time: 0.5985264778137207 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17475', '-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": 10.743166208267212}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17543', '-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": 10.699259996414185}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     107,  ..., 2499873,
+tensor(crow_indices=tensor([      0,      54,      99,  ..., 2499881,
                             2499945, 2500000]),
-       col_indices=tensor([ 1803,  2168,  2288,  ..., 48770, 49205, 49605]),
-       values=tensor([0.1814, 0.9281, 0.5481,  ..., 0.9692, 0.2397, 0.8106]),
+       col_indices=tensor([ 1025,  3202,  3517,  ..., 49482, 49487, 49789]),
+       values=tensor([0.3859, 0.1414, 0.1100,  ..., 0.9363, 0.6699, 0.1002]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8810, 0.5797, 0.1795,  ..., 0.7146, 0.8135, 0.6945])
+tensor([0.4003, 0.0598, 0.2302,  ..., 0.6994, 0.7206, 0.2744])
 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: 10.743166208267212 seconds
+Time: 10.699259996414185 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     107,  ..., 2499873,
+tensor(crow_indices=tensor([      0,      54,      99,  ..., 2499881,
                             2499945, 2500000]),
-       col_indices=tensor([ 1803,  2168,  2288,  ..., 48770, 49205, 49605]),
-       values=tensor([0.1814, 0.9281, 0.5481,  ..., 0.9692, 0.2397, 0.8106]),
+       col_indices=tensor([ 1025,  3202,  3517,  ..., 49482, 49487, 49789]),
+       values=tensor([0.3859, 0.1414, 0.1100,  ..., 0.9363, 0.6699, 0.1002]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8810, 0.5797, 0.1795,  ..., 0.7146, 0.8135, 0.6945])
+tensor([0.4003, 0.0598, 0.2302,  ..., 0.6994, 0.7206, 0.2744])
 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: 10.743166208267212 seconds
+Time: 10.699259996414185 seconds
 
-[18.24, 17.93, 17.9, 17.93, 18.17, 17.82, 18.06, 21.0, 18.11, 19.61]
-[87.46]
-14.809917688369751
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17475, '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': 10.743166208267212, 'TIME_S_1KI': 0.6147734597005557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2754010248184, 'W': 87.46}
-[18.24, 17.93, 17.9, 17.93, 18.17, 17.82, 18.06, 21.0, 18.11, 19.61, 18.37, 18.2, 17.93, 17.83, 17.81, 18.07, 18.0, 17.85, 21.15, 18.15]
-330.94500000000005
-16.547250000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17475, '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': 10.743166208267212, 'TIME_S_1KI': 0.6147734597005557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2754010248184, 'W': 87.46, 'J_1KI': 74.12162523747172, 'W_1KI': 5.004864091559369, 'W_D': 70.91274999999999, 'J_D': 1050.2119905559418, 'W_D_1KI': 4.057954220314734, 'J_D_1KI': 0.2322148337805284}
+[18.49, 17.83, 18.02, 18.17, 18.01, 17.87, 18.35, 18.02, 18.01, 17.87]
+[87.71]
+14.83059549331665
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17543, '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': 10.699259996414185, 'TIME_S_1KI': 0.6098877042931189, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.7915307188034, 'W': 87.71}
+[18.49, 17.83, 18.02, 18.17, 18.01, 17.87, 18.35, 18.02, 18.01, 17.87, 18.45, 18.41, 18.09, 17.88, 18.15, 18.34, 17.89, 18.68, 17.87, 18.16]
+326.075
+16.30375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17543, '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': 10.699259996414185, 'TIME_S_1KI': 0.6098877042931189, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.7915307188034, 'W': 87.71, 'J_1KI': 74.14875053974825, 'W_1KI': 4.9997149860343155, 'W_D': 71.40625, 'J_D': 1058.997209444642, 'W_D_1KI': 4.070355697429174, 'J_D_1KI': 0.2320216438140098}
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 598f4f1..8166250 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": 112560, "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": 10.602921962738037, "TIME_S_1KI": 0.0941979563143038, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1061.1344814062118, "W": 76.1, "J_1KI": 9.427278619458171, "W_1KI": 0.6760838663823738, "W_D": 59.91175, "J_D": 835.4063569827675, "W_D_1KI": 0.532265014214641, "J_D_1KI": 0.004728722585417919}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 109532, "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": 10.03889274597168, "TIME_S_1KI": 0.09165260148606508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1026.1644780921936, "W": 75.74, "J_1KI": 9.368627233066078, "W_1KI": 0.6914874192016944, "W_D": 59.13049999999999, "J_D": 801.1304287276266, "W_D_1KI": 0.539846802760837, "J_D_1KI": 0.004928667446598592}
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 fbbef6c..709a214 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', '1000', '-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": 0.10953974723815918}
+{"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": 0.12936139106750488}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([20679, 38088, 21453,  ..., 14604, 22112, 37567]),
-       values=tensor([0.0203, 0.9911, 0.7304,  ..., 0.1348, 0.2520, 0.4128]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([44477, 18295, 41758,  ..., 46506, 28720, 46164]),
+       values=tensor([0.4132, 0.4608, 0.2599,  ..., 0.0448, 0.1303, 0.6544]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2000, 0.8382, 0.5478,  ..., 0.6017, 0.0874, 0.6263])
+tensor([0.0039, 0.4422, 0.0639,  ..., 0.1130, 0.9521, 0.1334])
 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: 0.10953974723815918 seconds
+Time: 0.12936139106750488 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '95855', '-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": 8.94165301322937}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '81167', '-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": 7.780844211578369}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([31700, 16272, 20084,  ..., 46363,  9221, 39878]),
-       values=tensor([0.3577, 0.1970, 0.2573,  ..., 0.9498, 0.8667, 0.9638]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([38361, 15493, 29627,  ..., 27733, 22368, 35508]),
+       values=tensor([0.9149, 0.3524, 0.3637,  ..., 0.0393, 0.5821, 0.3741]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7704, 0.6386, 0.5878,  ..., 0.7750, 0.3511, 0.4334])
+tensor([0.5874, 0.0444, 0.7896,  ..., 0.3503, 0.3177, 0.2388])
 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: 8.94165301322937 seconds
+Time: 7.780844211578369 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '112560', '-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": 10.602921962738037}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '109532', '-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": 10.03889274597168}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([11228,  2410, 48293,  ..., 48555, 29403, 27641]),
-       values=tensor([0.9662, 0.4123, 0.9370,  ..., 0.4524, 0.0602, 0.8924]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24997, 24997, 25000]),
+       col_indices=tensor([17005,  6306, 21289,  ...,  8288,  8622, 19411]),
+       values=tensor([0.2779, 0.9469, 0.2610,  ..., 0.9922, 0.2668, 0.6005]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4903, 0.0715, 0.0009,  ..., 0.3750, 0.8526, 0.7709])
+tensor([0.1194, 0.4917, 0.3228,  ..., 0.2258, 0.0044, 0.3600])
 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: 10.602921962738037 seconds
+Time: 10.03889274597168 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([11228,  2410, 48293,  ..., 48555, 29403, 27641]),
-       values=tensor([0.9662, 0.4123, 0.9370,  ..., 0.4524, 0.0602, 0.8924]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24997, 24997, 25000]),
+       col_indices=tensor([17005,  6306, 21289,  ...,  8288,  8622, 19411]),
+       values=tensor([0.2779, 0.9469, 0.2610,  ..., 0.9922, 0.2668, 0.6005]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4903, 0.0715, 0.0009,  ..., 0.3750, 0.8526, 0.7709])
+tensor([0.1194, 0.4917, 0.3228,  ..., 0.2258, 0.0044, 0.3600])
 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: 10.602921962738037 seconds
+Time: 10.03889274597168 seconds
 
-[18.19, 18.01, 17.81, 17.69, 18.0, 18.19, 18.15, 17.85, 17.89, 17.99]
-[76.1]
-13.94394850730896
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112560, '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': 10.602921962738037, 'TIME_S_1KI': 0.0941979563143038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1061.1344814062118, 'W': 76.1}
-[18.19, 18.01, 17.81, 17.69, 18.0, 18.19, 18.15, 17.85, 17.89, 17.99, 18.3, 17.89, 18.04, 18.07, 17.99, 17.83, 18.01, 18.29, 17.73, 18.17]
-323.765
-16.18825
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112560, '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': 10.602921962738037, 'TIME_S_1KI': 0.0941979563143038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1061.1344814062118, 'W': 76.1, 'J_1KI': 9.427278619458171, 'W_1KI': 0.6760838663823738, 'W_D': 59.91175, 'J_D': 835.4063569827675, 'W_D_1KI': 0.532265014214641, 'J_D_1KI': 0.004728722585417919}
+[19.15, 18.96, 18.11, 18.03, 19.81, 17.96, 18.06, 18.12, 18.35, 17.94]
+[75.74]
+13.548514366149902
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109532, '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': 10.03889274597168, 'TIME_S_1KI': 0.09165260148606508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1026.1644780921936, 'W': 75.74}
+[19.15, 18.96, 18.11, 18.03, 19.81, 17.96, 18.06, 18.12, 18.35, 17.94, 18.12, 21.35, 17.92, 18.62, 18.1, 18.19, 17.98, 17.91, 18.07, 18.09]
+332.19000000000005
+16.609500000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109532, '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': 10.03889274597168, 'TIME_S_1KI': 0.09165260148606508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1026.1644780921936, 'W': 75.74, 'J_1KI': 9.368627233066078, 'W_1KI': 0.6914874192016944, 'W_D': 59.13049999999999, 'J_D': 801.1304287276266, 'W_D_1KI': 0.539846802760837, 'J_D_1KI': 0.004928667446598592}
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
new file mode 100644
index 0000000..3b1a4d8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 334616, "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": 10.390317678451538, "TIME_S_1KI": 0.03105146699037565, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1009.4583482408524, "W": 73.59, "J_1KI": 3.0167665271261757, "W_1KI": 0.21992373347359362, "W_D": 57.37650000000001, "J_D": 787.052410896063, "W_D_1KI": 0.17146968465345352, "J_D_1KI": 0.0005124371956315703}
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
new file mode 100644
index 0000000..ab60bf4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.0494227409362793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([2191, 1647, 4069,  ..., 3482,  688, 2162]),
+       values=tensor([0.7127, 0.2553, 0.3133,  ..., 0.9149, 0.5638, 0.5628]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.0865, 0.0532, 0.7203,  ..., 0.4777, 0.7863, 0.0162])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.0494227409362793 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '212452', '-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": 6.666574239730835}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([2208, 2123, 4174,  ..., 2091,   42, 2382]),
+       values=tensor([0.8755, 0.2371, 0.7047,  ..., 0.2373, 0.9261, 0.2864]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3651, 0.6415, 0.7426,  ..., 0.3371, 0.9910, 0.6174])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.666574239730835 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '334616', '-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": 10.390317678451538}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1385, 3626, 3706,  ...,  891, 2896, 4403]),
+       values=tensor([0.8264, 0.4439, 0.4297,  ..., 0.4171, 0.8922, 0.6160]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2966, 0.7201, 0.1357,  ..., 0.1499, 0.6981, 0.8153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.390317678451538 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1385, 3626, 3706,  ...,  891, 2896, 4403]),
+       values=tensor([0.8264, 0.4439, 0.4297,  ..., 0.4171, 0.8922, 0.6160]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2966, 0.7201, 0.1357,  ..., 0.1499, 0.6981, 0.8153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.390317678451538 seconds
+
+[18.39, 18.14, 17.96, 18.23, 18.13, 17.78, 18.17, 18.05, 18.11, 18.01]
+[73.59]
+13.71733045578003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334616, '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': 10.390317678451538, 'TIME_S_1KI': 0.03105146699037565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.4583482408524, 'W': 73.59}
+[18.39, 18.14, 17.96, 18.23, 18.13, 17.78, 18.17, 18.05, 18.11, 18.01, 18.29, 17.99, 18.0, 17.86, 17.97, 18.01, 17.96, 17.81, 17.79, 17.93]
+324.27
+16.2135
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334616, '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': 10.390317678451538, 'TIME_S_1KI': 0.03105146699037565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.4583482408524, 'W': 73.59, 'J_1KI': 3.0167665271261757, 'W_1KI': 0.21992373347359362, 'W_D': 57.37650000000001, 'J_D': 787.052410896063, 'W_D_1KI': 0.17146968465345352, 'J_D_1KI': 0.0005124371956315703}
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
new file mode 100644
index 0000000..e6fec4b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 248893, "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": 10.503267288208008, "TIME_S_1KI": 0.04219993044484179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1056.0920481681824, "W": 74.8, "J_1KI": 4.243156891387795, "W_1KI": 0.3005307501617161, "W_D": 58.488749999999996, "J_D": 825.7955051109194, "W_D_1KI": 0.2349955603411908, "J_D_1KI": 0.0009441629951070973}
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
new file mode 100644
index 0000000..d474848
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.0580594539642334}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 985, 1057,  218,  ..., 4882, 1671, 4380]),
+       values=tensor([0.5160, 0.3498, 0.0303,  ..., 0.2263, 0.8538, 0.6441]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8672, 0.0025, 0.6942,  ..., 0.2074, 0.2932, 0.8728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.0580594539642334 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '180849', '-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": 7.629418849945068}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 24984, 24994, 25000]),
+       col_indices=tensor([ 206,  438, 1117,  ..., 3589, 4561, 4654]),
+       values=tensor([0.7806, 0.0093, 0.9775,  ..., 0.2394, 0.5986, 0.1036]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9079, 0.6440, 0.7990,  ..., 0.4243, 0.2944, 0.4838])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.629418849945068 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '248893', '-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": 10.503267288208008}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([ 263, 1234, 1436,  ..., 3199, 3400, 4091]),
+       values=tensor([0.7110, 0.3838, 0.4652,  ..., 0.0537, 0.9297, 0.5811]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4941, 0.0109, 0.4935,  ..., 0.1517, 0.7151, 0.3544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.503267288208008 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([ 263, 1234, 1436,  ..., 3199, 3400, 4091]),
+       values=tensor([0.7110, 0.3838, 0.4652,  ..., 0.0537, 0.9297, 0.5811]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4941, 0.0109, 0.4935,  ..., 0.1517, 0.7151, 0.3544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.503267288208008 seconds
+
+[18.2, 18.48, 18.33, 18.03, 18.1, 18.08, 18.14, 18.78, 18.15, 17.99]
+[74.8]
+14.118877649307251
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248893, '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': 10.503267288208008, 'TIME_S_1KI': 0.04219993044484179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1056.0920481681824, 'W': 74.8}
+[18.2, 18.48, 18.33, 18.03, 18.1, 18.08, 18.14, 18.78, 18.15, 17.99, 18.23, 17.98, 17.97, 18.01, 17.96, 18.02, 17.91, 18.17, 17.92, 17.97]
+326.225
+16.31125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248893, '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': 10.503267288208008, 'TIME_S_1KI': 0.04219993044484179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1056.0920481681824, 'W': 74.8, 'J_1KI': 4.243156891387795, 'W_1KI': 0.3005307501617161, 'W_D': 58.488749999999996, 'J_D': 825.7955051109194, 'W_D_1KI': 0.2349955603411908, 'J_D_1KI': 0.0009441629951070973}
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
new file mode 100644
index 0000000..6c317ff
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 167260, "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": 10.636158227920532, "TIME_S_1KI": 0.0635905669491841, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1183.6357300853729, "W": 83.74, "J_1KI": 7.076621607589219, "W_1KI": 0.5006576587349038, "W_D": 67.26599999999999, "J_D": 950.7814786233901, "W_D_1KI": 0.40216429510941043, "J_D_1KI": 0.002404426014046457}
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
new file mode 100644
index 0000000..72ef990
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.0799260139465332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     35,     85,  ..., 249913, 249959,
+                            250000]),
+       col_indices=tensor([  50,   52,  142,  ..., 3906, 4174, 4757]),
+       values=tensor([0.0913, 0.8215, 0.1970,  ..., 0.8521, 0.9478, 0.8405]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5132, 0.5547, 0.3014,  ..., 0.6656, 0.4241, 0.0798])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.0799260139465332 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '131371', '-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": 8.24699854850769}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     34,     94,  ..., 249884, 249936,
+                            250000]),
+       col_indices=tensor([   2,  398,  450,  ..., 4930, 4969, 4985]),
+       values=tensor([0.5923, 0.5022, 0.7915,  ..., 0.6018, 0.8801, 0.8622]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1968, 0.0295, 0.9143,  ..., 0.4064, 0.2286, 0.1114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.24699854850769 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '167260', '-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": 10.636158227920532}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     97,  ..., 249903, 249957,
+                            250000]),
+       col_indices=tensor([   6,   32,   62,  ..., 4630, 4959, 4982]),
+       values=tensor([0.7649, 0.1722, 0.7795,  ..., 0.2616, 0.2192, 0.2761]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3363, 0.3219, 0.7361,  ..., 0.7182, 0.1290, 0.5403])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.636158227920532 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     97,  ..., 249903, 249957,
+                            250000]),
+       col_indices=tensor([   6,   32,   62,  ..., 4630, 4959, 4982]),
+       values=tensor([0.7649, 0.1722, 0.7795,  ..., 0.2616, 0.2192, 0.2761]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3363, 0.3219, 0.7361,  ..., 0.7182, 0.1290, 0.5403])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.636158227920532 seconds
+
+[18.39, 18.16, 18.03, 17.97, 18.05, 19.85, 18.01, 18.29, 18.12, 18.31]
+[83.74]
+14.13465166091919
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 167260, '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': 10.636158227920532, 'TIME_S_1KI': 0.0635905669491841, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.6357300853729, 'W': 83.74}
+[18.39, 18.16, 18.03, 17.97, 18.05, 19.85, 18.01, 18.29, 18.12, 18.31, 17.95, 19.62, 17.88, 18.37, 18.2, 18.02, 18.09, 18.08, 18.42, 17.99]
+329.48
+16.474
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 167260, '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': 10.636158227920532, 'TIME_S_1KI': 0.0635905669491841, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.6357300853729, 'W': 83.74, 'J_1KI': 7.076621607589219, 'W_1KI': 0.5006576587349038, 'W_D': 67.26599999999999, 'J_D': 950.7814786233901, 'W_D_1KI': 0.40216429510941043, 'J_D_1KI': 0.002404426014046457}
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
new file mode 100644
index 0000000..cee5ebd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46485, "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": 10.648370742797852, "TIME_S_1KI": 0.2290711141830236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1255.2527691650391, "W": 87.87, "J_1KI": 27.003393980101947, "W_1KI": 1.8902871894159408, "W_D": 71.29950000000001, "J_D": 1018.5375533752442, "W_D_1KI": 1.5338173604388514, "J_D_1KI": 0.03299596343850385}
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
new file mode 100644
index 0000000..cd79c3c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/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', '1000', '-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": 0.24121379852294922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     541,  ..., 1249516,
+                            1249748, 1250000]),
+       col_indices=tensor([  43,   75,  121,  ..., 4958, 4960, 4986]),
+       values=tensor([0.9222, 0.1508, 0.6151,  ..., 0.6191, 0.5090, 0.9494]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9528, 0.4494, 0.6520,  ..., 0.1607, 0.1619, 0.1321])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.24121379852294922 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43529', '-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": 9.83215594291687}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     232,     495,  ..., 1249518,
+                            1249779, 1250000]),
+       col_indices=tensor([  48,   77,  155,  ..., 4840, 4912, 4927]),
+       values=tensor([0.7412, 0.4704, 0.5361,  ..., 0.0050, 0.3320, 0.0792]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0891, 0.9943, 0.3145,  ..., 0.1784, 0.0363, 0.2532])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.83215594291687 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46485', '-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": 10.648370742797852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     231,     510,  ..., 1249526,
+                            1249780, 1250000]),
+       col_indices=tensor([  32,   71,  112,  ..., 4895, 4929, 4940]),
+       values=tensor([0.5396, 0.2475, 0.0729,  ..., 0.2451, 0.2187, 0.9449]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6746, 0.7318, 0.7509,  ..., 0.9415, 0.3905, 0.0197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.648370742797852 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     231,     510,  ..., 1249526,
+                            1249780, 1250000]),
+       col_indices=tensor([  32,   71,  112,  ..., 4895, 4929, 4940]),
+       values=tensor([0.5396, 0.2475, 0.0729,  ..., 0.2451, 0.2187, 0.9449]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6746, 0.7318, 0.7509,  ..., 0.9415, 0.3905, 0.0197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.648370742797852 seconds
+
+[18.37, 17.89, 18.14, 18.27, 18.28, 18.81, 17.93, 18.0, 17.89, 19.89]
+[87.87]
+14.28533935546875
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46485, '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': 10.648370742797852, 'TIME_S_1KI': 0.2290711141830236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1255.2527691650391, 'W': 87.87}
+[18.37, 17.89, 18.14, 18.27, 18.28, 18.81, 17.93, 18.0, 17.89, 19.89, 18.31, 18.14, 18.17, 17.98, 21.39, 18.55, 17.96, 18.39, 18.34, 17.99]
+331.40999999999997
+16.5705
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46485, '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': 10.648370742797852, 'TIME_S_1KI': 0.2290711141830236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1255.2527691650391, 'W': 87.87, 'J_1KI': 27.003393980101947, 'W_1KI': 1.8902871894159408, 'W_D': 71.29950000000001, 'J_D': 1018.5375533752442, 'W_D_1KI': 1.5338173604388514, 'J_D_1KI': 0.03299596343850385}
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
new file mode 100644
index 0000000..0fd50b0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19767, "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": 10.566095352172852, "TIME_S_1KI": 0.5345320661796353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1281.5856591796876, "W": 87.51, "J_1KI": 64.8346061202857, "W_1KI": 4.427075428744878, "W_D": 71.21225000000001, "J_D": 1042.904792114258, "W_D_1KI": 3.6025825871401835, "J_D_1KI": 0.18225236946123255}
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
new file mode 100644
index 0000000..df7022e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.5311744213104248}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     483,     993,  ..., 2498991,
+                            2499491, 2500000]),
+       col_indices=tensor([  15,   20,   28,  ..., 4987, 4988, 4995]),
+       values=tensor([0.8912, 0.6515, 0.2376,  ..., 0.2173, 0.7300, 0.9523]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3817, 0.2295, 0.0793,  ..., 0.5917, 0.1851, 0.3088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.5311744213104248 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19767', '-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": 10.566095352172852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1020,  ..., 2499029,
+                            2499506, 2500000]),
+       col_indices=tensor([   2,   18,   32,  ..., 4991, 4992, 4995]),
+       values=tensor([0.1206, 0.3118, 0.4014,  ..., 0.4488, 0.4763, 0.9896]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0140, 0.3283, 0.7098,  ..., 0.4613, 0.1962, 0.1627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.566095352172852 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,    1020,  ..., 2499029,
+                            2499506, 2500000]),
+       col_indices=tensor([   2,   18,   32,  ..., 4991, 4992, 4995]),
+       values=tensor([0.1206, 0.3118, 0.4014,  ..., 0.4488, 0.4763, 0.9896]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0140, 0.3283, 0.7098,  ..., 0.4613, 0.1962, 0.1627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.566095352172852 seconds
+
+[18.51, 17.88, 18.11, 18.97, 18.57, 17.75, 18.11, 17.81, 18.02, 17.72]
+[87.51]
+14.64501953125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19767, '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': 10.566095352172852, 'TIME_S_1KI': 0.5345320661796353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.5856591796876, 'W': 87.51}
+[18.51, 17.88, 18.11, 18.97, 18.57, 17.75, 18.11, 17.81, 18.02, 17.72, 18.1, 18.14, 18.04, 18.33, 18.06, 17.95, 18.01, 18.09, 18.02, 17.86]
+325.95500000000004
+16.29775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19767, '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': 10.566095352172852, 'TIME_S_1KI': 0.5345320661796353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.5856591796876, 'W': 87.51, 'J_1KI': 64.8346061202857, 'W_1KI': 4.427075428744878, 'W_D': 71.21225000000001, 'J_D': 1042.904792114258, 'W_D_1KI': 3.6025825871401835, 'J_D_1KI': 0.18225236946123255}
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
new file mode 100644
index 0000000..4be18ec
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 355144, "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": 10.430037498474121, "TIME_S_1KI": 0.02936847447366173, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.3673194527627, "W": 73.03, "J_1KI": 2.808346246741498, "W_1KI": 0.20563489739373325, "W_D": 56.462250000000004, "J_D": 771.1023268899322, "W_D_1KI": 0.15898410222332351, "J_D_1KI": 0.0004476609550585777}
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
new file mode 100644
index 0000000..1e00532
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,383 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-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": 0.07530069351196289}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([1366, 2183,  387, 4785,  591, 3875, 1782, 3853, 3491,
+                           1111, 4311, 1391, 2949, 4195, 1174,   98, 1356,  809,
+                           1785,  447, 2538, 4572, 2460, 1800,  303, 1931, 4013,
+                           4968, 4004, 1588, 1643, 1967, 3906, 4748, 1447, 2599,
+                            629, 3538, 4520, 4776, 4758, 2464, 1751, 3806,   96,
+                            198,  731, 3443, 3712, 4600, 4270, 2744, 4125,  400,
+                            468,  107, 2682, 4704,  252, 1804, 2511, 1911,  162,
+                           2509,  972, 3478,  980, 1895, 2935, 3965, 2890, 3988,
+                           2804, 3654, 1037, 4790, 2965,  394, 3461, 2942, 2671,
+                           4602,  851, 2319, 1925, 2531, 2262, 2466,  138, 3192,
+                           4165, 2776, 2205, 2786, 1112, 4160, 4088, 4917, 1466,
+                             32, 4695, 2757, 3360, 3218,  455,  480, 4012, 3928,
+                           3689, 1276, 1963, 1058, 3861, 2863, 4421, 4459, 4424,
+                           4964, 4366, 2158, 3511,  768, 3822, 1025, 3276, 1349,
+                           1095, 2928, 2660, 1067, 2626,  893, 4611, 4619, 1553,
+                           2755, 3328, 4431, 1950, 4722, 1972, 4066, 2996, 4851,
+                           2711, 2693, 4611, 1116, 4304, 1246, 2511, 2934, 4826,
+                           2926, 3416, 3468, 2846, 4286, 3701, 3015, 2373, 3319,
+                           2586, 1704, 3671, 1535, 4335, 3487, 2710, 3432, 1408,
+                           2336, 4517, 3976, 4761, 1747,  150, 3884, 4390, 3319,
+                           3373, 3574, 3662, 1429, 4058, 1144, 1909, 4439, 1862,
+                            343, 1833, 2363, 3001, 1926, 4696,  409, 4669, 2313,
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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.07530069351196289 seconds
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '139440', '-ss', '5000', '-sd', '1e-05']
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+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 4.12260627746582 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355144', '-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": 10.430037498474121}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 576,  858,  411, 4414, 2665,  376, 3054, 3322, 4372,
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+       layout=torch.sparse_csr)
+tensor([0.5879, 0.8514, 0.6272,  ..., 0.2435, 0.3582, 0.3734])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.430037498474121 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 576,  858,  411, 4414, 2665,  376, 3054, 3322, 4372,
+                           4279, 4109, 1090, 4955, 1792, 2761, 3831, 3980,  529,
+                           2775,  617, 4117, 1022, 2357, 2558, 3577, 3578, 3958,
+                           4584, 2525, 1559, 1731, 3457, 3297, 1685, 2202, 1452,
+                            491, 1458, 4726, 3575, 1883, 2403, 3952, 4222, 1553,
+                           2911, 2279, 1175, 2336, 4753, 2819, 4436,  482, 3199,
+                           4976, 1797, 1610, 3205, 1638, 3687, 4164, 2284, 2312,
+                           3201, 1175, 2223, 2205, 1659, 1685, 3876, 4867, 4503,
+                           2508, 1070, 2370, 1257,  578, 3738, 3473, 1417, 2544,
+                           2056, 4843, 1000, 3228, 4837, 4943, 1171, 1607, 3883,
+                            537, 4674, 2976, 4953, 4244, 3122, 4003, 2726, 2176,
+                           3401, 3187, 4115, 3515,   94, 3353, 4307,  545, 4985,
+                           4583, 3489, 3066, 4121, 3459, 1522,  677, 4486,  147,
+                           3866, 1597, 3765, 2455, 4064, 1457, 3132, 4642, 3434,
+                           4882, 2125, 3414,  394, 3741, 3553, 2336, 1556, 4256,
+                           1078, 4010,  148, 4755, 1924, 2289, 4358, 4904, 1449,
+                           2494, 2907, 2566, 4673,  214, 1941, 3465, 4474, 2630,
+                           2169, 4563, 4405, 2613, 3633, 1231, 2935, 3998, 3861,
+                           1642,  586, 3529, 1226, 3435, 3242, 4352, 3913, 4066,
+                           3077, 2516, 4422, 1989,  692, 2984, 4096,  402, 4733,
+                           2105, 3134, 4775,  589, 4044, 4752, 4541, 3171, 2469,
+                           3653, 4657, 4456, 2233, 2803, 4834, 4936, 3017,  295,
+                           4978, 3056, 4089, 3884, 2193,  857, 3649,  854,  903,
+                             28, 3897,  555, 2344,   28, 2417, 2346, 4647, 1068,
+                            320, 3342, 2217, 2395, 4836, 4346, 3869, 1532, 3168,
+                           2904, 3224, 1957,  350, 1919, 1414, 1439, 2678, 3944,
+                            694, 4893, 4079, 3781, 2587, 2843, 2494, 2488,  824,
+                           1995, 2151,  656,  824, 1220, 2366, 1835]),
+       values=tensor([3.0955e-01, 7.8676e-01, 4.1266e-01, 3.7203e-01,
+                      7.1730e-01, 3.7179e-01, 6.0963e-01, 1.0902e-02,
+                      1.1230e-01, 2.1823e-01, 1.9100e-01, 8.5284e-01,
+                      3.9664e-01, 7.2699e-01, 1.5904e-01, 3.5501e-01,
+                      7.5722e-01, 5.6198e-01, 5.1816e-01, 6.4843e-01,
+                      9.7108e-01, 5.2337e-01, 4.5987e-01, 1.8356e-01,
+                      3.1359e-01, 2.0336e-01, 9.3922e-01, 6.3176e-01,
+                      5.5921e-01, 9.2083e-01, 3.8441e-01, 4.1891e-01,
+                      4.9039e-02, 2.5835e-01, 1.4251e-01, 8.7986e-02,
+                      1.9179e-01, 4.9636e-02, 9.9221e-01, 8.8195e-01,
+                      3.6211e-01, 7.7986e-01, 8.8005e-01, 5.3709e-01,
+                      6.1723e-01, 2.3666e-01, 6.4046e-01, 7.4852e-01,
+                      8.6162e-01, 6.4736e-02, 6.4638e-01, 6.8790e-01,
+                      7.7258e-02, 9.2613e-01, 4.5329e-01, 3.8429e-01,
+                      4.4778e-01, 5.4974e-01, 7.1635e-02, 9.9247e-01,
+                      6.0152e-01, 9.9716e-01, 7.7326e-02, 6.0941e-01,
+                      4.9490e-01, 7.1856e-01, 9.5478e-01, 7.3740e-01,
+                      7.1156e-01, 7.7724e-01, 6.8908e-01, 8.4478e-01,
+                      5.3169e-01, 3.1838e-01, 6.4893e-01, 3.6731e-01,
+                      9.6217e-01, 9.5642e-01, 3.3310e-01, 8.0468e-01,
+                      4.4419e-01, 9.9457e-01, 9.4870e-01, 5.1652e-01,
+                      2.2471e-01, 4.9478e-02, 7.7952e-01, 3.1317e-01,
+                      4.6028e-01, 9.9118e-01, 2.1805e-01, 7.6144e-01,
+                      5.8009e-01, 5.8921e-01, 9.6946e-01, 3.7819e-02,
+                      8.9083e-01, 3.9045e-01, 4.6997e-01, 7.7548e-01,
+                      7.6016e-01, 9.9749e-01, 2.2222e-01, 8.7022e-01,
+                      1.7241e-01, 5.1297e-01, 5.3356e-01, 7.6400e-01,
+                      4.5765e-01, 9.3983e-01, 7.4746e-01, 2.2337e-02,
+                      4.6779e-01, 4.1228e-02, 4.0470e-01, 5.8279e-01,
+                      3.9830e-01, 7.9952e-01, 2.1413e-01, 6.9695e-01,
+                      8.4451e-01, 7.5133e-01, 6.1979e-01, 1.0235e-01,
+                      2.3922e-01, 9.7618e-01, 2.7859e-01, 9.1245e-01,
+                      1.8747e-01, 1.3708e-01, 4.3286e-01, 4.5125e-01,
+                      7.7463e-01, 6.6460e-01, 4.6171e-01, 5.2632e-01,
+                      1.3309e-01, 4.8984e-01, 6.6220e-01, 3.7532e-01,
+                      2.3458e-01, 9.8677e-01, 1.8606e-01, 5.8578e-01,
+                      2.0218e-01, 8.1884e-01, 1.6790e-01, 8.2955e-01,
+                      8.0990e-01, 7.9230e-01, 5.7415e-04, 1.5263e-01,
+                      3.0153e-02, 4.3910e-01, 1.1145e-01, 8.2933e-01,
+                      4.2403e-01, 9.4143e-01, 1.1893e-01, 2.2950e-01,
+                      4.0652e-01, 5.3859e-02, 3.4042e-01, 3.0550e-01,
+                      7.4631e-01, 2.0289e-01, 2.7832e-01, 9.2428e-02,
+                      8.1994e-01, 6.1876e-01, 8.1655e-01, 3.3884e-01,
+                      8.1926e-01, 3.0647e-01, 2.5277e-02, 6.7292e-01,
+                      6.3249e-01, 3.0699e-01, 8.3683e-02, 1.1258e-01,
+                      5.7451e-01, 9.9511e-01, 3.5203e-01, 6.1419e-01,
+                      7.8849e-01, 2.6274e-01, 6.6338e-01, 2.1944e-01,
+                      5.0745e-01, 9.4340e-02, 4.8396e-02, 5.6132e-01,
+                      9.5395e-01, 7.8119e-01, 2.9298e-01, 9.8647e-01,
+                      4.1870e-03, 7.2546e-01, 1.3543e-01, 1.4547e-01,
+                      9.5808e-01, 3.2689e-01, 3.3868e-01, 4.7652e-01,
+                      8.8370e-01, 6.0302e-01, 7.9645e-01, 6.6784e-01,
+                      5.1333e-01, 1.1003e-01, 1.8848e-01, 9.5891e-01,
+                      5.8130e-01, 8.9461e-01, 5.9679e-01, 7.2510e-01,
+                      6.8221e-01, 6.6161e-01, 2.4940e-01, 6.6307e-01,
+                      2.4001e-02, 4.4766e-02, 2.4703e-01, 5.2095e-02,
+                      8.5216e-01, 3.2978e-01, 6.8601e-01, 2.3333e-01,
+                      6.2542e-01, 6.6716e-01, 6.3532e-01, 9.7031e-01,
+                      2.6179e-01, 5.9241e-01, 6.1379e-01, 8.7532e-01,
+                      5.8130e-01, 3.7637e-01, 4.6468e-01, 2.0496e-01,
+                      7.4431e-01, 7.1477e-02, 8.7938e-01, 4.5946e-01,
+                      4.6023e-01, 7.9786e-01, 2.4383e-01, 3.7799e-01,
+                      1.9335e-01, 7.4334e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.5879, 0.8514, 0.6272,  ..., 0.2435, 0.3582, 0.3734])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.430037498474121 seconds
+
+[18.28, 21.02, 18.58, 17.81, 18.18, 17.91, 18.02, 17.91, 18.05, 17.86]
+[73.03]
+13.656953573226929
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355144, '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': 10.430037498474121, 'TIME_S_1KI': 0.02936847447366173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3673194527627, 'W': 73.03}
+[18.28, 21.02, 18.58, 17.81, 18.18, 17.91, 18.02, 17.91, 18.05, 17.86, 18.18, 17.95, 18.17, 18.08, 18.7, 17.95, 18.01, 17.92, 21.06, 17.75]
+331.35499999999996
+16.567749999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355144, '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': 10.430037498474121, 'TIME_S_1KI': 0.02936847447366173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3673194527627, 'W': 73.03, 'J_1KI': 2.808346246741498, 'W_1KI': 0.20563489739373325, 'W_D': 56.462250000000004, 'J_D': 771.1023268899322, 'W_D_1KI': 0.15898410222332351, 'J_D_1KI': 0.0004476609550585777}
diff --git a/pytorch/synthetic_sizes b/pytorch/synthetic_sizes
index fc7c6bc..5b351e3 100644
--- a/pytorch/synthetic_sizes
+++ b/pytorch/synthetic_sizes
@@ -1,5 +1,5 @@
+5000
 10000
 50000
 100000
 500000
-1000000