From a9a2b170fc13d87b903c64369f0735f96c887489 Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Tue, 17 Dec 2024 17:15:38 -0500
Subject: [PATCH] datarm ../pytorch-xeon_4216.Containerfile !

---
 ..._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 |    2 +-
 ...csr_10_10_10_synthetic_100000_0.001.output |   64 +-
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_1e-05.output |   98 +-
 ...6_csr_10_10_10_synthetic_100000_5e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_5e-05.output |   74 +-
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_10000_0.0001.output |   68 +-
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_0.001.output |   74 +-
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   64 +-
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   44 +-
 ...a_16_csr_10_10_10_synthetic_10000_0.1.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.1.output |   44 +-
 ...a_16_csr_10_10_10_synthetic_10000_0.2.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.2.output |   44 +-
 ...a_16_csr_10_10_10_synthetic_10000_0.3.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.3.output |   44 +-
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2538 +++++++--------
 ...16_csr_10_10_10_synthetic_10000_5e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_5e-05.output |   64 +-
 ..._csr_10_10_10_synthetic_500000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_500000_0.0001.output |   69 +-
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_500000_1e-05.output |   66 +-
 ...6_csr_10_10_10_synthetic_500000_5e-05.json |    2 +-
 ...csr_10_10_10_synthetic_500000_5e-05.output |   67 +-
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_50000_0.0001.output |   74 +-
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_0.001.output |   84 +-
 ..._16_csr_10_10_10_synthetic_50000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_50000_0.01.output |   44 +-
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   68 +-
 ...16_csr_10_10_10_synthetic_50000_5e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_5e-05.output |   74 +-
 ...16_csr_10_10_10_synthetic_5000_0.0001.json |    2 +-
 ..._csr_10_10_10_synthetic_5000_0.0001.output |   93 +-
 ..._16_csr_10_10_10_synthetic_5000_0.001.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_0.001.output |   68 +-
 ...a_16_csr_10_10_10_synthetic_5000_0.01.json |    2 +-
 ...16_csr_10_10_10_synthetic_5000_0.01.output |   74 +-
 ...a_16_csr_10_10_10_synthetic_5000_0.05.json |    2 +-
 ...16_csr_10_10_10_synthetic_5000_0.05.output |   64 +-
 ...ra_16_csr_10_10_10_synthetic_5000_0.1.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.1.output |   64 +-
 ...ra_16_csr_10_10_10_synthetic_5000_0.2.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.2.output |   44 +-
 ...ra_16_csr_10_10_10_synthetic_5000_0.3.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.3.output |   44 +-
 ...ra_16_csr_10_10_10_synthetic_5000_0.4.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.4.output |   45 +
 ...ra_16_csr_10_10_10_synthetic_5000_0.5.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.5.output |   45 +
 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  664 ++--
 ..._16_csr_10_10_10_synthetic_5000_5e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_5e-05.output |   68 +-
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_100000_0.0001.output |  120 +-
 ...6_csr_10_10_10_synthetic_100000_0.001.json |    2 +-
 ...csr_10_10_10_synthetic_100000_0.001.output |   80 +-
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_1e-05.output |   68 +-
 ...6_csr_10_10_10_synthetic_100000_5e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_5e-05.output |   68 +-
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_10000_0.0001.output |   68 +-
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_0.001.output |   89 +-
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   96 +-
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   76 +-
 ...p_16_csr_10_10_10_synthetic_10000_0.1.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.1.output |   78 +-
 ...p_16_csr_10_10_10_synthetic_10000_0.2.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.2.output |   76 +-
 ...p_16_csr_10_10_10_synthetic_10000_0.3.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.3.output |   60 +-
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2623 ++++++++--------
 ...16_csr_10_10_10_synthetic_10000_5e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_5e-05.output |   68 +-
 ..._csr_10_10_10_synthetic_500000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_500000_0.0001.output |   87 +-
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_500000_1e-05.output |  106 +-
 ...6_csr_10_10_10_synthetic_500000_5e-05.json |    2 +-
 ...csr_10_10_10_synthetic_500000_5e-05.output |   87 +-
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_50000_0.0001.output |  102 +-
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_0.001.output |   76 +-
 ..._16_csr_10_10_10_synthetic_50000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_50000_0.01.output |   80 +-
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   68 +-
 ...16_csr_10_10_10_synthetic_50000_5e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_5e-05.output |   68 +-
 ...16_csr_10_10_10_synthetic_5000_0.0001.json |    2 +-
 ..._csr_10_10_10_synthetic_5000_0.0001.output |   68 +-
 ..._16_csr_10_10_10_synthetic_5000_0.001.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_0.001.output |   68 +-
 ...p_16_csr_10_10_10_synthetic_5000_0.01.json |    2 +-
 ...16_csr_10_10_10_synthetic_5000_0.01.output |   68 +-
 ...p_16_csr_10_10_10_synthetic_5000_0.05.json |    2 +-
 ...16_csr_10_10_10_synthetic_5000_0.05.output |   98 +-
 ...3p_16_csr_10_10_10_synthetic_5000_0.1.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.1.output |   76 +-
 ...3p_16_csr_10_10_10_synthetic_5000_0.2.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.2.output |   76 +-
 ...3p_16_csr_10_10_10_synthetic_5000_0.3.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.3.output |   76 +-
 ...3p_16_csr_10_10_10_synthetic_5000_0.4.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.4.output |   85 +
 ...3p_16_csr_10_10_10_synthetic_5000_0.5.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.5.output |   85 +
 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  693 ++---
 ..._16_csr_10_10_10_synthetic_5000_5e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_5e-05.output |   68 +-
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_100000_0.0001.output |   80 +-
 ...6_csr_10_10_10_synthetic_100000_0.001.json |    2 +-
 ...csr_10_10_10_synthetic_100000_0.001.output |   80 +-
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_1e-05.output |   68 +-
 ...6_csr_10_10_10_synthetic_100000_5e-05.json |    2 +-
 ...csr_10_10_10_synthetic_100000_5e-05.output |   74 +-
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_10000_0.0001.output |   68 +-
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_0.001.output |   68 +-
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   80 +-
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    2 +-
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   80 +-
 ...6_16_csr_10_10_10_synthetic_10000_0.1.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.1.output |   80 +-
 ...6_16_csr_10_10_10_synthetic_10000_0.2.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.2.output |   60 +-
 ...6_16_csr_10_10_10_synthetic_10000_0.3.json |    2 +-
 ...16_csr_10_10_10_synthetic_10000_0.3.output |  104 +-
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2756 ++++++++---------
 ...16_csr_10_10_10_synthetic_10000_5e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_5e-05.output |   64 +-
 ..._csr_10_10_10_synthetic_500000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_500000_0.0001.output |   90 +-
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    2 +-
 ...csr_10_10_10_synthetic_500000_1e-05.output |   85 +-
 ...6_csr_10_10_10_synthetic_500000_5e-05.json |    2 +-
 ...csr_10_10_10_synthetic_500000_5e-05.output |   66 +-
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_50000_0.0001.output |   68 +-
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_0.001.output |   81 +-
 ..._16_csr_10_10_10_synthetic_50000_0.01.json |    2 +-
 ...6_csr_10_10_10_synthetic_50000_0.01.output |  104 +-
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   90 +-
 ...16_csr_10_10_10_synthetic_50000_5e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_5e-05.output |  118 +-
 ...16_csr_10_10_10_synthetic_5000_0.0001.json |    2 +-
 ..._csr_10_10_10_synthetic_5000_0.0001.output |   89 +-
 ..._16_csr_10_10_10_synthetic_5000_0.001.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_0.001.output |   68 +-
 ...6_16_csr_10_10_10_synthetic_5000_0.01.json |    2 +-
 ...16_csr_10_10_10_synthetic_5000_0.01.output |   68 +-
 ...6_16_csr_10_10_10_synthetic_5000_0.05.json |    2 +-
 ...16_csr_10_10_10_synthetic_5000_0.05.output |   76 +-
 ...16_16_csr_10_10_10_synthetic_5000_0.1.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.1.output |   80 +-
 ...16_16_csr_10_10_10_synthetic_5000_0.2.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.2.output |   82 +-
 ...16_16_csr_10_10_10_synthetic_5000_0.3.json |    2 +-
 ..._16_csr_10_10_10_synthetic_5000_0.3.output |   80 +-
 ...16_16_csr_10_10_10_synthetic_5000_0.4.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.4.output |   65 +
 ...16_16_csr_10_10_10_synthetic_5000_0.5.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.5.output |   85 +
 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  611 ++--
 ..._16_csr_10_10_10_synthetic_5000_5e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_5e-05.output |   64 +-
 pytorch/synthetic_densities                   |    2 +
 193 files changed, 8613 insertions(+), 8109 deletions(-)
 create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
 create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
 create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
 create mode 100644 pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
 create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
 create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
 create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
 create mode 100644 pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
 create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
 create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
 create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
 create mode 100644 pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output

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 980d684..e9b3498 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1748, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308, "TIME_S_1KI": 5.8030813455036085, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 530.3414198303223, "W": 36.295146632325824, "J_1KI": 303.3989815962942, "W_1KI": 20.763813862886625, "W_D": 17.602146632325823, "J_D": 257.20098424220083, "W_D_1KI": 10.069877936113171, "J_D_1KI": 5.760799734618519}
diff --git a/pytorch/output_synthetic_16core/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 365e810..2da7ef7 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": 5.980836629867554}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6005632877349854}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      22,  ...,  999980,
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999970,
                              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]),
+       col_indices=tensor([27708, 32922, 35240,  ..., 82805, 88487, 98517]),
+       values=tensor([0.0088, 0.7733, 0.0012,  ..., 0.6420, 0.7382, 0.2177]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6757, 0.5029, 0.1898,  ..., 0.2612, 0.6123, 0.0844])
+tensor([0.1129, 0.5965, 0.7496,  ..., 0.0902, 0.9107, 0.7724])
 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: 5.980836629867554 seconds
+Time: 0.6005632877349854 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1748 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,      10,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 8017, 17251, 18992,  ..., 72823, 91334, 91663]),
+       values=tensor([0.4596, 0.1797, 0.9797,  ..., 0.0499, 0.7967, 0.0183]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4750, 0.6821, 0.2847,  ..., 0.3502, 0.4038, 0.5877])
+tensor([0.8873, 0.5523, 0.3791,  ..., 0.8812, 0.4027, 0.2259])
 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: 10.253487825393677 seconds
+Time: 10.143786191940308 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,      10,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 8017, 17251, 18992,  ..., 72823, 91334, 91663]),
+       values=tensor([0.4596, 0.1797, 0.9797,  ..., 0.0499, 0.7967, 0.0183]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4750, 0.6821, 0.2847,  ..., 0.3502, 0.4038, 0.5877])
+tensor([0.8873, 0.5523, 0.3791,  ..., 0.8812, 0.4027, 0.2259])
 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: 10.253487825393677 seconds
+Time: 10.143786191940308 seconds
 
-[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}
+[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04]
+[20.76, 20.76, 21.04, 22.36, 24.68, 32.36, 38.56, 45.04, 50.12, 51.88, 51.52, 52.0, 52.0, 51.84]
+14.611910104751587
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824}
+[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04, 20.36, 20.64, 20.56, 20.72, 20.68, 20.52, 20.72, 20.76, 20.52, 20.48]
+373.86
+18.693
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824, 'J_1KI': 303.3989815962942, 'W_1KI': 20.763813862886625, 'W_D': 17.602146632325823, 'J_D': 257.20098424220083, 'W_D_1KI': 10.069877936113171, 'J_D_1KI': 5.760799734618519}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
index 0a97dfa..fdbe85f 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 175, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321, "TIME_S_1KI": 63.90815871102469, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 628.162894821167, "W": 35.42748603907242, "J_1KI": 3589.5022561209544, "W_1KI": 202.44277736612813, "W_D": 16.71548603907242, "J_D": 296.38140530395515, "W_D_1KI": 95.51706308041383, "J_D_1KI": 545.8117890309362}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
index 01b19d4..887a602 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.9738054275512695}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,      100,      229,  ...,  9999825,
+                             9999913, 10000000]),
+       col_indices=tensor([ 2839,  3131,  5153,  ..., 92533, 94576, 98932]),
+       values=tensor([0.4697, 0.9996, 0.7875,  ..., 0.5192, 0.5202, 0.9540]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3516, 0.3347, 0.9443,  ..., 0.8917, 0.2195, 0.8723])
+tensor([0.9598, 0.0952, 0.8851,  ..., 0.3844, 0.8104, 0.5939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 57.53653693199158 seconds
+Time: 5.9738054275512695 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 175 -ss 100000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,       95,      193,  ...,  9999801,
+                             9999901, 10000000]),
+       col_indices=tensor([ 2869,  4015,  6080,  ..., 94953, 95635, 98117]),
+       values=tensor([0.0857, 0.9758, 0.7363,  ..., 0.8151, 0.8595, 0.7723]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3516, 0.3347, 0.9443,  ..., 0.8917, 0.2195, 0.8723])
+tensor([0.7586, 0.5970, 0.0221,  ..., 0.6721, 0.2659, 0.4588])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 57.53653693199158 seconds
+Time: 11.183927774429321 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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       95,      193,  ...,  9999801,
+                             9999901, 10000000]),
+       col_indices=tensor([ 2869,  4015,  6080,  ..., 94953, 95635, 98117]),
+       values=tensor([0.0857, 0.9758, 0.7363,  ..., 0.8151, 0.8595, 0.7723]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7586, 0.5970, 0.0221,  ..., 0.6721, 0.2659, 0.4588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.183927774429321 seconds
+
+[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64]
+[20.64, 20.76, 20.84, 24.4, 27.0, 28.6, 31.96, 32.96, 34.0, 38.76, 44.2, 47.96, 51.6, 50.84, 50.88, 50.6, 50.76]
+17.730947494506836
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242}
+[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64, 20.64, 20.64, 20.64, 20.72, 20.76, 20.76, 21.0, 20.92, 21.36, 21.56]
+374.24
+18.712
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242, 'J_1KI': 3589.5022561209544, 'W_1KI': 202.44277736612813, 'W_D': 16.71548603907242, 'J_D': 296.38140530395515, 'W_D_1KI': 95.51706308041383, 'J_D_1KI': 545.8117890309362}
diff --git a/pytorch/output_synthetic_16core/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 9a23625..eff1e86 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11597, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856, "TIME_S_1KI": 0.8998928454926151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 483.6025880336762, "W": 33.00177532885384, "J_1KI": 41.700662932971994, "W_1KI": 2.845716592985586, "W_D": 14.225775328853839, "J_D": 208.462172027588, "W_D_1KI": 1.2266771862424626, "J_D_1KI": 0.10577538900081596}
diff --git a/pytorch/output_synthetic_16core/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 0a26541..cdfe200 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 +1,54 @@
-['apptainer', '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": 0.8802759647369385}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.12187767028808594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([99237, 81965, 52149,  ..., 94819, 50598, 82628]),
+       values=tensor([0.3300, 0.8237, 0.5005,  ..., 0.6469, 0.1010, 0.4687]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0038, 0.2456, 0.3182,  ..., 0.7163, 0.7510, 0.9775])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.12187767028808594 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8615 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.799410104751587}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([88588, 42232, 90125,  ..., 27244, 80106, 39636]),
+       values=tensor([0.8018, 0.8315, 0.5597,  ..., 0.5532, 0.0030, 0.5793]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1929, 0.1411, 0.4568,  ..., 0.6294, 0.2188, 0.4350])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 7.799410104751587 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 11597 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([50190, 32056, 73796,  ..., 55938, 31334, 37461]),
-       values=tensor([0.0722, 0.7116, 0.8310,  ..., 0.7930, 0.8115, 0.4149]),
+       col_indices=tensor([24172, 83350, 29274,  ..., 76990, 53592, 71081]),
+       values=tensor([0.7302, 0.8346, 0.3553,  ..., 0.4222, 0.8183, 0.0288]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5168, 0.3496, 0.0063,  ..., 0.9888, 0.0960, 0.5324])
+tensor([0.8537, 0.9431, 0.0277,  ..., 0.1357, 0.7019, 0.9196])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-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 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}
+Time: 10.436057329177856 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99995,  99996,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([15079, 22431, 71484,  ..., 38240, 57604, 63673]),
-       values=tensor([0.6856, 0.2309, 0.0261,  ..., 0.6883, 0.7108, 0.1151]),
+       col_indices=tensor([24172, 83350, 29274,  ..., 76990, 53592, 71081]),
+       values=tensor([0.7302, 0.8346, 0.3553,  ..., 0.4222, 0.8183, 0.0288]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6131, 0.6051, 0.4027,  ..., 0.3545, 0.9505, 0.4978])
+tensor([0.8537, 0.9431, 0.0277,  ..., 0.1357, 0.7019, 0.9196])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,30 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.779903888702393 seconds
+Time: 10.436057329177856 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99995,  99996,
-                            100000]),
-       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([0.6131, 0.6051, 0.4027,  ..., 0.3545, 0.9505, 0.4978])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 100000
-Density: 1e-05
-Time: 10.779903888702393 seconds
-
-[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}
+[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8]
+[20.64, 20.36, 20.72, 23.08, 24.72, 29.44, 35.88, 40.08, 43.56, 45.4, 45.96, 45.6, 45.36, 46.04]
+14.653835535049438
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384}
+[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8, 20.36, 20.32, 20.32, 20.44, 20.64, 20.8, 21.0, 21.16, 21.12, 21.04]
+375.52
+18.776
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384, 'J_1KI': 41.700662932971994, 'W_1KI': 2.845716592985586, 'W_D': 14.225775328853839, 'J_D': 208.462172027588, 'W_D_1KI': 1.2266771862424626, 'J_D_1KI': 0.10577538900081596}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
index 6ac986d..67cd5e0 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3268, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551287174224854, "TIME_S_1KI": 3.2286680459684374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 514.9588033294677, "W": 35.14704993762719, "J_1KI": 157.57613320975145, "W_1KI": 10.754911241623986, "W_D": 16.333049937627187, "J_D": 239.30451817512508, "W_D_1KI": 4.997873297927536, "J_D_1KI": 1.5293369944698703}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524, "TIME_S_1KI": 3.3034382352403777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 528.672490940094, "W": 36.05478479295784, "J_1KI": 160.34955745832394, "W_1KI": 10.935633846817664, "W_D": 17.45578479295784, "J_D": 255.95474444794658, "W_D_1KI": 5.2944448871573675, "J_D_1KI": 1.605837090432929}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
index c2c09d9..3c3d8e5 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.21274733543396}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.4573814868927002}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     10,  ..., 499991, 499996,
+tensor(crow_indices=tensor([     0,     10,     16,  ..., 499990, 499995,
                             500000]),
-       col_indices=tensor([ 6819, 16249, 65142,  ..., 35181, 90238, 95591]),
-       values=tensor([0.9907, 0.7784, 0.8470,  ..., 0.0401, 0.4552, 0.5172]),
+       col_indices=tensor([ 5164,  6869,  8448,  ..., 29154, 68140, 97893]),
+       values=tensor([0.8386, 0.0921, 0.7067,  ..., 0.9232, 0.1449, 0.6848]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.1211, 0.3699, 0.8120,  ..., 0.3387, 0.3308, 0.0427])
+tensor([0.0246, 0.8160, 0.3295,  ..., 0.0588, 0.6998, 0.9868])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 3.21274733543396 seconds
+Time: 0.4573814868927002 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3268 -ss 100000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551287174224854}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2295 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.306779146194458}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     10,  ..., 499988, 499994,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499989, 499995,
                             500000]),
-       col_indices=tensor([ 4698, 29712, 45324,  ..., 47109, 54294, 79244]),
-       values=tensor([0.0467, 0.0395, 0.2018,  ..., 0.4601, 0.1623, 0.8954]),
+       col_indices=tensor([ 2059, 19971, 54406,  ..., 65065, 65922, 83323]),
+       values=tensor([0.5530, 0.6181, 0.7781,  ..., 0.5380, 0.6243, 0.8378]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.1366, 0.5632, 0.5706,  ..., 0.2593, 0.9938, 0.0917])
+tensor([0.4055, 0.5945, 0.9428,  ..., 0.6446, 0.1456, 0.3700])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.551287174224854 seconds
+Time: 7.306779146194458 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3297 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     10,  ..., 499988, 499994,
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 499995, 499995,
                             500000]),
-       col_indices=tensor([ 4698, 29712, 45324,  ..., 47109, 54294, 79244]),
-       values=tensor([0.0467, 0.0395, 0.2018,  ..., 0.4601, 0.1623, 0.8954]),
+       col_indices=tensor([ 8913, 22689, 49331,  ..., 65321, 72756, 72788]),
+       values=tensor([0.7511, 0.0782, 0.7533,  ..., 0.6341, 0.1803, 0.2288]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.1366, 0.5632, 0.5706,  ..., 0.2593, 0.9938, 0.0917])
+tensor([0.5601, 0.4293, 0.2285,  ..., 0.5137, 0.5400, 0.5797])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.551287174224854 seconds
+Time: 10.891435861587524 seconds
 
-[20.52, 20.48, 20.56, 20.6, 20.6, 20.88, 21.08, 21.04, 21.04, 20.92]
-[20.68, 20.64, 20.84, 22.28, 23.8, 29.96, 35.72, 42.6, 47.2, 50.88, 50.56, 50.96, 50.68, 50.8]
-14.651551246643066
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551287174224854, 'TIME_S_1KI': 3.2286680459684374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 514.9588033294677, 'W': 35.14704993762719}
-[20.52, 20.48, 20.56, 20.6, 20.6, 20.88, 21.08, 21.04, 21.04, 20.92, 20.72, 21.08, 21.16, 21.16, 21.24, 21.28, 21.16, 20.88, 20.68, 20.56]
-376.28
-18.814
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551287174224854, 'TIME_S_1KI': 3.2286680459684374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 514.9588033294677, 'W': 35.14704993762719, 'J_1KI': 157.57613320975145, 'W_1KI': 10.754911241623986, 'W_D': 16.333049937627187, 'J_D': 239.30451817512508, 'W_D_1KI': 4.997873297927536, 'J_D_1KI': 1.5293369944698703}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 499995, 499995,
+                            500000]),
+       col_indices=tensor([ 8913, 22689, 49331,  ..., 65321, 72756, 72788]),
+       values=tensor([0.7511, 0.0782, 0.7533,  ..., 0.6341, 0.1803, 0.2288]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5601, 0.4293, 0.2285,  ..., 0.5137, 0.5400, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.891435861587524 seconds
+
+[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92]
+[20.6, 20.48, 20.72, 25.12, 26.88, 32.4, 38.84, 42.28, 46.96, 50.12, 51.2, 51.8, 51.56, 51.6]
+14.66303277015686
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784}
+[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92, 20.84, 21.04, 20.84, 20.84, 20.76, 20.8, 20.68, 20.44, 20.28, 20.24]
+371.98
+18.599
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784, 'J_1KI': 160.34955745832394, 'W_1KI': 10.935633846817664, 'W_D': 17.45578479295784, 'J_D': 255.95474444794658, 'W_D_1KI': 5.2944448871573675, 'J_D_1KI': 1.605837090432929}
diff --git a/pytorch/output_synthetic_16core/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 47534f3..6fa3845 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372, "TIME_S_1KI": 0.32081469877018387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.63640854835506, "W": 22.48260624860275, "J_1KI": 9.793982367580435, "W_1KI": 0.6888897612637195, "W_D": 3.984606248602752, "J_D": 56.64935891771315, "W_D_1KI": 0.12209235962136145, "J_D_1KI": 0.003741033203252894}
diff --git a/pytorch/output_synthetic_16core/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 0d66d25..21cdb17 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": 0.3622722625732422}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.050879478454589844}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([5382, 2827, 5658,  ..., 9195, 8647, 1137]),
+       values=tensor([0.6423, 0.5656, 0.8194,  ..., 0.3825, 0.7281, 0.0248]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9594, 0.1900, 0.3074,  ..., 0.8950, 0.9459, 0.6732])
+tensor([0.6609, 0.7541, 0.4159,  ..., 0.2180, 0.3481, 0.0053])
 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: 0.3622722625732422 seconds
+Time: 0.050879478454589844 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 20637 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.639445781707764}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([1538, 6690, 5733,  ..., 9607, 7438, 7782]),
+       values=tensor([0.7222, 0.1089, 0.5631,  ..., 0.3116, 0.0243, 0.6999]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.6771, 0.1497, 0.5070,  ..., 0.8092, 0.9643, 0.7887])
+tensor([0.2878, 0.8940, 0.0961,  ..., 0.0631, 0.2895, 0.2219])
 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: 9.27123761177063 seconds
+Time: 6.639445781707764 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32636 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     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]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([7014, 8766, 3433,  ..., 9466, 1431, 7728]),
+       values=tensor([0.0370, 0.3747, 0.2051,  ..., 0.2901, 0.3737, 0.7201]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2861, 0.2741, 0.4038,  ..., 0.8389, 0.9796, 0.7969])
+tensor([0.8451, 0.4833, 0.4298,  ..., 0.9015, 0.0937, 0.6764])
 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: 10.612937211990356 seconds
+Time: 10.47010850906372 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     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]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([7014, 8766, 3433,  ..., 9466, 1431, 7728]),
+       values=tensor([0.0370, 0.3747, 0.2051,  ..., 0.2901, 0.3737, 0.7201]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2861, 0.2741, 0.4038,  ..., 0.8389, 0.9796, 0.7969])
+tensor([0.8451, 0.4833, 0.4298,  ..., 0.9015, 0.0937, 0.6764])
 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: 10.612937211990356 seconds
+Time: 10.47010850906372 seconds
 
-[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}
+[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4]
+[20.72, 20.64, 21.24, 23.32, 25.32, 26.04, 26.72, 26.72, 26.48, 25.08, 23.72, 23.6, 23.56, 23.48]
+14.217053174972534
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275}
+[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4, 20.64, 20.56, 20.48, 20.52, 20.68, 20.84, 20.88, 20.88, 20.84, 20.48]
+369.96
+18.497999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275, 'J_1KI': 9.793982367580435, 'W_1KI': 0.6888897612637195, 'W_D': 3.984606248602752, 'J_D': 56.64935891771315, 'W_D_1KI': 0.12209235962136145, 'J_D_1KI': 0.003741033203252894}
diff --git a/pytorch/output_synthetic_16core/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 650d27e..c0d7ab3 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648, "TIME_S_1KI": 2.279479990598506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2094431686402, "W": 23.392743273719635, "J_1KI": 73.51392856132777, "W_1KI": 5.176530930232272, "W_D": 4.8837432737196345, "J_D": 69.35593720483783, "W_D_1KI": 1.0807132714582062, "J_D_1KI": 0.2391487655362262}
diff --git a/pytorch/output_synthetic_16core/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 7a35653..f0ce63e 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": 2.282747268676758}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2727935314178467}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     15,     26,  ...,  99983,  99992,
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99993,
                             100000]),
-       col_indices=tensor([ 746, 1254, 2691,  ..., 5665, 9904, 9986]),
-       values=tensor([0.7024, 0.2927, 0.8116,  ..., 0.2675, 0.5863, 0.1724]),
+       col_indices=tensor([2872, 4034, 5620,  ..., 6357, 6556, 9590]),
+       values=tensor([0.7995, 0.0045, 0.2448,  ..., 0.5761, 0.7842, 0.1546]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2042, 0.3555, 0.3767,  ..., 0.6038, 0.4952, 0.0036])
+tensor([0.8077, 0.7130, 0.7281,  ..., 0.3829, 0.9486, 0.9162])
 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: 2.282747268676758 seconds
+Time: 0.2727935314178467 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3849 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.942286252975464}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99989,
+tensor(crow_indices=tensor([     0,     12,     21,  ...,  99991,  99998,
                             100000]),
-       col_indices=tensor([5193, 5456, 6247,  ..., 5100, 5946, 8330]),
-       values=tensor([0.7086, 0.0012, 0.4180,  ..., 0.5448, 0.8405, 0.8114]),
+       col_indices=tensor([ 425,  574,  695,  ..., 9570, 6024, 9715]),
+       values=tensor([0.7410, 0.8879, 0.5840,  ..., 0.6995, 0.9280, 0.9465]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0495, 0.0946, 0.7654,  ..., 0.8976, 0.3544, 0.9283])
+tensor([0.2929, 0.5164, 0.5482,  ..., 0.5103, 0.5008, 0.9557])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.21649432182312 seconds
+Time: 8.942286252975464 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4519 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99989,
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99974,  99990,
                             100000]),
-       col_indices=tensor([5193, 5456, 6247,  ..., 5100, 5946, 8330]),
-       values=tensor([0.7086, 0.0012, 0.4180,  ..., 0.5448, 0.8405, 0.8114]),
+       col_indices=tensor([ 582, 1691, 2515,  ..., 7345, 7996, 8295]),
+       values=tensor([0.8177, 0.9283, 0.6030,  ..., 0.2647, 0.3717, 0.8633]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0495, 0.0946, 0.7654,  ..., 0.8976, 0.3544, 0.9283])
+tensor([0.2209, 0.7260, 0.0429,  ..., 0.4887, 0.7834, 0.0043])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.21649432182312 seconds
+Time: 10.300970077514648 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99974,  99990,
+                            100000]),
+       col_indices=tensor([ 582, 1691, 2515,  ..., 7345, 7996, 8295]),
+       values=tensor([0.8177, 0.9283, 0.6030,  ..., 0.2647, 0.3717, 0.8633]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2209, 0.7260, 0.0429,  ..., 0.4887, 0.7834, 0.0043])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.300970077514648 seconds
+
+[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48]
+[20.4, 20.36, 23.16, 24.08, 27.0, 27.6, 28.72, 28.72, 26.24, 26.28, 24.44, 24.28, 24.24, 24.08]
+14.201388835906982
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635}
+[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48, 20.52, 20.6, 20.68, 20.64, 20.64, 20.52, 20.52, 20.48, 20.68, 20.72]
+370.18
+18.509
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635, 'J_1KI': 73.51392856132777, 'W_1KI': 5.176530930232272, 'W_D': 4.8837432737196345, 'J_D': 69.35593720483783, 'W_D_1KI': 1.0807132714582062, 'J_D_1KI': 0.2391487655362262}
diff --git a/pytorch/output_synthetic_16core/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 d799afd..6d63757 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": 21.366477489471436, "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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 490, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521, "TIME_S_1KI": 21.416943900439204, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.6961988353729, "W": 22.678695903983957, "J_1KI": 658.5636710925977, "W_1KI": 46.283052865273376, "W_D": 4.3196959039839555, "J_D": 61.46515012335775, "W_D_1KI": 8.815705926497868, "J_D_1KI": 17.991236584689528}
diff --git a/pytorch/output_synthetic_16core/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 7e1dd33..8697ed9 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": 21.366477489471436}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.140977382659912}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,     103,     212,  ...,  999798,
+                             999901, 1000000]),
+       col_indices=tensor([  63,  140,  146,  ..., 9691, 9771, 9918]),
+       values=tensor([0.8748, 0.2571, 0.8906,  ..., 0.1504, 0.2890, 0.7825]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4749, 0.5757, 0.5717,  ..., 0.5026, 0.5396, 0.1085])
+tensor([0.5882, 0.3416, 0.1892,  ..., 0.3016, 0.5220, 0.0626])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.366477489471436 seconds
+Time: 2.140977382659912 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 490 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,     113,     202,  ...,  999820,
+                             999916, 1000000]),
+       col_indices=tensor([   2,   35,   39,  ..., 9519, 9605, 9656]),
+       values=tensor([0.0992, 0.7724, 0.9238,  ..., 0.3639, 0.9758, 0.0697]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4749, 0.5757, 0.5717,  ..., 0.5026, 0.5396, 0.1085])
+tensor([0.2199, 0.1288, 0.7757,  ..., 0.1449, 0.2950, 0.2928])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.366477489471436 seconds
+Time: 10.49430251121521 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     113,     202,  ...,  999820,
+                             999916, 1000000]),
+       col_indices=tensor([   2,   35,   39,  ..., 9519, 9605, 9656]),
+       values=tensor([0.0992, 0.7724, 0.9238,  ..., 0.3639, 0.9758, 0.0697]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2199, 0.1288, 0.7757,  ..., 0.1449, 0.2950, 0.2928])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.49430251121521 seconds
+
+[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92]
+[19.96, 20.36, 20.52, 22.04, 24.08, 26.92, 27.96, 27.96, 26.64, 25.04, 24.52, 24.4, 24.4, 24.52]
+14.229045629501343
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957}
+[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92, 20.44, 20.36, 20.4, 20.52, 20.4, 20.64, 20.76, 20.52, 20.52, 20.52]
+367.18
+18.359
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957, 'J_1KI': 658.5636710925977, 'W_1KI': 46.283052865273376, 'W_D': 4.3196959039839555, 'J_D': 61.46515012335775, 'W_D_1KI': 8.815705926497868, 'J_D_1KI': 17.991236584689528}
diff --git a/pytorch/output_synthetic_16core/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 c5bc81a..d53721b 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": 106.56549715995789, "TIME_S_1KI": 106.56549715995789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2685.1749729537964, "W": 24.108298502680796, "J_1KI": 2685.1749729537964, "W_1KI": 24.108298502680796, "W_D": 5.534298502680798, "J_D": 616.4084881644253, "W_D_1KI": 5.534298502680798, "J_D_1KI": 5.534298502680798}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151, "TIME_S_1KI": 105.75754880905151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.48722974777223, "W": 23.357767084816274, "J_1KI": 3574.8722974777224, "W_1KI": 233.57767084816274, "W_D": 4.891767084816273, "J_D": 74.86778412389756, "W_D_1KI": 48.91767084816273, "J_D_1KI": 489.1767084816273}
diff --git a/pytorch/output_synthetic_16core/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 1729b52..5bc40f5 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": 106.56549715995789}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     506,    1012,  ..., 4998963,
-                            4999486, 5000000]),
-       col_indices=tensor([  12,   18,   20,  ..., 9951, 9962, 9995]),
-       values=tensor([0.8717, 0.8420, 0.2460,  ..., 0.1141, 0.5771, 0.8192]),
+tensor(crow_indices=tensor([      0,     505,    1011,  ..., 4998999,
+                            4999505, 5000000]),
+       col_indices=tensor([  17,   34,   93,  ..., 9927, 9945, 9977]),
+       values=tensor([0.3942, 0.9668, 0.2842,  ..., 0.3748, 0.5474, 0.6270]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3519, 0.6034, 0.3549,  ..., 0.4924, 0.0253, 0.7056])
+tensor([0.3102, 0.7326, 0.1847,  ..., 0.2267, 0.2009, 0.0941])
 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: 106.56549715995789 seconds
+Time: 10.575754880905151 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     506,    1012,  ..., 4998963,
-                            4999486, 5000000]),
-       col_indices=tensor([  12,   18,   20,  ..., 9951, 9962, 9995]),
-       values=tensor([0.8717, 0.8420, 0.2460,  ..., 0.1141, 0.5771, 0.8192]),
+tensor(crow_indices=tensor([      0,     505,    1011,  ..., 4998999,
+                            4999505, 5000000]),
+       col_indices=tensor([  17,   34,   93,  ..., 9927, 9945, 9977]),
+       values=tensor([0.3942, 0.9668, 0.2842,  ..., 0.3748, 0.5474, 0.6270]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3519, 0.6034, 0.3549,  ..., 0.4924, 0.0253, 0.7056])
+tensor([0.3102, 0.7326, 0.1847,  ..., 0.2267, 0.2009, 0.0941])
 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: 106.56549715995789 seconds
+Time: 10.575754880905151 seconds
 
-[20.96, 20.76, 20.72, 20.52, 20.44, 20.56, 20.84, 20.84, 20.6, 20.6]
-[20.64, 20.64, 21.08, 23.16, 24.52, 27.56, 29.68, 29.72, 28.4, 27.2, 27.2, 24.36, 24.36, 24.36, 24.84, 24.88, 24.76, 24.96, 24.72, 24.4, 24.36, 24.16, 24.12, 24.12, 24.24, 24.64, 24.72, 24.76, 24.88, 24.76, 24.4, 24.4, 24.32, 24.32, 24.24, 24.24, 24.44, 24.36, 24.16, 24.16, 24.2, 24.24, 24.4, 24.68, 24.56, 24.6, 24.52, 24.48, 24.48, 24.24, 24.36, 24.36, 24.44, 24.52, 24.52, 24.32, 24.44, 24.28, 24.04, 23.96, 23.96, 24.04, 24.16, 24.28, 24.36, 24.44, 24.48, 24.56, 24.64, 24.56, 24.48, 24.24, 24.24, 24.24, 24.2, 24.28, 24.36, 24.2, 24.4, 24.12, 24.16, 24.24, 24.48, 24.48, 24.8, 24.8, 24.92, 24.92, 24.96, 24.84, 24.72, 24.88, 24.72, 24.68, 24.48, 24.28, 24.0, 24.04, 24.04, 24.16, 24.32, 24.28, 24.32, 24.32, 24.48, 24.4, 24.68, 24.8, 24.64, 24.48]
-111.37969660758972
-{'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': 106.56549715995789, 'TIME_S_1KI': 106.56549715995789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2685.1749729537964, 'W': 24.108298502680796}
-[20.96, 20.76, 20.72, 20.52, 20.44, 20.56, 20.84, 20.84, 20.6, 20.6, 20.64, 20.6, 20.6, 20.72, 20.8, 20.6, 20.52, 20.28, 20.68, 20.6]
-371.47999999999996
-18.573999999999998
-{'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': 106.56549715995789, 'TIME_S_1KI': 106.56549715995789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2685.1749729537964, 'W': 24.108298502680796, 'J_1KI': 2685.1749729537964, 'W_1KI': 24.108298502680796, 'W_D': 5.534298502680798, 'J_D': 616.4084881644253, 'W_D_1KI': 5.534298502680798, 'J_D_1KI': 5.534298502680798}
+[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32]
+[20.32, 20.16, 21.08, 22.56, 24.44, 27.6, 27.6, 29.28, 28.88, 28.12, 25.16, 24.16, 24.12, 24.4, 24.56]
+15.30485463142395
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274}
+[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32, 20.52, 20.56, 20.76, 20.76, 20.84, 20.84, 20.72, 20.56, 20.56, 20.48]
+369.32
+18.466
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274, 'J_1KI': 3574.8722974777224, 'W_1KI': 233.57767084816274, 'W_D': 4.891767084816273, 'J_D': 74.86778412389756, 'W_D_1KI': 48.91767084816273, 'J_D_1KI': 489.1767084816273}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
index f711a9d..d4908f3 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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": 213.33555269241333, "TIME_S_1KI": 213.33555269241333, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5226.200544624329, "W": 24.133137838334523, "J_1KI": 5226.200544624329, "W_1KI": 24.133137838334523, "W_D": 5.8551378383345245, "J_D": 1267.97123376751, "W_D_1KI": 5.8551378383345245, "J_D_1KI": 5.8551378383345245}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.58586049079895, "TIME_S_1KI": 215.8586049079895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 695.2596657943726, "W": 24.42843676698333, "J_1KI": 6952.596657943726, "W_1KI": 244.2843676698333, "W_D": 6.1854367669833294, "J_D": 176.04420374608034, "W_D_1KI": 61.854367669833294, "J_D_1KI": 618.543676698333}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
index dc4c095..b9e17f1 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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": 213.33555269241333}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.58586049079895}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1053,     2094,  ...,  9997974,
-                             9998964, 10000000]),
-       col_indices=tensor([   7,   12,   13,  ..., 9961, 9986, 9993]),
-       values=tensor([0.1704, 0.0678, 0.1172,  ..., 0.2208, 0.7370, 0.2820]),
+tensor(crow_indices=tensor([       0,      983,     1945,  ...,  9997995,
+                             9998975, 10000000]),
+       col_indices=tensor([   7,   29,   32,  ..., 9986, 9994, 9999]),
+       values=tensor([0.5805, 0.0545, 0.7779,  ..., 0.8799, 0.6314, 0.5149]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7446, 0.0915, 0.8679,  ..., 0.5982, 0.0596, 0.6566])
+tensor([0.1246, 0.2739, 0.0084,  ..., 0.7975, 0.3318, 0.0977])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 213.33555269241333 seconds
+Time: 21.58586049079895 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1053,     2094,  ...,  9997974,
-                             9998964, 10000000]),
-       col_indices=tensor([   7,   12,   13,  ..., 9961, 9986, 9993]),
-       values=tensor([0.1704, 0.0678, 0.1172,  ..., 0.2208, 0.7370, 0.2820]),
+tensor(crow_indices=tensor([       0,      983,     1945,  ...,  9997995,
+                             9998975, 10000000]),
+       col_indices=tensor([   7,   29,   32,  ..., 9986, 9994, 9999]),
+       values=tensor([0.5805, 0.0545, 0.7779,  ..., 0.8799, 0.6314, 0.5149]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7446, 0.0915, 0.8679,  ..., 0.5982, 0.0596, 0.6566])
+tensor([0.1246, 0.2739, 0.0084,  ..., 0.7975, 0.3318, 0.0977])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 213.33555269241333 seconds
+Time: 21.58586049079895 seconds
 
-[20.4, 20.4, 20.4, 20.36, 20.36, 20.16, 20.32, 20.2, 20.2, 20.28]
-[20.32, 20.48, 21.4, 22.68, 24.52, 24.52, 27.12, 28.4, 28.88, 28.48, 27.56, 25.92, 24.36, 24.48, 24.52, 24.56, 24.56, 24.24, 24.24, 24.2, 23.96, 23.92, 24.16, 24.36, 24.48, 24.52, 24.56, 24.56, 24.36, 24.28, 24.28, 24.48, 24.36, 24.36, 24.6, 24.36, 24.24, 24.2, 24.24, 24.12, 24.16, 24.12, 24.24, 24.24, 24.36, 24.48, 24.48, 24.56, 24.4, 24.4, 24.24, 24.56, 24.84, 24.76, 24.76, 24.76, 24.84, 24.52, 24.64, 24.64, 24.64, 24.64, 24.52, 24.52, 24.52, 24.4, 24.36, 24.36, 24.36, 24.32, 24.24, 24.44, 24.2, 24.08, 24.2, 24.0, 23.92, 23.88, 23.84, 24.08, 24.08, 24.4, 24.6, 24.72, 24.8, 24.68, 24.4, 24.36, 24.24, 24.12, 24.16, 24.2, 24.04, 24.04, 24.0, 24.12, 24.32, 24.36, 24.32, 24.4, 24.16, 24.08, 24.12, 24.32, 24.32, 24.32, 24.44, 24.56, 24.32, 24.36, 24.56, 24.68, 24.44, 24.48, 24.52, 24.44, 24.56, 24.56, 24.68, 24.6, 24.2, 24.6, 24.16, 24.24, 24.32, 24.56, 24.28, 24.48, 24.8, 24.68, 24.68, 24.84, 24.84, 24.84, 24.88, 24.56, 24.84, 24.68, 24.48, 24.76, 24.64, 24.64, 24.64, 24.52, 24.8, 24.68, 24.52, 24.68, 24.6, 24.44, 24.68, 24.76, 24.76, 24.64, 24.6, 24.6, 24.56, 24.44, 24.4, 24.52, 24.48, 24.44, 24.44, 24.32, 24.44, 24.28, 24.44, 24.44, 24.36, 24.16, 24.04, 24.0, 24.12, 24.0, 24.12, 24.4, 24.4, 24.32, 24.28, 24.28, 24.28, 24.2, 24.32, 24.32, 24.4, 24.6, 24.44, 24.56, 24.76, 24.84, 24.72, 24.72, 24.72, 24.56, 24.52, 24.56, 24.64, 24.6, 24.36, 24.44, 24.4, 24.4, 24.56, 24.72, 24.72, 24.6, 24.48, 24.36, 24.24, 24.24, 24.28, 24.44, 24.56, 24.56]
-216.55702543258667
-{'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': 213.33555269241333, 'TIME_S_1KI': 213.33555269241333, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5226.200544624329, 'W': 24.133137838334523}
-[20.4, 20.4, 20.4, 20.36, 20.36, 20.16, 20.32, 20.2, 20.2, 20.28, 20.2, 19.96, 20.08, 20.24, 20.24, 20.28, 20.48, 20.6, 20.64, 20.4]
-365.55999999999995
-18.278
-{'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': 213.33555269241333, 'TIME_S_1KI': 213.33555269241333, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5226.200544624329, 'W': 24.133137838334523, 'J_1KI': 5226.200544624329, 'W_1KI': 24.133137838334523, 'W_D': 5.8551378383345245, 'J_D': 1267.97123376751, 'W_D_1KI': 5.8551378383345245, 'J_D_1KI': 5.8551378383345245}
+[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04]
+[20.8, 20.6, 23.72, 23.72, 26.04, 27.56, 30.88, 32.68, 29.8, 29.08, 27.52, 26.28, 24.44, 24.48, 24.48, 24.2, 24.2, 24.2, 24.12, 24.12, 24.28, 24.28, 24.4, 24.32, 24.16, 24.0, 24.08, 24.16]
+28.461078882217407
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333}
+[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04, 19.8, 19.8, 19.8, 19.96, 20.0, 20.08, 20.12, 20.36, 20.32, 20.36]
+364.86
+18.243000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333, 'J_1KI': 6952.596657943726, 'W_1KI': 244.2843676698333, 'W_D': 6.1854367669833294, 'J_D': 176.04420374608034, 'W_D_1KI': 61.854367669833294, 'J_D_1KI': 618.543676698333}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
index 1e2c2f4..00303c3 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4943735599518, "TIME_S_1KI": 424.4943735599518, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10473.538437499996, "W": 24.301530579701517, "J_1KI": 10473.538437499996, "W_1KI": 24.301530579701517, "W_D": 5.865530579701517, "J_D": 2527.942006835934, "W_D_1KI": 5.865530579701517, "J_D_1KI": 5.865530579701517}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.006776571273804, "TIME_S_1KI": 420.06776571273804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.3857918548583, "W": 24.65839535462019, "J_1KI": 13233.857918548583, "W_1KI": 246.58395354620188, "W_D": 6.256395354620192, "J_D": 335.77305422592167, "W_D_1KI": 62.56395354620192, "J_D_1KI": 625.6395354620192}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
index a51b684..7c8bead 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.2 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4943735599518}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.006776571273804}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1949,     3966,  ..., 19996026,
-                            19998069, 20000000]),
-       col_indices=tensor([   7,   21,   26,  ..., 9986, 9990, 9999]),
-       values=tensor([0.4961, 0.8613, 0.9281,  ..., 0.1556, 0.7430, 0.8560]),
+tensor(crow_indices=tensor([       0,     1950,     3929,  ..., 19995954,
+                            19997973, 20000000]),
+       col_indices=tensor([   0,   10,   17,  ..., 9977, 9980, 9990]),
+       values=tensor([0.1470, 0.8510, 0.9446,  ..., 0.3735, 0.6466, 0.3885]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.5082, 0.3131, 0.5448,  ..., 0.5922, 0.3726, 0.5476])
+tensor([0.1499, 0.7404, 0.4886,  ..., 0.1182, 0.4158, 0.3615])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 424.4943735599518 seconds
+Time: 42.006776571273804 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1949,     3966,  ..., 19996026,
-                            19998069, 20000000]),
-       col_indices=tensor([   7,   21,   26,  ..., 9986, 9990, 9999]),
-       values=tensor([0.4961, 0.8613, 0.9281,  ..., 0.1556, 0.7430, 0.8560]),
+tensor(crow_indices=tensor([       0,     1950,     3929,  ..., 19995954,
+                            19997973, 20000000]),
+       col_indices=tensor([   0,   10,   17,  ..., 9977, 9980, 9990]),
+       values=tensor([0.1470, 0.8510, 0.9446,  ..., 0.3735, 0.6466, 0.3885]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.5082, 0.3131, 0.5448,  ..., 0.5922, 0.3726, 0.5476])
+tensor([0.1499, 0.7404, 0.4886,  ..., 0.1182, 0.4158, 0.3615])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 424.4943735599518 seconds
+Time: 42.006776571273804 seconds
 
-[20.56, 20.4, 20.48, 20.48, 20.56, 20.8, 20.96, 20.68, 20.68, 20.52]
-[20.4, 20.4, 20.84, 23.24, 23.96, 25.96, 29.08, 30.08, 29.8, 30.04, 30.84, 27.8, 27.8, 27.2, 26.24, 24.6, 24.44, 24.32, 24.24, 24.32, 24.2, 24.44, 24.6, 24.6, 24.72, 24.72, 24.84, 24.88, 24.92, 24.96, 24.6, 24.48, 24.36, 24.16, 24.36, 24.64, 24.68, 24.68, 24.72, 24.68, 24.52, 24.52, 24.4, 24.44, 24.24, 24.36, 24.4, 24.48, 24.44, 24.44, 24.44, 24.24, 24.28, 24.24, 24.44, 24.4, 24.28, 24.24, 24.28, 24.24, 24.48, 24.84, 24.84, 24.56, 24.64, 24.6, 24.88, 24.68, 24.56, 24.56, 24.4, 24.4, 24.32, 24.36, 24.44, 24.44, 24.36, 24.48, 24.64, 24.8, 24.68, 24.92, 24.88, 24.76, 24.88, 24.68, 24.36, 24.36, 24.4, 24.44, 24.68, 24.76, 24.56, 24.64, 24.24, 24.32, 24.72, 24.6, 24.96, 24.96, 25.12, 25.04, 24.44, 24.36, 24.28, 24.16, 24.4, 24.44, 24.56, 24.68, 24.72, 24.48, 24.48, 24.4, 24.36, 24.24, 24.2, 24.52, 24.64, 24.84, 24.8, 24.56, 24.28, 24.16, 24.16, 24.16, 24.28, 24.4, 24.6, 24.64, 24.52, 24.56, 24.44, 24.36, 24.56, 24.84, 24.64, 24.64, 24.84, 24.72, 24.48, 24.6, 24.64, 24.68, 24.6, 24.4, 24.4, 24.16, 24.12, 24.12, 24.28, 24.4, 24.48, 24.76, 24.6, 24.44, 24.28, 24.32, 24.24, 24.32, 24.4, 24.4, 24.56, 24.48, 24.48, 24.28, 24.68, 24.52, 24.32, 24.4, 24.56, 24.28, 24.4, 24.72, 24.72, 24.76, 24.76, 24.72, 24.64, 24.56, 24.6, 24.56, 24.64, 24.48, 24.44, 24.4, 24.4, 24.36, 24.32, 24.48, 24.48, 24.48, 24.32, 24.4, 24.08, 24.16, 24.28, 24.44, 24.48, 24.48, 24.6, 24.56, 24.44, 24.4, 24.44, 24.44, 24.64, 24.72, 24.44, 24.48, 24.32, 24.32, 24.4, 24.28, 24.52, 24.56, 24.68, 24.68, 24.72, 24.88, 24.96, 25.04, 24.84, 24.84, 24.84, 24.6, 24.64, 24.6, 24.6, 24.6, 24.36, 24.28, 24.32, 24.48, 24.48, 24.44, 24.44, 24.56, 24.56, 24.84, 24.72, 24.8, 24.72, 24.6, 24.56, 24.68, 24.88, 24.88, 24.88, 24.88, 24.48, 24.48, 24.32, 24.32, 24.36, 24.36, 24.44, 24.32, 24.36, 24.44, 24.32, 24.32, 24.28, 24.64, 24.8, 24.8, 24.96, 24.76, 24.8, 24.68, 24.52, 24.64, 24.48, 24.48, 24.56, 24.48, 24.32, 24.28, 24.32, 24.36, 24.28, 24.36, 24.4, 24.28, 24.36, 24.36, 24.32, 24.16, 24.04, 24.2, 24.2, 24.32, 24.36, 24.52, 24.36, 24.44, 24.56, 24.64, 24.64, 24.84, 24.8, 24.8, 24.64, 24.48, 24.52, 24.4, 24.6, 24.32, 24.24, 24.2, 24.2, 24.16, 24.2, 24.56, 24.56, 24.68, 24.8, 24.8, 24.68, 24.64, 24.68, 24.6, 24.64, 24.64, 24.6, 24.6, 24.4, 24.28, 24.36, 24.4, 24.48, 24.44, 24.48, 24.52, 24.36, 24.36, 24.44, 24.32, 24.24, 24.44, 24.44, 24.68, 24.56, 24.4, 24.52, 24.4, 24.24, 24.24, 24.4, 24.44, 24.48, 24.6, 24.76, 24.76, 24.64, 24.72, 24.44, 24.48, 24.6, 24.56, 24.56, 24.64, 24.72, 24.56, 24.4, 24.24, 24.2, 24.16, 24.08, 24.04, 24.12, 24.32, 24.32, 24.36, 24.36, 24.6, 24.56, 24.52, 24.76, 24.6, 24.68, 24.56, 24.76, 24.8, 24.76, 24.76, 24.88, 24.64, 24.76, 24.52, 24.44, 24.2, 24.68, 24.48, 24.84, 25.0, 25.12, 25.12, 24.96, 24.84, 24.52, 24.28, 24.2, 24.48, 24.48, 24.52, 24.52, 24.56, 24.6, 24.6, 24.76, 24.76, 25.0, 24.88, 24.92, 24.88, 24.64, 24.72, 24.64, 24.68, 24.68, 24.56, 24.56, 24.36, 24.28, 24.32]
-430.982666015625
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4943735599518, 'TIME_S_1KI': 424.4943735599518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10473.538437499996, 'W': 24.301530579701517}
-[20.56, 20.4, 20.48, 20.48, 20.56, 20.8, 20.96, 20.68, 20.68, 20.52, 20.2, 20.36, 20.48, 20.56, 20.28, 20.2, 20.2, 20.24, 20.52, 20.4]
-368.72
-18.436
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4943735599518, 'TIME_S_1KI': 424.4943735599518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10473.538437499996, 'W': 24.301530579701517, 'J_1KI': 10473.538437499996, 'W_1KI': 24.301530579701517, 'W_D': 5.865530579701517, 'J_D': 2527.942006835934, 'W_D_1KI': 5.865530579701517, 'J_D_1KI': 5.865530579701517}
+[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56]
+[20.6, 20.56, 20.76, 24.64, 26.0, 26.92, 30.76, 29.04, 28.68, 29.52, 30.24, 30.24, 27.76, 27.48, 26.4, 24.64, 24.48, 24.32, 24.28, 24.28, 24.28, 24.08, 24.28, 24.28, 24.36, 24.2, 24.24, 24.6, 24.64, 24.52, 24.72, 24.48, 24.64, 24.4, 24.52, 24.52, 24.36, 24.4, 24.4, 24.4, 24.48, 24.68, 24.76, 24.56, 24.36, 24.16, 24.24, 24.4, 24.4, 24.76, 24.88, 24.96, 24.92]
+53.668771743774414
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019}
+[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56, 20.12, 19.92, 20.0, 20.28, 20.44, 20.72, 20.96, 21.04, 21.04, 21.04]
+368.03999999999996
+18.401999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019, 'J_1KI': 13233.857918548583, 'W_1KI': 246.58395354620188, 'W_D': 6.256395354620192, 'J_D': 335.77305422592167, 'W_D_1KI': 62.56395354620192, 'J_D_1KI': 625.6395354620192}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
index 25c5266..efd7bc8 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 637.8268127441406, "TIME_S_1KI": 637.8268127441406, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 15996.775521488189, "W": 24.370595996658984, "J_1KI": 15996.775521488189, "W_1KI": 24.370595996658984, "W_D": 5.917595996658985, "J_D": 3884.2896906743035, "W_D_1KI": 5.917595996658985, "J_D_1KI": 5.917595996658985}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 67.20304369926453, "TIME_S_1KI": 672.0304369926453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1846.4787199783327, "W": 24.29422238106021, "J_1KI": 18464.787199783328, "W_1KI": 242.9422238106021, "W_D": 5.818222381060206, "J_D": 442.2131174325942, "W_D_1KI": 58.18222381060206, "J_D_1KI": 581.8222381060207}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
index 294dece..f1c9a57 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.3 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 637.8268127441406}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 67.20304369926453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2961,     5942,  ..., 29993896,
-                            29996981, 30000000]),
-       col_indices=tensor([   2,    5,   14,  ..., 9994, 9995, 9996]),
-       values=tensor([0.0155, 0.6045, 0.5805,  ..., 0.2136, 0.4050, 0.0107]),
+tensor(crow_indices=tensor([       0,     2926,     5920,  ..., 29993999,
+                            29997022, 30000000]),
+       col_indices=tensor([   1,    4,    6,  ..., 9978, 9982, 9992]),
+       values=tensor([0.3929, 0.6592, 0.7367,  ..., 0.3321, 0.3012, 0.1502]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.2173, 0.9101, 0.0107,  ..., 0.2401, 0.6161, 0.6478])
+tensor([0.6782, 0.5388, 0.0901,  ..., 0.7339, 0.4235, 0.1483])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 637.8268127441406 seconds
+Time: 67.20304369926453 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2961,     5942,  ..., 29993896,
-                            29996981, 30000000]),
-       col_indices=tensor([   2,    5,   14,  ..., 9994, 9995, 9996]),
-       values=tensor([0.0155, 0.6045, 0.5805,  ..., 0.2136, 0.4050, 0.0107]),
+tensor(crow_indices=tensor([       0,     2926,     5920,  ..., 29993999,
+                            29997022, 30000000]),
+       col_indices=tensor([   1,    4,    6,  ..., 9978, 9982, 9992]),
+       values=tensor([0.3929, 0.6592, 0.7367,  ..., 0.3321, 0.3012, 0.1502]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.2173, 0.9101, 0.0107,  ..., 0.2401, 0.6161, 0.6478])
+tensor([0.6782, 0.5388, 0.0901,  ..., 0.7339, 0.4235, 0.1483])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 637.8268127441406 seconds
+Time: 67.20304369926453 seconds
 
-[20.6, 20.64, 20.76, 20.6, 20.44, 20.44, 20.48, 20.64, 20.64, 20.88]
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-656.396565914154
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 637.8268127441406, 'TIME_S_1KI': 637.8268127441406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15996.775521488189, 'W': 24.370595996658984}
-[20.6, 20.64, 20.76, 20.6, 20.44, 20.44, 20.48, 20.64, 20.64, 20.88, 20.28, 20.44, 20.44, 20.64, 20.68, 20.44, 20.36, 20.36, 20.12, 20.12]
-369.06
-18.453
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 637.8268127441406, 'TIME_S_1KI': 637.8268127441406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15996.775521488189, 'W': 24.370595996658984, 'J_1KI': 15996.775521488189, 'W_1KI': 24.370595996658984, 'W_D': 5.917595996658985, 'J_D': 3884.2896906743035, 'W_D_1KI': 5.917595996658985, 'J_D_1KI': 5.917595996658985}
+[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6]
+[20.64, 20.6, 20.6, 21.32, 22.6, 24.24, 25.68, 28.84, 30.4, 30.64, 31.4, 31.32, 28.4, 28.16, 27.72, 26.8, 26.8, 25.96, 24.6, 24.4, 24.12, 24.12, 24.12, 24.08, 24.36, 24.56, 24.8, 24.88, 24.88, 24.92, 24.92, 24.72, 24.64, 24.44, 24.28, 24.52, 24.68, 24.64, 24.68, 24.64, 24.64, 24.52, 24.76, 24.84, 24.68, 24.72, 24.68, 24.68, 24.76, 24.68, 24.52, 24.2, 24.12, 24.12, 24.24, 24.48, 24.64, 24.76, 24.76, 24.52, 24.28, 24.32, 24.12, 24.04, 24.32, 24.32, 24.6, 24.52, 24.76, 24.72, 24.36, 24.12, 24.16, 24.36, 24.48]
+76.0048496723175
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021}
+[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6, 20.04, 20.16, 20.28, 20.16, 20.16, 20.64, 20.72, 20.72, 20.92, 20.68]
+369.52000000000004
+18.476000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021, 'J_1KI': 18464.787199783328, 'W_1KI': 242.9422238106021, 'W_D': 5.818222381060206, 'J_D': 442.2131174325942, 'W_D_1KI': 58.18222381060206, 'J_D_1KI': 581.8222381060207}
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 7258aa7..e0e6919 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 145476, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.560847282409668, "TIME_S_1KI": 0.07259511728676668, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.8679765701293, "W": 22.125839021878733, "J_1KI": 2.1643980902013342, "W_1KI": 0.15209270960075016, "W_D": 3.6298390218787304, "J_D": 51.65544533538807, "W_D_1KI": 0.024951462934633413, "J_D_1KI": 0.00017151600906426773}
diff --git a/pytorch/output_synthetic_16core/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 5a62a56..29cdb63 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
@@ -1,538 +1,373 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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": 0.08288788795471191}
-
-/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,    0,  ...,  999, 1000, 1000]),
-       col_indices=tensor([8276, 8512, 1857, 9453, 5594, 1786,  645, 6447, 5709,
-                           9757, 8196, 1491, 9162, 8503,  189,   10, 1296, 6876,
-                            119, 8563,  749, 1550, 2318, 8246, 5733, 8006, 6329,
-                           7471, 3720, 9382, 1064, 3289, 8282, 2516, 8872, 2976,
-                           6001, 4221, 9050,    2, 6851, 1283, 8840, 7445, 9952,
-                           3963, 7775, 4665, 7671, 1625, 7201, 7423, 9095, 4003,
-                           9039, 8222, 4002, 4851, 2216, 6397, 2573, 1155, 1658,
-                           5402, 1265, 1882, 3515, 3745, 1128, 1774,  309, 5274,
-                             84, 2261,  875, 7389, 8770, 9055, 1245, 4813, 5184,
-                           2172, 1801, 9623, 6859, 2196, 1926, 1262, 2389, 9131,
-                           4836,  349, 6978, 9280, 3446, 5839, 9885, 3634, 6691,
-                           6641, 6961, 8613, 9421, 2247, 8553,  789, 6806, 4532,
-                           3327, 6426, 7167, 5921, 1683, 7786, 6904, 6367, 1125,
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-                           4751, 4079,  855, 2750,  867, 3028,  909, 1327, 4954,
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-                           5229, 4813, 2971, 8367, 8880, 1956, 3166, 5488, 9128,
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-                           2324, 6868, 2121, 7163, 7046, 8735,  121, 8887, 9517,
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-                           4660, 3318, 7366, 7252, 3513, 8003, 1227, 4245,  740,
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4940, 0.1637, 0.9163,  ..., 0.2332, 0.8903, 0.4879])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-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 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015525579452514648}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      6.1330e-01, 3.2294e-01, 3.1984e-01, 5.8267e-01,
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+                      1.6081e-02, 9.8894e-01, 5.8987e-01, 3.5565e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.9110, 0.9462, 0.7927,  ..., 0.0987, 0.6084, 0.0709])
+tensor([0.3198, 0.6605, 0.6944,  ..., 0.6134, 0.5235, 0.6507])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -540,378 +375,650 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 9.372220277786255 seconds
+Time: 0.015525579452514648 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 67630 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.8812994956970215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
+       col_indices=tensor([8647, 4654, 5499, 4449, 7451, 3553, 6737, 3727, 9375,
+                            328, 3652, 7464, 1792, 3337, 3219, 9816, 8774,  722,
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+                           8379, 2007,  678, 8640,  712, 2714, 3092, 8404,  958,
+                           6892, 5229,  232, 1407, 5081, 5811, 8597, 2045,  959,
+                           9609, 9725,  137, 6909, 8887, 5531, 9907, 8259, 3598,
+                           3106, 4242, 1459,  975, 9373,  279, 5289,  239, 4982,
+                           7449, 2338, 3106, 7326, 5651, 7345, 5951, 2276, 9406,
+                           1555,  187, 5936, 3172, 5886, 8072, 5078, 8086, 5802,
+                           9928, 7066, 2942, 5468, 8880, 3550, 6036, 7923, 2059,
+                           5727, 8966, 3271, 5191, 8019,  143, 8926, 3410, 1927,
+                           5129, 6995, 4214, 8413, 2923, 5122,  289, 6094, 9084,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0493, 0.7033, 0.8450,  ..., 0.3708, 0.9702, 0.0106])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 4.8812994956970215 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 145476 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.560847282409668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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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+                      0.9874, 0.0901, 0.6221, 0.7762, 0.9765, 0.8525]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0747, 0.3561, 0.1255,  ..., 0.0664, 0.4841, 0.3262])
+tensor([0.4976, 0.0481, 0.1913,  ..., 0.0301, 0.3766, 0.0826])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -919,375 +1026,268 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.197850227355957 seconds
+Time: 10.560847282409668 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.9874, 0.0901, 0.6221, 0.7762, 0.9765, 0.8525]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0747, 0.3561, 0.1255,  ..., 0.0664, 0.4841, 0.3262])
+tensor([0.4976, 0.0481, 0.1913,  ..., 0.0301, 0.3766, 0.0826])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.197850227355957 seconds
+Time: 10.560847282409668 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}
+[20.4, 20.16, 20.52, 20.6, 20.92, 20.92, 20.96, 20.76, 20.68, 20.44]
+[20.4, 20.52, 20.92, 23.12, 24.52, 26.12, 26.6, 26.16, 24.72, 24.72, 23.36, 23.32, 23.4, 23.52]
+14.230781316757202
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 145476, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.560847282409668, 'TIME_S_1KI': 0.07259511728676668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.8679765701293, 'W': 22.125839021878733}
+[20.4, 20.16, 20.52, 20.6, 20.92, 20.92, 20.96, 20.76, 20.68, 20.44, 20.2, 20.44, 20.48, 20.56, 20.52, 20.48, 20.4, 20.4, 20.4, 20.4]
+369.9200000000001
+18.496000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 145476, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.560847282409668, 'TIME_S_1KI': 0.07259511728676668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.8679765701293, 'W': 22.125839021878733, 'J_1KI': 2.1643980902013342, 'W_1KI': 0.15209270960075016, 'W_D': 3.6298390218787304, 'J_D': 51.65544533538807, 'W_D_1KI': 0.024951462934633413, 'J_D_1KI': 0.00017151600906426773}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
index 282c250..2bcb239 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52721, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.043588876724243, "TIME_S_1KI": 0.20947229522816796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.41147632598876, "W": 21.8334966893401, "J_1KI": 5.8878146530981725, "W_1KI": 0.4141328254270613, "W_D": 3.2964966893401026, "J_D": 46.86699609327319, "W_D_1KI": 0.06252720337892116, "J_D_1KI": 0.0011860018470613448}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52342, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.44128155708313, "TIME_S_1KI": 0.19948189899283808, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.7413140869141, "W": 23.567352524316387, "J_1KI": 6.395271752835469, "W_1KI": 0.4502570120422679, "W_D": 3.4093525243163825, "J_D": 48.42508902931206, "W_D_1KI": 0.06513607665577133, "J_D_1KI": 0.0012444323231013588}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
index 2fcd1ba..0a68cee 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.24570083618164062}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.027875900268554688}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([7274, 1823, 9481,  ..., 3720, 7669, 6157]),
-       values=tensor([0.0699, 0.4403, 0.9366,  ..., 0.7220, 0.3462, 0.9666]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([4151, 7566,   61,  ..., 1923, 6890, 8738]),
+       values=tensor([0.6199, 0.1524, 0.8589,  ..., 0.4429, 0.5764, 0.1533]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.3652, 0.9468, 0.8818,  ..., 0.3143, 0.5478, 0.8274])
+tensor([0.5335, 0.6247, 0.4039,  ..., 0.6064, 0.4993, 0.4017])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.24570083618164062 seconds
+Time: 0.027875900268554688 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 42734 -ss 10000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.510959386825562}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37666 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.555848598480225}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
-       col_indices=tensor([3889, 8009,  975,  ...,  383, 3476, 3024]),
-       values=tensor([0.2888, 0.9236, 0.0703,  ..., 0.2234, 0.4670, 0.5913]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([4832, 7617, 3198,  ..., 2337, 8239, 2535]),
+       values=tensor([0.0012, 0.2497, 0.5477,  ..., 0.0331, 0.3343, 0.4565]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.8206, 0.5304, 0.1258,  ..., 0.8056, 0.8493, 0.1547])
+tensor([0.1414, 0.6293, 0.2915,  ..., 0.6179, 0.0556, 0.9688])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 8.510959386825562 seconds
+Time: 7.555848598480225 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52721 -ss 10000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.043588876724243}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52342 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.44128155708313}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([3785, 9007, 8216,  ..., 3831, 7720, 5051]),
-       values=tensor([0.5982, 0.6190, 0.5749,  ..., 0.7670, 0.1287, 0.1052]),
+       col_indices=tensor([6929, 6481, 6208,  ..., 5185, 5914, 4436]),
+       values=tensor([0.2292, 0.3731, 0.2148,  ..., 0.4978, 0.6385, 0.1071]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6986, 0.7258, 0.0612,  ..., 0.6419, 0.7078, 0.3008])
+tensor([0.7171, 0.2412, 0.9457,  ..., 0.4356, 0.0163, 0.8101])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 11.043588876724243 seconds
+Time: 10.44128155708313 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([3785, 9007, 8216,  ..., 3831, 7720, 5051]),
-       values=tensor([0.5982, 0.6190, 0.5749,  ..., 0.7670, 0.1287, 0.1052]),
+       col_indices=tensor([6929, 6481, 6208,  ..., 5185, 5914, 4436]),
+       values=tensor([0.2292, 0.3731, 0.2148,  ..., 0.4978, 0.6385, 0.1071]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6986, 0.7258, 0.0612,  ..., 0.6419, 0.7078, 0.3008])
+tensor([0.7171, 0.2412, 0.9457,  ..., 0.4356, 0.0163, 0.8101])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 11.043588876724243 seconds
+Time: 10.44128155708313 seconds
 
-[20.64, 20.64, 20.64, 20.64, 20.48, 20.52, 20.4, 20.52, 20.6, 20.56]
-[20.56, 20.6, 20.72, 22.44, 23.64, 25.6, 26.32, 25.84, 25.0, 23.2, 23.28, 23.24, 23.44, 23.44]
-14.217213153839111
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52721, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.043588876724243, 'TIME_S_1KI': 0.20947229522816796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.41147632598876, 'W': 21.8334966893401}
-[20.64, 20.64, 20.64, 20.64, 20.48, 20.52, 20.4, 20.52, 20.6, 20.56, 20.36, 20.48, 20.48, 20.44, 20.56, 20.68, 20.88, 20.96, 20.72, 20.64]
-370.74
-18.537
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52721, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.043588876724243, 'TIME_S_1KI': 0.20947229522816796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.41147632598876, 'W': 21.8334966893401, 'J_1KI': 5.8878146530981725, 'W_1KI': 0.4141328254270613, 'W_D': 3.2964966893401026, 'J_D': 46.86699609327319, 'W_D_1KI': 0.06252720337892116, 'J_D_1KI': 0.0011860018470613448}
+[25.48, 25.4, 25.4, 24.8, 24.68, 24.36, 24.4, 24.16, 24.24, 23.68]
+[23.36, 22.52, 25.48, 26.0, 27.84, 27.84, 27.8, 28.32, 24.88, 24.76, 23.72, 23.52, 23.72, 23.68]
+14.20360279083252
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52342, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.44128155708313, 'TIME_S_1KI': 0.19948189899283808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.7413140869141, 'W': 23.567352524316387}
+[25.48, 25.4, 25.4, 24.8, 24.68, 24.36, 24.4, 24.16, 24.24, 23.68, 20.0, 19.92, 19.76, 20.2, 20.12, 20.12, 20.36, 20.24, 20.24, 20.36]
+403.1600000000001
+20.158000000000005
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52342, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.44128155708313, 'TIME_S_1KI': 0.19948189899283808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.7413140869141, 'W': 23.567352524316387, 'J_1KI': 6.395271752835469, 'W_1KI': 0.4502570120422679, 'W_D': 3.4093525243163825, 'J_D': 48.42508902931206, 'W_D_1KI': 0.06513607665577133, 'J_D_1KI': 0.0012444323231013588}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
index 8015441..ece3918 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654, "TIME_S_1KI": 78.25872588157654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4354.838327121737, "W": 46.22946318790178, "J_1KI": 4354.838327121737, "W_1KI": 46.22946318790178, "W_D": 27.22746318790178, "J_D": 2564.8405165128734, "W_D_1KI": 27.22746318790178, "J_D_1KI": 27.22746318790178}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 137, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396055936813354, "TIME_S_1KI": 75.88361997673981, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 753.5857475948335, "W": 33.1694657622459, "J_1KI": 5500.625894852799, "W_1KI": 242.11288877551752, "W_D": 14.2684657622459, "J_D": 324.16899673771866, "W_D_1KI": 104.14938512588249, "J_D_1KI": 760.2144899699452}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
index f9d34b2..5419742 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.629654169082642}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       62,      108,  ..., 24999902,
-                            24999957, 25000000]),
-       col_indices=tensor([  8122,  13146,  17492,  ..., 475877, 485043,
-                           496973]),
-       values=tensor([0.3209, 0.8894, 0.3965,  ..., 0.0269, 0.4047, 0.8747]),
+tensor(crow_indices=tensor([       0,       44,       92,  ..., 24999905,
+                            24999955, 25000000]),
+       col_indices=tensor([  2191,   6192,  41052,  ..., 471066, 488040,
+                           493296]),
+       values=tensor([0.3986, 0.5227, 0.3241,  ..., 0.9261, 0.7192, 0.3287]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9161, 0.4114, 0.3584,  ..., 0.3416, 0.2120, 0.9282])
+tensor([0.0957, 0.3468, 0.1431,  ..., 0.5849, 0.2942, 0.3782])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 78.25872588157654 seconds
+Time: 7.629654169082642 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 137 -ss 500000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396055936813354}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       62,      108,  ..., 24999902,
-                            24999957, 25000000]),
-       col_indices=tensor([  8122,  13146,  17492,  ..., 475877, 485043,
-                           496973]),
-       values=tensor([0.3209, 0.8894, 0.3965,  ..., 0.0269, 0.4047, 0.8747]),
+tensor(crow_indices=tensor([       0,       53,       94,  ..., 24999919,
+                            24999951, 25000000]),
+       col_indices=tensor([  2485,  12624,  22152,  ..., 462150, 467889,
+                           476331]),
+       values=tensor([0.9572, 0.0985, 0.5455,  ..., 0.5648, 0.8530, 0.8208]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9161, 0.4114, 0.3584,  ..., 0.3416, 0.2120, 0.9282])
+tensor([0.3242, 0.9080, 0.9457,  ..., 0.2147, 0.3332, 0.4113])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 78.25872588157654 seconds
+Time: 10.396055936813354 seconds
 
-[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8]
-[20.96, 20.88, 21.6, 21.6, 22.56, 24.32, 26.6, 27.68, 30.32, 31.76, 31.8, 31.36, 31.8, 32.64, 38.12, 43.32, 49.08, 53.24, 53.16, 52.56, 52.56, 52.56, 52.6, 52.28, 52.68, 53.0, 53.04, 53.24, 53.08, 53.48, 53.36, 53.16, 53.16, 52.68, 52.84, 52.88, 53.12, 52.96, 52.88, 53.2, 53.16, 53.0, 53.0, 52.96, 53.12, 52.84, 52.76, 53.08, 52.96, 52.84, 52.96, 52.92, 53.12, 53.2, 53.04, 53.28, 53.16, 53.12, 52.68, 52.96, 52.88, 52.72, 53.04, 53.08, 52.76, 52.76, 52.92, 53.32, 53.28, 53.32, 53.44, 53.28, 53.32, 53.4, 53.48, 53.48, 53.4, 53.4, 53.44, 53.12, 53.32, 53.44, 53.56, 53.52, 53.4, 53.36, 53.12, 53.12, 53.12, 53.16]
-94.20049524307251
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178}
-[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8, 21.64, 21.52, 21.28, 21.2, 21.12, 20.8, 20.84, 20.76, 21.0, 21.08]
-380.03999999999996
-19.002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178, 'J_1KI': 4354.838327121737, 'W_1KI': 46.22946318790178, 'W_D': 27.22746318790178, 'J_D': 2564.8405165128734, 'W_D_1KI': 27.22746318790178, 'J_D_1KI': 27.22746318790178}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,       94,  ..., 24999919,
+                            24999951, 25000000]),
+       col_indices=tensor([  2485,  12624,  22152,  ..., 462150, 467889,
+                           476331]),
+       values=tensor([0.9572, 0.0985, 0.5455,  ..., 0.5648, 0.8530, 0.8208]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3242, 0.9080, 0.9457,  ..., 0.2147, 0.3332, 0.4113])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.396055936813354 seconds
+
+[20.64, 20.72, 20.84, 20.96, 21.08, 21.36, 21.56, 21.56, 21.36, 21.36]
+[21.36, 21.04, 20.8, 24.16, 25.16, 26.72, 28.8, 27.16, 30.52, 29.84, 29.76, 29.92, 29.92, 30.28, 32.88, 38.36, 44.52, 49.04, 53.12, 53.28, 53.2, 52.84]
+22.719260931015015
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396055936813354, 'TIME_S_1KI': 75.88361997673981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.5857475948335, 'W': 33.1694657622459}
+[20.64, 20.72, 20.84, 20.96, 21.08, 21.36, 21.56, 21.56, 21.36, 21.36, 20.8, 20.84, 20.88, 21.0, 21.04, 20.84, 20.96, 20.68, 20.68, 20.52]
+378.02
+18.901
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396055936813354, 'TIME_S_1KI': 75.88361997673981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.5857475948335, 'W': 33.1694657622459, 'J_1KI': 5500.625894852799, 'W_1KI': 242.11288877551752, 'W_D': 14.2684657622459, 'J_D': 324.16899673771866, 'W_D_1KI': 104.14938512588249, 'J_D_1KI': 760.2144899699452}
diff --git a/pytorch/output_synthetic_16core/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 35c105f..28ed47d 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1548, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.061279058456421, "TIME_S_1KI": 7.145529107529987, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 576.3272948646545, "W": 36.84956710355697, "J_1KI": 372.3044540469344, "W_1KI": 23.80462991185851, "W_D": 18.03556710355697, "J_D": 282.0763014917373, "W_D_1KI": 11.65088314183267, "J_D_1KI": 7.526410298341518}
diff --git a/pytorch/output_synthetic_16core/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 2875e7e..ddb3af3 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": 7.073613166809082}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.6782102584838867}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,       8,      13,  ..., 2499990,
+                            2499997, 2500000]),
+       col_indices=tensor([ 40175, 122073, 147940,  ..., 245767, 297950,
+                           495791]),
+       values=tensor([0.1248, 0.8645, 0.7112,  ..., 0.2227, 0.8085, 0.2637]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1896, 0.3447, 0.8973,  ..., 0.8957, 0.5716, 0.6993])
+tensor([0.3055, 0.1588, 0.3916,  ..., 0.3608, 0.8122, 0.4114])
 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: 7.073613166809082 seconds
+Time: 0.6782102584838867 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1548 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.061279058456421}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      10,      15,  ..., 2499994,
-                            2500000, 2500000]),
-       col_indices=tensor([ 19808,  30523,  42041,  ..., 253465, 473291,
-                           475423]),
-       values=tensor([0.2655, 0.1335, 0.5252,  ..., 0.0072, 0.8874, 0.1974]),
+tensor(crow_indices=tensor([      0,       0,       4,  ..., 2499989,
+                            2499994, 2500000]),
+       col_indices=tensor([ 33871,  87157, 252512,  ..., 380315, 410804,
+                           497208]),
+       values=tensor([0.0607, 0.8545, 0.0688,  ..., 0.9965, 0.6178, 0.6113]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7673, 0.2797, 0.0430,  ..., 0.8352, 0.7956, 0.1250])
+tensor([0.3094, 0.9384, 0.5289,  ..., 0.5205, 0.7717, 0.7334])
 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: 10.88543152809143 seconds
+Time: 11.061279058456421 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      10,      15,  ..., 2499994,
-                            2500000, 2500000]),
-       col_indices=tensor([ 19808,  30523,  42041,  ..., 253465, 473291,
-                           475423]),
-       values=tensor([0.2655, 0.1335, 0.5252,  ..., 0.0072, 0.8874, 0.1974]),
+tensor(crow_indices=tensor([      0,       0,       4,  ..., 2499989,
+                            2499994, 2500000]),
+       col_indices=tensor([ 33871,  87157, 252512,  ..., 380315, 410804,
+                           497208]),
+       values=tensor([0.0607, 0.8545, 0.0688,  ..., 0.9965, 0.6178, 0.6113]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7673, 0.2797, 0.0430,  ..., 0.8352, 0.7956, 0.1250])
+tensor([0.3094, 0.9384, 0.5289,  ..., 0.5205, 0.7717, 0.7334])
 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: 10.88543152809143 seconds
+Time: 11.061279058456421 seconds
 
-[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}
+[20.72, 20.76, 21.12, 20.92, 20.92, 20.92, 20.88, 20.44, 20.48, 20.48]
+[20.56, 20.56, 21.24, 22.4, 24.48, 28.6, 36.72, 41.88, 48.52, 48.52, 52.36, 52.92, 53.48, 53.4, 53.48]
+15.640001773834229
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.061279058456421, 'TIME_S_1KI': 7.145529107529987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.3272948646545, 'W': 36.84956710355697}
+[20.72, 20.76, 21.12, 20.92, 20.92, 20.92, 20.88, 20.44, 20.48, 20.48, 20.6, 20.68, 20.88, 21.24, 21.48, 21.32, 21.48, 20.96, 20.64, 20.52]
+376.28
+18.814
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.061279058456421, 'TIME_S_1KI': 7.145529107529987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.3272948646545, 'W': 36.84956710355697, 'J_1KI': 372.3044540469344, 'W_1KI': 23.80462991185851, 'W_D': 18.03556710355697, 'J_D': 282.0763014917373, 'W_D_1KI': 11.65088314183267, 'J_D_1KI': 7.526410298341518}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
index e64af38..9620135 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 37.50764799118042, "TIME_S_1KI": 37.50764799118042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2045.1465811538696, "W": 43.475713756728496, "J_1KI": 2045.1465811538699, "W_1KI": 43.475713756728496, "W_D": 24.798713756728496, "J_D": 1166.5594483480452, "W_D_1KI": 24.798713756728496, "J_D_1KI": 24.798713756728496}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 285, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.060697317123413, "TIME_S_1KI": 38.809464270608466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 641.8192787170411, "W": 34.323302144261945, "J_1KI": 2251.9974691826005, "W_1KI": 120.43263910267349, "W_D": 15.818302144261942, "J_D": 295.7900504469872, "W_D_1KI": 55.50281454126997, "J_D_1KI": 194.74671768866656}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
index c540d96..0f40f5a 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 37.50764799118042}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.6834404468536377}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       37,       66,  ..., 12499959,
+tensor(crow_indices=tensor([       0,       36,       64,  ..., 12499962,
                             12499977, 12500000]),
-       col_indices=tensor([  2833,  12397,  17330,  ..., 457394, 482426,
-                           493028]),
-       values=tensor([0.7637, 0.4211, 0.1191,  ..., 0.6468, 0.8330, 0.4871]),
+       col_indices=tensor([  6540,  37225,  45963,  ..., 476281, 491551,
+                           491729]),
+       values=tensor([0.4995, 0.3434, 0.2289,  ..., 0.9980, 0.3953, 0.2839]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3810, 0.7581, 0.5448,  ..., 0.8790, 0.4682, 0.1184])
+tensor([0.6494, 0.0196, 0.1697,  ..., 0.1655, 0.3294, 0.8926])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 37.50764799118042 seconds
+Time: 3.6834404468536377 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 285 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.060697317123413}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       37,       66,  ..., 12499959,
-                            12499977, 12500000]),
-       col_indices=tensor([  2833,  12397,  17330,  ..., 457394, 482426,
-                           493028]),
-       values=tensor([0.7637, 0.4211, 0.1191,  ..., 0.6468, 0.8330, 0.4871]),
+tensor(crow_indices=tensor([       0,       30,       51,  ..., 12499947,
+                            12499979, 12500000]),
+       col_indices=tensor([ 32854,  40713,  51141,  ..., 464012, 471829,
+                           496055]),
+       values=tensor([0.7704, 0.8573, 0.2864,  ..., 0.9432, 0.9508, 0.7094]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3810, 0.7581, 0.5448,  ..., 0.8790, 0.4682, 0.1184])
+tensor([0.9627, 0.2273, 0.6691,  ..., 0.1238, 0.9472, 0.0057])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 37.50764799118042 seconds
+Time: 11.060697317123413 seconds
 
-[21.08, 20.96, 20.88, 20.72, 20.6, 20.6, 20.64, 20.2, 20.28, 20.6]
-[20.72, 20.68, 21.2, 23.0, 24.88, 26.4, 29.84, 29.72, 30.96, 36.72, 36.72, 41.28, 45.64, 50.76, 52.76, 52.48, 53.28, 52.92, 52.8, 52.76, 52.48, 52.52, 52.68, 52.72, 53.4, 53.44, 53.4, 53.36, 53.12, 53.4, 53.2, 53.0, 53.0, 53.24, 53.08, 52.92, 53.0, 52.72, 52.8, 52.64, 52.8, 52.96, 52.8, 52.8, 52.68]
-47.04112720489502
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 37.50764799118042, 'TIME_S_1KI': 37.50764799118042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.1465811538696, 'W': 43.475713756728496}
-[21.08, 20.96, 20.88, 20.72, 20.6, 20.6, 20.64, 20.2, 20.28, 20.6, 20.92, 21.12, 21.0, 20.84, 20.92, 20.72, 20.72, 20.56, 21.0, 20.96]
-373.54
-18.677
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 37.50764799118042, 'TIME_S_1KI': 37.50764799118042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.1465811538696, 'W': 43.475713756728496, 'J_1KI': 2045.1465811538699, 'W_1KI': 43.475713756728496, 'W_D': 24.798713756728496, 'J_D': 1166.5594483480452, 'W_D_1KI': 24.798713756728496, 'J_D_1KI': 24.798713756728496}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       51,  ..., 12499947,
+                            12499979, 12500000]),
+       col_indices=tensor([ 32854,  40713,  51141,  ..., 464012, 471829,
+                           496055]),
+       values=tensor([0.7704, 0.8573, 0.2864,  ..., 0.9432, 0.9508, 0.7094]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9627, 0.2273, 0.6691,  ..., 0.1238, 0.9472, 0.0057])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 11.060697317123413 seconds
+
+[20.52, 20.84, 20.92, 20.88, 20.88, 20.72, 20.52, 20.32, 20.36, 20.2]
+[20.44, 20.6, 21.48, 21.48, 23.84, 25.24, 27.32, 30.28, 29.76, 32.76, 37.84, 41.44, 46.92, 51.96, 52.12, 52.64, 52.6, 52.8]
+18.699228763580322
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.060697317123413, 'TIME_S_1KI': 38.809464270608466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 641.8192787170411, 'W': 34.323302144261945}
+[20.52, 20.84, 20.92, 20.88, 20.88, 20.72, 20.52, 20.32, 20.36, 20.2, 20.52, 20.44, 20.6, 20.44, 20.44, 20.48, 20.48, 20.36, 20.44, 20.72]
+370.1
+18.505000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.060697317123413, 'TIME_S_1KI': 38.809464270608466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 641.8192787170411, 'W': 34.323302144261945, 'J_1KI': 2251.9974691826005, 'W_1KI': 120.43263910267349, 'W_D': 15.818302144261942, 'J_D': 295.7900504469872, 'W_D_1KI': 55.50281454126997, 'J_D_1KI': 194.74671768866656}
diff --git a/pytorch/output_synthetic_16core/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 0acd95a..29a7f3d 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3424, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.731184720993042, "TIME_S_1KI": 3.1341076872059115, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 463.79336466789243, "W": 31.765422510706816, "J_1KI": 135.4536695875854, "W_1KI": 9.27728461177185, "W_D": 13.349422510706813, "J_D": 194.90921553230277, "W_D_1KI": 3.8987799388746534, "J_D_1KI": 1.1386623653255412}
diff --git a/pytorch/output_synthetic_16core/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 f123524..192c47b 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": 3.0953831672668457}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.4611988067626953}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249991,
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 249990, 249996,
                             250000]),
-       col_indices=tensor([  782, 10679, 21591,  ..., 21721, 25862, 26402]),
-       values=tensor([0.1080, 0.2599, 0.9753,  ..., 0.8598, 0.0309, 0.7621]),
+       col_indices=tensor([ 1266,  4071, 18947,  ..., 33754, 36171, 46993]),
+       values=tensor([0.2894, 0.3028, 0.5808,  ..., 0.9499, 0.5530, 0.4490]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0624, 0.3415, 0.4601,  ..., 0.0482, 0.7737, 0.1465])
+tensor([0.9097, 0.0887, 0.0049,  ..., 0.6179, 0.8641, 0.1772])
 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: 3.0953831672668457 seconds
+Time: 0.4611988067626953 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2276 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.978086233139038}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 249992, 249997,
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249995,
                             250000]),
-       col_indices=tensor([29888, 37512, 45145,  ..., 10362, 27481, 28096]),
-       values=tensor([0.5987, 0.4413, 0.1210,  ..., 0.9023, 0.1888, 0.1246]),
+       col_indices=tensor([ 2233,  6887, 19755,  ..., 38632, 41476, 48223]),
+       values=tensor([0.3109, 0.9167, 0.4160,  ..., 0.6671, 0.8506, 0.5777]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0260, 0.0462, 0.3716,  ..., 0.4992, 0.3586, 0.2225])
+tensor([0.7812, 0.4224, 0.5960,  ..., 0.2514, 0.6292, 0.3012])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.727018594741821 seconds
+Time: 6.978086233139038 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3424 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.731184720993042}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 249992, 249997,
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 249990, 249996,
                             250000]),
-       col_indices=tensor([29888, 37512, 45145,  ..., 10362, 27481, 28096]),
-       values=tensor([0.5987, 0.4413, 0.1210,  ..., 0.9023, 0.1888, 0.1246]),
+       col_indices=tensor([12104, 14436, 24112,  ..., 12878, 32819, 38734]),
+       values=tensor([0.5759, 0.9600, 0.3696,  ..., 0.0040, 0.7766, 0.9665]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0260, 0.0462, 0.3716,  ..., 0.4992, 0.3586, 0.2225])
+tensor([0.4011, 0.3434, 0.3941,  ..., 0.7256, 0.6030, 0.5117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.727018594741821 seconds
+Time: 10.731184720993042 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([12104, 14436, 24112,  ..., 12878, 32819, 38734]),
+       values=tensor([0.5759, 0.9600, 0.3696,  ..., 0.0040, 0.7766, 0.9665]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4011, 0.3434, 0.3941,  ..., 0.7256, 0.6030, 0.5117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.731184720993042 seconds
+
+[20.72, 20.68, 20.48, 20.6, 20.64, 20.32, 20.32, 20.36, 20.32, 20.4]
+[20.52, 20.76, 22.24, 22.24, 24.16, 27.64, 32.84, 38.08, 39.84, 44.4, 44.52, 43.96, 44.12, 44.28]
+14.60057282447815
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3424, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.731184720993042, 'TIME_S_1KI': 3.1341076872059115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.79336466789243, 'W': 31.765422510706816}
+[20.72, 20.68, 20.48, 20.6, 20.64, 20.32, 20.32, 20.36, 20.32, 20.4, 20.68, 20.44, 20.48, 20.48, 20.2, 20.32, 20.44, 20.44, 20.6, 20.6]
+368.32000000000005
+18.416000000000004
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3424, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.731184720993042, 'TIME_S_1KI': 3.1341076872059115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.79336466789243, 'W': 31.765422510706816, 'J_1KI': 135.4536695875854, 'W_1KI': 9.27728461177185, 'W_D': 13.349422510706813, 'J_D': 194.90921553230277, 'W_D_1KI': 3.8987799388746534, 'J_D_1KI': 1.1386623653255412}
diff --git a/pytorch/output_synthetic_16core/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 3f36d4c..e699ae2 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 385, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.064192295074463, "TIME_S_1KI": 28.738161805388216, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 468.0283072185517, "W": 32.037671646494225, "J_1KI": 1215.657940827407, "W_1KI": 83.21473154933565, "W_D": 13.341671646494227, "J_D": 194.90430094528205, "W_D_1KI": 34.65369258829669, "J_D_1KI": 90.00959113843297}
diff --git a/pytorch/output_synthetic_16core/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 c095d19..c964dd6 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": 29.441463470458984}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.9453940391540527}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      37,      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]),
+tensor(crow_indices=tensor([      0,      37,      86,  ..., 2499902,
+                            2499952, 2500000]),
+       col_indices=tensor([  541,  1139,  1813,  ..., 42919, 43072, 44933]),
+       values=tensor([0.0452, 0.1724, 0.8861,  ..., 0.4157, 0.9772, 0.2120]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6700, 0.5614, 0.5608,  ..., 0.8928, 0.8615, 0.5607])
+tensor([0.1045, 0.1557, 0.1178,  ..., 0.5894, 0.9079, 0.5773])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 29.441463470458984 seconds
+Time: 2.9453940391540527 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 356 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.708060026168823}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      46,      99,  ..., 2499902,
+                            2499948, 2500000]),
+       col_indices=tensor([ 4031,  7226,  7309,  ..., 44877, 48582, 49711]),
+       values=tensor([0.9329, 0.4420, 0.5313,  ..., 0.9423, 0.2849, 0.2389]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6700, 0.5614, 0.5608,  ..., 0.8928, 0.8615, 0.5607])
+tensor([0.0088, 0.8123, 0.3302,  ..., 0.9483, 0.6171, 0.9552])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +36,50 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 29.441463470458984 seconds
+Time: 9.708060026168823 seconds
 
-[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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 385 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.064192295074463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      67,     125,  ..., 2499902,
+                            2499956, 2500000]),
+       col_indices=tensor([ 1129,  2884,  2891,  ..., 49010, 49022, 49816]),
+       values=tensor([0.8127, 0.7656, 0.2912,  ..., 0.8978, 0.1718, 0.1428]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7569, 0.5985, 0.1427,  ..., 0.6714, 0.1732, 0.3064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 11.064192295074463 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      67,     125,  ..., 2499902,
+                            2499956, 2500000]),
+       col_indices=tensor([ 1129,  2884,  2891,  ..., 49010, 49022, 49816]),
+       values=tensor([0.8127, 0.7656, 0.2912,  ..., 0.8978, 0.1718, 0.1428]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7569, 0.5985, 0.1427,  ..., 0.6714, 0.1732, 0.3064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 11.064192295074463 seconds
+
+[20.72, 20.52, 20.56, 20.48, 20.48, 20.52, 20.56, 20.6, 20.36, 20.6]
+[20.48, 20.52, 21.48, 23.4, 25.44, 29.64, 35.04, 38.48, 42.0, 43.12, 43.12, 43.84, 43.68, 43.32]
+14.608686685562134
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 385, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.064192295074463, 'TIME_S_1KI': 28.738161805388216, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 468.0283072185517, 'W': 32.037671646494225}
+[20.72, 20.52, 20.56, 20.48, 20.48, 20.52, 20.56, 20.6, 20.36, 20.6, 20.84, 20.8, 21.0, 21.24, 21.16, 21.08, 21.28, 20.92, 20.84, 20.88]
+373.91999999999996
+18.695999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 385, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.064192295074463, 'TIME_S_1KI': 28.738161805388216, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 468.0283072185517, 'W': 32.037671646494225, 'J_1KI': 1215.657940827407, 'W_1KI': 83.21473154933565, 'W_D': 13.341671646494227, 'J_D': 194.90430094528205, 'W_D_1KI': 34.65369258829669, 'J_D_1KI': 90.00959113843297}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json
index cc8d9c0..3c1f90f 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317, "TIME_S_1KI": 324.79648518562317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 11877.425473241823, "W": 35.646067141867775, "J_1KI": 11877.425473241823, "W_1KI": 35.646067141867775, "W_D": 16.970067141867776, "J_D": 5654.50059192373, "W_D_1KI": 16.970067141867776, "J_D_1KI": 16.970067141867776}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 32.80737018585205, "TIME_S_1KI": 328.0737018585205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1398.267660713196, "W": 32.405416577679354, "J_1KI": 13982.676607131958, "W_1KI": 324.0541657767935, "W_D": 13.697416577679352, "J_D": 591.0325080986024, "W_D_1KI": 136.97416577679354, "J_D_1KI": 1369.7416577679353}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output
index b2a5634..0d92e6b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 32.80737018585205}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      495,      976,  ..., 24999061,
-                            24999546, 25000000]),
-       col_indices=tensor([  151,   490,   496,  ..., 49361, 49363, 49505]),
-       values=tensor([0.8177, 0.6830, 0.3837,  ..., 0.7178, 0.0658, 0.9045]),
+tensor(crow_indices=tensor([       0,      489,      963,  ..., 24999055,
+                            24999529, 25000000]),
+       col_indices=tensor([   18,   157,   241,  ..., 49747, 49771, 49960]),
+       values=tensor([0.7706, 0.7949, 0.9210,  ..., 0.0962, 0.6322, 0.0053]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7632, 0.2578, 0.8207,  ..., 0.6267, 0.5426, 0.4264])
+tensor([0.2729, 0.2896, 0.6966,  ..., 0.6831, 0.4086, 0.6520])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 324.79648518562317 seconds
+Time: 32.80737018585205 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      495,      976,  ..., 24999061,
-                            24999546, 25000000]),
-       col_indices=tensor([  151,   490,   496,  ..., 49361, 49363, 49505]),
-       values=tensor([0.8177, 0.6830, 0.3837,  ..., 0.7178, 0.0658, 0.9045]),
+tensor(crow_indices=tensor([       0,      489,      963,  ..., 24999055,
+                            24999529, 25000000]),
+       col_indices=tensor([   18,   157,   241,  ..., 49747, 49771, 49960]),
+       values=tensor([0.7706, 0.7949, 0.9210,  ..., 0.0962, 0.6322, 0.0053]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7632, 0.2578, 0.8207,  ..., 0.6267, 0.5426, 0.4264])
+tensor([0.2729, 0.2896, 0.6966,  ..., 0.6831, 0.4086, 0.6520])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 324.79648518562317 seconds
+Time: 32.80737018585205 seconds
 
-[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88]
-[21.16, 21.04, 21.12, 26.2, 28.16, 29.12, 31.64, 29.84, 30.96, 30.96, 31.32, 31.32, 31.52, 31.84, 31.92, 33.6, 35.32, 36.84, 37.0, 36.88, 36.84, 36.12, 36.76, 36.76, 36.4, 37.24, 37.32, 37.12, 37.84, 37.0, 37.76, 37.32, 37.0, 35.96, 36.28, 35.4, 35.8, 36.36, 37.12, 37.12, 38.8, 38.96, 38.72, 38.44, 37.4, 38.36, 38.0, 38.04, 37.84, 37.56, 36.92, 36.92, 36.84, 36.48, 36.64, 36.64, 36.52, 35.64, 36.12, 36.84, 37.56, 37.52, 38.6, 37.96, 37.32, 37.44, 36.76, 37.36, 37.44, 37.76, 37.8, 37.8, 38.68, 38.12, 37.96, 37.72, 37.88, 37.92, 37.84, 36.92, 36.6, 36.4, 36.48, 36.4, 37.2, 37.04, 37.16, 36.8, 36.8, 36.72, 37.72, 38.0, 37.96, 37.76, 37.08, 37.48, 36.64, 36.8, 36.88, 37.2, 37.24, 37.44, 37.48, 38.04, 38.04, 38.16, 38.68, 37.96, 38.24, 37.16, 36.68, 36.84, 36.88, 37.16, 37.76, 37.92, 37.76, 37.56, 36.52, 36.0, 37.0, 36.44, 36.36, 36.68, 37.08, 37.4, 37.24, 37.32, 36.6, 36.2, 37.16, 37.32, 37.6, 37.6, 37.6, 37.56, 37.56, 37.24, 36.48, 36.28, 36.48, 36.64, 37.68, 38.24, 37.72, 37.64, 38.24, 37.6, 37.0, 37.0, 36.88, 36.88, 37.28, 38.48, 39.08, 38.28, 38.04, 37.48, 36.64, 36.72, 36.84, 36.84, 37.2, 37.36, 37.76, 37.96, 38.24, 37.88, 37.88, 37.12, 37.6, 36.76, 37.52, 37.68, 36.76, 37.72, 37.48, 38.04, 37.88, 37.6, 37.56, 36.96, 37.0, 37.0, 37.92, 37.08, 37.44, 36.8, 36.84, 36.08, 36.52, 36.48, 36.56, 36.84, 37.2, 37.36, 36.52, 37.0, 36.12, 36.12, 37.0, 36.68, 36.88, 37.56, 37.72, 38.36, 38.2, 38.48, 38.72, 38.36, 38.28, 37.96, 37.76, 36.36, 37.0, 36.48, 36.52, 36.52, 37.16, 36.6, 36.52, 36.6, 37.52, 37.12, 37.8, 37.88, 37.04, 36.64, 36.44, 36.04, 36.32, 37.68, 37.88, 37.88, 38.04, 38.04, 37.68, 37.92, 37.96, 36.92, 37.64, 36.4, 36.32, 36.4, 36.32, 36.2, 37.04, 37.16, 37.68, 37.64, 37.64, 38.12, 38.04, 37.64, 37.24, 36.56, 36.48, 37.28, 36.6, 36.44, 37.08, 37.08, 36.56, 37.48, 38.08, 37.2, 37.2, 36.96, 36.72, 36.64, 36.24, 37.32, 37.96, 38.2, 38.28, 38.36, 38.36, 37.88, 38.36, 37.64, 36.88, 36.88, 37.2, 36.4, 36.52, 37.2, 37.52, 37.44, 36.8, 37.48, 36.92, 37.32, 38.0, 37.76, 36.72, 37.84, 37.64, 37.64, 38.32, 38.32, 37.88, 38.16, 38.24, 37.64, 37.76, 37.12, 37.04, 36.24, 36.68, 36.4, 36.6, 36.6, 36.64, 37.16, 38.08, 37.28, 37.28, 37.24, 36.52, 36.4, 36.88, 36.2, 36.92]
-333.20437359809875
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775}
-[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88, 20.44, 20.44, 20.64, 20.68, 20.68, 20.68, 20.68, 20.84, 20.88, 20.88]
-373.52
-18.676
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775, 'J_1KI': 11877.425473241823, 'W_1KI': 35.646067141867775, 'W_D': 16.970067141867776, 'J_D': 5654.50059192373, 'W_D_1KI': 16.970067141867776, 'J_D_1KI': 16.970067141867776}
+[20.96, 20.68, 20.68, 20.68, 20.72, 20.64, 20.64, 20.76, 20.84, 20.92]
+[20.92, 20.84, 21.32, 22.6, 25.12, 26.44, 26.44, 28.96, 28.92, 32.68, 31.76, 31.16, 31.4, 31.52, 32.12, 34.6, 36.28, 37.8, 37.28, 37.56, 37.96, 37.96, 38.28, 38.36, 37.96, 37.32, 36.48, 36.68, 36.84, 37.44, 37.44, 37.76, 38.0, 39.0, 37.68, 36.84, 37.4, 37.4, 36.8, 37.32, 37.6, 37.72]
+43.14919567108154
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 32.80737018585205, 'TIME_S_1KI': 328.0737018585205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.267660713196, 'W': 32.405416577679354}
+[20.96, 20.68, 20.68, 20.68, 20.72, 20.64, 20.64, 20.76, 20.84, 20.92, 21.04, 20.84, 20.72, 20.68, 20.72, 20.6, 20.92, 21.2, 20.96, 20.84]
+374.16
+18.708000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 32.80737018585205, 'TIME_S_1KI': 328.0737018585205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.267660713196, 'W': 32.405416577679354, 'J_1KI': 13982.676607131958, 'W_1KI': 324.0541657767935, 'W_D': 13.697416577679352, 'J_D': 591.0325080986024, 'W_D_1KI': 136.97416577679354, 'J_D_1KI': 1369.7416577679353}
diff --git a/pytorch/output_synthetic_16core/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 719c8cd..a4de095 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": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 19951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462696075439453, "TIME_S_1KI": 0.5244196318700542, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 488.71538955688476, "W": 33.3802333465878, "J_1KI": 24.49578414900931, "W_1KI": 1.6731107887618566, "W_D": 15.011233346587801, "J_D": 219.77739569807053, "W_D_1KI": 0.752405059725718, "J_D_1KI": 0.03771264897627778}
diff --git a/pytorch/output_synthetic_16core/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 b0c7cd1..06a37e6 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": 0.648245096206665}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05949115753173828}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([35821, 49411,  3789,  ..., 32092, 27347, 39445]),
+       values=tensor([0.1439, 0.1701, 0.0383,  ..., 0.6521, 0.3755, 0.5678]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8531, 0.5584, 0.8209,  ..., 0.8853, 0.7506, 0.6837])
+tensor([0.8709, 0.6173, 0.3475,  ..., 0.7020, 0.1451, 0.7453])
 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: 0.648245096206665 seconds
+Time: 0.05949115753173828 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17649 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.288093090057373}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([10903, 22613,  1325,  ...,  4616, 25772, 38217]),
+       values=tensor([0.1548, 0.5404, 0.0562,  ..., 0.6796, 0.5534, 0.6437]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6191, 0.3887, 0.4199,  ..., 0.2754, 0.8424, 0.8817])
+tensor([0.8066, 0.3465, 0.3699,  ..., 0.5654, 0.2544, 0.1290])
 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: 8.461615800857544 seconds
+Time: 9.288093090057373 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 19951 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462696075439453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 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]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([36526, 27522,  9271,  ..., 28337, 20494, 41611]),
+       values=tensor([0.2838, 0.5711, 0.3512,  ..., 0.1758, 0.7475, 0.3339]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7523, 0.4685, 0.7648,  ..., 0.0829, 0.9708, 0.7467])
+tensor([0.9803, 0.0496, 0.4924,  ..., 0.5397, 0.0486, 0.3592])
 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: 10.650188207626343 seconds
+Time: 10.462696075439453 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 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]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([36526, 27522,  9271,  ..., 28337, 20494, 41611]),
+       values=tensor([0.2838, 0.5711, 0.3512,  ..., 0.1758, 0.7475, 0.3339]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7523, 0.4685, 0.7648,  ..., 0.0829, 0.9708, 0.7467])
+tensor([0.9803, 0.0496, 0.4924,  ..., 0.5397, 0.0486, 0.3592])
 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: 10.650188207626343 seconds
+Time: 10.462696075439453 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}
+[20.44, 20.56, 20.6, 20.64, 20.64, 20.44, 20.44, 20.44, 20.4, 20.56]
+[20.44, 20.64, 23.28, 24.2, 26.6, 32.52, 37.24, 40.44, 44.28, 44.96, 44.96, 44.92, 44.44, 43.84]
+14.640861988067627
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19951, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.462696075439453, 'TIME_S_1KI': 0.5244196318700542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 488.71538955688476, 'W': 33.3802333465878}
+[20.44, 20.56, 20.6, 20.64, 20.64, 20.44, 20.44, 20.44, 20.4, 20.56, 20.32, 20.32, 20.12, 20.24, 20.32, 20.56, 20.52, 20.28, 20.2, 20.0]
+367.38
+18.369
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19951, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.462696075439453, 'TIME_S_1KI': 0.5244196318700542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 488.71538955688476, 'W': 33.3802333465878, 'J_1KI': 24.49578414900931, 'W_1KI': 1.6731107887618566, 'W_D': 15.011233346587801, 'J_D': 219.77739569807053, 'W_D_1KI': 0.752405059725718, 'J_D_1KI': 0.03771264897627778}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
index f5cb3fd..cff7542 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6265, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.399809837341309, "TIME_S_1KI": 1.6599856085141753, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 409.2376235961914, "W": 30.158092261325162, "J_1KI": 65.32124877832264, "W_1KI": 4.813741781536339, "W_D": 11.570092261325161, "J_D": 157.0032023506164, "W_D_1KI": 1.846782483850784, "J_D_1KI": 0.2947777308620565}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6322, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.657772779464722, "TIME_S_1KI": 1.68582296416715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 425.8721401214599, "W": 31.344652680689325, "J_1KI": 67.3635147297469, "W_1KI": 4.958027946961298, "W_D": 12.573652680689325, "J_D": 170.83514789009087, "W_D_1KI": 1.9888726163697126, "J_D_1KI": 0.31459547870447846}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
index 80257e9..57f8461 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6757559776306152}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.3423471450805664}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      4,  ..., 124996, 124997,
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 124998, 124999,
                             125000]),
-       col_indices=tensor([11324, 36531, 41582,  ..., 26561, 37075, 42675]),
-       values=tensor([0.0907, 0.5500, 0.9495,  ..., 0.7742, 0.3202, 0.5187]),
+       col_indices=tensor([  303, 26221, 28347,  ...,  8622, 14261,  4291]),
+       values=tensor([0.9240, 0.5223, 0.0365,  ..., 0.6044, 0.0072, 0.5479]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.4295, 0.8994, 0.1269,  ..., 0.0289, 0.7051, 0.4729])
+tensor([0.1523, 0.9417, 0.1754,  ..., 0.6908, 0.2427, 0.5501])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 1.6757559776306152 seconds
+Time: 0.3423471450805664 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6265 -ss 50000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.399809837341309}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3067 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 5.0935986042022705}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 124998, 125000,
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 124997, 125000,
                             125000]),
-       col_indices=tensor([ 5059, 34750, 35724,  ..., 34703,  6591, 31118]),
-       values=tensor([0.4217, 0.1867, 0.1593,  ..., 0.5339, 0.2274, 0.5888]),
+       col_indices=tensor([ 1194,  5034,  6320,  ..., 11179, 21504, 33093]),
+       values=tensor([0.7209, 0.3055, 0.4482,  ..., 0.3076, 0.8643, 0.0918]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7798, 0.1756, 0.8709,  ..., 0.5047, 0.8577, 0.3016])
+tensor([0.9680, 0.6265, 0.9723,  ..., 0.1304, 0.1284, 0.7215])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.399809837341309 seconds
+Time: 5.0935986042022705 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6322 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.657772779464722}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 124998, 125000,
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124992, 124996,
                             125000]),
-       col_indices=tensor([ 5059, 34750, 35724,  ..., 34703,  6591, 31118]),
-       values=tensor([0.4217, 0.1867, 0.1593,  ..., 0.5339, 0.2274, 0.5888]),
+       col_indices=tensor([41720,  5446, 23409,  ..., 23991, 37197, 42632]),
+       values=tensor([0.7857, 0.2010, 0.0929,  ..., 0.8446, 0.3352, 0.3559]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7798, 0.1756, 0.8709,  ..., 0.5047, 0.8577, 0.3016])
+tensor([0.5851, 0.1828, 0.1733,  ..., 0.7326, 0.4663, 0.8685])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.399809837341309 seconds
+Time: 10.657772779464722 seconds
 
-[20.92, 20.84, 20.96, 20.84, 20.68, 20.64, 20.84, 20.76, 20.84, 21.08]
-[21.08, 21.16, 21.84, 21.84, 23.04, 26.04, 31.36, 35.8, 40.08, 43.12, 43.68, 43.84, 43.84]
-13.569745063781738
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6265, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.399809837341309, 'TIME_S_1KI': 1.6599856085141753, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 409.2376235961914, 'W': 30.158092261325162}
-[20.92, 20.84, 20.96, 20.84, 20.68, 20.64, 20.84, 20.76, 20.84, 21.08, 20.04, 19.96, 20.16, 20.28, 20.56, 20.88, 20.76, 20.68, 20.68, 20.76]
-371.76000000000005
-18.588
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6265, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.399809837341309, 'TIME_S_1KI': 1.6599856085141753, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 409.2376235961914, 'W': 30.158092261325162, 'J_1KI': 65.32124877832264, 'W_1KI': 4.813741781536339, 'W_D': 11.570092261325161, 'J_D': 157.0032023506164, 'W_D_1KI': 1.846782483850784, 'J_D_1KI': 0.2947777308620565}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124992, 124996,
+                            125000]),
+       col_indices=tensor([41720,  5446, 23409,  ..., 23991, 37197, 42632]),
+       values=tensor([0.7857, 0.2010, 0.0929,  ..., 0.8446, 0.3352, 0.3559]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5851, 0.1828, 0.1733,  ..., 0.7326, 0.4663, 0.8685])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.657772779464722 seconds
+
+[20.44, 20.32, 20.44, 20.56, 20.76, 20.84, 20.88, 21.04, 21.08, 21.08]
+[21.08, 20.88, 20.92, 24.6, 25.64, 31.32, 35.8, 37.92, 41.28, 43.2, 43.12, 43.4, 43.48]
+13.586755752563477
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.657772779464722, 'TIME_S_1KI': 1.68582296416715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 425.8721401214599, 'W': 31.344652680689325}
+[20.44, 20.32, 20.44, 20.56, 20.76, 20.84, 20.88, 21.04, 21.08, 21.08, 20.84, 20.88, 20.84, 20.72, 20.96, 20.96, 21.08, 21.16, 21.2, 21.04]
+375.42
+18.771
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.657772779464722, 'TIME_S_1KI': 1.68582296416715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 425.8721401214599, 'W': 31.344652680689325, 'J_1KI': 67.3635147297469, 'W_1KI': 4.958027946961298, 'W_D': 12.573652680689325, 'J_D': 170.83514789009087, 'W_D_1KI': 1.9888726163697126, 'J_D_1KI': 0.31459547870447846}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
index c3c7f57..3609346 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 98325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519834756851196, "TIME_S_1KI": 0.10699043739487614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.21768825531, "W": 22.200046075478067, "J_1KI": 3.216045647142741, "W_1KI": 0.22578231452304162, "W_D": 3.72304607547807, "J_D": 53.03110719919203, "W_D_1KI": 0.03786469438574187, "J_D_1KI": 0.0003850973240350051}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
index 61cae4f..555507d 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,32 +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 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018892765045166016}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([1451, 2006, 3586,  ..., 3975, 4446, 2086]),
-       values=tensor([0.6609, 0.8356, 0.1353,  ..., 0.7408, 0.3224, 0.8471]),
+       col_indices=tensor([3456, 1605,  749,  ..., 2516,  837, 4620]),
+       values=tensor([0.8429, 0.4221, 0.2092,  ..., 0.3256, 0.3578, 0.9398]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2892, 0.1223, 0.3419,  ..., 0.7884, 0.7802, 0.0113])
+tensor([0.1595, 0.2560, 0.8545,  ..., 0.4673, 0.4412, 0.6412])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +15,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 9.897401094436646 seconds
+Time: 0.018892765045166016 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 55576 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.934850692749023}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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])
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2304, 3497, 2599,  ..., 3517, 2336, 3180]),
+       values=tensor([7.9793e-01, 1.3489e-04, 7.1193e-01,  ...,
+                      7.4115e-01, 8.0632e-01, 9.8789e-03]), size=(5000, 5000),
+       nnz=2500, layout=torch.sparse_csr)
+tensor([0.4232, 0.5545, 0.0889,  ..., 0.2237, 0.6245, 0.5041])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +35,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.663233041763306 seconds
+Time: 5.934850692749023 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 98325 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519834756851196}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 417, 1523, 4116,  ..., 1599, 2107, 3220]),
+       values=tensor([0.7284, 0.4903, 0.1270,  ..., 0.3684, 0.2323, 0.2388]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.1659, 0.6941, 0.7553,  ..., 0.7483, 0.8019, 0.7277])
+tensor([0.8570, 0.2399, 0.2271,  ..., 0.1785, 0.2270, 0.3588])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +54,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.663233041763306 seconds
+Time: 10.519834756851196 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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 417, 1523, 4116,  ..., 1599, 2107, 3220]),
+       values=tensor([0.7284, 0.4903, 0.1270,  ..., 0.3684, 0.2323, 0.2388]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8570, 0.2399, 0.2271,  ..., 0.1785, 0.2270, 0.3588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.519834756851196 seconds
+
+[20.24, 20.24, 20.16, 20.08, 20.32, 20.6, 20.6, 20.56, 20.52, 20.6]
+[20.68, 20.8, 21.12, 22.88, 24.6, 25.72, 26.32, 26.12, 25.12, 25.12, 23.36, 23.52, 23.52, 23.76]
+14.24401044845581
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 98325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519834756851196, 'TIME_S_1KI': 0.10699043739487614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.21768825531, 'W': 22.200046075478067}
+[20.24, 20.24, 20.16, 20.08, 20.32, 20.6, 20.6, 20.56, 20.52, 20.6, 20.68, 20.68, 20.72, 20.52, 20.44, 20.56, 20.8, 20.84, 20.76, 20.76]
+369.53999999999996
+18.476999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 98325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519834756851196, 'TIME_S_1KI': 0.10699043739487614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.21768825531, 'W': 22.200046075478067, 'J_1KI': 3.216045647142741, 'W_1KI': 0.22578231452304162, 'W_D': 3.72304607547807, 'J_D': 53.03110719919203, 'W_D_1KI': 0.03786469438574187, 'J_D_1KI': 0.0003850973240350051}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
index 810fc32..2fa1f21 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 17780, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.605318784713745, "TIME_S_1KI": 0.5964746223123591, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 313.57909806251524, "W": 22.019197605482734, "J_1KI": 17.636619688555413, "W_1KI": 1.2384250621756319, "W_D": 3.4661976054827335, "J_D": 49.36270332407948, "W_D_1KI": 0.19494924665257218, "J_D_1KI": 0.010964524558637355}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output
index 5d5c1f0..8d3b43b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06766819953918457}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     7,    15,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([ 734,  800, 1880,  ..., 3125, 3280, 3794]),
+       values=tensor([0.0540, 0.4911, 0.3592,  ..., 0.2590, 0.5736, 0.3057]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1847, 0.5253, 0.6086,  ..., 0.9552, 0.0514, 0.1920])
+tensor([0.9823, 0.9343, 0.9377,  ..., 0.0786, 0.0908, 0.1511])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.6220724582672119 seconds
+Time: 0.06766819953918457 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15516 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.162637948989868}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     8,    11,  ..., 24988, 24995, 25000]),
+       col_indices=tensor([  62,  227,  575,  ..., 2337, 2631, 3700]),
+       values=tensor([0.5265, 0.4146, 0.5026,  ..., 0.0706, 0.1241, 0.5991]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6729, 0.2847, 0.7618,  ..., 0.5837, 0.8359, 0.7138])
+tensor([0.6610, 0.4053, 0.0257,  ..., 0.7779, 0.2973, 0.6422])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 9.927400827407837 seconds
+Time: 9.162637948989868 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17780 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.605318784713745}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24997, 25000]),
+       col_indices=tensor([ 423, 1662, 2124,  ...,  288, 1379, 2658]),
+       values=tensor([0.1096, 0.1453, 0.3978,  ..., 0.4089, 0.5724, 0.6122]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7581, 0.5458, 0.9932,  ..., 0.3205, 0.5744, 0.9847])
+tensor([0.2174, 0.6127, 0.5782,  ..., 0.6057, 0.7055, 0.7233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.54783010482788 seconds
+Time: 10.605318784713745 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24997, 25000]),
+       col_indices=tensor([ 423, 1662, 2124,  ...,  288, 1379, 2658]),
+       values=tensor([0.1096, 0.1453, 0.3978,  ..., 0.4089, 0.5724, 0.6122]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7581, 0.5458, 0.9932,  ..., 0.3205, 0.5744, 0.9847])
+tensor([0.2174, 0.6127, 0.5782,  ..., 0.6057, 0.7055, 0.7233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.54783010482788 seconds
+Time: 10.605318784713745 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}
+[20.64, 20.56, 20.28, 20.4, 20.28, 20.28, 20.36, 20.44, 20.4, 20.4]
+[20.64, 20.56, 20.76, 22.16, 23.52, 25.36, 26.08, 26.2, 25.48, 23.84, 23.92, 23.84, 23.84, 23.88]
+14.24116826057434
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.605318784713745, 'TIME_S_1KI': 0.5964746223123591, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.57909806251524, 'W': 22.019197605482734}
+[20.64, 20.56, 20.28, 20.4, 20.28, 20.28, 20.36, 20.44, 20.4, 20.4, 20.84, 20.68, 20.92, 20.96, 20.88, 20.88, 21.0, 20.8, 20.68, 20.64]
+371.06
+18.553
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.605318784713745, 'TIME_S_1KI': 0.5964746223123591, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.57909806251524, 'W': 22.019197605482734, 'J_1KI': 17.636619688555413, 'W_1KI': 1.2384250621756319, 'W_D': 3.4661976054827335, 'J_D': 49.36270332407948, 'W_D_1KI': 0.19494924665257218, 'J_D_1KI': 0.010964524558637355}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
index cca3281..84660f7 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1921, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.388477563858032, "TIME_S_1KI": 5.407848809920892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.91259566307076, "W": 21.966357313024826, "J_1KI": 162.89047145396708, "W_1KI": 11.434855446655297, "W_D": 3.429357313024827, "J_D": 48.85148151707659, "W_D_1KI": 1.785193812089967, "J_D_1KI": 0.9293044310723411}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output
index 3224ea8..1a4afe9 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.593717098236084}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     36,     79,  ..., 249900, 249941,
+tensor(crow_indices=tensor([     0,     39,     88,  ..., 249897, 249951,
                             250000]),
-       col_indices=tensor([  80,  388,  404,  ..., 4737, 4807, 4857]),
-       values=tensor([0.4885, 0.5213, 0.1721,  ..., 0.5810, 0.1625, 0.7107]),
+       col_indices=tensor([   1,   41,  120,  ..., 4868, 4902, 4963]),
+       values=tensor([0.6487, 0.6379, 0.3189,  ..., 0.3941, 0.1960, 0.9453]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1545, 0.4718, 0.9539,  ..., 0.2261, 0.6017, 0.7355])
+tensor([0.9493, 0.7713, 0.4212,  ..., 0.5345, 0.1694, 0.1229])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 5.431562900543213 seconds
+Time: 0.593717098236084 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1768 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.65909719467163}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     40,     89,  ..., 249917, 249964,
+tensor(crow_indices=tensor([     0,     53,    105,  ..., 249907, 249948,
                             250000]),
-       col_indices=tensor([ 165,  177,  195,  ..., 4656, 4719, 4927]),
-       values=tensor([0.2100, 0.9405, 0.2582,  ..., 0.7931, 0.5258, 0.8197]),
+       col_indices=tensor([ 103,  261,  471,  ..., 4857, 4933, 4959]),
+       values=tensor([0.8889, 0.3073, 0.1638,  ..., 0.6109, 0.3049, 0.0052]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9173, 0.2185, 0.4076,  ..., 0.3362, 0.1795, 0.2923])
+tensor([0.5335, 0.0728, 0.9615,  ..., 0.8926, 0.1348, 0.8188])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.477078676223755 seconds
+Time: 9.65909719467163 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1921 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.388477563858032}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     40,     89,  ..., 249917, 249964,
+tensor(crow_indices=tensor([     0,     65,     98,  ..., 249897, 249948,
                             250000]),
-       col_indices=tensor([ 165,  177,  195,  ..., 4656, 4719, 4927]),
-       values=tensor([0.2100, 0.9405, 0.2582,  ..., 0.7931, 0.5258, 0.8197]),
+       col_indices=tensor([ 141,  179,  219,  ..., 4719, 4923, 4985]),
+       values=tensor([0.6589, 0.9882, 0.9555,  ..., 0.3007, 0.0365, 0.3378]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9173, 0.2185, 0.4076,  ..., 0.3362, 0.1795, 0.2923])
+tensor([0.9859, 0.3282, 0.7924,  ..., 0.6550, 0.5905, 0.4141])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.477078676223755 seconds
+Time: 10.388477563858032 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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     65,     98,  ..., 249897, 249948,
+                            250000]),
+       col_indices=tensor([ 141,  179,  219,  ..., 4719, 4923, 4985]),
+       values=tensor([0.6589, 0.9882, 0.9555,  ..., 0.3007, 0.0365, 0.3378]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9859, 0.3282, 0.7924,  ..., 0.6550, 0.5905, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.388477563858032 seconds
+
+[21.12, 20.84, 20.6, 20.44, 20.44, 20.44, 20.52, 20.52, 20.6, 20.96]
+[20.8, 20.8, 20.76, 21.68, 22.28, 24.84, 25.84, 26.2, 25.88, 24.96, 23.84, 23.92, 23.72, 23.88]
+14.245083570480347
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1921, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.388477563858032, 'TIME_S_1KI': 5.407848809920892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.91259566307076, 'W': 21.966357313024826}
+[21.12, 20.84, 20.6, 20.44, 20.44, 20.44, 20.52, 20.52, 20.6, 20.96, 20.44, 20.44, 20.44, 20.48, 20.56, 20.68, 20.68, 20.56, 20.84, 20.8]
+370.74
+18.537
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1921, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.388477563858032, 'TIME_S_1KI': 5.407848809920892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.91259566307076, 'W': 21.966357313024826, 'J_1KI': 162.89047145396708, 'W_1KI': 11.434855446655297, 'W_D': 3.429357313024827, 'J_D': 48.85148151707659, 'W_D_1KI': 1.785193812089967, 'J_D_1KI': 0.9293044310723411}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
index 8f974e7..50afdc0 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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": 26.830852508544922, "TIME_S_1KI": 26.830852508544922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 724.3529372215271, "W": 23.831275335163394, "J_1KI": 724.3529372215271, "W_1KI": 23.831275335163394, "W_D": 5.2862753351633955, "J_D": 160.67663237214092, "W_D_1KI": 5.2862753351633955, "J_D_1KI": 5.2862753351633955}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 396, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.481398582458496, "TIME_S_1KI": 26.468178238531554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 323.34643746376037, "W": 22.685982650996596, "J_1KI": 816.5314077367685, "W_1KI": 57.28783497726413, "W_D": 4.214982650996596, "J_D": 60.07672866272925, "W_D_1KI": 10.643895583324738, "J_D_1KI": 26.878524200314995}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
index e6b75ff..07e41de 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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": 26.830852508544922}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6507530212402344}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     229,     490,  ..., 1249494,
-                            1249740, 1250000]),
-       col_indices=tensor([  18,   28,   58,  ..., 4928, 4934, 4938]),
-       values=tensor([0.5574, 0.5312, 0.0435,  ..., 0.2785, 0.4540, 0.5116]),
+tensor(crow_indices=tensor([      0,     267,     531,  ..., 1249531,
+                            1249748, 1250000]),
+       col_indices=tensor([  12,   24,   45,  ..., 4958, 4983, 4986]),
+       values=tensor([0.7384, 0.2434, 0.0755,  ..., 0.4736, 0.1384, 0.4678]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.0667, 0.4882, 0.3630,  ..., 0.8923, 0.8020, 0.4280])
+tensor([0.2921, 0.5624, 0.4015,  ..., 0.8005, 0.9400, 0.6114])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 26.830852508544922 seconds
+Time: 2.6507530212402344 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 396 -ss 5000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.481398582458496}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     229,     490,  ..., 1249494,
-                            1249740, 1250000]),
-       col_indices=tensor([  18,   28,   58,  ..., 4928, 4934, 4938]),
-       values=tensor([0.5574, 0.5312, 0.0435,  ..., 0.2785, 0.4540, 0.5116]),
+tensor(crow_indices=tensor([      0,     247,     505,  ..., 1249488,
+                            1249757, 1250000]),
+       col_indices=tensor([  27,   35,   41,  ..., 4930, 4938, 4952]),
+       values=tensor([0.8294, 0.9821, 0.6691,  ..., 0.3905, 0.4873, 0.1672]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.0667, 0.4882, 0.3630,  ..., 0.8923, 0.8020, 0.4280])
+tensor([0.8352, 0.4457, 0.1150,  ..., 0.9988, 0.2164, 0.9018])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +36,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 26.830852508544922 seconds
+Time: 10.481398582458496 seconds
 
-[20.52, 20.64, 20.8, 20.76, 20.84, 20.84, 20.8, 20.88, 20.8, 20.88]
-[20.64, 20.72, 20.92, 24.36, 26.68, 28.84, 29.6, 27.08, 27.08, 26.84, 23.8, 24.0, 24.32, 24.44, 24.48, 24.52, 24.36, 24.12, 24.24, 24.2, 24.2, 24.28, 24.28, 24.28, 24.2, 24.28, 24.16, 24.12, 24.08, 24.08]
-30.395055532455444
-{'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': 26.830852508544922, 'TIME_S_1KI': 26.830852508544922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.3529372215271, 'W': 23.831275335163394}
-[20.52, 20.64, 20.8, 20.76, 20.84, 20.84, 20.8, 20.88, 20.8, 20.88, 20.44, 20.56, 20.56, 20.6, 20.6, 20.48, 20.32, 20.2, 20.2, 20.2]
-370.9
-18.544999999999998
-{'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': 26.830852508544922, 'TIME_S_1KI': 26.830852508544922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.3529372215271, 'W': 23.831275335163394, 'J_1KI': 724.3529372215271, 'W_1KI': 23.831275335163394, 'W_D': 5.2862753351633955, 'J_D': 160.67663237214092, 'W_D_1KI': 5.2862753351633955, 'J_D_1KI': 5.2862753351633955}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     247,     505,  ..., 1249488,
+                            1249757, 1250000]),
+       col_indices=tensor([  27,   35,   41,  ..., 4930, 4938, 4952]),
+       values=tensor([0.8294, 0.9821, 0.6691,  ..., 0.3905, 0.4873, 0.1672]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8352, 0.4457, 0.1150,  ..., 0.9988, 0.2164, 0.9018])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.481398582458496 seconds
+
+[20.72, 20.84, 20.92, 20.68, 20.6, 20.44, 20.48, 20.24, 20.44, 20.24]
+[20.24, 20.52, 20.72, 22.36, 24.28, 26.72, 27.0, 27.56, 26.28, 25.88, 24.68, 24.52, 24.36, 24.36]
+14.253137826919556
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.481398582458496, 'TIME_S_1KI': 26.468178238531554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.34643746376037, 'W': 22.685982650996596}
+[20.72, 20.84, 20.92, 20.68, 20.6, 20.44, 20.48, 20.24, 20.44, 20.24, 20.24, 20.24, 20.36, 20.48, 20.48, 20.6, 20.68, 20.64, 20.4, 20.6]
+369.42
+18.471
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.481398582458496, 'TIME_S_1KI': 26.468178238531554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.34643746376037, 'W': 22.685982650996596, 'J_1KI': 816.5314077367685, 'W_1KI': 57.28783497726413, 'W_D': 4.214982650996596, 'J_D': 60.07672866272925, 'W_D_1KI': 10.643895583324738, 'J_D_1KI': 26.878524200314995}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
index db59bda..721e6aa 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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": 52.60684037208557, "TIME_S_1KI": 52.60684037208557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1396.747187700272, "W": 24.19749919495284, "J_1KI": 1396.747187700272, "W_1KI": 24.19749919495284, "W_D": 5.574499194952839, "J_D": 321.77565171742464, "W_D_1KI": 5.574499194952839, "J_D_1KI": 5.574499194952839}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 199, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.477930784225464, "TIME_S_1KI": 52.6529185136958, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 366.7794834327698, "W": 23.985504753820422, "J_1KI": 1843.11298207422, "W_1KI": 120.53017464231368, "W_D": 5.225504753820424, "J_D": 79.90692520141607, "W_D_1KI": 26.25881785839409, "J_D_1KI": 131.95385858489493}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
index 919a009..a6a4a54 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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": 52.60684037208557}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.267488241195679}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     495,    1009,  ..., 2499036,
-                            2499501, 2500000]),
-       col_indices=tensor([   3,    5,    7,  ..., 4975, 4989, 4996]),
-       values=tensor([0.3153, 0.9204, 0.9402,  ..., 0.8644, 0.1243, 0.2567]),
+tensor(crow_indices=tensor([      0,     489,     972,  ..., 2499002,
+                            2499515, 2500000]),
+       col_indices=tensor([   0,    4,   21,  ..., 4965, 4988, 4998]),
+       values=tensor([0.4985, 0.2439, 0.0801,  ..., 0.3726, 0.6532, 0.2308]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0882, 0.6551, 0.2953,  ..., 0.5102, 0.2382, 0.8339])
+tensor([0.7620, 0.1310, 0.6898,  ..., 0.4324, 0.6267, 0.4614])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 52.60684037208557 seconds
+Time: 5.267488241195679 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 199 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.477930784225464}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     495,    1009,  ..., 2499036,
-                            2499501, 2500000]),
-       col_indices=tensor([   3,    5,    7,  ..., 4975, 4989, 4996]),
-       values=tensor([0.3153, 0.9204, 0.9402,  ..., 0.8644, 0.1243, 0.2567]),
+tensor(crow_indices=tensor([      0,     479,     980,  ..., 2498983,
+                            2499479, 2500000]),
+       col_indices=tensor([   7,   13,   23,  ..., 4987, 4988, 4998]),
+       values=tensor([0.4519, 0.3203, 0.6830,  ..., 0.2361, 0.6866, 0.7928]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0882, 0.6551, 0.2953,  ..., 0.5102, 0.2382, 0.8339])
+tensor([0.4502, 0.7188, 0.8112,  ..., 0.2797, 0.2285, 0.9848])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +36,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 52.60684037208557 seconds
+Time: 10.477930784225464 seconds
 
-[20.32, 20.32, 20.52, 20.36, 20.68, 20.64, 20.64, 20.84, 20.84, 20.64]
-[21.04, 20.8, 23.84, 25.56, 28.32, 28.32, 30.0, 30.76, 27.04, 26.12, 23.96, 24.04, 23.96, 24.4, 24.48, 24.44, 24.4, 24.4, 24.4, 24.44, 24.52, 24.64, 24.44, 24.48, 24.36, 24.52, 24.36, 24.36, 24.28, 24.28, 24.2, 24.2, 24.04, 24.12, 24.04, 24.04, 24.08, 24.16, 24.12, 23.96, 24.0, 24.04, 24.04, 23.92, 23.92, 24.16, 24.32, 24.52, 24.68, 24.64, 24.24, 24.12, 24.2, 24.2, 24.2, 24.36, 24.36]
-57.72279095649719
-{'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': 52.60684037208557, 'TIME_S_1KI': 52.60684037208557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.747187700272, 'W': 24.19749919495284}
-[20.32, 20.32, 20.52, 20.36, 20.68, 20.64, 20.64, 20.84, 20.84, 20.64, 20.48, 20.4, 20.44, 20.76, 20.76, 20.96, 21.12, 21.08, 21.0, 20.76]
-372.46000000000004
-18.623
-{'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': 52.60684037208557, 'TIME_S_1KI': 52.60684037208557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.747187700272, 'W': 24.19749919495284, 'J_1KI': 1396.747187700272, 'W_1KI': 24.19749919495284, 'W_D': 5.574499194952839, 'J_D': 321.77565171742464, 'W_D_1KI': 5.574499194952839, 'J_D_1KI': 5.574499194952839}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     479,     980,  ..., 2498983,
+                            2499479, 2500000]),
+       col_indices=tensor([   7,   13,   23,  ..., 4987, 4988, 4998]),
+       values=tensor([0.4519, 0.3203, 0.6830,  ..., 0.2361, 0.6866, 0.7928]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4502, 0.7188, 0.8112,  ..., 0.2797, 0.2285, 0.9848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.477930784225464 seconds
+
+[20.56, 20.84, 21.36, 21.88, 21.64, 21.6, 21.4, 20.76, 20.76, 20.72]
+[21.16, 21.2, 21.4, 25.96, 27.32, 30.6, 31.24, 28.68, 27.4, 26.08, 24.2, 24.2, 24.4, 24.36, 24.2]
+15.291714191436768
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.477930784225464, 'TIME_S_1KI': 52.6529185136958, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 366.7794834327698, 'W': 23.985504753820422}
+[20.56, 20.84, 21.36, 21.88, 21.64, 21.6, 21.4, 20.76, 20.76, 20.72, 20.44, 20.32, 20.44, 20.64, 20.64, 20.44, 20.4, 20.36, 20.44, 20.84]
+375.2
+18.759999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.477930784225464, 'TIME_S_1KI': 52.6529185136958, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 366.7794834327698, 'W': 23.985504753820422, 'J_1KI': 1843.11298207422, 'W_1KI': 120.53017464231368, 'W_D': 5.225504753820424, 'J_D': 79.90692520141607, 'W_D_1KI': 26.25881785839409, 'J_D_1KI': 131.95385858489493}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
index 72274ad..328683d 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.2479407787323, "TIME_S_1KI": 105.2479407787323, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2877.626370677949, "W": 24.097306664358964, "J_1KI": 2877.626370677949, "W_1KI": 24.097306664358964, "W_D": 5.6223066643589625, "J_D": 671.3985984718811, "W_D_1KI": 5.6223066643589625, "J_D_1KI": 5.6223066643589625}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.774195671081543, "TIME_S_1KI": 107.74195671081543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.5530287361145, "W": 23.422029760012947, "J_1KI": 3575.5302873611454, "W_1KI": 234.22029760012947, "W_D": 5.2480297600129475, "J_D": 80.11470204830175, "W_D_1KI": 52.480297600129475, "J_D_1KI": 524.8029760012947}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
index f4956b7..4419f1e 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.2 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.2479407787323}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.774195671081543}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     987,    1961,  ..., 4998062,
-                            4998993, 5000000]),
-       col_indices=tensor([   2,    8,   14,  ..., 4993, 4994, 4998]),
-       values=tensor([0.2058, 0.7964, 0.8021,  ..., 0.0689, 0.5253, 0.0161]),
+tensor(crow_indices=tensor([      0,    1044,    1986,  ..., 4998023,
+                            4998990, 5000000]),
+       col_indices=tensor([   2,   11,   17,  ..., 4984, 4985, 4991]),
+       values=tensor([0.4872, 0.8747, 0.2341,  ..., 0.7866, 0.4499, 0.5164]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1259, 0.8957, 0.2222,  ..., 0.6970, 0.5570, 0.6933])
+tensor([0.5529, 0.0016, 0.5040,  ..., 0.3915, 0.6771, 0.4202])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 105.2479407787323 seconds
+Time: 10.774195671081543 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     987,    1961,  ..., 4998062,
-                            4998993, 5000000]),
-       col_indices=tensor([   2,    8,   14,  ..., 4993, 4994, 4998]),
-       values=tensor([0.2058, 0.7964, 0.8021,  ..., 0.0689, 0.5253, 0.0161]),
+tensor(crow_indices=tensor([      0,    1044,    1986,  ..., 4998023,
+                            4998990, 5000000]),
+       col_indices=tensor([   2,   11,   17,  ..., 4984, 4985, 4991]),
+       values=tensor([0.4872, 0.8747, 0.2341,  ..., 0.7866, 0.4499, 0.5164]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1259, 0.8957, 0.2222,  ..., 0.6970, 0.5570, 0.6933])
+tensor([0.5529, 0.0016, 0.5040,  ..., 0.3915, 0.6771, 0.4202])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 105.2479407787323 seconds
+Time: 10.774195671081543 seconds
 
-[20.6, 20.48, 20.6, 20.6, 20.72, 20.72, 20.68, 20.68, 20.52, 20.76]
-[20.8, 20.6, 20.6, 24.4, 25.4, 28.96, 30.96, 31.56, 28.6, 27.8, 25.4, 24.24, 24.24, 24.24, 24.24, 24.24, 24.16, 24.04, 24.12, 24.2, 24.2, 24.28, 24.36, 24.2, 24.32, 24.4, 24.56, 24.56, 24.56, 24.44, 24.44, 24.24, 24.2, 24.16, 24.04, 24.32, 24.24, 24.32, 24.4, 24.4, 24.36, 24.4, 24.6, 24.8, 24.72, 24.88, 24.88, 24.64, 24.48, 24.48, 24.2, 24.12, 24.12, 24.28, 24.48, 24.56, 24.56, 24.6, 24.28, 24.16, 24.16, 24.04, 24.08, 24.24, 24.24, 24.64, 24.72, 24.6, 24.48, 24.12, 24.16, 24.08, 24.16, 24.2, 23.84, 23.92, 23.92, 23.92, 23.76, 24.2, 24.16, 24.28, 24.64, 24.44, 24.36, 24.52, 24.36, 24.4, 24.48, 24.48, 24.56, 24.56, 24.56, 24.36, 24.36, 24.2, 24.32, 24.08, 24.16, 24.24, 24.4, 24.4, 24.44, 24.68, 24.56, 24.4, 24.28, 24.4, 24.32, 24.4, 24.6, 24.48, 24.44, 24.6, 24.6, 24.48, 24.28, 24.24]
-119.4169294834137
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.2479407787323, 'TIME_S_1KI': 105.2479407787323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2877.626370677949, 'W': 24.097306664358964}
-[20.6, 20.48, 20.6, 20.6, 20.72, 20.72, 20.68, 20.68, 20.52, 20.76, 20.2, 20.32, 20.2, 20.12, 20.32, 20.24, 20.6, 20.72, 20.8, 20.8]
-369.5
-18.475
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.2479407787323, 'TIME_S_1KI': 105.2479407787323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2877.626370677949, 'W': 24.097306664358964, 'J_1KI': 2877.626370677949, 'W_1KI': 24.097306664358964, 'W_D': 5.6223066643589625, 'J_D': 671.3985984718811, 'W_D_1KI': 5.6223066643589625, 'J_D_1KI': 5.6223066643589625}
+[20.36, 20.12, 20.12, 20.04, 20.28, 20.12, 20.08, 20.4, 20.4, 20.72]
+[20.8, 21.08, 22.4, 23.32, 25.48, 25.48, 27.96, 29.08, 28.16, 27.04, 25.64, 24.2, 24.2, 24.4, 24.44]
+15.265672206878662
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.774195671081543, 'TIME_S_1KI': 107.74195671081543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.5530287361145, 'W': 23.422029760012947}
+[20.36, 20.12, 20.12, 20.04, 20.28, 20.12, 20.08, 20.4, 20.4, 20.72, 20.32, 20.16, 20.04, 20.08, 20.08, 20.0, 20.16, 20.28, 20.28, 20.28]
+363.48
+18.174
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.774195671081543, 'TIME_S_1KI': 107.74195671081543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.5530287361145, 'W': 23.422029760012947, 'J_1KI': 3575.5302873611454, 'W_1KI': 234.22029760012947, 'W_D': 5.2480297600129475, 'J_D': 80.11470204830175, 'W_D_1KI': 52.480297600129475, 'J_D_1KI': 524.8029760012947}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
index 46e1ba0..1787dbd 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 171.51510739326477, "TIME_S_1KI": 171.51510739326477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4017.952876434326, "W": 24.359705854077962, "J_1KI": 4017.9528764343263, "W_1KI": 24.359705854077962, "W_D": 5.79370585407796, "J_D": 955.6288257770532, "W_D_1KI": 5.79370585407796, "J_D_1KI": 5.79370585407796}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.919427633285522, "TIME_S_1KI": 159.19427633285522, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.52752479553226, "W": 23.776826253573663, "J_1KI": 5065.275247955323, "W_1KI": 237.76826253573662, "W_D": 5.445826253573667, "J_D": 116.01468014574058, "W_D_1KI": 54.45826253573667, "J_D_1KI": 544.5826253573666}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
index 2f85bb6..2f8bfb8 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.3 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 171.51510739326477}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.919427633285522}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1461,    2933,  ..., 7497082,
-                            7498527, 7500000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4992, 4997, 4999]),
-       values=tensor([0.2348, 0.4295, 0.7390,  ..., 0.0266, 0.0621, 0.6356]),
+tensor(crow_indices=tensor([      0,    1464,    2929,  ..., 7497018,
+                            7498512, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4985, 4989, 4990]),
+       values=tensor([0.4014, 0.1905, 0.8906,  ..., 0.4332, 0.9731, 0.1283]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.3584, 0.9157, 0.1902,  ..., 0.2272, 0.0135, 0.3908])
+tensor([0.5776, 0.8031, 0.5959,  ..., 0.3626, 0.0858, 0.0842])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 171.51510739326477 seconds
+Time: 15.919427633285522 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1461,    2933,  ..., 7497082,
-                            7498527, 7500000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4992, 4997, 4999]),
-       values=tensor([0.2348, 0.4295, 0.7390,  ..., 0.0266, 0.0621, 0.6356]),
+tensor(crow_indices=tensor([      0,    1464,    2929,  ..., 7497018,
+                            7498512, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4985, 4989, 4990]),
+       values=tensor([0.4014, 0.1905, 0.8906,  ..., 0.4332, 0.9731, 0.1283]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.3584, 0.9157, 0.1902,  ..., 0.2272, 0.0135, 0.3908])
+tensor([0.5776, 0.8031, 0.5959,  ..., 0.3626, 0.0858, 0.0842])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 171.51510739326477 seconds
+Time: 15.919427633285522 seconds
 
-[21.16, 20.96, 20.88, 20.76, 20.72, 20.72, 20.72, 20.6, 20.56, 20.56]
-[20.6, 20.48, 20.88, 21.96, 23.44, 26.4, 27.84, 28.8, 28.48, 27.04, 25.6, 24.28, 24.28, 24.48, 24.64, 24.64, 24.6, 24.6, 24.4, 24.24, 24.36, 24.48, 24.44, 24.52, 24.6, 24.6, 24.96, 25.04, 25.04, 25.12, 24.68, 24.56, 24.64, 24.6, 24.68, 24.92, 24.84, 24.84, 24.56, 24.48, 24.8, 24.56, 24.76, 24.76, 24.68, 24.64, 24.72, 24.64, 24.6, 24.6, 24.68, 24.68, 24.56, 24.48, 24.6, 24.48, 24.48, 24.56, 24.64, 24.52, 24.48, 24.6, 24.6, 24.52, 24.24, 24.32, 24.44, 24.32, 24.44, 24.44, 24.64, 24.84, 24.6, 24.76, 24.76, 24.8, 24.92, 25.08, 25.04, 24.92, 24.52, 24.6, 24.56, 24.6, 24.48, 24.6, 24.56, 24.56, 24.4, 24.36, 24.48, 24.48, 24.64, 24.64, 24.52, 24.56, 24.52, 24.44, 24.52, 24.52, 24.52, 24.48, 24.32, 24.52, 24.84, 25.04, 25.04, 25.0, 24.92, 24.68, 24.36, 24.36, 24.36, 24.24, 24.36, 24.36, 24.8, 24.72, 24.84, 24.68, 24.44, 24.56, 24.64, 24.72, 24.72, 25.16, 25.56, 25.68, 25.56, 25.36, 24.88, 24.8, 24.72, 24.52, 24.48, 24.6, 24.6, 24.6, 24.44, 24.44, 24.36, 24.44, 24.56, 24.56, 24.76, 24.64, 24.48, 24.72, 24.72, 24.72, 24.56, 24.92, 24.8, 24.56, 24.72, 24.8, 24.88, 24.88, 24.96, 24.84, 24.6, 24.6, 24.16]
-164.9425859451294
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 171.51510739326477, 'TIME_S_1KI': 171.51510739326477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4017.952876434326, 'W': 24.359705854077962}
-[21.16, 20.96, 20.88, 20.76, 20.72, 20.72, 20.72, 20.6, 20.56, 20.56, 20.0, 20.24, 20.64, 20.6, 20.8, 20.76, 20.44, 20.48, 20.4, 20.36]
-371.32000000000005
-18.566000000000003
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 171.51510739326477, 'TIME_S_1KI': 171.51510739326477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4017.952876434326, 'W': 24.359705854077962, 'J_1KI': 4017.9528764343263, 'W_1KI': 24.359705854077962, 'W_D': 5.79370585407796, 'J_D': 955.6288257770532, 'W_D_1KI': 5.79370585407796, 'J_D_1KI': 5.79370585407796}
+[20.2, 20.32, 20.32, 20.32, 20.32, 20.24, 20.52, 20.56, 20.64, 20.76]
+[20.76, 20.64, 21.8, 21.8, 22.88, 24.6, 28.28, 30.28, 29.56, 29.28, 26.08, 25.32, 24.68, 24.72, 24.64, 24.44, 24.44, 24.48, 24.24, 24.32, 24.48]
+21.303411960601807
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.919427633285522, 'TIME_S_1KI': 159.19427633285522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.52752479553226, 'W': 23.776826253573663}
+[20.2, 20.32, 20.32, 20.32, 20.32, 20.24, 20.52, 20.56, 20.64, 20.76, 20.12, 20.12, 19.96, 20.2, 20.04, 20.4, 20.6, 20.56, 20.64, 20.64]
+366.61999999999995
+18.330999999999996
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.919427633285522, 'TIME_S_1KI': 159.19427633285522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.52752479553226, 'W': 23.776826253573663, 'J_1KI': 5065.275247955323, 'W_1KI': 237.76826253573662, 'W_D': 5.445826253573667, 'J_D': 116.01468014574058, 'W_D_1KI': 54.45826253573667, 'J_D_1KI': 544.5826253573666}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d18f4ce
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.475390195846558, "TIME_S_1KI": 214.75390195846558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 659.3497357940673, "W": 24.06026504427972, "J_1KI": 6593.497357940673, "W_1KI": 240.60265044279723, "W_D": 5.657265044279722, "J_D": 155.03221620368953, "W_D_1KI": 56.57265044279722, "J_D_1KI": 565.7265044279723}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..6dfec86
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.475390195846558}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2112,     4108,  ...,  9995977,
+                             9998003, 10000000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4993, 4997, 4998]),
+       values=tensor([0.6521, 0.2294, 0.7060,  ..., 0.9592, 0.5713, 0.6385]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2067, 0.4320, 0.3905,  ..., 0.7782, 0.8244, 0.2696])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.475390195846558 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2112,     4108,  ...,  9995977,
+                             9998003, 10000000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4993, 4997, 4998]),
+       values=tensor([0.6521, 0.2294, 0.7060,  ..., 0.9592, 0.5713, 0.6385]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2067, 0.4320, 0.3905,  ..., 0.7782, 0.8244, 0.2696])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.475390195846558 seconds
+
+[20.36, 20.32, 20.12, 20.16, 20.12, 20.28, 20.68, 20.96, 21.08, 21.04]
+[20.56, 20.44, 20.44, 23.96, 25.32, 27.12, 30.0, 32.28, 29.04, 28.16, 26.48, 25.72, 24.44, 24.36, 24.24, 24.24, 24.2, 24.16, 24.28, 24.32, 24.36, 24.48, 24.04, 23.92, 23.8, 23.68, 23.88]
+27.40409278869629
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.475390195846558, 'TIME_S_1KI': 214.75390195846558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 659.3497357940673, 'W': 24.06026504427972}
+[20.36, 20.32, 20.12, 20.16, 20.12, 20.28, 20.68, 20.96, 21.08, 21.04, 20.4, 20.4, 20.36, 20.56, 20.6, 20.4, 20.24, 20.36, 20.28, 20.48]
+368.06
+18.403
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.475390195846558, 'TIME_S_1KI': 214.75390195846558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 659.3497357940673, 'W': 24.06026504427972, 'J_1KI': 6593.497357940673, 'W_1KI': 240.60265044279723, 'W_D': 5.657265044279722, 'J_D': 155.03221620368953, 'W_D_1KI': 56.57265044279722, 'J_D_1KI': 565.7265044279723}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..dc66501
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.60726523399353, "TIME_S_1KI": 266.0726523399353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 821.7415859985354, "W": 24.511735271206813, "J_1KI": 8217.415859985353, "W_1KI": 245.11735271206814, "W_D": 6.057735271206813, "J_D": 203.08203129005446, "W_D_1KI": 60.57735271206813, "J_D_1KI": 605.7735271206813}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..ad4e507
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.60726523399353}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2547,     5140,  ..., 12494977,
+                            12497506, 12500000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4994, 4995, 4998]),
+       values=tensor([0.6176, 0.1216, 0.2065,  ..., 0.5783, 0.0575, 0.3833]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3583, 0.1424, 0.2491,  ..., 0.0607, 0.2583, 0.4693])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.60726523399353 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2547,     5140,  ..., 12494977,
+                            12497506, 12500000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4994, 4995, 4998]),
+       values=tensor([0.6176, 0.1216, 0.2065,  ..., 0.5783, 0.0575, 0.3833]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3583, 0.1424, 0.2491,  ..., 0.0607, 0.2583, 0.4693])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.60726523399353 seconds
+
+[20.28, 20.52, 20.68, 20.56, 20.44, 20.52, 20.44, 20.44, 20.72, 21.0]
+[20.92, 20.68, 20.68, 23.84, 26.2, 27.84, 31.04, 29.96, 30.76, 29.48, 26.6, 26.6, 26.28, 24.68, 24.6, 24.48, 24.4, 24.24, 24.32, 24.32, 24.48, 24.52, 24.44, 24.52, 24.52, 24.56, 24.4, 24.56, 24.72, 24.68, 24.84, 24.8, 24.64]
+33.524415016174316
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.60726523399353, 'TIME_S_1KI': 266.0726523399353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7415859985354, 'W': 24.511735271206813}
+[20.28, 20.52, 20.68, 20.56, 20.44, 20.52, 20.44, 20.44, 20.72, 21.0, 20.24, 20.2, 20.4, 20.6, 20.6, 20.6, 20.44, 20.56, 20.52, 20.16]
+369.08000000000004
+18.454
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.60726523399353, 'TIME_S_1KI': 266.0726523399353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7415859985354, 'W': 24.511735271206813, 'J_1KI': 8217.415859985353, 'W_1KI': 245.11735271206814, 'W_D': 6.057735271206813, 'J_D': 203.08203129005446, 'W_D_1KI': 60.57735271206813, 'J_D_1KI': 605.7735271206813}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
index aba0d5e..b24de3c 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 284909, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.16942024230957, "TIME_S_1KI": 0.035693573184102885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.9542731094361, "W": 22.304497332617817, "J_1KI": 1.1124754679895548, "W_1KI": 0.07828639085679223, "W_D": 3.6764973326178207, "J_D": 52.24424125194558, "W_D_1KI": 0.012904110900736098, "J_D_1KI": 4.529204377796454e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
index ebedd90..ece41ca 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012842655181884766}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 683, 1119, 1321, 2450, 3482, 3631, 1761, 3022,  756,
-                           3517,   37, 3468, 4655, 1287,  913, 1692, 3561, 1823,
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        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7002, 0.3467, 0.9676,  ..., 0.8135, 0.6463, 0.9360])
+tensor([0.0363, 0.1704, 0.8959,  ..., 0.1381, 0.6314, 0.8045])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,188 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.04628562927246094 seconds
+Time: 0.012842655181884766 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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)
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-                      8.0477e-01, 2.3035e-01, 5.4135e-01, 3.8689e-01,
-                      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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 81758 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.01309871673584}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+       col_indices=tensor([3901, 3663,  229, 3286, 2751, 3483, 2128, 1446, 4503,
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+                      0.1725, 0.3312, 0.1791, 0.6141, 0.0562, 0.4774, 0.5212,
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+                      0.0444, 0.0991, 0.8125, 0.2081, 0.2559, 0.6572, 0.3238,
+                      0.3534, 0.8270, 0.9704, 0.5262, 0.1397]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.5019, 0.1367, 0.6742,  ..., 0.0249, 0.2703, 0.5698])
+tensor([0.2610, 0.0051, 0.8611,  ..., 0.6706, 0.7457, 0.2823])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -266,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 9.940149784088135 seconds
+Time: 3.01309871673584 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 284909 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.16942024230957}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([3878, 1793,  196, 4523, 2590, 2367,  223, 4753,  811,
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-                           4913,  361, 4684, 2741,  722, 1718, 3274,  513, 1785,
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-       values=tensor([0.8076, 0.3370, 0.6780, 0.9299, 0.5410, 0.0897, 0.3343,
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-                      0.7238, 0.8002, 0.6243, 0.9654, 0.2803]),
+       col_indices=tensor([1669, 2388, 3410,  214, 4888, 2047, 1859, 3824, 1130,
+                           3331, 4650,  808, 1845, 4600, 2980, 4756, 2639, 4242,
+                            120, 4542, 2175, 1322,  104,  704,  854, 2110, 1063,
+                           1256, 2794, 2665, 1239, 4623, 2397, 2905, 1669, 3634,
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+                           4849, 3844, 4805, 2751,  822,  856, 3866, 2362, 4396,
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+                           2427, 4542,  135, 3704, 3578, 4193, 3743, 3465, 2179,
+                           4188, 2714, 3316, 1323, 3063, 3972, 3355, 1842, 1656,
+                           2481, 1669, 1106, 4204, 1040,  565, 3967, 2999,  776,
+                           1132, 4335,  252, 3480, 3592, 4417, 2743,  508, 1998,
+                           2250, 4747, 3247, 3778, 2520, 4340, 4333,  889, 3347,
+                           1306,  252, 3840, 4251, 3753,  922, 1530,  732, 4724,
+                           4652, 2305,  676, 3763, 2577,  479, 3149, 3237,  682,
+                           2204, 1170, 4037, 1115,  902, 2463, 2133,   49, 3338,
+                            846, 2596, 1254,  611,  336, 2556, 4596, 3162, 2347,
+                           1052, 1946, 3013, 1910, 3262,  793,  681, 3061, 4097,
+                            649, 4096, 3982, 4856, 2244,  770, 1157, 3683, 1150,
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+                           1530, 1512, 2880, 1124,  741, 2337, 2820, 1096,  969,
+                           4662, 1861, 4067, 2109, 3996, 1635,  499]),
+       values=tensor([0.8132, 0.1702, 0.5583, 0.1261, 0.6291, 0.5508, 0.1330,
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+                      0.3478, 0.4454, 0.7734, 0.2847, 0.4512, 0.5866, 0.1633,
+                      0.7139, 0.4511, 0.5642, 0.2230, 0.1384, 0.2467, 0.5114,
+                      0.5149, 0.4901, 0.7340, 0.5840, 0.0495, 0.1493, 0.4501,
+                      0.5299, 0.1752, 0.0737, 0.0887, 0.7004, 0.7171, 0.6451,
+                      0.1099, 0.6191, 0.3209, 0.2667, 0.2735, 0.3592, 0.7035,
+                      0.1766, 0.2292, 0.6138, 0.2492, 0.8422, 0.5205, 0.0949,
+                      0.6311, 0.1200, 0.6842, 0.3167, 0.3418, 0.7978, 0.1885,
+                      0.9433, 0.6390, 0.5217, 0.8313, 0.4066, 0.8623, 0.9330,
+                      0.7999, 0.0688, 0.3315, 0.2496, 0.2006, 0.0199, 0.1239,
+                      0.0030, 0.9251, 0.8374, 0.2492, 0.6001, 0.0171, 0.3645,
+                      0.9564, 0.7314, 0.8427, 0.8917, 0.1465, 0.2355, 0.6975,
+                      0.9025, 0.0358, 0.2860, 0.4051, 0.9734, 0.8626, 0.4028,
+                      0.9642, 0.0743, 0.8714, 0.6919, 0.3640, 0.9239, 0.1573,
+                      0.9549, 0.3068, 0.2789, 0.0169, 0.6253, 0.7318, 0.1857,
+                      0.1394, 0.2220, 0.2355, 0.9726, 0.9750]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4617, 0.6014, 0.4133,  ..., 0.3579, 0.3877, 0.5185])
+tensor([0.9060, 0.0911, 0.6185,  ..., 0.7353, 0.0547, 0.2301])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -347,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.89356017112732 seconds
+Time: 10.16942024230957 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([3878, 1793,  196, 4523, 2590, 2367,  223, 4753,  811,
-                           1344, 3831, 3891, 1471,  342,   75, 2706, 3424, 2402,
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-                           2173, 3425, 2770, 4498,  897,  827,  829, 4189, 4991,
-                           1934, 1179, 1128, 2114, 2395, 2017, 3561, 4691, 2694,
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-                           2811, 4794, 2166, 3385, 3144,  590,  756, 1857, 2472,
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-                           4913,  361, 4684, 2741,  722, 1718, 3274,  513, 1785,
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-       values=tensor([0.8076, 0.3370, 0.6780, 0.9299, 0.5410, 0.0897, 0.3343,
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-                      0.7238, 0.8002, 0.6243, 0.9654, 0.2803]),
+       col_indices=tensor([1669, 2388, 3410,  214, 4888, 2047, 1859, 3824, 1130,
+                           3331, 4650,  808, 1845, 4600, 2980, 4756, 2639, 4242,
+                            120, 4542, 2175, 1322,  104,  704,  854, 2110, 1063,
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+                           1132, 4335,  252, 3480, 3592, 4417, 2743,  508, 1998,
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+                           4652, 2305,  676, 3763, 2577,  479, 3149, 3237,  682,
+                           2204, 1170, 4037, 1115,  902, 2463, 2133,   49, 3338,
+                            846, 2596, 1254,  611,  336, 2556, 4596, 3162, 2347,
+                           1052, 1946, 3013, 1910, 3262,  793,  681, 3061, 4097,
+                            649, 4096, 3982, 4856, 2244,  770, 1157, 3683, 1150,
+                           4034, 4307, 4867,  947, 1680, 3888,  190,  677, 2841,
+                            816,  454, 4546, 1683, 1115, 4528, 4055,  324, 2442,
+                           1530, 1512, 2880, 1124,  741, 2337, 2820, 1096,  969,
+                           4662, 1861, 4067, 2109, 3996, 1635,  499]),
+       values=tensor([0.8132, 0.1702, 0.5583, 0.1261, 0.6291, 0.5508, 0.1330,
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+                      0.5149, 0.4901, 0.7340, 0.5840, 0.0495, 0.1493, 0.4501,
+                      0.5299, 0.1752, 0.0737, 0.0887, 0.7004, 0.7171, 0.6451,
+                      0.1099, 0.6191, 0.3209, 0.2667, 0.2735, 0.3592, 0.7035,
+                      0.1766, 0.2292, 0.6138, 0.2492, 0.8422, 0.5205, 0.0949,
+                      0.6311, 0.1200, 0.6842, 0.3167, 0.3418, 0.7978, 0.1885,
+                      0.9433, 0.6390, 0.5217, 0.8313, 0.4066, 0.8623, 0.9330,
+                      0.7999, 0.0688, 0.3315, 0.2496, 0.2006, 0.0199, 0.1239,
+                      0.0030, 0.9251, 0.8374, 0.2492, 0.6001, 0.0171, 0.3645,
+                      0.9564, 0.7314, 0.8427, 0.8917, 0.1465, 0.2355, 0.6975,
+                      0.9025, 0.0358, 0.2860, 0.4051, 0.9734, 0.8626, 0.4028,
+                      0.9642, 0.0743, 0.8714, 0.6919, 0.3640, 0.9239, 0.1573,
+                      0.9549, 0.3068, 0.2789, 0.0169, 0.6253, 0.7318, 0.1857,
+                      0.1394, 0.2220, 0.2355, 0.9726, 0.9750]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4617, 0.6014, 0.4133,  ..., 0.3579, 0.3877, 0.5185])
+tensor([0.9060, 0.0911, 0.6185,  ..., 0.7353, 0.0547, 0.2301])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -425,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.89356017112732 seconds
+Time: 10.16942024230957 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}
+[20.28, 20.4, 20.4, 20.48, 20.76, 21.28, 21.16, 20.96, 20.76, 20.4]
+[20.12, 20.44, 21.32, 22.44, 25.12, 25.12, 25.76, 26.28, 25.96, 25.44, 23.88, 24.04, 24.16, 23.84]
+14.210330247879028
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 284909, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.16942024230957, 'TIME_S_1KI': 0.035693573184102885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.9542731094361, 'W': 22.304497332617817}
+[20.28, 20.4, 20.4, 20.48, 20.76, 21.28, 21.16, 20.96, 20.76, 20.4, 20.8, 20.88, 20.72, 20.76, 20.84, 20.84, 20.44, 20.52, 20.32, 20.6]
+372.55999999999995
+18.627999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 284909, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.16942024230957, 'TIME_S_1KI': 0.035693573184102885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.9542731094361, 'W': 22.304497332617817, 'J_1KI': 1.1124754679895548, 'W_1KI': 0.07828639085679223, 'W_D': 3.6764973326178207, 'J_D': 52.24424125194558, 'W_D_1KI': 0.012904110900736098, 'J_D_1KI': 4.529204377796454e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
index c47b89b..758834a 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 151147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.619585275650024, "TIME_S_1KI": 0.07025998051995755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.34527337074286, "W": 23.06683265939357, "J_1KI": 2.1723571977660345, "W_1KI": 0.15261191197571614, "W_D": 4.6928326593935665, "J_D": 66.80021679544454, "W_D_1KI": 0.031048136313612352, "J_D_1KI": 0.00020541682146263144}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 154432, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.621034145355225, "TIME_S_1KI": 0.06877482740206191, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.4244461250305, "W": 22.262246477486542, "J_1KI": 2.0489564735613763, "W_1KI": 0.144155657360434, "W_D": 3.7392464774865424, "J_D": 53.14778078484536, "W_D_1KI": 0.024212899382812774, "J_D_1KI": 0.00015678680184685024}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
index b0a08a7..8f01bb5 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.07715368270874023}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014879941940307617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4186,  604, 2911,  ..., 3524, 2664,  807]),
-       values=tensor([0.1303, 0.5472, 0.9541,  ..., 0.4453, 0.4813, 0.2933]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1250, 1250]),
+       col_indices=tensor([1397, 3608,  621,  ..., 1983, 2722, 4972]),
+       values=tensor([0.7898, 0.8890, 0.9853,  ..., 0.2806, 0.4332, 0.7785]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2363, 0.5745, 0.8536,  ..., 0.3028, 0.7626, 0.7945])
+tensor([0.8515, 0.1205, 0.1290,  ..., 0.0596, 0.1294, 0.2178])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.07715368270874023 seconds
+Time: 0.014879941940307617 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 136092 -ss 5000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.454103946685791}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 70564 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.797720670700073}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2642, 3295, 3317,  ...,  552, 1688, 3754]),
-       values=tensor([0.5853, 0.8410, 0.7758,  ..., 0.7543, 0.4171, 0.3907]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([4236, 1927,  389,  ..., 3900, 4084, 4178]),
+       values=tensor([0.5819, 0.5926, 0.4032,  ..., 0.1422, 0.8129, 0.9187]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.4145, 0.1634, 0.4401,  ..., 0.9903, 0.7928, 0.8495])
+tensor([0.4782, 0.7587, 0.6755,  ..., 0.4641, 0.3230, 0.1517])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 9.454103946685791 seconds
+Time: 4.797720670700073 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 151147 -ss 5000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.619585275650024}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 154432 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.621034145355225}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2120, 4070, 3230,  ..., 2901, 3405,  168]),
-       values=tensor([0.1519, 0.7232, 0.6282,  ..., 0.2528, 0.5199, 0.9452]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([  91, 2944, 3974,  ..., 4430,   70, 3263]),
+       values=tensor([0.2553, 0.0855, 0.4739,  ..., 0.3797, 0.6721, 0.4378]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2553, 0.4766, 0.2217,  ..., 0.4056, 0.3500, 0.9553])
+tensor([0.8009, 0.9874, 0.1682,  ..., 0.8612, 0.3697, 0.0752])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.619585275650024 seconds
+Time: 10.621034145355225 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2120, 4070, 3230,  ..., 2901, 3405,  168]),
-       values=tensor([0.1519, 0.7232, 0.6282,  ..., 0.2528, 0.5199, 0.9452]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([  91, 2944, 3974,  ..., 4430,   70, 3263]),
+       values=tensor([0.2553, 0.0855, 0.4739,  ..., 0.3797, 0.6721, 0.4378]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2553, 0.4766, 0.2217,  ..., 0.4056, 0.3500, 0.9553])
+tensor([0.8009, 0.9874, 0.1682,  ..., 0.8612, 0.3697, 0.0752])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.619585275650024 seconds
+Time: 10.621034145355225 seconds
 
-[20.44, 20.36, 20.28, 20.16, 20.04, 20.16, 20.28, 20.32, 20.56, 20.56]
-[20.72, 20.76, 20.8, 24.32, 26.28, 28.48, 29.36, 29.52, 26.16, 23.92, 23.8, 23.64, 23.64, 23.56]
-14.234519243240356
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 151147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.619585275650024, 'TIME_S_1KI': 0.07025998051995755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.34527337074286, 'W': 23.06683265939357}
-[20.44, 20.36, 20.28, 20.16, 20.04, 20.16, 20.28, 20.32, 20.56, 20.56, 20.24, 20.2, 20.36, 20.68, 20.96, 20.8, 20.68, 20.4, 20.44, 20.36]
-367.48
-18.374000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 151147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.619585275650024, 'TIME_S_1KI': 0.07025998051995755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.34527337074286, 'W': 23.06683265939357, 'J_1KI': 2.1723571977660345, 'W_1KI': 0.15261191197571614, 'W_D': 4.6928326593935665, 'J_D': 66.80021679544454, 'W_D_1KI': 0.031048136313612352, 'J_D_1KI': 0.00020541682146263144}
+[20.4, 20.68, 20.44, 20.16, 20.16, 20.48, 20.44, 20.68, 20.64, 20.88]
+[20.92, 20.96, 21.52, 22.88, 25.2, 25.52, 26.2, 26.2, 25.6, 24.92, 23.32, 23.36, 23.56, 23.44]
+14.213500261306763
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 154432, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.621034145355225, 'TIME_S_1KI': 0.06877482740206191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.4244461250305, 'W': 22.262246477486542}
+[20.4, 20.68, 20.44, 20.16, 20.16, 20.48, 20.44, 20.68, 20.64, 20.88, 20.72, 20.64, 20.68, 20.52, 20.64, 20.8, 20.76, 20.76, 20.68, 20.6]
+370.46
+18.523
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 154432, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.621034145355225, 'TIME_S_1KI': 0.06877482740206191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.4244461250305, 'W': 22.262246477486542, 'J_1KI': 2.0489564735613763, 'W_1KI': 0.144155657360434, 'W_D': 3.7392464774865424, 'J_D': 53.14778078484536, 'W_D_1KI': 0.024212899382812774, 'J_D_1KI': 0.00015678680184685024}
diff --git a/pytorch/output_synthetic_16core/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 f73c7ea..a438e21 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 65446, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.748796463012695, "TIME_S_1KI": 0.16423916607604278, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1866.1015530323982, "W": 143.13, "J_1KI": 28.51360744785622, "W_1KI": 2.1869938575314, "W_D": 106.99974999999999, "J_D": 1395.0422668139338, "W_D_1KI": 1.634931852214039, "J_D_1KI": 0.024981386978792274}
diff --git a/pytorch/output_synthetic_16core/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 b2fc454..7f0e014 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": 0.20868682861328125}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.060246944427490234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      23,  ...,  999975,
+tensor(crow_indices=tensor([      0,      16,      25,  ...,  999980,
+                             999989, 1000000]),
+       col_indices=tensor([ 4573,  4595,  4948,  ..., 71788, 92544, 99741]),
+       values=tensor([0.3512, 0.1040, 0.2729,  ..., 0.2513, 0.9554, 0.9408]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1257, 0.5794, 0.5612,  ..., 0.8235, 0.1474, 0.3975])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.060246944427490234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17428', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7960927486419678}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999980,
+                             999989, 1000000]),
+       col_indices=tensor([11836, 34889, 39226,  ..., 79566, 86668, 94364]),
+       values=tensor([0.7886, 0.3777, 0.4340,  ..., 0.5250, 0.8836, 0.4934]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9435, 0.7532, 0.3829,  ..., 0.0561, 0.6547, 0.0145])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.7960927486419678 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65446', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.748796463012695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
                              999990, 1000000]),
-       col_indices=tensor([ 1102,  1885,  5689,  ..., 70464, 82505, 82637]),
-       values=tensor([0.9145, 0.6563, 0.0210,  ..., 0.3467, 0.9517, 0.4307]),
+       col_indices=tensor([ 6624,  6694, 37331,  ..., 71444, 97628, 99166]),
+       values=tensor([0.8094, 0.0427, 0.0622,  ..., 0.4502, 0.4633, 0.1157]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3954, 0.8531, 0.4592,  ..., 0.1653, 0.9288, 0.8508])
+tensor([0.2357, 0.1643, 0.3206,  ..., 0.7759, 0.8620, 0.1771])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,39 +56,16 @@ 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}
+Time: 10.748796463012695 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
-                             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]),
+                             999990, 1000000]),
+       col_indices=tensor([ 6624,  6694, 37331,  ..., 71444, 97628, 99166]),
+       values=tensor([0.8094, 0.0427, 0.0622,  ..., 0.4502, 0.4633, 0.1157]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5397, 0.2720, 0.7091,  ..., 0.7919, 0.2241, 0.5973])
+tensor([0.2357, 0.1643, 0.3206,  ..., 0.7759, 0.8620, 0.1771])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,30 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.264819622039795 seconds
+Time: 10.748796463012695 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
-                             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([0.5397, 0.2720, 0.7091,  ..., 0.7919, 0.2241, 0.5973])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 10.264819622039795 seconds
-
-[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}
+[40.51, 40.31, 40.08, 39.71, 39.8, 39.59, 39.58, 41.48, 39.56, 40.06]
+[143.13]
+13.037808656692505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 65446, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.748796463012695, 'TIME_S_1KI': 0.16423916607604278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.1015530323982, 'W': 143.13}
+[40.51, 40.31, 40.08, 39.71, 39.8, 39.59, 39.58, 41.48, 39.56, 40.06, 41.48, 39.67, 40.17, 39.98, 41.44, 39.98, 40.5, 40.23, 39.59, 39.82]
+722.605
+36.130250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 65446, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.748796463012695, 'TIME_S_1KI': 0.16423916607604278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.1015530323982, 'W': 143.13, 'J_1KI': 28.51360744785622, 'W_1KI': 2.1869938575314, 'W_D': 106.99974999999999, 'J_D': 1395.0422668139338, 'W_D_1KI': 1.634931852214039, 'J_D_1KI': 0.024981386978792274}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
index 47e1eaa..d0be3f6 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.009309530258179, "TIME_S_1KI": 2.558983492490556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2204.305100774765, "W": 128.95, "J_1KI": 469.7006394150362, "W_1KI": 27.477093543575535, "W_D": 92.7465, "J_D": 1585.4329820008277, "W_D_1KI": 19.76273172810569, "J_D_1KI": 4.211108401471487}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
index 2b06cc0..561971c 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.27472805976867676}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      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]),
+tensor(crow_indices=tensor([       0,      103,      224,  ...,  9999788,
+                             9999890, 10000000]),
+       col_indices=tensor([  311,  3365,  5161,  ..., 98602, 99530, 99576]),
+       values=tensor([0.9917, 0.0583, 0.3712,  ..., 0.9136, 0.4986, 0.7909]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0022, 0.6683, 0.3307,  ..., 0.4747, 0.3475, 0.4636])
+tensor([0.4323, 0.4083, 0.9080,  ..., 0.7530, 0.1922, 0.7136])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 2.4475483894348145 seconds
+Time: 0.27472805976867676 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3821', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.548935651779175}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      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]),
+tensor(crow_indices=tensor([       0,       86,      193,  ...,  9999790,
+                             9999889, 10000000]),
+       col_indices=tensor([  598,  3163,  6325,  ..., 93333, 94869, 95502]),
+       values=tensor([0.3479, 0.2007, 0.7107,  ..., 0.5121, 0.1193, 0.0296]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1529, 0.0141, 0.4287,  ..., 0.1937, 0.2308, 0.9820])
+tensor([0.9967, 0.6546, 0.0107,  ..., 0.1473, 0.4856, 0.1261])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.725477457046509 seconds
+Time: 8.548935651779175 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4693', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.009309530258179}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      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]),
+tensor(crow_indices=tensor([       0,       80,      177,  ...,  9999782,
+                             9999890, 10000000]),
+       col_indices=tensor([ 1894,  3295,  3747,  ..., 98404, 98823, 99018]),
+       values=tensor([0.1540, 0.7163, 0.3077,  ..., 0.3211, 0.5255, 0.5012]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1529, 0.0141, 0.4287,  ..., 0.1937, 0.2308, 0.9820])
+tensor([0.8104, 0.7178, 0.6885,  ..., 0.8661, 0.7147, 0.1559])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.725477457046509 seconds
+Time: 12.009309530258179 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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       80,      177,  ...,  9999782,
+                             9999890, 10000000]),
+       col_indices=tensor([ 1894,  3295,  3747,  ..., 98404, 98823, 99018]),
+       values=tensor([0.1540, 0.7163, 0.3077,  ..., 0.3211, 0.5255, 0.5012]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8104, 0.7178, 0.6885,  ..., 0.8661, 0.7147, 0.1559])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 12.009309530258179 seconds
+
+[41.32, 39.94, 39.97, 39.81, 39.83, 40.24, 40.5, 40.22, 40.21, 41.33]
+[128.95]
+17.09426212310791
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.009309530258179, 'TIME_S_1KI': 2.558983492490556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2204.305100774765, 'W': 128.95}
+[41.32, 39.94, 39.97, 39.81, 39.83, 40.24, 40.5, 40.22, 40.21, 41.33, 40.63, 40.93, 40.28, 39.66, 39.87, 41.7, 39.67, 40.03, 39.68, 39.78]
+724.0699999999999
+36.2035
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.009309530258179, 'TIME_S_1KI': 2.558983492490556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2204.305100774765, 'W': 128.95, 'J_1KI': 469.7006394150362, 'W_1KI': 27.477093543575535, 'W_D': 92.7465, 'J_D': 1585.4329820008277, 'W_D_1KI': 19.76273172810569, 'J_D_1KI': 4.211108401471487}
diff --git a/pytorch/output_synthetic_16core/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 7f413fc..5f6b7ac 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 99857, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.432250738143921, "TIME_S_1KI": 0.10447190220158749, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.9044136524199, "W": 115.09999999999998, "J_1KI": 15.391053342804408, "W_1KI": 1.152648287050482, "W_D": 79.15799999999997, "J_D": 1056.9789711198803, "W_D_1KI": 0.7927135804200004, "J_D_1KI": 0.007938487841813797}
diff --git a/pytorch/output_synthetic_16core/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 fb29049..5aa8a41 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": 0.12978029251098633}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.043670654296875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      4,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99998,
                             100000]),
-       col_indices=tensor([21616, 77637, 85619,  ..., 53732, 81470,  6094]),
-       values=tensor([0.4857, 0.1991, 0.9153,  ..., 0.9203, 0.8308, 0.8562]),
+       col_indices=tensor([ 6609, 19255, 81333,  ..., 81128, 51531, 76130]),
+       values=tensor([0.9876, 0.0139, 0.8085,  ..., 0.3685, 0.4758, 0.0266]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0197, 0.8164, 0.2872,  ..., 0.9903, 0.3891, 0.9778])
+tensor([0.1735, 0.8240, 0.8190,  ..., 0.4288, 0.7745, 0.1715])
 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: 0.12978029251098633 seconds
+Time: 0.043670654296875 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24043', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.5281074047088623}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99999,
                             100000]),
-       col_indices=tensor([18950, 61338, 17160,  ..., 57514, 79997, 96494]),
-       values=tensor([0.7220, 0.1840, 0.6067,  ..., 0.9597, 0.4652, 0.5228]),
+       col_indices=tensor([69039, 75318, 84133,  ..., 16483, 23976, 47642]),
+       values=tensor([0.3961, 0.2517, 0.3876,  ..., 0.3761, 0.7912, 0.1675]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0221, 0.6414, 0.1516,  ..., 0.3018, 0.8902, 0.3461])
+tensor([0.9918, 0.3750, 0.7737,  ..., 0.5214, 0.0832, 0.2225])
 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: 8.253613233566284 seconds
+Time: 2.5281074047088623 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '99857', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.432250738143921}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([ 4611, 80501,  8771,  ..., 95435, 27789, 45343]),
-       values=tensor([0.8274, 0.0201, 0.6109,  ..., 0.4116, 0.6491, 0.0785]),
+       col_indices=tensor([18969, 38131, 43029,  ..., 81495,  1519, 27704]),
+       values=tensor([0.3850, 0.3770, 0.8820,  ..., 0.3865, 0.0804, 0.8829]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0461, 0.3256, 0.3375,  ..., 0.6234, 0.9526, 0.7301])
+tensor([0.4374, 0.1348, 0.8967,  ..., 0.5157, 0.0353, 0.0014])
 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: 10.60866904258728 seconds
+Time: 10.432250738143921 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([ 4611, 80501,  8771,  ..., 95435, 27789, 45343]),
-       values=tensor([0.8274, 0.0201, 0.6109,  ..., 0.4116, 0.6491, 0.0785]),
+       col_indices=tensor([18969, 38131, 43029,  ..., 81495,  1519, 27704]),
+       values=tensor([0.3850, 0.3770, 0.8820,  ..., 0.3865, 0.0804, 0.8829]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0461, 0.3256, 0.3375,  ..., 0.6234, 0.9526, 0.7301])
+tensor([0.4374, 0.1348, 0.8967,  ..., 0.5157, 0.0353, 0.0014])
 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: 10.60866904258728 seconds
+Time: 10.432250738143921 seconds
 
-[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}
+[40.36, 39.67, 39.74, 39.69, 39.75, 39.62, 40.16, 41.41, 40.17, 40.09]
+[115.1]
+13.35277509689331
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99857, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.432250738143921, 'TIME_S_1KI': 0.10447190220158749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.9044136524199, 'W': 115.09999999999998}
+[40.36, 39.67, 39.74, 39.69, 39.75, 39.62, 40.16, 41.41, 40.17, 40.09, 40.33, 39.61, 39.56, 41.58, 39.51, 39.51, 39.84, 39.34, 39.38, 39.82]
+718.8400000000001
+35.94200000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99857, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.432250738143921, 'TIME_S_1KI': 0.10447190220158749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.9044136524199, 'W': 115.09999999999998, 'J_1KI': 15.391053342804408, 'W_1KI': 1.152648287050482, 'W_D': 79.15799999999997, 'J_D': 1056.9789711198803, 'W_D_1KI': 0.7927135804200004, 'J_D_1KI': 0.007938487841813797}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
index 4188099..80ab5da 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 85448, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.79839825630188, "TIME_S_1KI": 0.12637391461826936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1847.2158340501787, "W": 132.18, "J_1KI": 21.618011352520583, "W_1KI": 1.5469057204381613, "W_D": 96.35400000000001, "J_D": 1346.5473935093883, "W_D_1KI": 1.127633180413819, "J_D_1KI": 0.013196718242835633}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 81276, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.078009605407715, "TIME_S_1KI": 0.12399736214144047, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1722.4750410318375, "W": 132.75, "J_1KI": 21.192911081153568, "W_1KI": 1.6333234903292484, "W_D": 96.411, "J_D": 1250.9645286698342, "W_D_1KI": 1.1862173335301935, "J_D_1KI": 0.014594927574317062}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
index be89160..0f8dc83 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.157515287399292}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04085874557495117}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      8,  ..., 499988, 499996,
+tensor(crow_indices=tensor([     0,      4,      9,  ..., 499987, 499993,
                             500000]),
-       col_indices=tensor([50162, 75153, 30191,  ..., 32389, 47580, 60210]),
-       values=tensor([0.9007, 0.9447, 0.0410,  ..., 0.6472, 0.2952, 0.4267]),
+       col_indices=tensor([ 4658, 51132, 55767,  ..., 77897, 84680, 91168]),
+       values=tensor([0.8716, 0.7460, 0.9968,  ..., 0.7762, 0.8585, 0.9878]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.3259, 0.8902, 0.7186,  ..., 0.8330, 0.5312, 0.8917])
+tensor([0.7678, 0.5187, 0.4774,  ..., 0.8664, 0.3724, 0.0254])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.157515287399292 seconds
+Time: 0.04085874557495117 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66660', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.191283702850342}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25698', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.3198819160461426}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      8,  ..., 499990, 499997,
+tensor(crow_indices=tensor([     0,      4,      4,  ..., 499992, 499998,
                             500000]),
-       col_indices=tensor([ 3937, 41482, 51345,  ..., 57028, 62776, 96568]),
-       values=tensor([0.3669, 0.7790, 0.6636,  ..., 0.0088, 0.3191, 0.1015]),
+       col_indices=tensor([33478, 35089, 63624,  ..., 93258,  3464, 77760]),
+       values=tensor([0.8303, 0.5286, 0.9064,  ..., 0.8655, 0.5788, 0.5903]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.1888, 0.6317, 0.9833,  ..., 0.5078, 0.6417, 0.5906])
+tensor([0.0892, 0.6340, 0.1475,  ..., 0.5230, 0.0009, 0.8265])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 8.191283702850342 seconds
+Time: 3.3198819160461426 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '85448', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.79839825630188}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '81276', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.078009605407715}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     13,  ..., 499988, 499995,
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 499997, 500000,
                             500000]),
-       col_indices=tensor([ 3698, 26087, 35796,  ..., 95832, 96289, 98226]),
-       values=tensor([0.4478, 0.6896, 0.5878,  ..., 0.3885, 0.0788, 0.0500]),
+       col_indices=tensor([38450, 44227, 69625,  ...,  8507, 39094, 82179]),
+       values=tensor([0.2677, 0.9845, 0.1042,  ..., 0.9974, 0.0756, 0.3422]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6913, 0.6407, 0.8664,  ..., 0.8625, 0.1823, 0.9429])
+tensor([0.8400, 0.1962, 0.3075,  ..., 0.6034, 0.5737, 0.0994])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.79839825630188 seconds
+Time: 10.078009605407715 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     13,  ..., 499988, 499995,
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 499997, 500000,
                             500000]),
-       col_indices=tensor([ 3698, 26087, 35796,  ..., 95832, 96289, 98226]),
-       values=tensor([0.4478, 0.6896, 0.5878,  ..., 0.3885, 0.0788, 0.0500]),
+       col_indices=tensor([38450, 44227, 69625,  ...,  8507, 39094, 82179]),
+       values=tensor([0.2677, 0.9845, 0.1042,  ..., 0.9974, 0.0756, 0.3422]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6913, 0.6407, 0.8664,  ..., 0.8625, 0.1823, 0.9429])
+tensor([0.8400, 0.1962, 0.3075,  ..., 0.6034, 0.5737, 0.0994])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.79839825630188 seconds
+Time: 10.078009605407715 seconds
 
-[40.27, 39.58, 39.76, 40.26, 40.31, 39.92, 40.36, 39.5, 39.65, 39.54]
-[132.18]
-13.975002527236938
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 85448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.79839825630188, 'TIME_S_1KI': 0.12637391461826936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1847.2158340501787, 'W': 132.18}
-[40.27, 39.58, 39.76, 40.26, 40.31, 39.92, 40.36, 39.5, 39.65, 39.54, 40.41, 39.45, 40.31, 39.36, 39.58, 39.39, 39.62, 39.75, 39.86, 39.5]
-716.52
-35.826
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 85448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.79839825630188, 'TIME_S_1KI': 0.12637391461826936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1847.2158340501787, 'W': 132.18, 'J_1KI': 21.618011352520583, 'W_1KI': 1.5469057204381613, 'W_D': 96.35400000000001, 'J_D': 1346.5473935093883, 'W_D_1KI': 1.127633180413819, 'J_D_1KI': 0.013196718242835633}
+[41.19, 39.74, 39.62, 40.23, 39.69, 39.5, 44.76, 39.57, 39.56, 39.98]
+[132.75]
+12.975329875946045
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 81276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.078009605407715, 'TIME_S_1KI': 0.12399736214144047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1722.4750410318375, 'W': 132.75}
+[41.19, 39.74, 39.62, 40.23, 39.69, 39.5, 44.76, 39.57, 39.56, 39.98, 40.29, 39.57, 39.62, 45.06, 39.69, 39.48, 40.04, 39.95, 40.05, 39.84]
+726.78
+36.339
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 81276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.078009605407715, 'TIME_S_1KI': 0.12399736214144047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1722.4750410318375, 'W': 132.75, 'J_1KI': 21.192911081153568, 'W_1KI': 1.6333234903292484, 'W_D': 96.411, 'J_D': 1250.9645286698342, 'W_D_1KI': 1.1862173335301935, 'J_D_1KI': 0.014594927574317062}
diff --git a/pytorch/output_synthetic_16core/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 cb11125..0d37837 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 280711, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.449347734451294, "TIME_S_1KI": 0.03722457521953644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.5745413303375, "W": 98.5, "J_1KI": 4.593957989998032, "W_1KI": 0.35089469240606885, "W_D": 62.83475, "J_D": 822.6405473183394, "W_D_1KI": 0.22384142409809377, "J_D_1KI": 0.0007974088086968226}
diff --git a/pytorch/output_synthetic_16core/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 26aaa7d..d302c45 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": 0.05305743217468262}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019724130630493164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([ 730, 4220, 7544,  ..., 4458, 7562, 5619]),
+       values=tensor([0.0181, 0.7832, 0.5914,  ..., 0.2469, 0.2734, 0.2796]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2095, 0.5712, 0.5435,  ..., 0.2564, 0.5818, 0.1577])
+tensor([0.6994, 0.7339, 0.7582,  ..., 0.9456, 0.1186, 0.3856])
 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: 0.05305743217468262 seconds
+Time: 0.019724130630493164 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53234', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.991217851638794}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     5,     5,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([2031, 5960, 7493,  ..., 3747, 8534, 6060]),
+       values=tensor([0.1847, 0.1000, 0.1920,  ..., 0.9911, 0.4392, 0.2330]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5895, 0.0291, 0.5304,  ..., 0.4324, 0.9976, 0.6205])
+tensor([0.7239, 0.0636, 0.4781,  ..., 0.2276, 0.2279, 0.8613])
 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: 7.456049680709839 seconds
+Time: 1.991217851638794 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '280711', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.449347734451294}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([8732,   42, 2512,  ..., 1373, 9550, 9690]),
+       values=tensor([0.4706, 0.1126, 0.6045,  ..., 0.0102, 0.1178, 0.6557]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4368, 0.0363, 0.2687,  ..., 0.3029, 0.2331, 0.6830])
+tensor([0.4976, 0.6299, 0.3127,  ..., 0.9623, 0.9434, 0.7070])
 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: 10.3841392993927 seconds
+Time: 10.449347734451294 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([8732,   42, 2512,  ..., 1373, 9550, 9690]),
+       values=tensor([0.4706, 0.1126, 0.6045,  ..., 0.0102, 0.1178, 0.6557]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4368, 0.0363, 0.2687,  ..., 0.3029, 0.2331, 0.6830])
+tensor([0.4976, 0.6299, 0.3127,  ..., 0.9623, 0.9434, 0.7070])
 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: 10.3841392993927 seconds
+Time: 10.449347734451294 seconds
 
-[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}
+[40.09, 39.2, 39.41, 39.39, 39.38, 42.02, 40.51, 39.25, 39.25, 39.51]
+[98.5]
+13.092127323150635
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 280711, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.449347734451294, 'TIME_S_1KI': 0.03722457521953644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.5745413303375, 'W': 98.5}
+[40.09, 39.2, 39.41, 39.39, 39.38, 42.02, 40.51, 39.25, 39.25, 39.51, 40.09, 39.17, 39.95, 40.04, 39.24, 39.23, 39.51, 39.16, 39.2, 39.1]
+713.3050000000001
+35.66525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 280711, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.449347734451294, 'TIME_S_1KI': 0.03722457521953644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.5745413303375, 'W': 98.5, 'J_1KI': 4.593957989998032, 'W_1KI': 0.35089469240606885, 'W_D': 62.83475, 'J_D': 822.6405473183394, 'W_D_1KI': 0.22384142409809377, 'J_D_1KI': 0.0007974088086968226}
diff --git a/pytorch/output_synthetic_16core/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 9de9eeb..9665b3b 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 193546, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.657205820083618, "TIME_S_1KI": 0.05506290917964524, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1424.268603067398, "W": 108.67, "J_1KI": 7.358811874528009, "W_1KI": 0.5614685914459612, "W_D": 72.91375, "J_D": 955.6341663467883, "W_D_1KI": 0.3767256879501513, "J_D_1KI": 0.001946440060503195}
diff --git a/pytorch/output_synthetic_16core/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 62ff1c2..77b5a62 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": 0.07387351989746094}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.025844097137451172}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     20,  ...,  99983,  99988,
+tensor(crow_indices=tensor([     0,     12,     28,  ...,  99969,  99983,
                             100000]),
-       col_indices=tensor([2080, 2520, 2867,  ..., 8307, 8901, 9286]),
-       values=tensor([0.8261, 0.1055, 0.9939,  ..., 0.1447, 0.1951, 0.2617]),
+       col_indices=tensor([1079, 2122, 3254,  ..., 9373, 9823, 9958]),
+       values=tensor([0.1589, 0.8596, 0.7837,  ..., 0.1493, 0.1272, 0.2084]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7373, 0.8108, 0.8070,  ..., 0.3032, 0.8916, 0.0356])
+tensor([0.0719, 0.4122, 0.7875,  ..., 0.0407, 0.8322, 0.6511])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,20 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.07387351989746094 seconds
+Time: 0.025844097137451172 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '40628', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.4707634449005127}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     16,  ...,  99977,  99988,
+tensor(crow_indices=tensor([     0,     12,     18,  ...,  99974,  99987,
                             100000]),
-       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]),
+       col_indices=tensor([ 792, 1032, 1238,  ..., 8561, 8731, 9370]),
+       values=tensor([0.4488, 0.9659, 0.1268,  ..., 0.7863, 0.6709, 0.3638]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4055, 0.0658, 0.7904,  ..., 0.2959, 0.0826, 0.7426])
+tensor([0.8213, 0.7389, 0.9585,  ..., 0.8858, 0.0787, 0.3979])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -37,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 8.216149806976318 seconds
+Time: 2.4707634449005127 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '172656', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.366683721542358}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     23,  ...,  99984,  99996,
+tensor(crow_indices=tensor([     0,     10,     24,  ...,  99973,  99986,
                             100000]),
-       col_indices=tensor([2026, 2065, 2399,  ..., 4623, 7297, 9355]),
-       values=tensor([0.4157, 0.6883, 0.2119,  ..., 0.3441, 0.2622, 0.5721]),
+       col_indices=tensor([ 684, 3301, 3344,  ..., 8499, 8709, 9229]),
+       values=tensor([0.0104, 0.6771, 0.5927,  ..., 0.6883, 0.2524, 0.4550]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4888, 0.3451, 0.6891,  ..., 0.9797, 0.8702, 0.1612])
+tensor([0.4786, 0.6837, 0.1379,  ..., 0.3005, 0.2266, 0.1673])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -57,16 +56,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.058825254440308 seconds
+Time: 9.366683721542358 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193546', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.657205820083618}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     23,  ...,  99984,  99996,
+tensor(crow_indices=tensor([     0,     13,     24,  ...,  99982,  99990,
                             100000]),
-       col_indices=tensor([2026, 2065, 2399,  ..., 4623, 7297, 9355]),
-       values=tensor([0.4157, 0.6883, 0.2119,  ..., 0.3441, 0.2622, 0.5721]),
+       col_indices=tensor([ 667,  823, 2535,  ..., 7218, 8112, 8309]),
+       values=tensor([0.9044, 0.9079, 0.6825,  ..., 0.1587, 0.6143, 0.0618]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4888, 0.3451, 0.6891,  ..., 0.9797, 0.8702, 0.1612])
+tensor([0.5914, 0.6686, 0.5823,  ..., 0.5362, 0.3609, 0.2297])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -74,13 +76,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.058825254440308 seconds
+Time: 10.657205820083618 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     24,  ...,  99982,  99990,
+                            100000]),
+       col_indices=tensor([ 667,  823, 2535,  ..., 7218, 8112, 8309]),
+       values=tensor([0.9044, 0.9079, 0.6825,  ..., 0.1587, 0.6143, 0.0618]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5914, 0.6686, 0.5823,  ..., 0.5362, 0.3609, 0.2297])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.657205820083618 seconds
+
+[40.9, 39.63, 39.53, 39.38, 39.44, 39.45, 39.92, 39.64, 39.85, 39.99]
+[108.67]
+13.106364250183105
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 193546, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.657205820083618, 'TIME_S_1KI': 0.05506290917964524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.268603067398, 'W': 108.67}
+[40.9, 39.63, 39.53, 39.38, 39.44, 39.45, 39.92, 39.64, 39.85, 39.99, 39.96, 39.48, 39.45, 39.41, 39.63, 39.38, 40.94, 39.79, 39.91, 39.74]
+715.125
+35.75625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 193546, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.657205820083618, 'TIME_S_1KI': 0.05506290917964524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.268603067398, 'W': 108.67, 'J_1KI': 7.358811874528009, 'W_1KI': 0.5614685914459612, 'W_D': 72.91375, 'J_D': 955.6341663467883, 'W_D_1KI': 0.3767256879501513, 'J_D_1KI': 0.001946440060503195}
diff --git a/pytorch/output_synthetic_16core/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 37875a8..4c6ec73 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102691, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.217256307601929, "TIME_S_1KI": 0.09949514862648069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1713.9247414016722, "W": 132.92, "J_1KI": 16.690116382172462, "W_1KI": 1.294368542520766, "W_D": 96.75025, "J_D": 1247.5372194688318, "W_D_1KI": 0.9421492633239523, "J_D_1KI": 0.009174604038561825}
diff --git a/pytorch/output_synthetic_16core/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 0c99b95..7818ee2 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": 0.14159297943115234}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.027860403060913086}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     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]),
+tensor(crow_indices=tensor([      0,      86,     184,  ...,  999787,
+                             999901, 1000000]),
+       col_indices=tensor([  81,   93,  211,  ..., 9891, 9936, 9983]),
+       values=tensor([0.0273, 0.9948, 0.2764,  ..., 0.0318, 0.5538, 0.8532]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0358, 0.2032, 0.7087,  ..., 0.4931, 0.1706, 0.1726])
+tensor([0.8459, 0.7440, 0.9932,  ..., 0.5464, 0.7654, 0.2266])
 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: 0.14159297943115234 seconds
+Time: 0.027860403060913086 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37687', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.853411912918091}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      90,     178,  ...,  999807,
+                             999899, 1000000]),
+       col_indices=tensor([   9,   87,  435,  ..., 9776, 9821, 9947]),
+       values=tensor([0.6051, 0.3509, 0.6551,  ..., 0.3060, 0.1178, 0.2325]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9536, 0.3002, 0.1616,  ..., 0.3121, 0.8413, 0.9505])
+tensor([0.6802, 0.0969, 0.8232,  ..., 0.8757, 0.6573, 0.4893])
 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: 7.9134438037872314 seconds
+Time: 3.853411912918091 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102691', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.217256307601929}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      98,     191,  ...,  999800,
+                             999904, 1000000]),
+       col_indices=tensor([  18,   19,   89,  ..., 9675, 9719, 9959]),
+       values=tensor([0.5811, 0.2000, 0.4195,  ..., 0.8918, 0.7545, 0.5786]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2767, 0.4380, 0.7945,  ..., 0.2102, 0.5547, 0.8740])
+tensor([0.3032, 0.6522, 0.8844,  ..., 0.7793, 0.6874, 0.5546])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,19 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-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}
+Time: 10.217256307601929 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     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]),
+tensor(crow_indices=tensor([      0,      98,     191,  ...,  999800,
+                             999904, 1000000]),
+       col_indices=tensor([  18,   19,   89,  ..., 9675, 9719, 9959]),
+       values=tensor([0.5811, 0.2000, 0.4195,  ..., 0.8918, 0.7545, 0.5786]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7936, 0.9785, 0.9590,  ..., 0.6005, 0.0137, 0.6516])
+tensor([0.3032, 0.6522, 0.8844,  ..., 0.7793, 0.6874, 0.5546])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -76,30 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.469164609909058 seconds
+Time: 10.217256307601929 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     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}
+[40.93, 39.91, 40.43, 39.4, 39.51, 40.07, 39.51, 39.35, 39.39, 44.7]
+[132.92]
+12.894408226013184
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102691, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.217256307601929, 'TIME_S_1KI': 0.09949514862648069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.9247414016722, 'W': 132.92}
+[40.93, 39.91, 40.43, 39.4, 39.51, 40.07, 39.51, 39.35, 39.39, 44.7, 40.04, 39.9, 39.44, 39.42, 39.81, 39.87, 45.14, 39.82, 39.78, 39.62]
+723.395
+36.16975
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102691, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.217256307601929, 'TIME_S_1KI': 0.09949514862648069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.9247414016722, 'W': 132.92, 'J_1KI': 16.690116382172462, 'W_1KI': 1.294368542520766, 'W_D': 96.75025, 'J_D': 1247.5372194688318, 'W_D_1KI': 0.9421492633239523, 'J_D_1KI': 0.009174604038561825}
diff --git a/pytorch/output_synthetic_16core/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 5302269..52cad91 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": 27894, "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": 10.822905540466309, "TIME_S_1KI": 0.3880012024258374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2101.6757108688353, "W": 151.95, "J_1KI": 75.34508176915593, "W_1KI": 5.447408044740804, "W_D": 116.03349999999999, "J_D": 1604.90153732872, "W_D_1KI": 4.159801390980138, "J_D_1KI": 0.14912889477952743}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.346900463104248, "TIME_S_1KI": 0.3725256692386768, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2138.1740951538086, "W": 151.25, "J_1KI": 76.98196562209931, "W_1KI": 5.445544554455445, "W_D": 115.164, "J_D": 1628.037563598633, "W_D_1KI": 4.146318631863187, "J_D_1KI": 0.1492823989869734}
diff --git a/pytorch/output_synthetic_16core/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 3e6dd10..19caf4c 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": 0.4579291343688965}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07912850379943848}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     485,    1001,  ..., 4998993,
-                            4999541, 5000000]),
-       col_indices=tensor([   5,   20,   61,  ..., 9897, 9942, 9998]),
-       values=tensor([0.7241, 0.0945, 0.6836,  ..., 0.9220, 0.2796, 0.2745]),
+tensor(crow_indices=tensor([      0,     485,     973,  ..., 4998984,
+                            4999512, 5000000]),
+       col_indices=tensor([  23,   33,   35,  ..., 9878, 9920, 9946]),
+       values=tensor([0.8956, 0.5440, 0.5650,  ..., 0.6571, 0.0981, 0.4530]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6528, 0.9454, 0.7224,  ..., 0.5670, 0.2826, 0.8750])
+tensor([0.3320, 0.0557, 0.6993,  ..., 0.8374, 0.3528, 0.6849])
 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: 0.4579291343688965 seconds
+Time: 0.07912850379943848 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22929', '-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": 8.630967855453491}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13269', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.016182899475098}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     483,     987,  ..., 4998961,
-                            4999465, 5000000]),
-       col_indices=tensor([  47,   67,   96,  ..., 9993, 9994, 9997]),
-       values=tensor([0.9705, 0.3882, 0.2458,  ..., 0.5796, 0.8899, 0.6056]),
+tensor(crow_indices=tensor([      0,     490,     975,  ..., 4999017,
+                            4999511, 5000000]),
+       col_indices=tensor([   5,    7,   17,  ..., 9925, 9927, 9956]),
+       values=tensor([0.3061, 0.0982, 0.7519,  ..., 0.4711, 0.1343, 0.2753]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2409, 0.7584, 0.7571,  ..., 0.5444, 0.5564, 0.6333])
+tensor([0.4300, 0.5593, 0.7816,  ..., 0.7590, 0.1985, 0.5681])
 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: 8.630967855453491 seconds
+Time: 5.016182899475098 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27894', '-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": 10.822905540466309}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27775', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.346900463104248}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     505,     994,  ..., 4999000,
-                            4999548, 5000000]),
-       col_indices=tensor([  26,   89,   94,  ..., 9963, 9967, 9969]),
-       values=tensor([0.5738, 0.3729, 0.9664,  ..., 0.7914, 0.9130, 0.5932]),
+tensor(crow_indices=tensor([      0,     516,     985,  ..., 4998986,
+                            4999503, 5000000]),
+       col_indices=tensor([   0,   38,   62,  ..., 9969, 9984, 9993]),
+       values=tensor([0.4538, 0.1922, 0.3497,  ..., 0.8541, 0.7038, 0.0561]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2560, 0.0745, 0.3692,  ..., 0.2024, 0.4618, 0.6540])
+tensor([0.3516, 0.9610, 0.6827,  ..., 0.5287, 0.4040, 0.0575])
 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: 10.822905540466309 seconds
+Time: 10.346900463104248 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     505,     994,  ..., 4999000,
-                            4999548, 5000000]),
-       col_indices=tensor([  26,   89,   94,  ..., 9963, 9967, 9969]),
-       values=tensor([0.5738, 0.3729, 0.9664,  ..., 0.7914, 0.9130, 0.5932]),
+tensor(crow_indices=tensor([      0,     516,     985,  ..., 4998986,
+                            4999503, 5000000]),
+       col_indices=tensor([   0,   38,   62,  ..., 9969, 9984, 9993]),
+       values=tensor([0.4538, 0.1922, 0.3497,  ..., 0.8541, 0.7038, 0.0561]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2560, 0.0745, 0.3692,  ..., 0.2024, 0.4618, 0.6540])
+tensor([0.3516, 0.9610, 0.6827,  ..., 0.5287, 0.4040, 0.0575])
 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: 10.822905540466309 seconds
+Time: 10.346900463104248 seconds
 
-[41.38, 40.1, 39.88, 40.17, 39.72, 39.62, 39.69, 39.64, 39.9, 39.73]
-[151.95]
-13.831363677978516
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27894, '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': 10.822905540466309, 'TIME_S_1KI': 0.3880012024258374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.6757108688353, 'W': 151.95}
-[41.38, 40.1, 39.88, 40.17, 39.72, 39.62, 39.69, 39.64, 39.9, 39.73, 40.26, 39.63, 39.55, 39.93, 41.22, 40.33, 39.48, 39.56, 39.48, 39.49]
-718.3299999999999
-35.9165
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27894, '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': 10.822905540466309, 'TIME_S_1KI': 0.3880012024258374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.6757108688353, 'W': 151.95, 'J_1KI': 75.34508176915593, 'W_1KI': 5.447408044740804, 'W_D': 116.03349999999999, 'J_D': 1604.90153732872, 'W_D_1KI': 4.159801390980138, 'J_D_1KI': 0.14912889477952743}
+[41.25, 39.91, 39.68, 39.83, 39.58, 41.43, 40.59, 39.78, 40.26, 39.69]
+[151.25]
+14.136688232421875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.346900463104248, 'TIME_S_1KI': 0.3725256692386768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2138.1740951538086, 'W': 151.25}
+[41.25, 39.91, 39.68, 39.83, 39.58, 41.43, 40.59, 39.78, 40.26, 39.69, 40.82, 40.72, 40.11, 39.71, 39.6, 39.85, 39.65, 39.99, 39.77, 40.76]
+721.72
+36.086
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.346900463104248, 'TIME_S_1KI': 0.3725256692386768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2138.1740951538086, 'W': 151.25, 'J_1KI': 76.98196562209931, 'W_1KI': 5.445544554455445, 'W_D': 115.164, 'J_D': 1628.037563598633, 'W_D_1KI': 4.146318631863187, 'J_D_1KI': 0.1492823989869734}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
index a6d8fa4..6336d4d 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4679, "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": 11.427535057067871, "TIME_S_1KI": 2.4423028546843066, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1873.982270450592, "W": 124.72, "J_1KI": 400.5091409383612, "W_1KI": 26.655268219705064, "W_D": 88.71, "J_D": 1332.9134638524054, "W_D_1KI": 18.959179311818765, "J_D_1KI": 4.051972496648593}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4427, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.78234076499939, "TIME_S_1KI": 2.4355863485428935, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1855.6078462219239, "W": 121.94, "J_1KI": 419.15695645401485, "W_1KI": 27.544612604472555, "W_D": 85.48649999999999, "J_D": 1300.8809262428283, "W_D_1KI": 19.310255251863563, "J_D_1KI": 4.361927999065633}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
index a87f9d5..68c803b 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 2.382111072540283}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.23713254928588867}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1024,     2032,  ...,  9997994,
+tensor(crow_indices=tensor([       0,      967,     1927,  ...,  9997983,
                              9998974, 10000000]),
-       col_indices=tensor([  25,   59,   80,  ..., 9969, 9975, 9986]),
-       values=tensor([0.6759, 0.5147, 0.7066,  ..., 0.5276, 0.4088, 0.2550]),
+       col_indices=tensor([   2,    7,   17,  ..., 9977, 9981, 9986]),
+       values=tensor([0.0113, 0.4578, 0.3712,  ..., 0.8300, 0.4518, 0.5288]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6863, 0.6243, 0.0191,  ..., 0.9166, 0.1487, 0.8503])
+tensor([0.6464, 0.5946, 0.9135,  ..., 0.7384, 0.8851, 0.3138])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 2.382111072540283 seconds
+Time: 0.23713254928588867 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4407', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.887728929519653}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4427', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.78234076499939}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      980,     2002,  ...,  9998027,
-                             9998983, 10000000]),
-       col_indices=tensor([   0,    5,   25,  ..., 9979, 9984, 9986]),
-       values=tensor([0.6732, 0.9055, 0.4649,  ..., 0.1468, 0.7629, 0.6148]),
+tensor(crow_indices=tensor([       0,     1000,     1968,  ...,  9997976,
+                             9998957, 10000000]),
+       col_indices=tensor([  18,   35,   37,  ..., 9972, 9974, 9993]),
+       values=tensor([0.5495, 0.5155, 0.6909,  ..., 0.5748, 0.2988, 0.6189]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4732, 0.0327, 0.4956,  ..., 0.7189, 0.9869, 0.4026])
+tensor([0.2327, 0.3005, 0.5005,  ..., 0.5867, 0.2890, 0.0524])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 9.887728929519653 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4679', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.427535057067871}
+Time: 10.78234076499939 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1053,     2097,  ...,  9998095,
-                             9999109, 10000000]),
-       col_indices=tensor([  10,   15,   24,  ..., 9977, 9991, 9995]),
-       values=tensor([0.4155, 0.1468, 0.0149,  ..., 0.0226, 0.9383, 0.4708]),
+tensor(crow_indices=tensor([       0,     1000,     1968,  ...,  9997976,
+                             9998957, 10000000]),
+       col_indices=tensor([  18,   35,   37,  ..., 9972, 9974, 9993]),
+       values=tensor([0.5495, 0.5155, 0.6909,  ..., 0.5748, 0.2988, 0.6189]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3975, 0.8045, 0.4645,  ..., 0.1781, 0.4097, 0.1046])
+tensor([0.2327, 0.3005, 0.5005,  ..., 0.5867, 0.2890, 0.0524])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 11.427535057067871 seconds
+Time: 10.78234076499939 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1053,     2097,  ...,  9998095,
-                             9999109, 10000000]),
-       col_indices=tensor([  10,   15,   24,  ..., 9977, 9991, 9995]),
-       values=tensor([0.4155, 0.1468, 0.0149,  ..., 0.0226, 0.9383, 0.4708]),
-       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3975, 0.8045, 0.4645,  ..., 0.1781, 0.4097, 0.1046])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 10000000
-Density: 0.1
-Time: 11.427535057067871 seconds
-
-[40.62, 41.67, 40.6, 39.62, 39.77, 39.59, 39.62, 40.85, 40.24, 40.01]
-[124.72]
-15.02551531791687
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4679, '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': 11.427535057067871, 'TIME_S_1KI': 2.4423028546843066, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.982270450592, 'W': 124.72}
-[40.62, 41.67, 40.6, 39.62, 39.77, 39.59, 39.62, 40.85, 40.24, 40.01, 40.22, 39.64, 40.11, 39.47, 39.58, 39.84, 39.97, 39.93, 39.51, 39.53]
-720.2
-36.010000000000005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4679, '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': 11.427535057067871, 'TIME_S_1KI': 2.4423028546843066, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.982270450592, 'W': 124.72, 'J_1KI': 400.5091409383612, 'W_1KI': 26.655268219705064, 'W_D': 88.71, 'J_D': 1332.9134638524054, 'W_D_1KI': 18.959179311818765, 'J_D_1KI': 4.051972496648593}
+[40.15, 39.73, 39.55, 39.93, 39.94, 39.96, 44.96, 39.82, 40.15, 39.43]
+[121.94]
+15.217384338378906
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.78234076499939, 'TIME_S_1KI': 2.4355863485428935, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1855.6078462219239, 'W': 121.94}
+[40.15, 39.73, 39.55, 39.93, 39.94, 39.96, 44.96, 39.82, 40.15, 39.43, 40.99, 45.07, 39.68, 40.13, 40.01, 39.56, 39.72, 40.75, 40.01, 39.63]
+729.07
+36.453500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.78234076499939, 'TIME_S_1KI': 2.4355863485428935, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1855.6078462219239, 'W': 121.94, 'J_1KI': 419.15695645401485, 'W_1KI': 27.544612604472555, 'W_D': 85.48649999999999, 'J_D': 1300.8809262428283, 'W_D_1KI': 19.310255251863563, 'J_D_1KI': 4.361927999065633}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
index 4f270a6..3673122 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2251, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.887785911560059, "TIME_S_1KI": 4.83686624236342, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2027.23151884079, "W": 120.43, "J_1KI": 900.591523252239, "W_1KI": 53.500666370501996, "W_D": 84.27900000000001, "J_D": 1418.6917311000825, "W_D_1KI": 37.44069302532208, "J_D_1KI": 16.632915604319006}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2210, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.475984573364258, "TIME_S_1KI": 4.740264512834506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.6901198005676, "W": 119.44, "J_1KI": 934.701411674465, "W_1KI": 54.04524886877828, "W_D": 83.048, "J_D": 1436.2979995746614, "W_D_1KI": 37.57828054298643, "J_D_1KI": 17.00374685203006}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
index c0dab55..5e9953a 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.959461212158203}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5103092193603516}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1995,     4040,  ..., 19996024,
-                            19998015, 20000000]),
-       col_indices=tensor([   0,   11,   12,  ..., 9985, 9992, 9995]),
-       values=tensor([0.1207, 0.1695, 0.9340,  ..., 0.6555, 0.7804, 0.2569]),
+tensor(crow_indices=tensor([       0,     2001,     3993,  ..., 19996027,
+                            19997998, 20000000]),
+       col_indices=tensor([   4,    8,   12,  ..., 9988, 9991, 9998]),
+       values=tensor([0.1397, 0.5991, 0.8904,  ..., 0.1163, 0.3047, 0.7503]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.9596, 0.9534, 0.3471,  ..., 0.1162, 0.8421, 0.0589])
+tensor([0.7325, 0.0863, 0.4494,  ..., 0.5445, 0.3494, 0.7015])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 4.959461212158203 seconds
+Time: 0.5103092193603516 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2117', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.870691061019897}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2057', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.769307136535645}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2060,     4088,  ..., 19995982,
-                            19997996, 20000000]),
-       col_indices=tensor([   3,    8,   18,  ..., 9972, 9995, 9999]),
-       values=tensor([0.9088, 0.2769, 0.7723,  ..., 0.9463, 0.8275, 0.8743]),
+tensor(crow_indices=tensor([       0,     1965,     3996,  ..., 19995929,
+                            19997992, 20000000]),
+       col_indices=tensor([   4,    9,   15,  ..., 9975, 9986, 9992]),
+       values=tensor([0.0708, 0.7889, 0.9973,  ..., 0.4384, 0.2830, 0.3299]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1663, 0.5238, 0.4734,  ..., 0.4751, 0.9551, 0.4862])
+tensor([0.8359, 0.1884, 0.2769,  ..., 0.8252, 0.8191, 0.5472])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 9.870691061019897 seconds
+Time: 9.769307136535645 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2251', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.887785911560059}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2210', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.475984573364258}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1987,     4056,  ..., 19996028,
-                            19998022, 20000000]),
-       col_indices=tensor([   5,   19,   20,  ..., 9991, 9993, 9995]),
-       values=tensor([0.9673, 0.5215, 0.1097,  ..., 0.0767, 0.9506, 0.8361]),
+tensor(crow_indices=tensor([       0,     2066,     4070,  ..., 19995990,
+                            19998002, 20000000]),
+       col_indices=tensor([   1,    2,    8,  ..., 9986, 9990, 9993]),
+       values=tensor([0.6258, 0.8376, 0.0180,  ..., 0.7990, 0.4511, 0.0511]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1308, 0.6150, 0.9184,  ..., 0.0574, 0.4962, 0.6674])
+tensor([0.7373, 0.4078, 0.5568,  ..., 0.6016, 0.2858, 0.4434])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.887785911560059 seconds
+Time: 10.475984573364258 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1987,     4056,  ..., 19996028,
-                            19998022, 20000000]),
-       col_indices=tensor([   5,   19,   20,  ..., 9991, 9993, 9995]),
-       values=tensor([0.9673, 0.5215, 0.1097,  ..., 0.0767, 0.9506, 0.8361]),
+tensor(crow_indices=tensor([       0,     2066,     4070,  ..., 19995990,
+                            19998002, 20000000]),
+       col_indices=tensor([   1,    2,    8,  ..., 9986, 9990, 9993]),
+       values=tensor([0.6258, 0.8376, 0.0180,  ..., 0.7990, 0.4511, 0.0511]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1308, 0.6150, 0.9184,  ..., 0.0574, 0.4962, 0.6674])
+tensor([0.7373, 0.4078, 0.5568,  ..., 0.6016, 0.2858, 0.4434])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.887785911560059 seconds
+Time: 10.475984573364258 seconds
 
-[40.74, 39.87, 40.58, 40.31, 40.56, 40.27, 40.45, 40.39, 39.9, 40.0]
-[120.43]
-16.833276748657227
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2251, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.887785911560059, 'TIME_S_1KI': 4.83686624236342, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2027.23151884079, 'W': 120.43}
-[40.74, 39.87, 40.58, 40.31, 40.56, 40.27, 40.45, 40.39, 39.9, 40.0, 40.84, 39.74, 40.34, 40.47, 40.2, 40.2, 39.72, 39.73, 39.66, 39.68]
-723.02
-36.150999999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2251, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.887785911560059, 'TIME_S_1KI': 4.83686624236342, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2027.23151884079, 'W': 120.43, 'J_1KI': 900.591523252239, 'W_1KI': 53.500666370501996, 'W_D': 84.27900000000001, 'J_D': 1418.6917311000825, 'W_D_1KI': 37.44069302532208, 'J_D_1KI': 16.632915604319006}
+[40.41, 45.21, 40.37, 40.41, 40.26, 39.65, 40.47, 41.87, 39.92, 39.65]
+[119.44]
+17.294793367385864
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2210, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.475984573364258, 'TIME_S_1KI': 4.740264512834506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.6901198005676, 'W': 119.44}
+[40.41, 45.21, 40.37, 40.41, 40.26, 39.65, 40.47, 41.87, 39.92, 39.65, 40.39, 39.85, 39.69, 40.06, 40.15, 39.58, 40.59, 39.58, 39.95, 40.01]
+727.8399999999999
+36.391999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2210, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.475984573364258, 'TIME_S_1KI': 4.740264512834506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.6901198005676, 'W': 119.44, 'J_1KI': 934.701411674465, 'W_1KI': 54.04524886877828, 'W_D': 83.048, 'J_D': 1436.2979995746614, 'W_D_1KI': 37.57828054298643, 'J_D_1KI': 17.00374685203006}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
index 0f6b467..21b0219 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1475, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.550647735595703, "TIME_S_1KI": 7.152981515658104, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2246.3171599555017, "W": 116.09, "J_1KI": 1522.9268881054247, "W_1KI": 78.7050847457627, "W_D": 80.02975, "J_D": 1548.5588830385805, "W_D_1KI": 54.25745762711865, "J_D_1KI": 36.78471703533467}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1434, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.430570602416992, "TIME_S_1KI": 7.273759136971403, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2171.006755914688, "W": 115.95999999999998, "J_1KI": 1513.9517126322787, "W_1KI": 80.8647140864714, "W_D": 79.27299999999998, "J_D": 1484.1515915973182, "W_D_1KI": 55.281032078103195, "J_D_1KI": 38.55023157468842}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
index d7eb761..29bb118 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.117977619171143}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7320935726165771}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2996,     5947,  ..., 29993941,
-                            29997016, 30000000]),
-       col_indices=tensor([   2,    4,    5,  ..., 9994, 9995, 9997]),
-       values=tensor([0.7643, 0.7440, 0.4862,  ..., 0.6436, 0.3641, 0.9418]),
+tensor(crow_indices=tensor([       0,     2976,     5887,  ..., 29993981,
+                            29996974, 30000000]),
+       col_indices=tensor([   2,   12,   13,  ..., 9995, 9997, 9999]),
+       values=tensor([0.2872, 0.6919, 0.0045,  ..., 0.7234, 0.8152, 0.1470]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.8566, 0.8595, 0.2293,  ..., 0.0057, 0.7338, 0.0583])
+tensor([0.3759, 0.5048, 0.7452,  ..., 0.9323, 0.0206, 0.6020])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 7.117977619171143 seconds
+Time: 0.7320935726165771 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1475', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.550647735595703}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1434', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.430570602416992}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2975,     5983,  ..., 29994011,
-                            29997029, 30000000]),
-       col_indices=tensor([   7,    9,   13,  ..., 9994, 9995, 9998]),
-       values=tensor([0.9780, 0.9139, 0.8008,  ..., 0.7340, 0.6785, 0.0713]),
+tensor(crow_indices=tensor([       0,     2946,     5856,  ..., 29993956,
+                            29997054, 30000000]),
+       col_indices=tensor([   1,    3,   10,  ..., 9992, 9994, 9995]),
+       values=tensor([0.6658, 0.8893, 0.2640,  ..., 0.2436, 0.9944, 0.7745]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.9158, 0.5873, 0.3730,  ..., 0.5358, 0.1305, 0.5051])
+tensor([0.7478, 0.4417, 0.0487,  ..., 0.7713, 0.8445, 0.5646])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.550647735595703 seconds
+Time: 10.430570602416992 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2975,     5983,  ..., 29994011,
-                            29997029, 30000000]),
-       col_indices=tensor([   7,    9,   13,  ..., 9994, 9995, 9998]),
-       values=tensor([0.9780, 0.9139, 0.8008,  ..., 0.7340, 0.6785, 0.0713]),
+tensor(crow_indices=tensor([       0,     2946,     5856,  ..., 29993956,
+                            29997054, 30000000]),
+       col_indices=tensor([   1,    3,   10,  ..., 9992, 9994, 9995]),
+       values=tensor([0.6658, 0.8893, 0.2640,  ..., 0.2436, 0.9944, 0.7745]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.9158, 0.5873, 0.3730,  ..., 0.5358, 0.1305, 0.5051])
+tensor([0.7478, 0.4417, 0.0487,  ..., 0.7713, 0.8445, 0.5646])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.550647735595703 seconds
+Time: 10.430570602416992 seconds
 
-[41.39, 39.87, 39.86, 40.02, 39.88, 39.87, 40.22, 40.42, 40.2, 40.31]
-[116.09]
-19.349790334701538
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.550647735595703, 'TIME_S_1KI': 7.152981515658104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2246.3171599555017, 'W': 116.09}
-[41.39, 39.87, 39.86, 40.02, 39.88, 39.87, 40.22, 40.42, 40.2, 40.31, 41.51, 39.76, 39.79, 39.7, 39.79, 40.38, 40.29, 39.67, 39.96, 39.84]
-721.2049999999999
-36.060249999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.550647735595703, 'TIME_S_1KI': 7.152981515658104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2246.3171599555017, 'W': 116.09, 'J_1KI': 1522.9268881054247, 'W_1KI': 78.7050847457627, 'W_D': 80.02975, 'J_D': 1548.5588830385805, 'W_D_1KI': 54.25745762711865, 'J_D_1KI': 36.78471703533467}
+[40.39, 40.27, 40.31, 40.01, 39.78, 40.12, 45.36, 39.94, 40.02, 52.21]
+[115.96]
+18.722031354904175
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1434, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.430570602416992, 'TIME_S_1KI': 7.273759136971403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2171.006755914688, 'W': 115.95999999999998}
+[40.39, 40.27, 40.31, 40.01, 39.78, 40.12, 45.36, 39.94, 40.02, 52.21, 41.63, 40.16, 40.0, 40.35, 41.93, 39.55, 39.78, 39.55, 39.63, 39.73]
+733.74
+36.687
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1434, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.430570602416992, 'TIME_S_1KI': 7.273759136971403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2171.006755914688, 'W': 115.95999999999998, 'J_1KI': 1513.9517126322787, 'W_1KI': 80.8647140864714, 'W_D': 79.27299999999998, 'J_D': 1484.1515915973182, 'W_D_1KI': 55.281032078103195, 'J_D_1KI': 38.55023157468842}
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 6beae0d..7eea71b 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 349456, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.173964023590088, "TIME_S_1KI": 0.03197531026392475, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1252.9686733055114, "W": 97.63, "J_1KI": 3.585483360724988, "W_1KI": 0.2793770889611282, "W_D": 61.798, "J_D": 793.1061976127625, "W_D_1KI": 0.17684057506524428, "J_D_1KI": 0.0005060453249200021}
diff --git a/pytorch/output_synthetic_16core/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 f912c9b..518e71c 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
@@ -1,266 +1,373 @@
-['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": 0.1263408660888672}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.018783092498779297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([6511, 7342, 6569, 9549, 4200, 6366, 6222, 7592,  169,
-                           4880, 5467, 9729, 3186, 7579, 7366, 6461, 1569,  540,
-                           4760, 8910, 3523, 1918, 8918, 3090, 6190,  499, 9266,
-                           7460, 2296, 3773, 5490,  884, 9714, 1811, 1020, 9037,
-                           8808, 4866, 6500, 7251, 3472, 4368, 6660, 7858, 2942,
-                           5413, 1404, 5690, 9364, 8617, 6277, 1831,  560, 3190,
-                           1663,  819, 9507, 4286, 7801, 2558, 3419, 9656, 3132,
-                           7435, 3025, 8945, 7422, 9343, 1144, 3849, 1843, 8839,
-                           6036, 1338, 5977, 5948, 8060, 2140, 9438, 3733, 4406,
-                           5939, 9926, 2756, 7803, 4028, 4889, 5230, 7472, 8834,
-                           3528, 4118, 8239, 1176, 9979,  731, 8697, 6303,  596,
-                           6780, 2484,  641, 8003, 4632, 6436, 9612, 7360, 8016,
-                            621, 8128,  463, 4301, 2226, 5312, 7004, 6810, 3999,
-                           4752, 5969,   34, 4043, 3161, 9457,  286, 6473, 2546,
-                           2948, 1709, 8722,  220, 6967, 9215,  550, 2682, 1046,
-                           1411, 5120, 1681,  795, 7027, 1138, 7720, 5262, 9156,
-                           6114, 3094, 5529, 9897, 5383, 2658, 5925, 4585, 1891,
-                           5947, 1641, 3669, 2374, 4271, 1873, 3950, 8634, 9898,
-                           2804, 4238, 6088, 7480, 3866, 2931,  158,  762, 7315,
-                           3431, 9534, 8813, 8729, 7588, 5653, 2233, 8886, 2117,
-                           5788, 7121,  687, 4736, 1055, 4450, 4608,   65, 8244,
-                           2012, 6731, 5872, 7793, 1928, 6136, 4684, 8489, 8964,
-                           3740, 2505, 3086,  567, 7565, 6980, 8106, 6069, 7130,
-                           6970, 9760, 2913, 3403, 1406, 7675,  386, 5541, 6017,
-                           9133, 5436, 8960, 7368, 6345, 3739, 2407, 5067, 8091,
-                           3266, 3124, 5929, 6713, 6112, 8945, 2328, 7728, 4986,
-                           8539, 2814, 6319,  705, 1165, 9519, 3981, 5584, 6598,
-                           2738, 5532, 5237, 6331, 6207, 8710,  995, 1064, 3408,
-                           6852,   23, 6885, 9749, 2209, 7966, 1743, 7553, 7522,
-                           7688, 3322, 2692, 4077,  524, 1297, 4398, 4096, 1166,
-                           5811, 9504, 9733, 2814, 8085, 4424, 3816, 2029, 7548,
-                           8169, 3026, 1340, 5158, 5626, 7898, 9054, 3947, 2974,
-                           8879, 2439, 4906, 5376, 7674, 5565, 4834, 1951, 8992,
-                           2602, 4891, 1422, 7414, 4875, 8525, 5441, 9284,  999,
-                           7732, 4808, 2724, 1701, 1389, 3674, 4863, 3129, 2538,
-                           6570, 3018, 4218, 7797, 9608, 1867, 6688, 2972, 1313,
-                           6883, 6461, 1346, 4019, 3075, 3279, 8085, 7848, 9504,
-                           3827, 2046, 5854,  837, 9356, 3800, 7458, 9224, 1721,
-                           4056, 5289,  942, 2304, 5616, 3284, 4339, 5079,  870,
-                           9407, 2635, 5239, 2058, 2138, 7880, 4585, 3515, 9934,
-                            866, 9761, 8919, 2186,  800,  572, 8912, 8785, 4800,
-                           5094, 6357, 8393, 6810, 1477, 5566, 9893,  349, 3779,
-                           8545, 1987, 7795, 9938, 6031, 9207, 8810, 6010, 4262,
-                           5989, 7188, 9196, 6390, 7391, 9696, 2839, 9975, 2181,
-                           2674, 1526, 8229, 3221, 2082, 4838, 7371,   86, 8603,
-                           1062, 8643, 4915, 5544, 7520, 8254, 9621,  153, 2491,
-                           8229, 4372, 2665, 4480, 3338, 4223, 3871, 8012, 4932,
-                           8756, 3433, 2881, 6190, 3712, 7183, 4288, 7905, 7231,
-                           8297, 6210, 5179, 8257, 6157, 3488, 6273, 9080, 4379,
-                           5031, 8844,  157, 4802,  414,  188, 6362,  490, 9145,
-                           8499, 4374, 6930, 3177, 8307, 8277, 6077,  290, 2856,
-                           6353, 3841,  363, 9590, 8189, 5238, 5239, 3246, 7783,
-                           5125, 1355, 7133, 8323, 9928, 5054, 1300, 7618, 7017,
-                             20, 6323, 9040, 4476, 3660, 9493, 2606, 5099, 8989,
-                           4686, 4749, 8776, 7509, 5402, 8359, 4880, 1488, 5157,
-                            230, 5242, 2209, 1343, 2485,  558, 5693, 1249, 4396,
-                           2349, 6899, 7592, 9403, 4311,  564, 7436, 7000,  380,
-                           9168, 6194, 2637, 2512, 3205, 2559, 7975, 9819, 6809,
-                           3325, 1840, 4305, 5238, 9210, 2227, 6924, 8354, 3045,
-                           3286, 3664, 6276, 8603, 6294, 1616, 3859, 5674, 2042,
-                           4316, 8399, 3534, 2694, 6067, 6420, 9170, 8993, 3488,
-                           8272, 5531, 4465, 9064, 8332,  990, 5946, 5090, 3858,
-                           8019, 8546, 3535, 1789, 3116, 6602, 8289, 9270, 3315,
-                           3607, 5332, 5665, 4255, 9462, 3870, 5110, 7175,   59,
-                           8950, 4474,  898, 3887, 9586, 3479,  624, 2157, 8013,
-                           5204,  446, 1227, 6497, 4047, 2825, 4969,  207, 9153,
-                           3558, 2821, 5665, 1108, 8779, 8273, 3826, 1024, 2831,
-                           8755,  975, 6214, 8135, 9347, 2576, 7238,  260, 9515,
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-                           6785, 4432, 9084, 9287, 1308, 6562, 2916, 5846, 4040,
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-                           4244, 8881, 3409, 5409, 2501, 2137, 7118, 7535, 5989,
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-                           2427, 5637, 5250,  785, 6753, 8646, 5170, 7589, 4599,
-                           6101, 5880, 4216, 4352, 6075, 8934, 4178, 4278, 9476,
-                            891, 1828, 2805, 9509, 7190, 8284, 7824, 2775, 8561,
-                           5451, 4644,  949, 2504, 4485, 5019, 3536, 9441, 2681,
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-                           2312, 3478, 3018, 8148,   35, 1239, 3737,  276, 1484,
-                           5891]),
-       values=tensor([0.0306, 0.2996, 0.3526, 0.5136, 0.1960, 0.0494, 0.4248,
-                      0.9003, 0.3088, 0.8066, 0.0284, 0.9628, 0.6970, 0.3929,
-                      0.9036, 0.7489, 0.7075, 0.5515, 0.0416, 0.3000, 0.2939,
-                      0.4662, 0.0532, 0.2066, 0.1559, 0.5483, 0.1194, 0.6794,
-                      0.0379, 0.2707, 0.3144, 0.3029, 0.5991, 0.1291, 0.8872,
-                      0.0571, 0.4748, 0.2201, 0.3840, 0.3049, 0.2970, 0.9700,
-                      0.8529, 0.1435, 0.5565, 0.5637, 0.9930, 0.1648, 0.4519,
-                      0.4565, 0.0369, 0.8511, 0.6180, 0.7898, 0.5871, 0.6839,
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+                      5.4582e-01, 4.2012e-01, 6.6184e-01, 5.0529e-01,
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+                      5.8909e-01, 6.1073e-01, 5.5245e-01, 6.0942e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7754, 0.7775, 0.1646,  ..., 0.9136, 0.8074, 0.4658])
+tensor([0.4323, 0.3291, 0.3785,  ..., 0.2185, 0.3372, 0.4003])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,378 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.1263408660888672 seconds
+Time: 0.018783092498779297 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55901', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.679638147354126}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8610, 0.2031, 0.4276,  ..., 0.7862, 0.1485, 0.1233])
+tensor([0.3431, 0.4493, 0.0337,  ..., 0.1419, 0.4440, 0.1516])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -647,378 +754,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 2.457648515701294 seconds
+Time: 1.679638147354126 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '349456', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.173964023590088}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1885, 0.9110, 0.8668,  ..., 0.5988, 0.5354, 0.4490])
+tensor([0.5348, 0.7986, 0.2200,  ..., 0.0453, 0.2085, 0.0080])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,375 +1026,268 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.454679489135742 seconds
+Time: 11.173964023590088 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1885, 0.9110, 0.8668,  ..., 0.5988, 0.5354, 0.4490])
+tensor([0.5348, 0.7986, 0.2200,  ..., 0.0453, 0.2085, 0.0080])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1402,13 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.454679489135742 seconds
+Time: 11.173964023590088 seconds
 
-[39.64, 44.74, 39.71, 38.92, 39.06, 38.91, 39.09, 38.87, 39.14, 39.48]
-[97.06]
-13.222800016403198
-{'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
-35.57875
-{'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}
+[40.48, 41.2, 39.7, 39.62, 39.96, 39.55, 39.84, 39.41, 40.04, 39.52]
+[97.63]
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+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349456, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.173964023590088, 'TIME_S_1KI': 0.03197531026392475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1252.9686733055114, 'W': 97.63}
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+716.6399999999999
+35.831999999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349456, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.173964023590088, 'TIME_S_1KI': 0.03197531026392475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1252.9686733055114, 'W': 97.63, 'J_1KI': 3.585483360724988, 'W_1KI': 0.2793770889611282, 'W_D': 61.798, 'J_D': 793.1061976127625, 'W_D_1KI': 0.17684057506524428, 'J_D_1KI': 0.0005060453249200021}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
index b4ec291..6ded063 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 307566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.194913387298584, "TIME_S_1KI": 0.033147075383165185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1254.457565395832, "W": 97.91, "J_1KI": 4.078661378032136, "W_1KI": 0.3183381778219959, "W_D": 62.19175, "J_D": 796.8227075141073, "W_D_1KI": 0.2022061931422849, "J_D_1KI": 0.0006574400068352317}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 308023, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.28289532661438, "TIME_S_1KI": 0.03338353086170311, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1226.4741826629638, "W": 98.08, "J_1KI": 3.9817616952726382, "W_1KI": 0.3184177804904179, "W_D": 61.967, "J_D": 774.8870888772011, "W_D_1KI": 0.20117653551845155, "J_D_1KI": 0.0006531217977828004}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
index 037a253..df70699 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04550504684448242}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019933462142944336}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 5000, 5000]),
-       col_indices=tensor([9281,  526, 5110,  ..., 4172,  680, 4833]),
-       values=tensor([0.9710, 0.4177, 0.1273,  ..., 0.7621, 0.2431, 0.8030]),
+tensor(crow_indices=tensor([   0,    2,    5,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([4080, 6557, 3158,  ..., 4357, 6307, 2550]),
+       values=tensor([0.9910, 0.3414, 0.4855,  ..., 0.2598, 0.6108, 0.2815]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6244, 0.3231, 0.3638,  ..., 0.2586, 0.1943, 0.4038])
+tensor([0.2787, 0.7388, 0.8319,  ..., 0.5413, 0.0496, 0.2437])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.04550504684448242 seconds
+Time: 0.019933462142944336 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '230743', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.877320289611816}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52675', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.7956035137176514}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 4996, 4999, 5000]),
-       col_indices=tensor([5149,  830, 3827,  ..., 6947, 7825, 8143]),
-       values=tensor([0.7974, 0.8672, 0.6352,  ..., 0.0945, 0.9729, 0.8206]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([ 408,  476, 3837,  ..., 3097, 8388, 8856]),
+       values=tensor([0.3698, 0.9808, 0.6496,  ..., 0.7839, 0.4021, 0.3346]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.8724, 0.1762, 0.3345,  ..., 0.8958, 0.7321, 0.5036])
+tensor([0.1775, 0.8809, 0.7204,  ..., 0.4994, 0.6943, 0.3851])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 7.877320289611816 seconds
+Time: 1.7956035137176514 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '307566', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.194913387298584}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '308023', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.28289532661438}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([6013, 2562, 3841,  ..., 8262, 3270, 1424]),
-       values=tensor([0.2685, 0.3885, 0.6243,  ..., 0.1198, 0.9466, 0.9233]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2483, 6584, 3017,  ...,  870, 3138, 2052]),
+       values=tensor([0.7385, 0.7043, 0.9061,  ..., 0.4377, 0.8515, 0.3180]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.2690, 0.9597, 0.8597,  ..., 0.6996, 0.9887, 0.3737])
+tensor([0.1916, 0.9837, 0.2990,  ..., 0.4110, 0.2807, 0.4933])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.194913387298584 seconds
+Time: 10.28289532661438 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([6013, 2562, 3841,  ..., 8262, 3270, 1424]),
-       values=tensor([0.2685, 0.3885, 0.6243,  ..., 0.1198, 0.9466, 0.9233]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2483, 6584, 3017,  ...,  870, 3138, 2052]),
+       values=tensor([0.7385, 0.7043, 0.9061,  ..., 0.4377, 0.8515, 0.3180]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.2690, 0.9597, 0.8597,  ..., 0.6996, 0.9887, 0.3737])
+tensor([0.1916, 0.9837, 0.2990,  ..., 0.4110, 0.2807, 0.4933])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.194913387298584 seconds
+Time: 10.28289532661438 seconds
 
-[40.9, 39.57, 39.65, 39.48, 39.52, 39.37, 39.42, 39.88, 40.21, 39.84]
-[97.91]
-12.81235384941101
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 307566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.194913387298584, 'TIME_S_1KI': 0.033147075383165185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1254.457565395832, 'W': 97.91}
-[40.9, 39.57, 39.65, 39.48, 39.52, 39.37, 39.42, 39.88, 40.21, 39.84, 41.61, 39.93, 39.26, 40.39, 39.17, 39.17, 39.39, 39.38, 39.35, 40.1]
-714.365
-35.71825
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 307566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.194913387298584, 'TIME_S_1KI': 0.033147075383165185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1254.457565395832, 'W': 97.91, 'J_1KI': 4.078661378032136, 'W_1KI': 0.3183381778219959, 'W_D': 62.19175, 'J_D': 796.8227075141073, 'W_D_1KI': 0.2022061931422849, 'J_D_1KI': 0.0006574400068352317}
+[40.25, 39.88, 39.44, 44.89, 40.01, 39.55, 39.45, 39.48, 39.91, 41.29]
+[98.08]
+12.504834651947021
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 308023, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.28289532661438, 'TIME_S_1KI': 0.03338353086170311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1226.4741826629638, 'W': 98.08}
+[40.25, 39.88, 39.44, 44.89, 40.01, 39.55, 39.45, 39.48, 39.91, 41.29, 44.96, 39.78, 39.79, 40.18, 39.36, 39.46, 39.39, 39.37, 39.43, 39.28]
+722.26
+36.113
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 308023, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.28289532661438, 'TIME_S_1KI': 0.03338353086170311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1226.4741826629638, 'W': 98.08, 'J_1KI': 3.9817616952726382, 'W_1KI': 0.3184177804904179, 'W_D': 61.967, 'J_D': 774.8870888772011, 'W_D_1KI': 0.20117653551845155, 'J_D_1KI': 0.0006531217977828004}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
index f4ead28..f68b6b9 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1316, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738, "TIME_S_1KI": 7.964192915107703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2349.371359462738, "W": 119.72, "J_1KI": 1785.2365953364272, "W_1KI": 90.9726443768997, "W_D": 83.6515, "J_D": 1641.5673093559742, "W_D_1KI": 63.564969604863215, "J_D_1KI": 48.3016486359143}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1275, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.370937824249268, "TIME_S_1KI": 8.134068881764131, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2317.954887828827, "W": 118.94, "J_1KI": 1818.0038335912368, "W_1KI": 93.28627450980392, "W_D": 82.976, "J_D": 1617.072681793213, "W_D_1KI": 65.07921568627451, "J_D_1KI": 51.04252210688197}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
index a121e20..8fe4739 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.476217031478882}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8229217529296875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       58,      110,  ..., 24999904,
-                            24999948, 25000000]),
-       col_indices=tensor([  6107,  36475,  44542,  ..., 455197, 482838,
-                           484709]),
-       values=tensor([0.8741, 0.1087, 0.7265,  ..., 0.9387, 0.2139, 0.8984]),
+tensor(crow_indices=tensor([       0,       48,       92,  ..., 24999906,
+                            24999954, 25000000]),
+       col_indices=tensor([ 13687,  16103,  22085,  ..., 466250, 497468,
+                           498839]),
+       values=tensor([0.1763, 0.0612, 0.1831,  ..., 0.7206, 0.9735, 0.4201]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2008, 0.1464, 0.5363,  ..., 0.5258, 0.1478, 0.0153])
+tensor([0.0392, 0.3068, 0.8540,  ..., 0.0771, 0.2433, 0.8939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 8.476217031478882 seconds
+Time: 0.8229217529296875 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1238', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.872223138809204}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1275', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.370937824249268}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       42,      103,  ..., 24999895,
-                            24999951, 25000000]),
-       col_indices=tensor([  1782,  32597,  35292,  ..., 490788, 494408,
-                           495086]),
-       values=tensor([0.7532, 0.4055, 0.7849,  ..., 0.0826, 0.2837, 0.9366]),
+tensor(crow_indices=tensor([       0,       45,       89,  ..., 24999893,
+                            24999957, 25000000]),
+       col_indices=tensor([ 25264,  35882,  38786,  ..., 487781, 491680,
+                           492236]),
+       values=tensor([0.0901, 0.4292, 0.0295,  ..., 0.7641, 0.5758, 0.3435]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.6389, 0.8135, 0.6286,  ..., 0.0387, 0.4513, 0.2151])
+tensor([0.7878, 0.6485, 0.9023,  ..., 0.5055, 0.2764, 0.4227])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 9.872223138809204 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1316', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738}
+Time: 10.370937824249268 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       49,       93,  ..., 24999907,
-                            24999956, 25000000]),
-       col_indices=tensor([  1315,   9605,  36829,  ..., 467295, 483577,
-                           490282]),
-       values=tensor([0.4504, 0.6377, 0.8962,  ..., 0.8735, 0.2973, 0.1824]),
+tensor(crow_indices=tensor([       0,       45,       89,  ..., 24999893,
+                            24999957, 25000000]),
+       col_indices=tensor([ 25264,  35882,  38786,  ..., 487781, 491680,
+                           492236]),
+       values=tensor([0.0901, 0.4292, 0.0295,  ..., 0.7641, 0.5758, 0.3435]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1673, 0.3950, 0.6065,  ..., 0.2020, 0.8580, 0.9739])
+tensor([0.7878, 0.6485, 0.9023,  ..., 0.5055, 0.2764, 0.4227])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.480877876281738 seconds
+Time: 10.370937824249268 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       49,       93,  ..., 24999907,
-                            24999956, 25000000]),
-       col_indices=tensor([  1315,   9605,  36829,  ..., 467295, 483577,
-                           490282]),
-       values=tensor([0.4504, 0.6377, 0.8962,  ..., 0.8735, 0.2973, 0.1824]),
-       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1673, 0.3950, 0.6065,  ..., 0.2020, 0.8580, 0.9739])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 25000000
-Density: 0.0001
-Time: 10.480877876281738 seconds
-
-[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68]
-[119.72]
-19.623883724212646
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72}
-[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68, 41.19, 39.75, 40.31, 41.27, 39.67, 39.76, 39.72, 39.58, 39.67, 40.17]
-721.3699999999999
-36.06849999999999
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72, 'J_1KI': 1785.2365953364272, 'W_1KI': 90.9726443768997, 'W_D': 83.6515, 'J_D': 1641.5673093559742, 'W_D_1KI': 63.564969604863215, 'J_D_1KI': 48.3016486359143}
+[40.75, 40.02, 39.59, 39.62, 39.74, 39.84, 39.97, 39.74, 40.73, 39.5]
+[118.94]
+19.488438606262207
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.370937824249268, 'TIME_S_1KI': 8.134068881764131, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2317.954887828827, 'W': 118.94}
+[40.75, 40.02, 39.59, 39.62, 39.74, 39.84, 39.97, 39.74, 40.73, 39.5, 41.45, 40.23, 40.12, 39.57, 39.77, 39.94, 39.75, 39.69, 40.29, 39.64]
+719.28
+35.964
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.370937824249268, 'TIME_S_1KI': 8.134068881764131, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2317.954887828827, 'W': 118.94, 'J_1KI': 1818.0038335912368, 'W_1KI': 93.28627450980392, 'W_D': 82.976, 'J_D': 1617.072681793213, 'W_D_1KI': 65.07921568627451, 'J_D_1KI': 51.04252210688197}
diff --git a/pytorch/output_synthetic_16core/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 98c0109..24823a1 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20602, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.051029920578003, "TIME_S_1KI": 0.4878667081146492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2012.3513662075998, "W": 152.11, "J_1KI": 97.67747627451702, "W_1KI": 7.383263760799923, "W_D": 115.75800000000001, "J_D": 1531.429685421467, "W_D_1KI": 5.61877487622561, "J_D_1KI": 0.272729583352374}
diff --git a/pytorch/output_synthetic_16core/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 4dbd4ff..60d167b 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 0.5443899631500244}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08110809326171875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([ 61750, 191731, 192878,  ..., 292292, 347392,
+                           413452]),
+       values=tensor([0.4333, 0.7749, 0.6975,  ..., 0.5571, 0.2303, 0.6423]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3188, 0.4041, 0.2486,  ..., 0.5189, 0.6175, 0.2446])
+tensor([0.7573, 0.7811, 0.2609,  ..., 0.7028, 0.0683, 0.1077])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,41 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.5443899631500244 seconds
+Time: 0.08110809326171875 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12945', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.597289562225342}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,      12,  ..., 2499994,
+tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499996,
+                            2499997, 2500000]),
+       col_indices=tensor([304373, 374974, 396567,  ..., 161828, 243938,
+                           306700]),
+       values=tensor([0.0234, 0.0111, 0.7752,  ..., 0.4123, 0.0911, 0.7333]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2563, 0.2400, 0.1997,  ..., 0.9331, 0.1838, 0.9541])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 6.597289562225342 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20602', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.051029920578003}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499995,
                             2499998, 2500000]),
-       col_indices=tensor([131466, 192610, 285983,  ..., 398857,   7127,
-                           216070]),
-       values=tensor([0.3766, 0.1095, 0.0818,  ..., 0.7673, 0.9998, 0.7256]),
+       col_indices=tensor([ 84683, 221772, 250792,  ..., 457280, 123381,
+                           490345]),
+       values=tensor([0.6671, 0.6498, 0.8275,  ..., 0.5282, 0.6912, 0.3058]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6672, 0.9862, 0.6354,  ..., 0.4943, 0.9100, 0.2548])
+tensor([0.8099, 0.6830, 0.6662,  ..., 0.4435, 0.6731, 0.4595])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +59,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 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}
+Time: 10.051029920578003 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 84683, 221772, 250792,  ..., 457280, 123381,
+                           490345]),
+       values=tensor([0.6671, 0.6498, 0.8275,  ..., 0.5282, 0.6912, 0.3058]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9645, 0.6044, 0.9036,  ..., 0.9779, 0.7664, 0.8298])
+tensor([0.8099, 0.6830, 0.6662,  ..., 0.4435, 0.6731, 0.4595])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +77,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.492619514465332 seconds
+Time: 10.051029920578003 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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([0.9645, 0.6044, 0.9036,  ..., 0.9779, 0.7664, 0.8298])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 10.492619514465332 seconds
-
-[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}
+[40.55, 45.24, 39.91, 39.85, 39.84, 39.77, 40.43, 40.43, 42.0, 39.73]
+[152.11]
+13.22957968711853
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.051029920578003, 'TIME_S_1KI': 0.4878667081146492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2012.3513662075998, 'W': 152.11}
+[40.55, 45.24, 39.91, 39.85, 39.84, 39.77, 40.43, 40.43, 42.0, 39.73, 40.67, 40.21, 39.79, 39.83, 39.69, 40.04, 39.86, 39.69, 40.08, 39.81]
+727.04
+36.352
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.051029920578003, 'TIME_S_1KI': 0.4878667081146492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2012.3513662075998, 'W': 152.11, 'J_1KI': 97.67747627451702, 'W_1KI': 7.383263760799923, 'W_D': 115.75800000000001, 'J_D': 1531.429685421467, 'W_D_1KI': 5.61877487622561, 'J_D_1KI': 0.272729583352374}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
index 340d1c5..ac74c89 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2443, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.091211557388306, "TIME_S_1KI": 4.5399965441622205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2093.1614422798157, "W": 125.74, "J_1KI": 856.7996079737272, "W_1KI": 51.469504707327054, "W_D": 89.3905, "J_D": 1488.0606641173363, "W_D_1KI": 36.59046254604994, "J_D_1KI": 14.977676031948402}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2268, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.338330507278442, "TIME_S_1KI": 4.558346784514304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1929.4526886749268, "W": 124.72, "J_1KI": 850.7286987102852, "W_1KI": 54.99118165784832, "W_D": 88.6155, "J_D": 1370.9061476368904, "W_D_1KI": 39.07208994708994, "J_D_1KI": 17.227552886723963}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
index 6a66c91..4f74cbe 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
-['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', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.620434761047363}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.46280455589294434}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       28,       55,  ..., 12499942,
-                            12499972, 12500000]),
-       col_indices=tensor([ 19855,  24177,  33309,  ..., 430292, 468270,
-                           470726]),
-       values=tensor([0.1735, 0.2720, 0.9086,  ..., 0.2697, 0.0473, 0.0416]),
+tensor(crow_indices=tensor([       0,       21,       39,  ..., 12499960,
+                            12499981, 12500000]),
+       col_indices=tensor([  5530,  18658,  36900,  ..., 388989, 426254,
+                           497258]),
+       values=tensor([0.8053, 0.3880, 0.4779,  ..., 0.4773, 0.4279, 0.6817]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.2844, 0.4487, 0.9137,  ..., 0.5004, 0.3000, 0.1233])
+tensor([0.5886, 0.4606, 0.7255,  ..., 0.1606, 0.2608, 0.5232])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 4.620434761047363 seconds
+Time: 0.46280455589294434 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2272', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.761992931365967}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2268', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.338330507278442}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       25,       50,  ..., 12499939,
-                            12499968, 12500000]),
-       col_indices=tensor([ 35309, 102593, 109410,  ..., 438712, 452154,
-                           489935]),
-       values=tensor([0.4991, 0.7582, 0.4985,  ..., 0.8355, 0.6986, 0.3665]),
+tensor(crow_indices=tensor([       0,       30,       63,  ..., 12499957,
+                            12499979, 12500000]),
+       col_indices=tensor([ 14790,  16334,  55074,  ..., 466420, 486794,
+                           499923]),
+       values=tensor([0.8543, 0.1686, 0.8292,  ..., 0.6567, 0.2357, 0.6950]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.1755, 0.5499, 0.0031,  ..., 0.2944, 0.6143, 0.3232])
+tensor([0.8639, 0.3423, 0.4800,  ..., 0.1443, 0.7816, 0.0060])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 9.761992931365967 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2443', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.091211557388306}
+Time: 10.338330507278442 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       29,       54,  ..., 12499940,
-                            12499966, 12500000]),
-       col_indices=tensor([ 15104,  53699,  66016,  ..., 451008, 478949,
-                           498027]),
-       values=tensor([0.3834, 0.0166, 0.8269,  ..., 0.7083, 0.6634, 0.5753]),
+tensor(crow_indices=tensor([       0,       30,       63,  ..., 12499957,
+                            12499979, 12500000]),
+       col_indices=tensor([ 14790,  16334,  55074,  ..., 466420, 486794,
+                           499923]),
+       values=tensor([0.8543, 0.1686, 0.8292,  ..., 0.6567, 0.2357, 0.6950]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3028, 0.2567, 0.4384,  ..., 0.4923, 0.2329, 0.9462])
+tensor([0.8639, 0.3423, 0.4800,  ..., 0.1443, 0.7816, 0.0060])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 11.091211557388306 seconds
+Time: 10.338330507278442 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       29,       54,  ..., 12499940,
-                            12499966, 12500000]),
-       col_indices=tensor([ 15104,  53699,  66016,  ..., 451008, 478949,
-                           498027]),
-       values=tensor([0.3834, 0.0166, 0.8269,  ..., 0.7083, 0.6634, 0.5753]),
-       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3028, 0.2567, 0.4384,  ..., 0.4923, 0.2329, 0.9462])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 12500000
-Density: 5e-05
-Time: 11.091211557388306 seconds
-
-[41.47, 41.03, 39.87, 40.83, 39.9, 40.09, 40.23, 40.2, 39.74, 39.65]
-[125.74]
-16.646742820739746
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.091211557388306, 'TIME_S_1KI': 4.5399965441622205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2093.1614422798157, 'W': 125.74}
-[41.47, 41.03, 39.87, 40.83, 39.9, 40.09, 40.23, 40.2, 39.74, 39.65, 40.36, 39.88, 39.75, 39.86, 39.72, 40.13, 40.1, 39.56, 45.29, 40.14]
-726.99
-36.3495
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.091211557388306, 'TIME_S_1KI': 4.5399965441622205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2093.1614422798157, 'W': 125.74, 'J_1KI': 856.7996079737272, 'W_1KI': 51.469504707327054, 'W_D': 89.3905, 'J_D': 1488.0606641173363, 'W_D_1KI': 36.59046254604994, 'J_D_1KI': 14.977676031948402}
+[40.4, 42.56, 40.55, 40.15, 39.72, 40.38, 39.75, 39.66, 39.85, 39.57]
+[124.72]
+15.470274925231934
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.338330507278442, 'TIME_S_1KI': 4.558346784514304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.4526886749268, 'W': 124.72}
+[40.4, 42.56, 40.55, 40.15, 39.72, 40.38, 39.75, 39.66, 39.85, 39.57, 40.6, 40.35, 40.27, 40.13, 39.75, 39.68, 39.73, 39.6, 39.89, 39.57]
+722.09
+36.1045
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.338330507278442, 'TIME_S_1KI': 4.558346784514304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.4526886749268, 'W': 124.72, 'J_1KI': 850.7286987102852, 'W_1KI': 54.99118165784832, 'W_D': 88.6155, 'J_D': 1370.9061476368904, 'W_D_1KI': 39.07208994708994, 'J_D_1KI': 17.227552886723963}
diff --git a/pytorch/output_synthetic_16core/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 c71ff3b..366bd80 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 89538, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.289806604385376, "TIME_S_1KI": 0.11492111287258344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1521.4007213592529, "W": 116.96, "J_1KI": 16.991676398392332, "W_1KI": 1.3062610288369183, "W_D": 80.71074999999999, "J_D": 1049.8751134699583, "W_D_1KI": 0.9014133663919228, "J_D_1KI": 0.010067383305322017}
diff --git a/pytorch/output_synthetic_16core/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 504044c..aedb6df 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": 0.14647722244262695}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.049301862716674805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      8,  ..., 249988, 249995,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 249993, 249996,
                             250000]),
-       col_indices=tensor([  544,  6056, 19594,  ..., 16208, 31107, 37035]),
-       values=tensor([0.8576, 0.5005, 0.2810,  ..., 0.0063, 0.7171, 0.8258]),
+       col_indices=tensor([ 1709, 19790, 28830,  ...,  3831, 22257, 48856]),
+       values=tensor([0.9244, 0.7522, 0.6687,  ..., 0.7540, 0.7318, 0.7260]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4318, 0.7107, 0.2576,  ..., 0.8496, 0.3705, 0.3608])
+tensor([0.0785, 0.8938, 0.5541,  ..., 0.5935, 0.2052, 0.2232])
 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: 0.14647722244262695 seconds
+Time: 0.049301862716674805 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21297', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7824935913085938}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     10,  ..., 249988, 249994,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 249989, 249993,
                             250000]),
-       col_indices=tensor([ 4979, 12449, 23825,  ..., 32585, 40358, 48594]),
-       values=tensor([0.7825, 0.8569, 0.5029,  ..., 0.3250, 0.4106, 0.3303]),
+       col_indices=tensor([16415, 16632, 32449,  ..., 45169, 45288, 48610]),
+       values=tensor([0.0101, 0.6954, 0.6241,  ..., 0.3711, 0.7246, 0.3748]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8033, 0.4755, 0.5204,  ..., 0.8611, 0.9528, 0.0172])
+tensor([0.6515, 0.7514, 0.0204,  ..., 0.8861, 0.6124, 0.4798])
 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: 9.233005046844482 seconds
+Time: 2.7824935913085938 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '80366', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.42440915107727}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     11,  ..., 249983, 249992,
+tensor(crow_indices=tensor([     0,      7,     10,  ..., 249987, 249993,
                             250000]),
-       col_indices=tensor([ 7422, 17911, 31055,  ..., 30707, 32021, 38558]),
-       values=tensor([0.7718, 0.8036, 0.8293,  ..., 0.2159, 0.0251, 0.0647]),
+       col_indices=tensor([ 2445, 24855, 26173,  ..., 23560, 26333, 46130]),
+       values=tensor([0.2012, 0.2713, 0.8391,  ..., 0.5844, 0.7972, 0.4463]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3183, 0.3041, 0.1046,  ..., 0.2603, 0.8118, 0.2097])
+tensor([0.5580, 0.1767, 0.6905,  ..., 0.9860, 0.6709, 0.2165])
 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: 9.805182695388794 seconds
+Time: 9.42440915107727 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89538', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.289806604385376}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      5,  ..., 249989, 249993,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249988, 249996,
                             250000]),
-       col_indices=tensor([19530, 21432, 40127,  ..., 33319, 45642, 48654]),
-       values=tensor([0.8438, 0.0330, 0.2387,  ..., 0.6115, 0.5796, 0.5067]),
+       col_indices=tensor([ 2244, 34732,  7243,  ...,  9132, 13610, 19520]),
+       values=tensor([0.6983, 0.0446, 0.9216,  ..., 0.0232, 0.0374, 0.6300]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1992, 0.5617, 0.3460,  ..., 0.4818, 0.9372, 0.6597])
+tensor([0.8539, 0.6321, 0.4259,  ..., 0.2899, 0.6274, 0.3350])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -76,19 +76,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-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}
+Time: 10.289806604385376 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     12,  ..., 249987, 249995,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249988, 249996,
                             250000]),
-       col_indices=tensor([ 2714,  5631, 18387,  ..., 39061, 48792, 49070]),
-       values=tensor([0.1970, 0.9435, 0.9859,  ..., 0.7944, 0.6863, 0.0587]),
+       col_indices=tensor([ 2244, 34732,  7243,  ...,  9132, 13610, 19520]),
+       values=tensor([0.6983, 0.0446, 0.9216,  ..., 0.0232, 0.0374, 0.6300]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5128, 0.8861, 0.8900,  ..., 0.3721, 0.4809, 0.7353])
+tensor([0.8539, 0.6321, 0.4259,  ..., 0.2899, 0.6274, 0.3350])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -96,30 +93,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.82576298713684 seconds
+Time: 10.289806604385376 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      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}
+[40.27, 39.54, 39.5, 39.65, 39.93, 39.44, 40.01, 39.51, 45.05, 39.37]
+[116.96]
+13.007872104644775
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 89538, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.289806604385376, 'TIME_S_1KI': 0.11492111287258344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1521.4007213592529, 'W': 116.96}
+[40.27, 39.54, 39.5, 39.65, 39.93, 39.44, 40.01, 39.51, 45.05, 39.37, 40.45, 39.41, 39.86, 39.36, 39.91, 44.54, 39.53, 39.97, 39.84, 39.78]
+724.985
+36.24925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 89538, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.289806604385376, 'TIME_S_1KI': 0.11492111287258344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1521.4007213592529, 'W': 116.96, 'J_1KI': 16.991676398392332, 'W_1KI': 1.3062610288369183, 'W_D': 80.71074999999999, 'J_D': 1049.8751134699583, 'W_D_1KI': 0.9014133663919228, 'J_D_1KI': 0.010067383305322017}
diff --git a/pytorch/output_synthetic_16core/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 3676d30..16ebf8c 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 45908, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.364075660705566, "TIME_S_1KI": 0.22575750763931268, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1982.6904290771486, "W": 146.58, "J_1KI": 43.18834253457238, "W_1KI": 3.192907554238913, "W_D": 110.64150000000001, "J_D": 1496.5741786651613, "W_D_1KI": 2.410070140280561, "J_D_1KI": 0.05249782478610615}
diff --git a/pytorch/output_synthetic_16core/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 3112aac..f9b44d5 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": 0.29659008979797363}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0605926513671875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      42,      99,  ..., 2499902,
+                            2499955, 2500000]),
+       col_indices=tensor([ 1009,  1628,  5292,  ..., 43455, 47256, 47946]),
+       values=tensor([0.2339, 0.7843, 0.8407,  ..., 0.0388, 0.2390, 0.6904]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5080, 0.1629, 0.0847,  ..., 0.6599, 0.4582, 0.2341])
+tensor([0.3494, 0.3893, 0.8826,  ..., 0.0693, 0.0070, 0.7582])
 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: 0.29659008979797363 seconds
+Time: 0.0605926513671875 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17328', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.963207721710205}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      45,      85,  ..., 2499905,
+                            2499952, 2500000]),
+       col_indices=tensor([ 2138,  2192,  2629,  ..., 48532, 49646, 49876]),
+       values=tensor([0.7824, 0.0061, 0.7967,  ..., 0.1635, 0.4732, 0.5157]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2903, 0.7408, 0.0968,  ..., 0.3344, 0.5691, 0.3821])
+tensor([0.8165, 0.7580, 0.0903,  ..., 0.6290, 0.7559, 0.6116])
 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: 7.946921348571777 seconds
+Time: 3.963207721710205 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45908', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.364075660705566}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      48,      99,  ..., 2499901,
+                            2499955, 2500000]),
+       col_indices=tensor([ 2242,  2630,  4307,  ..., 47333, 48170, 49131]),
+       values=tensor([0.3970, 0.2919, 0.1690,  ..., 0.5693, 0.6652, 0.4283]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5238, 0.6081, 0.9163,  ..., 0.2866, 0.2457, 0.9117])
+tensor([0.7545, 0.7866, 0.4331,  ..., 0.1722, 0.5406, 0.9467])
 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: 10.762548208236694 seconds
+Time: 10.364075660705566 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      48,      99,  ..., 2499901,
+                            2499955, 2500000]),
+       col_indices=tensor([ 2242,  2630,  4307,  ..., 47333, 48170, 49131]),
+       values=tensor([0.3970, 0.2919, 0.1690,  ..., 0.5693, 0.6652, 0.4283]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5238, 0.6081, 0.9163,  ..., 0.2866, 0.2457, 0.9117])
+tensor([0.7545, 0.7866, 0.4331,  ..., 0.1722, 0.5406, 0.9467])
 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: 10.762548208236694 seconds
+Time: 10.364075660705566 seconds
 
-[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}
+[40.59, 39.91, 39.52, 39.54, 39.7, 39.41, 39.47, 40.95, 40.05, 39.39]
+[146.58]
+13.526336669921875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45908, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.364075660705566, 'TIME_S_1KI': 0.22575750763931268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.6904290771486, 'W': 146.58}
+[40.59, 39.91, 39.52, 39.54, 39.7, 39.41, 39.47, 40.95, 40.05, 39.39, 40.78, 39.79, 39.69, 41.93, 40.24, 39.57, 39.58, 39.7, 39.52, 39.64]
+718.77
+35.9385
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45908, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.364075660705566, 'TIME_S_1KI': 0.22575750763931268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.6904290771486, 'W': 146.58, 'J_1KI': 43.18834253457238, 'W_1KI': 3.192907554238913, 'W_D': 110.64150000000001, 'J_D': 1496.5741786651613, 'W_D_1KI': 2.410070140280561, 'J_D_1KI': 0.05249782478610615}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
index 032b229..20ca6ca 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1728, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495, "TIME_S_1KI": 6.277456741642069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2262.675802116394, "W": 116.47, "J_1KI": 1309.418866965506, "W_1KI": 67.40162037037037, "W_D": 80.83224999999999, "J_D": 1570.3372207918164, "W_D_1KI": 46.7779224537037, "J_D_1KI": 27.070556975522976}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1726, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.692668676376343, "TIME_S_1KI": 6.1950571705540805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2256.2120828294755, "W": 117.97000000000001, "J_1KI": 1307.1912415002755, "W_1KI": 68.34878331402086, "W_D": 81.57950000000002, "J_D": 1560.2327168872362, "W_D_1KI": 47.26506373117035, "J_D_1KI": 27.384162069044237}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
index 44057a0..939a7fb 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.073575019836426}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6629180908203125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      511,      999,  ..., 24998996,
-                            24999515, 25000000]),
-       col_indices=tensor([   50,   140,   163,  ..., 49849, 49891, 49909]),
-       values=tensor([0.6896, 0.4241, 0.6835,  ..., 0.3809, 0.0330, 0.1679]),
+tensor(crow_indices=tensor([       0,      514,      996,  ..., 24998956,
+                            24999472, 25000000]),
+       col_indices=tensor([    1,    94,   348,  ..., 49850, 49922, 49959]),
+       values=tensor([0.7408, 0.6252, 0.3689,  ..., 0.4667, 0.9642, 0.0582]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.8743, 0.1159, 0.4633,  ..., 0.1043, 0.2471, 0.3798])
+tensor([0.9247, 0.2733, 0.8266,  ..., 0.5422, 0.1520, 0.6812])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 6.073575019836426 seconds
+Time: 0.6629180908203125 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1728', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1583', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.627561807632446}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      553,     1047,  ..., 24998955,
-                            24999507, 25000000]),
-       col_indices=tensor([   47,   133,   262,  ..., 49731, 49773, 49776]),
-       values=tensor([0.8665, 0.6889, 0.7366,  ..., 0.8541, 0.3572, 0.3739]),
+tensor(crow_indices=tensor([       0,      562,     1043,  ..., 24999032,
+                            24999504, 25000000]),
+       col_indices=tensor([    4,    50,    78,  ..., 49916, 49920, 49965]),
+       values=tensor([0.0759, 0.2514, 0.9400,  ..., 0.2240, 0.9432, 0.3438]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.0392, 0.1826, 0.6698,  ..., 0.4937, 0.5808, 0.8286])
+tensor([0.3732, 0.6785, 0.5695,  ..., 0.6003, 0.8169, 0.4003])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.847445249557495 seconds
+Time: 9.627561807632446 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1726', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.692668676376343}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      553,     1047,  ..., 24998955,
-                            24999507, 25000000]),
-       col_indices=tensor([   47,   133,   262,  ..., 49731, 49773, 49776]),
-       values=tensor([0.8665, 0.6889, 0.7366,  ..., 0.8541, 0.3572, 0.3739]),
+tensor(crow_indices=tensor([       0,      457,      970,  ..., 24999002,
+                            24999486, 25000000]),
+       col_indices=tensor([  100,   360,   480,  ..., 49859, 49889, 49953]),
+       values=tensor([0.3856, 0.6378, 0.2660,  ..., 0.6784, 0.6537, 0.7029]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.0392, 0.1826, 0.6698,  ..., 0.4937, 0.5808, 0.8286])
+tensor([0.4800, 0.0280, 0.3242,  ..., 0.3544, 0.6298, 0.7207])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.847445249557495 seconds
+Time: 10.692668676376343 seconds
 
-[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51]
-[116.47]
-19.427112579345703
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47}
-[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51, 40.76, 39.62, 39.66, 39.43, 39.52, 39.33, 39.97, 39.4, 39.29, 39.41]
-712.7550000000001
-35.637750000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47, 'J_1KI': 1309.418866965506, 'W_1KI': 67.40162037037037, 'W_D': 80.83224999999999, 'J_D': 1570.3372207918164, 'W_D_1KI': 46.7779224537037, 'J_D_1KI': 27.070556975522976}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      457,      970,  ..., 24999002,
+                            24999486, 25000000]),
+       col_indices=tensor([  100,   360,   480,  ..., 49859, 49889, 49953]),
+       values=tensor([0.3856, 0.6378, 0.2660,  ..., 0.6784, 0.6537, 0.7029]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4800, 0.0280, 0.3242,  ..., 0.3544, 0.6298, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.692668676376343 seconds
+
+[41.47, 39.7, 39.74, 40.1, 40.28, 40.05, 40.37, 39.58, 39.73, 40.01]
+[117.97]
+19.125303745269775
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.692668676376343, 'TIME_S_1KI': 6.1950571705540805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2256.2120828294755, 'W': 117.97000000000001}
+[41.47, 39.7, 39.74, 40.1, 40.28, 40.05, 40.37, 39.58, 39.73, 40.01, 41.18, 40.13, 45.73, 39.77, 39.88, 40.17, 39.76, 40.02, 41.27, 40.4]
+727.81
+36.390499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.692668676376343, 'TIME_S_1KI': 6.1950571705540805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2256.2120828294755, 'W': 117.97000000000001, 'J_1KI': 1307.1912415002755, 'W_1KI': 68.34878331402086, 'W_D': 81.57950000000002, 'J_D': 1560.2327168872362, 'W_D_1KI': 47.26506373117035, 'J_D_1KI': 27.384162069044237}
diff --git a/pytorch/output_synthetic_16core/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 efee96e..53cb9a4 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": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 125418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.786596775054932, "TIME_S_1KI": 0.0860051729022543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1353.9629184293747, "W": 103.38, "J_1KI": 10.795602851499583, "W_1KI": 0.8242835956561259, "W_D": 67.61375, "J_D": 885.5340518084168, "W_D_1KI": 0.5391072254381349, "J_D_1KI": 0.004298483674098893}
diff --git a/pytorch/output_synthetic_16core/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 1baca85..b030af9 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": 0.1170039176940918}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05780529975891113}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([26335, 27290, 38418,  ..., 19756, 20120,  4010]),
+       values=tensor([0.3389, 0.0656, 0.4529,  ..., 0.8287, 0.8944, 0.8355]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4235, 0.9189, 0.0697,  ..., 0.8234, 0.9093, 0.0251])
+tensor([0.6330, 0.8862, 0.5805,  ..., 0.8180, 0.2124, 0.8337])
 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: 0.1170039176940918 seconds
+Time: 0.05780529975891113 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18164', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5206849575042725}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([47070, 16594, 28343,  ..., 43440, 28747, 28655]),
+       values=tensor([0.5955, 0.4100, 0.8378,  ..., 0.8449, 0.3361, 0.6219]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2017, 0.4349, 0.5577,  ..., 0.2868, 0.8229, 0.7966])
+tensor([0.0973, 0.1697, 0.0749,  ..., 0.0145, 0.6554, 0.2719])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 7.3589677810668945 seconds
+Time: 1.5206849575042725 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '125418', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.786596775054932}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27120, 12941, 15664,  ...,  3161, 41560, 29450]),
+       values=tensor([0.4509, 0.2974, 0.8733,  ..., 0.8770, 0.0483, 0.7990]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1994, 0.9899, 0.9038,  ..., 0.7869, 0.4416, 0.9952])
+tensor([0.0817, 0.8974, 0.0414,  ..., 0.9825, 0.3309, 0.2047])
 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: 10.502545356750488 seconds
+Time: 10.786596775054932 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27120, 12941, 15664,  ...,  3161, 41560, 29450]),
+       values=tensor([0.4509, 0.2974, 0.8733,  ..., 0.8770, 0.0483, 0.7990]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1994, 0.9899, 0.9038,  ..., 0.7869, 0.4416, 0.9952])
+tensor([0.0817, 0.8974, 0.0414,  ..., 0.9825, 0.3309, 0.2047])
 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: 10.502545356750488 seconds
+Time: 10.786596775054932 seconds
 
-[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}
+[40.88, 40.06, 39.87, 40.02, 39.61, 39.65, 39.87, 39.87, 39.51, 39.91]
+[103.38]
+13.096952199935913
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.786596775054932, 'TIME_S_1KI': 0.0860051729022543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1353.9629184293747, 'W': 103.38}
+[40.88, 40.06, 39.87, 40.02, 39.61, 39.65, 39.87, 39.87, 39.51, 39.91, 40.56, 39.51, 39.82, 39.66, 39.34, 39.38, 39.55, 39.32, 39.66, 39.9]
+715.325
+35.76625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.786596775054932, 'TIME_S_1KI': 0.0860051729022543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1353.9629184293747, 'W': 103.38, 'J_1KI': 10.795602851499583, 'W_1KI': 0.8242835956561259, 'W_D': 67.61375, 'J_D': 885.5340518084168, 'W_D_1KI': 0.5391072254381349, 'J_D_1KI': 0.004298483674098893}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
index e2a64dc..d831091 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 110048, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.860910654067993, "TIME_S_1KI": 0.09869248558872486, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1513.3099088716508, "W": 110.42, "J_1KI": 13.751362213503661, "W_1KI": 1.0033803431230008, "W_D": 74.49324999999999, "J_D": 1020.9325608499645, "W_D_1KI": 0.6769159821168944, "J_D_1KI": 0.006151097540317811}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 106823, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.303539752960205, "TIME_S_1KI": 0.09645431932224527, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1429.2441740632057, "W": 111.25, "J_1KI": 13.379554721953191, "W_1KI": 1.0414423860030144, "W_D": 75.065, "J_D": 964.37046225667, "W_D_1KI": 0.7027044737556518, "J_D_1KI": 0.006578213247668122}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
index 99bfaff..a27a2d5 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1521902084350586}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.05483698844909668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      4,  ..., 124998, 124999,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([28568, 23377, 33207,  ..., 35070, 20237, 35086]),
-       values=tensor([0.0970, 0.0746, 0.1789,  ..., 0.4665, 0.3762, 0.5874]),
+       col_indices=tensor([28119, 29640, 21715,  ..., 29199, 13516, 45728]),
+       values=tensor([0.3782, 0.4368, 0.3959,  ..., 0.8630, 0.5532, 0.4165]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5202, 0.5834, 0.5039,  ..., 0.2581, 0.4110, 0.2043])
+tensor([0.8607, 0.2103, 0.4385,  ..., 0.0263, 0.3906, 0.3161])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.1521902084350586 seconds
+Time: 0.05483698844909668 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68992', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.582725286483765}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19147', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.8820090293884277}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      4,  ..., 124994, 124996,
+tensor(crow_indices=tensor([     0,      2,      6,  ..., 124991, 124992,
                             125000]),
-       col_indices=tensor([13407, 30849, 37582,  ...,  4235,  7510, 16049]),
-       values=tensor([0.4132, 0.1824, 0.9780,  ..., 0.4864, 0.4697, 0.1823]),
+       col_indices=tensor([32530, 36762,   311,  ..., 24158, 32618, 44758]),
+       values=tensor([0.9615, 0.3318, 0.5732,  ..., 0.8773, 0.1422, 0.4683]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.9509, 0.2372, 0.8108,  ..., 0.6237, 0.0261, 0.7128])
+tensor([0.1372, 0.3779, 0.3457,  ..., 0.7036, 0.6193, 0.2501])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 6.582725286483765 seconds
+Time: 1.8820090293884277 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '110048', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.860910654067993}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '106823', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.303539752960205}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 124995, 124996,
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([ 8770, 24657, 47529,  ..., 37234, 42798, 47480]),
-       values=tensor([0.5915, 0.6574, 0.6205,  ..., 0.3170, 0.3438, 0.3659]),
+       col_indices=tensor([16502, 37527, 11294,  ..., 28497,  8084, 35661]),
+       values=tensor([0.2823, 0.8232, 0.0849,  ..., 0.6885, 0.2665, 0.0851]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7258, 0.6385, 0.7203,  ..., 0.8359, 0.3176, 0.3735])
+tensor([0.8734, 0.5898, 0.3749,  ..., 0.2817, 0.4056, 0.3872])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.860910654067993 seconds
+Time: 10.303539752960205 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 124995, 124996,
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([ 8770, 24657, 47529,  ..., 37234, 42798, 47480]),
-       values=tensor([0.5915, 0.6574, 0.6205,  ..., 0.3170, 0.3438, 0.3659]),
+       col_indices=tensor([16502, 37527, 11294,  ..., 28497,  8084, 35661]),
+       values=tensor([0.2823, 0.8232, 0.0849,  ..., 0.6885, 0.2665, 0.0851]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7258, 0.6385, 0.7203,  ..., 0.8359, 0.3176, 0.3735])
+tensor([0.8734, 0.5898, 0.3749,  ..., 0.2817, 0.4056, 0.3872])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.860910654067993 seconds
+Time: 10.303539752960205 seconds
 
-[40.58, 39.41, 39.64, 39.39, 39.67, 39.83, 39.65, 39.43, 39.45, 39.8]
-[110.42]
-13.705034494400024
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110048, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.860910654067993, 'TIME_S_1KI': 0.09869248558872486, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1513.3099088716508, 'W': 110.42}
-[40.58, 39.41, 39.64, 39.39, 39.67, 39.83, 39.65, 39.43, 39.45, 39.8, 40.29, 39.44, 40.08, 39.34, 39.59, 39.49, 39.42, 44.77, 39.96, 39.28]
-718.5350000000001
-35.926750000000006
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110048, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.860910654067993, 'TIME_S_1KI': 0.09869248558872486, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1513.3099088716508, 'W': 110.42, 'J_1KI': 13.751362213503661, 'W_1KI': 1.0033803431230008, 'W_D': 74.49324999999999, 'J_D': 1020.9325608499645, 'W_D_1KI': 0.6769159821168944, 'J_D_1KI': 0.006151097540317811}
+[40.0, 39.65, 39.5, 39.28, 44.73, 39.57, 39.57, 39.75, 39.32, 39.43]
+[111.25]
+12.84713864326477
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.303539752960205, 'TIME_S_1KI': 0.09645431932224527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1429.2441740632057, 'W': 111.25}
+[40.0, 39.65, 39.5, 39.28, 44.73, 39.57, 39.57, 39.75, 39.32, 39.43, 41.22, 44.93, 39.95, 39.46, 39.81, 39.42, 39.55, 39.68, 39.52, 39.37]
+723.7
+36.185
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.303539752960205, 'TIME_S_1KI': 0.09645431932224527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1429.2441740632057, 'W': 111.25, 'J_1KI': 13.379554721953191, 'W_1KI': 1.0414423860030144, 'W_D': 75.065, 'J_D': 964.37046225667, 'W_D_1KI': 0.7027044737556518, 'J_D_1KI': 0.006578213247668122}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
index 4891577..59bfbf5 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 423606, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.169308185577393, "TIME_S_1KI": 0.024006525369275677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1199.050221145153, "W": 95.75, "J_1KI": 2.8305789369016328, "W_1KI": 0.2260355141334164, "W_D": 60.24975, "J_D": 754.4906116077303, "W_D_1KI": 0.14223063412699538, "J_D_1KI": 0.0003357616136858198}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
index 0251f49..ae86751 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018892288208007812}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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]),
+tensor(crow_indices=tensor([   0,    0,    4,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1108, 1116, 4456,  ..., 2396,  548, 1385]),
+       values=tensor([0.8638, 0.8794, 0.8595,  ..., 0.2787, 0.2270, 0.2436]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7803, 0.2089, 0.7573,  ..., 0.7596, 0.3125, 0.6078])
+tensor([0.1028, 0.3454, 0.0668,  ..., 0.0203, 0.1099, 0.2752])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.040769100189208984 seconds
+Time: 0.018892288208007812 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55578', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.3776216506958008}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    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]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1067, 4726, 2617,  ..., 4515, 4937,  207]),
+       values=tensor([0.7749, 0.8447, 0.6931,  ..., 0.5698, 0.5658, 0.7624]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.0086, 0.7636, 0.4685,  ..., 0.9955, 0.7657, 0.7966])
+tensor([0.8259, 0.6183, 0.2744,  ..., 0.6644, 0.1716, 0.4385])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 6.205128908157349 seconds
+Time: 1.3776216506958008 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '423606', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.169308185577393}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([ 723, 3357, 4021,  ..., 1038, 2195, 2669]),
+       values=tensor([0.5380, 0.6250, 0.5522,  ..., 0.2239, 0.7354, 0.7870]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3918, 0.7384, 0.9927,  ..., 0.9998, 0.6009, 0.1634])
+tensor([0.8647, 0.0404, 0.2214,  ..., 0.2716, 0.6887, 0.8481])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.511547565460205 seconds
+Time: 10.169308185577393 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([ 723, 3357, 4021,  ..., 1038, 2195, 2669]),
+       values=tensor([0.5380, 0.6250, 0.5522,  ..., 0.2239, 0.7354, 0.7870]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3918, 0.7384, 0.9927,  ..., 0.9998, 0.6009, 0.1634])
+tensor([0.8647, 0.0404, 0.2214,  ..., 0.2716, 0.6887, 0.8481])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.511547565460205 seconds
+Time: 10.169308185577393 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}
+[40.09, 39.04, 39.2, 39.28, 39.5, 39.12, 39.02, 39.36, 39.0, 39.1]
+[95.75]
+12.522717714309692
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 423606, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.169308185577393, 'TIME_S_1KI': 0.024006525369275677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.050221145153, 'W': 95.75}
+[40.09, 39.04, 39.2, 39.28, 39.5, 39.12, 39.02, 39.36, 39.0, 39.1, 39.76, 39.19, 39.37, 38.99, 39.74, 39.45, 39.52, 39.05, 39.58, 44.24]
+710.005
+35.50025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 423606, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.169308185577393, 'TIME_S_1KI': 0.024006525369275677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.050221145153, 'W': 95.75, 'J_1KI': 2.8305789369016328, 'W_1KI': 0.2260355141334164, 'W_D': 60.24975, 'J_D': 754.4906116077303, 'W_D_1KI': 0.14223063412699538, 'J_D_1KI': 0.0003357616136858198}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
index fe8abca..1eeb5de 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 247437, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.32590627670288, "TIME_S_1KI": 0.04173145599365851, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1233.848487944603, "W": 98.03, "J_1KI": 4.986515710846005, "W_1KI": 0.39618165432008956, "W_D": 62.5795, "J_D": 787.6529781835079, "W_D_1KI": 0.2529108419516887, "J_D_1KI": 0.001022122164234487}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
index 79cf966..f512a45 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.016702651977539062}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     4,    10,  ..., 24993, 24996, 25000]),
+       col_indices=tensor([ 203, 3164, 3874,  ..., 1660, 2575, 4898]),
+       values=tensor([0.2509, 0.0733, 0.7857,  ..., 0.9782, 0.1584, 0.7182]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5025, 0.8306, 0.5455,  ..., 0.1180, 0.7485, 0.4884])
+tensor([0.6754, 0.2035, 0.5445,  ..., 0.8964, 0.5875, 0.7630])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.05801510810852051 seconds
+Time: 0.016702651977539062 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '62864', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.6676299571990967}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     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]),
+tensor(crow_indices=tensor([    0,     4,     7,  ..., 24992, 24998, 25000]),
+       col_indices=tensor([1528, 1565, 2407,  ..., 4843,  196, 1526]),
+       values=tensor([0.9615, 0.4377, 0.6921,  ..., 0.0433, 0.3280, 0.0962]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5831, 0.9483, 0.7910,  ..., 0.0226, 0.1378, 0.9053])
+tensor([0.1897, 0.1898, 0.6419,  ..., 0.9248, 0.4513, 0.5147])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 7.7333667278289795 seconds
+Time: 2.6676299571990967 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '247437', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.32590627670288}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([ 429,  548,  735,  ..., 2923, 3331, 3611]),
+       values=tensor([0.1470, 0.7094, 0.7244,  ..., 0.3013, 0.3840, 0.1701]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3455, 0.7497, 0.7321,  ..., 0.5403, 0.0178, 0.6295])
+tensor([0.0349, 0.9280, 0.8549,  ..., 0.2131, 0.1223, 0.0130])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.373013734817505 seconds
+Time: 10.32590627670288 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([ 429,  548,  735,  ..., 2923, 3331, 3611]),
+       values=tensor([0.1470, 0.7094, 0.7244,  ..., 0.3013, 0.3840, 0.1701]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3455, 0.7497, 0.7321,  ..., 0.5403, 0.0178, 0.6295])
+tensor([0.0349, 0.9280, 0.8549,  ..., 0.2131, 0.1223, 0.0130])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.373013734817505 seconds
+Time: 10.32590627670288 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}
+[40.5, 39.52, 39.15, 39.13, 39.57, 40.5, 39.17, 39.06, 39.52, 39.1]
+[98.03]
+12.586437702178955
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.32590627670288, 'TIME_S_1KI': 0.04173145599365851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1233.848487944603, 'W': 98.03}
+[40.5, 39.52, 39.15, 39.13, 39.57, 40.5, 39.17, 39.06, 39.52, 39.1, 40.17, 39.6, 39.6, 39.15, 39.15, 39.05, 39.05, 39.08, 39.25, 39.15]
+709.01
+35.4505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.32590627670288, 'TIME_S_1KI': 0.04173145599365851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1233.848487944603, 'W': 98.03, 'J_1KI': 4.986515710846005, 'W_1KI': 0.39618165432008956, 'W_D': 62.5795, 'J_D': 787.6529781835079, 'W_D_1KI': 0.2529108419516887, 'J_D_1KI': 0.001022122164234487}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
index 65befab..685c3bf 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 163068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.72166895866394, "TIME_S_1KI": 0.07188209187985345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1665.769057817459, "W": 116.82, "J_1KI": 10.215180524796153, "W_1KI": 0.7163882552064169, "W_D": 81.24949999999998, "J_D": 1158.5593482549189, "W_D_1KI": 0.4982553290651751, "J_D_1KI": 0.0030555064700933054}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
index 930f25a..bc4d2ed 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.02829742431640625}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     51,     93,  ..., 249898, 249945,
+tensor(crow_indices=tensor([     0,     58,    108,  ..., 249919, 249959,
                             250000]),
-       col_indices=tensor([ 121,  263,  268,  ..., 4347, 4657, 4780]),
-       values=tensor([0.9155, 0.4457, 0.5767,  ..., 0.8561, 0.2482, 0.9078]),
+       col_indices=tensor([  73,  104,  551,  ..., 4719, 4888, 4958]),
+       values=tensor([0.4939, 0.4915, 0.0888,  ..., 0.3493, 0.1552, 0.1459]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9909, 0.5337, 0.2877,  ..., 0.9413, 0.4687, 0.7116])
+tensor([0.1377, 0.0837, 0.3150,  ..., 0.5794, 0.8670, 0.3865])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.10852384567260742 seconds
+Time: 0.02829742431640625 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37105', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.3891899585723877}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     54,    108,  ..., 249899, 249958,
+tensor(crow_indices=tensor([     0,     55,     99,  ..., 249890, 249937,
                             250000]),
-       col_indices=tensor([  30,   44,  230,  ..., 4553, 4620, 4987]),
-       values=tensor([0.7207, 0.9659, 0.8009,  ..., 0.1897, 0.2795, 0.9074]),
+       col_indices=tensor([   6,   32,   44,  ..., 4844, 4921, 4988]),
+       values=tensor([0.1281, 0.2469, 0.7745,  ..., 0.0638, 0.9042, 0.9189]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9275, 0.8053, 0.7107,  ..., 0.1305, 0.9789, 0.9894])
+tensor([0.6302, 0.1474, 0.6987,  ..., 0.1092, 0.0062, 0.2645])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 6.974123954772949 seconds
+Time: 2.3891899585723877 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '163068', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.72166895866394}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     49,    108,  ..., 249901, 249948,
+tensor(crow_indices=tensor([     0,     45,     94,  ..., 249900, 249951,
                             250000]),
-       col_indices=tensor([ 207,  226,  430,  ..., 4797, 4906, 4947]),
-       values=tensor([0.9242, 0.6665, 0.8223,  ..., 0.0998, 0.8618, 0.4766]),
+       col_indices=tensor([  17,  114,  188,  ..., 4806, 4921, 4968]),
+       values=tensor([0.1229, 0.8785, 0.6808,  ..., 0.9268, 0.7326, 0.7148]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6439, 0.4458, 0.8465,  ..., 0.5021, 0.5940, 0.7614])
+tensor([0.4545, 0.7872, 0.8321,  ..., 0.0206, 0.6423, 0.1627])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.058181524276733 seconds
+Time: 11.72166895866394 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     49,    108,  ..., 249901, 249948,
+tensor(crow_indices=tensor([     0,     45,     94,  ..., 249900, 249951,
                             250000]),
-       col_indices=tensor([ 207,  226,  430,  ..., 4797, 4906, 4947]),
-       values=tensor([0.9242, 0.6665, 0.8223,  ..., 0.0998, 0.8618, 0.4766]),
+       col_indices=tensor([  17,  114,  188,  ..., 4806, 4921, 4968]),
+       values=tensor([0.1229, 0.8785, 0.6808,  ..., 0.9268, 0.7326, 0.7148]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6439, 0.4458, 0.8465,  ..., 0.5021, 0.5940, 0.7614])
+tensor([0.4545, 0.7872, 0.8321,  ..., 0.0206, 0.6423, 0.1627])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.058181524276733 seconds
+Time: 11.72166895866394 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}
+[40.87, 39.29, 39.18, 39.72, 39.08, 39.54, 39.6, 39.54, 39.86, 39.27]
+[116.82]
+14.259279727935791
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 163068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.72166895866394, 'TIME_S_1KI': 0.07188209187985345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.769057817459, 'W': 116.82}
+[40.87, 39.29, 39.18, 39.72, 39.08, 39.54, 39.6, 39.54, 39.86, 39.27, 40.71, 39.19, 39.43, 39.27, 39.46, 39.83, 39.54, 39.53, 39.37, 39.11]
+711.4100000000001
+35.5705
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 163068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.72166895866394, 'TIME_S_1KI': 0.07188209187985345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.769057817459, 'W': 116.82, 'J_1KI': 10.215180524796153, 'W_1KI': 0.7163882552064169, 'W_D': 81.24949999999998, 'J_D': 1158.5593482549189, 'W_D_1KI': 0.4982553290651751, 'J_D_1KI': 0.0030555064700933054}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
index 11bcfb3..022fbf8 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 92460, "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": 10.897745609283447, "TIME_S_1KI": 0.1178644344503942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1737.196626586914, "W": 132.16, "J_1KI": 18.788628883700127, "W_1KI": 1.4293748648064026, "W_D": 96.69825, "J_D": 1271.0644196190835, "W_D_1KI": 1.0458387410772227, "J_D_1KI": 0.011311256122401284}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 90395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.401180028915405, "TIME_S_1KI": 0.11506366534559882, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1713.355565071106, "W": 132.0, "J_1KI": 18.954096632237466, "W_1KI": 1.4602577576193374, "W_D": 96.25175, "J_D": 1249.344481138885, "W_D_1KI": 1.0647906410752808, "J_D_1KI": 0.011779309044474592}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
index f6c9fdd..6ede2ab 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,34 +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', '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": 0.15929031372070312}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03801298141479492}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     260,     503,  ..., 1249486,
-                            1249755, 1250000]),
-       col_indices=tensor([   4,   17,   88,  ..., 4971, 4985, 4987]),
-       values=tensor([0.6362, 0.8148, 0.9153,  ..., 0.4566, 0.5649, 0.0413]),
-       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.3069, 0.3781, 0.2833,  ..., 0.0090, 0.7599, 0.7166])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250000
-Density: 0.05
-Time: 0.15929031372070312 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65917', '-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": 7.485628128051758}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     243,     508,  ..., 1249492,
+tensor(crow_indices=tensor([      0,     279,     546,  ..., 1249528,
                             1249760, 1250000]),
-       col_indices=tensor([  22,   30,   57,  ..., 4895, 4917, 4934]),
-       values=tensor([0.3367, 0.7320, 0.6215,  ..., 0.3721, 0.4144, 0.7665]),
+       col_indices=tensor([  17,   21,   26,  ..., 4944, 4980, 4991]),
+       values=tensor([0.9138, 0.1459, 0.7159,  ..., 0.0773, 0.4834, 0.3377]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8371, 0.0930, 0.7011,  ..., 0.7680, 0.1649, 0.0938])
+tensor([0.0568, 0.1587, 0.8688,  ..., 0.8476, 0.8640, 0.6593])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 7.485628128051758 seconds
+Time: 0.03801298141479492 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '92460', '-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": 10.897745609283447}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27622', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.2084648609161377}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     258,     501,  ..., 1249501,
-                            1249766, 1250000]),
-       col_indices=tensor([   3,   26,   36,  ..., 4973, 4974, 4975]),
-       values=tensor([0.1574, 0.4230, 0.3584,  ..., 0.2058, 0.0488, 0.1761]),
+tensor(crow_indices=tensor([      0,     251,     530,  ..., 1249473,
+                            1249739, 1250000]),
+       col_indices=tensor([  53,   63,   72,  ..., 4941, 4984, 4995]),
+       values=tensor([0.4190, 0.6332, 0.1682,  ..., 0.1102, 0.0295, 0.1696]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.1704, 0.2409, 0.1947,  ..., 0.5321, 0.6051, 0.5827])
+tensor([0.4081, 0.8253, 0.9060,  ..., 0.4379, 0.8960, 0.7193])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.897745609283447 seconds
+Time: 3.2084648609161377 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '90395', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.401180028915405}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     258,     501,  ..., 1249501,
-                            1249766, 1250000]),
-       col_indices=tensor([   3,   26,   36,  ..., 4973, 4974, 4975]),
-       values=tensor([0.1574, 0.4230, 0.3584,  ..., 0.2058, 0.0488, 0.1761]),
+tensor(crow_indices=tensor([      0,     225,     477,  ..., 1249532,
+                            1249754, 1250000]),
+       col_indices=tensor([   0,    4,   16,  ..., 4911, 4963, 4980]),
+       values=tensor([0.5027, 0.8615, 0.8405,  ..., 0.5051, 0.1395, 0.2376]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.1704, 0.2409, 0.1947,  ..., 0.5321, 0.6051, 0.5827])
+tensor([0.7929, 0.2058, 0.1103,  ..., 0.0989, 0.8674, 0.8642])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.897745609283447 seconds
+Time: 10.401180028915405 seconds
 
-[39.99, 38.92, 38.9, 38.99, 39.15, 39.51, 39.42, 41.37, 39.31, 39.41]
-[132.16]
-13.144647598266602
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92460, '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': 10.897745609283447, 'TIME_S_1KI': 0.1178644344503942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1737.196626586914, 'W': 132.16}
-[39.99, 38.92, 38.9, 38.99, 39.15, 39.51, 39.42, 41.37, 39.31, 39.41, 40.8, 39.06, 39.17, 39.04, 39.15, 39.09, 39.05, 39.4, 39.89, 39.43]
-709.2349999999999
-35.461749999999995
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92460, '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': 10.897745609283447, 'TIME_S_1KI': 0.1178644344503942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1737.196626586914, 'W': 132.16, 'J_1KI': 18.788628883700127, 'W_1KI': 1.4293748648064026, 'W_D': 96.69825, 'J_D': 1271.0644196190835, 'W_D_1KI': 1.0458387410772227, 'J_D_1KI': 0.011311256122401284}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     225,     477,  ..., 1249532,
+                            1249754, 1250000]),
+       col_indices=tensor([   0,    4,   16,  ..., 4911, 4963, 4980]),
+       values=tensor([0.5027, 0.8615, 0.8405,  ..., 0.5051, 0.1395, 0.2376]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7929, 0.2058, 0.1103,  ..., 0.0989, 0.8674, 0.8642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.401180028915405 seconds
+
+[40.46, 40.0, 39.58, 39.65, 39.39, 39.96, 39.38, 39.29, 39.31, 39.34]
+[132.0]
+12.979966402053833
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 90395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.401180028915405, 'TIME_S_1KI': 0.11506366534559882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.355565071106, 'W': 132.0}
+[40.46, 40.0, 39.58, 39.65, 39.39, 39.96, 39.38, 39.29, 39.31, 39.34, 40.66, 39.72, 41.06, 39.66, 39.37, 39.71, 39.69, 39.36, 39.44, 40.33]
+714.9649999999999
+35.74825
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 90395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.401180028915405, 'TIME_S_1KI': 0.11506366534559882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.355565071106, 'W': 132.0, 'J_1KI': 18.954096632237466, 'W_1KI': 1.4602577576193374, 'W_D': 96.25175, 'J_D': 1249.344481138885, 'W_D_1KI': 1.0647906410752808, 'J_D_1KI': 0.011779309044474592}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
index 0b5b4c3..28b4bfb 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53552, "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": 10.574474811553955, "TIME_S_1KI": 0.19746180929851276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1844.3806961250305, "W": 137.23, "J_1KI": 34.44093023836702, "W_1KI": 2.562556020316701, "W_D": 101.71924999999999, "J_D": 1367.113758830547, "W_D_1KI": 1.8994481998804897, "J_D_1KI": 0.03546922990514808}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.472357749938965, "TIME_S_1KI": 0.19817871335728413, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1834.1895242333412, "W": 138.65, "J_1KI": 34.710170206713116, "W_1KI": 2.623810154608936, "W_D": 102.62450000000001, "J_D": 1357.611127513051, "W_D_1KI": 1.9420642279961398, "J_D_1KI": 0.036751589198117815}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
index ab97b45..05a194a 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 0.24988102912902832}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.05003714561462402}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     499,    1048,  ..., 2499022,
-                            2499519, 2500000]),
-       col_indices=tensor([   0,   10,   32,  ..., 4963, 4977, 4991]),
-       values=tensor([0.6726, 0.1161, 0.9278,  ..., 0.2840, 0.7697, 0.2554]),
+tensor(crow_indices=tensor([      0,     498,     977,  ..., 2499006,
+                            2499489, 2500000]),
+       col_indices=tensor([   2,   20,   22,  ..., 4935, 4945, 4946]),
+       values=tensor([0.7520, 0.3359, 0.1395,  ..., 0.8155, 0.8337, 0.5892]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1233, 0.5107, 0.9675,  ..., 0.9055, 0.5032, 0.4140])
+tensor([0.3287, 0.7670, 0.4633,  ..., 0.8662, 0.8996, 0.4236])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.24988102912902832 seconds
+Time: 0.05003714561462402 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42019', '-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": 8.238577604293823}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20984', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 4.169527769088745}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     520,    1002,  ..., 2498984,
-                            2499504, 2500000]),
-       col_indices=tensor([   4,   20,   21,  ..., 4945, 4966, 4991]),
-       values=tensor([0.6805, 0.1607, 0.7488,  ..., 0.5436, 0.2045, 0.6809]),
+tensor(crow_indices=tensor([      0,     527,    1017,  ..., 2498957,
+                            2499469, 2500000]),
+       col_indices=tensor([   0,    2,   46,  ..., 4971, 4981, 4983]),
+       values=tensor([0.4264, 0.7891, 0.1289,  ..., 0.9402, 0.9265, 0.8274]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8501, 0.5629, 0.1238,  ..., 0.7287, 0.6927, 0.0708])
+tensor([0.1592, 0.0229, 0.7345,  ..., 0.9022, 0.9396, 0.4003])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 8.238577604293823 seconds
+Time: 4.169527769088745 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53552', '-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": 10.574474811553955}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52843', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.472357749938965}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     495,    1006,  ..., 2499053,
-                            2499503, 2500000]),
-       col_indices=tensor([   5,   23,   39,  ..., 4985, 4986, 4988]),
-       values=tensor([0.9333, 0.4318, 0.5588,  ..., 0.9224, 0.9203, 0.9677]),
+tensor(crow_indices=tensor([      0,     536,    1043,  ..., 2499033,
+                            2499531, 2500000]),
+       col_indices=tensor([   2,    4,    9,  ..., 4990, 4992, 4998]),
+       values=tensor([0.3950, 0.1857, 0.2386,  ..., 0.4312, 0.4990, 0.5416]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4521, 0.6662, 0.5969,  ..., 0.4716, 0.4029, 0.2909])
+tensor([0.5233, 0.3838, 0.2090,  ..., 0.9440, 0.1891, 0.8384])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.574474811553955 seconds
+Time: 10.472357749938965 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     495,    1006,  ..., 2499053,
-                            2499503, 2500000]),
-       col_indices=tensor([   5,   23,   39,  ..., 4985, 4986, 4988]),
-       values=tensor([0.9333, 0.4318, 0.5588,  ..., 0.9224, 0.9203, 0.9677]),
+tensor(crow_indices=tensor([      0,     536,    1043,  ..., 2499033,
+                            2499531, 2500000]),
+       col_indices=tensor([   2,    4,    9,  ..., 4990, 4992, 4998]),
+       values=tensor([0.3950, 0.1857, 0.2386,  ..., 0.4312, 0.4990, 0.5416]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4521, 0.6662, 0.5969,  ..., 0.4716, 0.4029, 0.2909])
+tensor([0.5233, 0.3838, 0.2090,  ..., 0.9440, 0.1891, 0.8384])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.574474811553955 seconds
+Time: 10.472357749938965 seconds
 
-[40.03, 39.26, 39.99, 39.12, 39.6, 39.27, 39.21, 40.1, 39.2, 39.55]
-[137.23]
-13.440069198608398
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53552, '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': 10.574474811553955, 'TIME_S_1KI': 0.19746180929851276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1844.3806961250305, 'W': 137.23}
-[40.03, 39.26, 39.99, 39.12, 39.6, 39.27, 39.21, 40.1, 39.2, 39.55, 39.94, 39.28, 39.24, 39.36, 39.26, 39.73, 39.25, 39.64, 39.4, 39.09]
-710.2149999999999
-35.510749999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53552, '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': 10.574474811553955, 'TIME_S_1KI': 0.19746180929851276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1844.3806961250305, 'W': 137.23, 'J_1KI': 34.44093023836702, 'W_1KI': 2.562556020316701, 'W_D': 101.71924999999999, 'J_D': 1367.113758830547, 'W_D_1KI': 1.8994481998804897, 'J_D_1KI': 0.03546922990514808}
+[40.4, 39.54, 40.0, 39.38, 39.54, 39.86, 39.47, 40.22, 39.37, 39.4]
+[138.65]
+13.228918313980103
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.472357749938965, 'TIME_S_1KI': 0.19817871335728413, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.1895242333412, 'W': 138.65}
+[40.4, 39.54, 40.0, 39.38, 39.54, 39.86, 39.47, 40.22, 39.37, 39.4, 41.49, 40.09, 39.59, 39.6, 39.65, 39.39, 39.42, 39.69, 45.2, 39.71]
+720.51
+36.0255
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.472357749938965, 'TIME_S_1KI': 0.19817871335728413, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.1895242333412, 'W': 138.65, 'J_1KI': 34.710170206713116, 'W_1KI': 2.623810154608936, 'W_D': 102.62450000000001, 'J_D': 1357.611127513051, 'W_D_1KI': 1.9420642279961398, 'J_D_1KI': 0.036751589198117815}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
index 3628bb4..3a471cf 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28750, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.407469034194946, "TIME_S_1KI": 0.3619989229285199, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1928.9614020395277, "W": 139.14, "J_1KI": 67.09430963615748, "W_1KI": 4.839652173913042, "W_D": 103.18299999999998, "J_D": 1430.47307996726, "W_D_1KI": 3.588973913043478, "J_D_1KI": 0.12483387523629487}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28798, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.444172620773315, "TIME_S_1KI": 0.36267006808713503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1937.146224603653, "W": 139.06, "J_1KI": 67.26669298575086, "W_1KI": 4.8288075560802834, "W_D": 102.9745, "J_D": 1434.4647195847035, "W_D_1KI": 3.5757517883186334, "J_D_1KI": 0.12416667089098664}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
index de85415..fd0984f 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43890881538391113}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.06763482093811035}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1013,    1983,  ..., 4997973,
-                            4999012, 5000000]),
-       col_indices=tensor([   4,   12,   14,  ..., 4994, 4995, 4999]),
-       values=tensor([0.7248, 0.5151, 0.3464,  ..., 0.0289, 0.6444, 0.6982]),
+tensor(crow_indices=tensor([      0,     956,    1961,  ..., 4997943,
+                            4998975, 5000000]),
+       col_indices=tensor([   6,   18,   19,  ..., 4986, 4993, 4998]),
+       values=tensor([0.4638, 0.8169, 0.7421,  ..., 0.5926, 0.1207, 0.0279]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8578, 0.9750, 0.0786,  ..., 0.8483, 0.5183, 0.1076])
+tensor([0.1902, 0.8341, 0.1608,  ..., 0.1172, 0.3175, 0.0262])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 0.43890881538391113 seconds
+Time: 0.06763482093811035 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23922', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 8.73651671409607}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15524', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.659992933273315}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1001,    1993,  ..., 4997975,
-                            4998974, 5000000]),
-       col_indices=tensor([   0,    6,    7,  ..., 4986, 4993, 4997]),
-       values=tensor([0.9981, 0.9465, 0.8571,  ..., 0.5801, 0.6800, 0.5830]),
+tensor(crow_indices=tensor([      0,     987,    2000,  ..., 4998018,
+                            4999001, 5000000]),
+       col_indices=tensor([   4,   16,   17,  ..., 4983, 4988, 4998]),
+       values=tensor([0.3168, 0.3066, 0.1113,  ..., 0.0328, 0.5136, 0.1275]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7841, 0.9192, 0.6592,  ..., 0.6410, 0.6781, 0.3236])
+tensor([0.9704, 0.5285, 0.3815,  ..., 0.6149, 0.3291, 0.1983])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 8.73651671409607 seconds
+Time: 5.659992933273315 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28750', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.407469034194946}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28798', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.444172620773315}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     962,    2002,  ..., 4997962,
-                            4998964, 5000000]),
-       col_indices=tensor([   3,    4,    7,  ..., 4990, 4997, 4999]),
-       values=tensor([0.7874, 0.2567, 0.8616,  ..., 0.3490, 0.8581, 0.1320]),
+tensor(crow_indices=tensor([      0,    1005,    2014,  ..., 4998103,
+                            4999056, 5000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 4980, 4983, 4993]),
+       values=tensor([0.7293, 0.3445, 0.3834,  ..., 0.7374, 0.4715, 0.7945]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5757, 0.3666, 0.5675,  ..., 0.4574, 0.0199, 0.8861])
+tensor([0.9531, 0.9906, 0.0327,  ..., 0.2819, 0.9884, 0.0185])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.407469034194946 seconds
+Time: 10.444172620773315 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     962,    2002,  ..., 4997962,
-                            4998964, 5000000]),
-       col_indices=tensor([   3,    4,    7,  ..., 4990, 4997, 4999]),
-       values=tensor([0.7874, 0.2567, 0.8616,  ..., 0.3490, 0.8581, 0.1320]),
+tensor(crow_indices=tensor([      0,    1005,    2014,  ..., 4998103,
+                            4999056, 5000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 4980, 4983, 4993]),
+       values=tensor([0.7293, 0.3445, 0.3834,  ..., 0.7374, 0.4715, 0.7945]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5757, 0.3666, 0.5675,  ..., 0.4574, 0.0199, 0.8861])
+tensor([0.9531, 0.9906, 0.0327,  ..., 0.2819, 0.9884, 0.0185])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.407469034194946 seconds
+Time: 10.444172620773315 seconds
 
-[40.02, 39.78, 39.89, 39.91, 39.49, 39.87, 40.34, 39.56, 39.63, 39.54]
-[139.14]
-13.863456964492798
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.407469034194946, 'TIME_S_1KI': 0.3619989229285199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1928.9614020395277, 'W': 139.14}
-[40.02, 39.78, 39.89, 39.91, 39.49, 39.87, 40.34, 39.56, 39.63, 39.54, 42.22, 40.02, 39.42, 39.79, 40.33, 39.7, 39.95, 40.8, 39.86, 39.82]
-719.1400000000001
-35.95700000000001
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.407469034194946, 'TIME_S_1KI': 0.3619989229285199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1928.9614020395277, 'W': 139.14, 'J_1KI': 67.09430963615748, 'W_1KI': 4.839652173913042, 'W_D': 103.18299999999998, 'J_D': 1430.47307996726, 'W_D_1KI': 3.588973913043478, 'J_D_1KI': 0.12483387523629487}
+[40.89, 39.68, 39.94, 39.87, 39.8, 39.94, 40.85, 40.96, 39.65, 40.01]
+[139.06]
+13.930290699005127
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28798, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.444172620773315, 'TIME_S_1KI': 0.36267006808713503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1937.146224603653, 'W': 139.06}
+[40.89, 39.68, 39.94, 39.87, 39.8, 39.94, 40.85, 40.96, 39.65, 40.01, 41.32, 40.08, 40.09, 40.7, 40.16, 39.61, 39.76, 39.88, 39.81, 39.64]
+721.71
+36.0855
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28798, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.444172620773315, 'TIME_S_1KI': 0.36267006808713503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1937.146224603653, 'W': 139.06, 'J_1KI': 67.26669298575086, 'W_1KI': 4.8288075560802834, 'W_D': 102.9745, 'J_D': 1434.4647195847035, 'W_D_1KI': 3.5757517883186334, 'J_D_1KI': 0.12416667089098664}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
index b67d5e2..6f0dd43 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.31110143661499, "TIME_S_1KI": 0.5428895612391402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1973.4541555023193, "W": 138.56, "J_1KI": 103.90428871175271, "W_1KI": 7.295319328173537, "W_D": 102.64975000000001, "J_D": 1461.9989585650565, "W_D_1KI": 5.404609593007951, "J_D_1KI": 0.2845579736222793}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.148674011230469, "TIME_S_1KI": 0.5515283958062317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1912.232966468334, "W": 137.63, "J_1KI": 103.92005687018825, "W_1KI": 7.479484810608119, "W_D": 101.70774999999999, "J_D": 1413.1287691296934, "W_D_1KI": 5.5272947122439, "J_D_1KI": 0.3003801267454975}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
index 95ac928..60a702d 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.6186671257019043}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.093505859375}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1447,    2956,  ..., 7496936,
-                            7498490, 7500000]),
-       col_indices=tensor([   4,    5,    6,  ..., 4990, 4991, 4998]),
-       values=tensor([0.3748, 0.3720, 0.6580,  ..., 0.1069, 0.0058, 0.6452]),
+tensor(crow_indices=tensor([      0,    1476,    2946,  ..., 7497092,
+                            7498499, 7500000]),
+       col_indices=tensor([   3,    4,    9,  ..., 4993, 4995, 4998]),
+       values=tensor([0.7692, 0.9577, 0.6421,  ..., 0.4974, 0.8037, 0.4799]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.1517, 0.2007, 0.5208,  ..., 0.9824, 0.7905, 0.4002])
+tensor([0.8552, 0.1634, 0.3191,  ..., 0.0243, 0.9305, 0.7580])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 0.6186671257019043 seconds
+Time: 0.093505859375 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16971', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.381728649139404}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11229', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.407301664352417}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1500,    3040,  ..., 7497021,
-                            7498445, 7500000]),
-       col_indices=tensor([   1,    5,    9,  ..., 4989, 4995, 4997]),
-       values=tensor([0.5527, 0.4050, 0.9810,  ..., 0.2200, 0.7595, 0.4370]),
+tensor(crow_indices=tensor([      0,    1491,    2991,  ..., 7496987,
+                            7498491, 7500000]),
+       col_indices=tensor([   6,    7,   10,  ..., 4987, 4988, 4999]),
+       values=tensor([0.9932, 0.6823, 0.0941,  ..., 0.3170, 0.1700, 0.5277]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.0260, 0.1554, 0.7193,  ..., 0.7760, 0.7155, 0.7186])
+tensor([0.7857, 0.3541, 0.7153,  ..., 0.0858, 0.7918, 0.2952])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 9.381728649139404 seconds
+Time: 6.407301664352417 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18993', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.31110143661499}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18401', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.148674011230469}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1505,    3034,  ..., 7497030,
-                            7498523, 7500000]),
-       col_indices=tensor([   3,    4,    6,  ..., 4987, 4992, 4993]),
-       values=tensor([0.4621, 0.5996, 0.9420,  ..., 0.5197, 0.1170, 0.3387]),
+tensor(crow_indices=tensor([      0,    1524,    2999,  ..., 7496978,
+                            7498500, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4975, 4979, 4999]),
+       values=tensor([0.7847, 0.2112, 0.1435,  ..., 0.9949, 0.2225, 0.8434]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.7133, 0.0520, 0.6752,  ..., 0.8724, 0.9726, 0.0718])
+tensor([0.9436, 0.7820, 0.2976,  ..., 0.7279, 0.8012, 0.5089])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.31110143661499 seconds
+Time: 10.148674011230469 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1505,    3034,  ..., 7497030,
-                            7498523, 7500000]),
-       col_indices=tensor([   3,    4,    6,  ..., 4987, 4992, 4993]),
-       values=tensor([0.4621, 0.5996, 0.9420,  ..., 0.5197, 0.1170, 0.3387]),
+tensor(crow_indices=tensor([      0,    1524,    2999,  ..., 7496978,
+                            7498500, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4975, 4979, 4999]),
+       values=tensor([0.7847, 0.2112, 0.1435,  ..., 0.9949, 0.2225, 0.8434]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.7133, 0.0520, 0.6752,  ..., 0.8724, 0.9726, 0.0718])
+tensor([0.9436, 0.7820, 0.2976,  ..., 0.7279, 0.8012, 0.5089])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.31110143661499 seconds
+Time: 10.148674011230469 seconds
 
-[41.45, 39.54, 41.3, 40.23, 39.52, 39.85, 39.55, 39.54, 39.68, 39.47]
-[138.56]
-14.24259638786316
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.31110143661499, 'TIME_S_1KI': 0.5428895612391402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1973.4541555023193, 'W': 138.56}
-[41.45, 39.54, 41.3, 40.23, 39.52, 39.85, 39.55, 39.54, 39.68, 39.47, 40.91, 39.98, 39.59, 39.45, 39.99, 39.93, 40.26, 39.5, 39.6, 39.56]
-718.2049999999999
-35.91025
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.31110143661499, 'TIME_S_1KI': 0.5428895612391402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1973.4541555023193, 'W': 138.56, 'J_1KI': 103.90428871175271, 'W_1KI': 7.295319328173537, 'W_D': 102.64975000000001, 'J_D': 1461.9989585650565, 'W_D_1KI': 5.404609593007951, 'J_D_1KI': 0.2845579736222793}
+[40.88, 40.15, 40.48, 39.7, 39.9, 39.72, 39.73, 39.77, 39.68, 39.5]
+[137.63]
+13.894012689590454
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.148674011230469, 'TIME_S_1KI': 0.5515283958062317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1912.232966468334, 'W': 137.63}
+[40.88, 40.15, 40.48, 39.7, 39.9, 39.72, 39.73, 39.77, 39.68, 39.5, 40.73, 39.74, 41.44, 39.63, 39.52, 39.59, 39.98, 39.43, 39.67, 39.52]
+718.445
+35.922250000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.148674011230469, 'TIME_S_1KI': 0.5515283958062317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1912.232966468334, 'W': 137.63, 'J_1KI': 103.92005687018825, 'W_1KI': 7.479484810608119, 'W_D': 101.70774999999999, 'J_D': 1413.1287691296934, 'W_D_1KI': 5.5272947122439, 'J_D_1KI': 0.3003801267454975}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..b33500c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4623, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.869210720062256, "TIME_S_1KI": 2.3511163140952314, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1715.391318473816, "W": 123.94000000000001, "J_1KI": 371.0558768059303, "W_1KI": 26.809431105342853, "W_D": 87.73350000000002, "J_D": 1214.2753287019732, "W_D_1KI": 18.977611940298512, "J_D_1KI": 4.105042600107833}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..915d5a7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2556135654449463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2026,     4021,  ...,  9995990,
+                             9997978, 10000000]),
+       col_indices=tensor([   1,    6,    9,  ..., 4991, 4993, 4997]),
+       values=tensor([0.3323, 0.6585, 0.3485,  ..., 0.6316, 0.2886, 0.7495]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3914, 0.7431, 0.0627,  ..., 0.4218, 0.6007, 0.5832])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.2556135654449463 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4107', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.327423810958862}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2032,     4046,  ...,  9995943,
+                             9997941, 10000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4988, 4990, 4991]),
+       values=tensor([0.7887, 0.1218, 0.0752,  ..., 0.7697, 0.6176, 0.4928]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3655, 0.9637, 0.0803,  ..., 0.0942, 0.4831, 0.3974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 9.327423810958862 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4623', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.869210720062256}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2080,     4074,  ...,  9996120,
+                             9998055, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4988, 4989, 4998]),
+       values=tensor([0.8721, 0.2802, 0.5674,  ..., 0.7807, 0.4474, 0.7441]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9197, 0.0161, 0.2580,  ..., 0.5344, 0.2373, 0.6957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.869210720062256 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2080,     4074,  ...,  9996120,
+                             9998055, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4988, 4989, 4998]),
+       values=tensor([0.8721, 0.2802, 0.5674,  ..., 0.7807, 0.4474, 0.7441]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9197, 0.0161, 0.2580,  ..., 0.5344, 0.2373, 0.6957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.869210720062256 seconds
+
+[40.45, 40.3, 45.54, 40.13, 39.83, 39.68, 39.66, 40.04, 39.63, 41.25]
+[123.94]
+13.840497970581055
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.869210720062256, 'TIME_S_1KI': 2.3511163140952314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1715.391318473816, 'W': 123.94000000000001}
+[40.45, 40.3, 45.54, 40.13, 39.83, 39.68, 39.66, 40.04, 39.63, 41.25, 41.52, 40.34, 39.67, 39.9, 39.74, 39.58, 39.63, 39.49, 39.57, 39.58]
+724.1299999999999
+36.20649999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.869210720062256, 'TIME_S_1KI': 2.3511163140952314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1715.391318473816, 'W': 123.94000000000001, 'J_1KI': 371.0558768059303, 'W_1KI': 26.809431105342853, 'W_D': 87.73350000000002, 'J_D': 1214.2753287019732, 'W_D_1KI': 18.977611940298512, 'J_D_1KI': 4.105042600107833}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..ef8b561
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.108771324157715, "TIME_S_1KI": 2.94272088057158, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1948.3000312638283, "W": 122.57, "J_1KI": 516.1059685467094, "W_1KI": 32.46887417218543, "W_D": 86.1335, "J_D": 1369.1270355132817, "W_D_1KI": 22.816821192052977, "J_D_1KI": 6.044191044252445}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..05d0f73
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.2961087226867676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2490,     4975,  ..., 12494955,
+                            12497491, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4994, 4996, 4998]),
+       values=tensor([0.9230, 0.7404, 0.0716,  ..., 0.8209, 0.3183, 0.8676]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4086, 0.9880, 0.1016,  ..., 0.8907, 0.8066, 0.6446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.2961087226867676 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3545', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.858964443206787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2500,     5017,  ..., 12494975,
+                            12497542, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4994, 4997, 4999]),
+       values=tensor([0.5138, 0.3202, 0.6371,  ..., 0.0572, 0.7854, 0.0609]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5537, 0.0044, 0.4461,  ..., 0.4637, 0.2205, 0.0434])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.858964443206787 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3775', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.108771324157715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2457,     4970,  ..., 12495118,
+                            12497586, 12500000]),
+       col_indices=tensor([   0,    5,    6,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5982, 0.0244, 0.5821,  ..., 0.2791, 0.2569, 0.9852]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.1130, 0.4034, 0.5297,  ..., 0.0598, 0.7079, 0.8853])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 11.108771324157715 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2457,     4970,  ..., 12495118,
+                            12497586, 12500000]),
+       col_indices=tensor([   0,    5,    6,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5982, 0.0244, 0.5821,  ..., 0.2791, 0.2569, 0.9852]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.1130, 0.4034, 0.5297,  ..., 0.0598, 0.7079, 0.8853])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 11.108771324157715 seconds
+
+[40.6, 40.02, 45.54, 40.47, 40.31, 40.18, 40.34, 40.25, 40.23, 39.68]
+[122.57]
+15.89540696144104
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.108771324157715, 'TIME_S_1KI': 2.94272088057158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1948.3000312638283, 'W': 122.57}
+[40.6, 40.02, 45.54, 40.47, 40.31, 40.18, 40.34, 40.25, 40.23, 39.68, 40.3, 40.16, 40.89, 40.3, 40.11, 40.08, 40.03, 39.96, 39.79, 39.56]
+728.73
+36.4365
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.108771324157715, 'TIME_S_1KI': 2.94272088057158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1948.3000312638283, 'W': 122.57, 'J_1KI': 516.1059685467094, 'W_1KI': 32.46887417218543, 'W_D': 86.1335, 'J_D': 1369.1270355132817, 'W_D_1KI': 22.816821192052977, 'J_D_1KI': 6.044191044252445}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
index f4429df..aad5720 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 475914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.308423519134521, "TIME_S_1KI": 0.021660265340239036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.9414702892302, "W": 94.85, "J_1KI": 2.5003287784961783, "W_1KI": 0.19930071399454524, "W_D": 59.248999999999995, "J_D": 743.3088262853622, "W_D_1KI": 0.12449518190261265, "J_D_1KI": 0.000261591762172604}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
index 5c32150..c0883dc 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
-['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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04127812385559082}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4395, 1896, 1016, 4725, 4287, 4964, 4709, 2475, 4723,
-                           2193, 4334, 4011, 1534,  947, 2980, 1276, 2745, 2145,
-                           4595, 3295, 1907, 4436,  575, 2869, 2437, 1774,  103,
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-                           2930,  693, 1558, 1071, 3383, 2339, 3436,  478, 4648,
-                           3106, 1411, 4257,  307, 1671, 1884, 1213, 4984,  642,
-                           1762, 3957, 2642, 3601, 1788,  779,  952, 1165, 4886,
-                           1883, 1290, 3845,  617, 3725, 3513, 4081, 2223, 1340,
-                           3232, 3261, 4162, 1911, 3991, 2182, 2166, 4202, 2629,
-                           1539, 1110,  990, 1798, 3362,   92, 4378, 3447, 4318,
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-                           3363, 1715,  457, 1006,  888, 2409, 2177, 4389, 4129,
-                           1812, 2617, 4717, 2316, 4949, 4158, 4435, 1917, 1201,
-                           1815,  715,  270,  923, 1913, 3452, 2985, 4782, 1099,
-                           4541, 1002, 2896, 4712, 4267, 2282,  628, 3973, 2938,
-                            376, 3252,   94, 2656, 4853, 4987, 1689, 1656,  463,
-                           3165,  992, 2823, 3447, 1273, 2259, 3674, 3345, 2191,
-                           1553, 3931,  925, 4111, 4050, 2652, 4860, 4434, 4407,
-                           4679, 4167, 4708, 2520, 3526, 2887, 3132, 3816, 2503,
-                           1957, 3455, 1933, 2402, 1540, 2844, 1178, 2305, 1831,
-                           1888, 1548, 3851, 4681,  615, 1793,  720, 2902,  503,
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-                           4882, 4297, 3385, 3297, 4242, 2939,  945,  273, 1189,
-                           1168, 4866,  495, 4965, 2390, 1391, 2738, 4804, 1124,
-                           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,
-                      0.8083, 0.2140, 0.4448, 0.7196, 0.7942, 0.0476, 0.7765,
-                      0.8012, 0.3506, 0.5836, 0.4105, 0.9051, 0.1137, 0.9336,
-                      0.6799, 0.2082, 0.3357, 0.2380, 0.0294, 0.3136, 0.6271,
-                      0.0480, 0.8189, 0.3762, 0.4307, 0.7550, 0.2975, 0.8129,
-                      0.6595, 0.2962, 0.6547, 0.2906, 0.5665, 0.2166, 0.0083,
-                      0.8507, 0.4177, 0.3111, 0.7802, 0.8212, 0.9638, 0.3557,
-                      0.0980, 0.2482, 0.5366, 0.7901, 0.2480, 0.2830, 0.7633,
-                      0.5347, 0.7196, 0.5079, 0.6330, 0.0116, 0.5729, 0.0163,
-                      0.3271, 0.1166, 0.7494, 0.8340, 0.1356, 0.0263, 0.4976,
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-                      0.2835, 0.3411, 0.2947, 0.0888, 0.7701, 0.5229, 0.9266,
-                      0.0848, 0.6607, 0.1111, 0.4010, 0.5304, 0.7457, 0.6466,
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-                      0.5142, 0.7264, 0.5640, 0.9227, 0.8507, 0.0543, 0.7639,
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-                      0.9443, 0.9108, 0.4282, 0.6493, 0.3251, 0.2113, 0.5069,
-                      0.2668, 0.6773, 0.2164, 0.4803, 0.1428, 0.5884, 0.9624,
-                      0.2800, 0.1414, 0.8042, 0.8031, 0.1028, 0.1173, 0.0795,
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-                      0.9400, 0.0108, 0.8998, 0.5855, 0.1393, 0.5266, 0.4851,
-                      0.4774, 0.1186, 0.4945, 0.1561, 0.6695]),
+       col_indices=tensor([ 980, 1760, 2093,  785, 4671, 1948,  723, 1459, 1601,
+                           3059, 3354, 3009, 3506, 1670, 2673, 2868, 3157, 4447,
+                           1531,  659, 4281, 2627, 1377, 2950, 2583, 1810, 1775,
+                           4655, 1859, 3752, 2371, 1562,  678, 1349,  716, 1427,
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+                           3966,  441, 2333,  946, 4406, 4968, 4149, 1833, 4307,
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+                           3606,  244,  568, 1109, 1740, 4991, 4457, 3089, 4428,
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+                           2436,  524, 4077, 4995, 1729, 2451, 3127, 2410, 3894,
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+                           4431,  906,  341, 4151,  340, 1004, 4160, 1654, 1155,
+                            188, 4818, 2576,  447, 1886, 1055, 2211, 1261, 3706,
+                           4104, 4081, 2022, 1659, 4405,  105,  257, 1519, 1573,
+                           2647,  334, 3953, 2722, 2554,  628, 1350, 4587, 1239,
+                           3896,   49, 2034, 3369,  750, 1198,  743, 3025, 4827,
+                           4181, 3975, 2556, 3695, 4820, 3552, 3237]),
+       values=tensor([0.9726, 0.4932, 0.3912, 0.9738, 0.2382, 0.2194, 0.1583,
+                      0.5409, 0.3403, 0.0032, 0.6572, 0.0849, 0.3926, 0.9886,
+                      0.6424, 0.9153, 0.1836, 0.7579, 0.8401, 0.5061, 0.5178,
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+                      0.9123, 0.3294, 0.9018, 0.9354, 0.4913, 0.5484, 0.4661,
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+                      0.6623, 0.0188, 0.0129, 0.1565, 0.2382, 0.5939, 0.9749,
+                      0.6008, 0.1917, 0.4414, 0.2563, 0.1692, 0.9585, 0.0472,
+                      0.8804, 0.5964, 0.2177, 0.3908, 0.2373, 0.0395, 0.7888,
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+                      0.4941, 0.2709, 0.6765, 0.8735, 0.4097, 0.3394, 0.4724,
+                      0.7144, 0.5758, 0.2577, 0.2371, 0.8221, 0.9059, 0.5592,
+                      0.3289, 0.0171, 0.1330, 0.9826, 0.1215, 0.7990, 0.5552,
+                      0.1342, 0.7031, 0.1802, 0.8204, 0.3147, 0.1663, 0.8508,
+                      0.4793, 0.5702, 0.1389, 0.1455, 0.9150, 0.8457, 0.2787,
+                      0.6364, 0.0265, 0.9823, 0.1357, 0.7315, 0.5366, 0.2059,
+                      0.1967, 0.4234, 0.3059, 0.0470, 0.8504, 0.4796, 0.2864,
+                      0.4369, 0.4481, 0.7929, 0.8452, 0.0958, 0.5059, 0.6459,
+                      0.8388, 0.7761, 0.1938, 0.8212, 0.1919, 0.8336, 0.3582,
+                      0.0664, 0.5404, 0.5179, 0.4249, 0.9245, 0.0841, 0.7587,
+                      0.7759, 0.0382, 0.0647, 0.6744, 0.6055, 0.1518, 0.6047,
+                      0.4888, 0.3468, 0.0096, 0.8517, 0.8972, 0.6357, 0.5150,
+                      0.0879, 0.9902, 0.5975, 0.0921, 0.1553, 0.8301, 0.3740,
+                      0.3374, 0.9082, 0.3028, 0.9956, 0.7728, 0.1467, 0.8331,
+                      0.1315, 0.6195, 0.4346, 0.3339, 0.9836, 0.5643, 0.7400,
+                      0.8152, 0.3695, 0.3467, 0.0410, 0.0075]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1727, 0.0592, 0.5429,  ..., 0.7822, 0.3152, 0.8983])
+tensor([0.4444, 0.7904, 0.6664,  ..., 0.7984, 0.5487, 0.5407])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.0375218391418457 seconds
+Time: 0.04127812385559082 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25437', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5612115859985352}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1000,  776, 2038, 1918, 4703, 1006, 4783,  345, 4138,
-                           3890, 4809, 2179, 2654,   39, 3277, 4397,  222, 2644,
-                           2751, 2925, 4735, 3220, 3118, 2167,  634, 4745, 1720,
-                           1787, 1820, 1926,  473, 4992, 3200, 1675, 2855, 1802,
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-                            391,  713,  190, 1392, 2043, 4585,  625, 4376,  675,
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-                             63, 3878, 2875, 4861, 4483,  644,  592, 3560, 3073,
-                           4937,  789,  208, 3509, 3077, 4039, 4563, 1839,  362,
-                           2824, 2672, 4373, 1492, 1152, 3845,  328, 2405, 4091,
-                           2931, 2541, 2530, 3217, 4233, 1852, 2606, 3892,  380,
-                           2119, 1221, 1290,  592, 3077, 4909,  282, 3215,  863,
-                           1452, 3214, 1592,  795,  193, 4254, 3986, 1847, 2461,
-                           4353, 1361, 3013, 1482, 4277, 3046,  277]),
-       values=tensor([0.1993, 0.0335, 0.2936, 0.2778, 0.6825, 0.6252, 0.3746,
-                      0.6011, 0.3211, 0.0488, 0.6153, 0.4477, 0.3116, 0.5339,
-                      0.8158, 0.9445, 0.2638, 0.2848, 0.0424, 0.7741, 0.0547,
-                      0.0033, 0.5605, 0.2034, 0.9731, 0.4334, 0.6773, 0.7018,
-                      0.1534, 0.3665, 0.8519, 0.3002, 0.6885, 0.4688, 0.2572,
-                      0.6610, 0.5022, 0.8309, 0.6908, 0.4905, 0.2911, 0.9203,
-                      0.1018, 0.0930, 0.0540, 0.4357, 0.3509, 0.7870, 0.0358,
-                      0.8075, 0.3342, 0.2290, 0.0496, 0.3593, 0.2995, 0.8746,
-                      0.4914, 0.4993, 0.3891, 0.1546, 0.8356, 0.6696, 0.4824,
-                      0.2231, 0.1034, 0.1057, 0.9353, 0.7565, 0.0205, 0.6134,
-                      0.2384, 0.3674, 0.3962, 0.9296, 0.6846, 0.4976, 0.1741,
-                      0.5769, 0.7161, 0.8852, 0.4021, 0.6679, 0.8123, 0.7585,
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        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7126, 0.1651, 0.2523,  ..., 0.5242, 0.8574, 0.9519])
+tensor([0.8285, 0.4420, 0.7749,  ..., 0.7287, 0.4578, 0.9435])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 6.313635349273682 seconds
+Time: 0.5612115859985352 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '475914', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.308423519134521}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.7083, 0.6529, 0.8556, 0.1421, 0.4528, 0.4045, 0.9221,
+                      0.6914, 0.6437, 0.8815, 0.0609, 0.9680, 0.2115, 0.5295,
+                      0.5418, 0.8646, 0.6735, 0.1927, 0.2578, 0.4564, 0.0603,
+                      0.1414, 0.3382, 0.0772, 0.6503, 0.3586, 0.8775, 0.8840,
+                      0.9215, 0.4825, 0.2733, 0.0423, 0.6825, 0.8144, 0.0837,
+                      0.2758, 0.6188, 0.3276, 0.0762, 0.7932, 0.5621, 0.9067,
+                      0.7339, 0.1976, 0.8462, 0.5736, 0.2659, 0.7486, 0.3053,
+                      0.7429, 0.4272, 0.8072, 0.5183, 0.4677]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.5720, 0.9223, 0.3340,  ..., 0.6697, 0.2837, 0.3607])
+tensor([0.4130, 0.3379, 0.7498,  ..., 0.0848, 0.9618, 0.5893])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,107 +239,77 @@ 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}
+Time: 10.308423519134521 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1815,  573,  313, 4753, 4998, 3650,  708, 3756, 4632,
-                           3171, 1063, 2555, 1629, 3246, 3070, 2176, 3197, 2451,
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-                           3029, 4444, 3612, 4409, 2915, 1931,  518, 3028, 4272,
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-                      3.1890e-01, 2.8569e-01, 1.1579e-01, 7.3771e-01,
-                      8.3701e-01, 7.5848e-01]), size=(5000, 5000), nnz=250,
-       layout=torch.sparse_csr)
-tensor([0.7970, 0.8043, 0.6125,  ..., 0.7108, 0.2175, 0.0136])
+       col_indices=tensor([ 698, 3054, 4592, 1629, 4601, 3886, 4804,  318, 1415,
+                            433, 1872, 1429, 1550, 3511, 4304, 3637, 1101, 2710,
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+                            181, 2166, 1855, 2452,  189, 3074, 2522, 3426,  135,
+                           1267,  666, 4928, 2908, 4053, 4593,  448]),
+       values=tensor([0.8620, 0.7365, 0.7531, 0.5477, 0.5295, 0.4268, 0.2903,
+                      0.3296, 0.4852, 0.0295, 0.6605, 0.4770, 0.1707, 0.4638,
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+                      0.4532, 0.0609, 0.2279, 0.8651, 0.8162, 0.8251, 0.1216,
+                      0.3049, 0.0805, 0.1284, 0.1859, 0.3690, 0.3435, 0.7762,
+                      0.7083, 0.6529, 0.8556, 0.1421, 0.4528, 0.4045, 0.9221,
+                      0.6914, 0.6437, 0.8815, 0.0609, 0.9680, 0.2115, 0.5295,
+                      0.5418, 0.8646, 0.6735, 0.1927, 0.2578, 0.4564, 0.0603,
+                      0.1414, 0.3382, 0.0772, 0.6503, 0.3586, 0.8775, 0.8840,
+                      0.9215, 0.4825, 0.2733, 0.0423, 0.6825, 0.8144, 0.0837,
+                      0.2758, 0.6188, 0.3276, 0.0762, 0.7932, 0.5621, 0.9067,
+                      0.7339, 0.1976, 0.8462, 0.5736, 0.2659, 0.7486, 0.3053,
+                      0.7429, 0.4272, 0.8072, 0.5183, 0.4677]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4130, 0.3379, 0.7498,  ..., 0.0848, 0.9618, 0.5893])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -347,118 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.363765478134155 seconds
+Time: 10.308423519134521 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1815,  573,  313, 4753, 4998, 3650,  708, 3756, 4632,
-                           3171, 1063, 2555, 1629, 3246, 3070, 2176, 3197, 2451,
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-                           3029, 4444, 3612, 4409, 2915, 1931,  518, 3028, 4272,
-                           2556, 3052, 1905, 3640, 2925, 2354, 3707]),
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-                      4.3469e-01, 7.3495e-01, 1.5216e-01, 3.9507e-01,
-                      7.1334e-01, 7.7117e-01, 9.9550e-01, 9.2278e-01,
-                      3.0890e-01, 6.6914e-01, 1.2145e-01, 9.1632e-01,
-                      5.0784e-01, 6.2243e-01, 6.5077e-01, 6.2687e-01,
-                      2.0114e-01, 7.5097e-01, 2.0777e-01, 4.2757e-01,
-                      2.2520e-01, 5.5414e-01, 9.1256e-01, 1.3031e-01,
-                      1.5351e-01, 4.1244e-01, 2.4735e-01, 9.5465e-01,
-                      3.7976e-01, 3.1882e-01, 2.8598e-02, 8.3393e-01,
-                      7.4047e-01, 7.3298e-01, 9.7843e-01, 4.0729e-01,
-                      9.2998e-02, 4.3465e-01, 3.2636e-01, 9.5106e-02,
-                      4.8367e-02, 3.1339e-01, 4.7275e-01, 6.9317e-01,
-                      6.7922e-01, 7.2355e-01, 6.1366e-01, 7.6219e-01,
-                      2.1995e-01, 3.9216e-01, 8.5252e-01, 7.1761e-01,
-                      4.5198e-01, 9.8165e-01, 7.6941e-01, 8.2823e-01,
-                      7.6982e-01, 4.3963e-01, 2.2626e-01, 2.9003e-01,
-                      7.3718e-01, 8.0941e-01, 4.5213e-01, 1.9323e-01,
-                      3.6014e-01, 6.7950e-02, 2.6777e-01, 7.5770e-01,
-                      8.8988e-01, 1.1815e-01, 1.1244e-01, 9.2625e-01,
-                      7.6156e-01, 9.7142e-01, 2.3564e-01, 3.8882e-01,
-                      5.9567e-01, 4.8258e-01, 5.5462e-01, 2.7503e-01,
-                      2.0411e-01, 3.1168e-01, 7.6951e-01, 7.2732e-01,
-                      4.6023e-02, 4.7740e-01, 9.9557e-01, 7.3789e-02,
-                      6.2383e-02, 3.5543e-01, 1.8242e-01, 3.6846e-01,
-                      5.3628e-02, 5.3874e-01, 3.0038e-01, 9.6174e-01,
-                      9.6554e-01, 4.7430e-01, 2.2738e-01, 8.6557e-01,
-                      5.4122e-02, 8.5019e-01, 5.0852e-01, 5.3410e-01,
-                      1.7285e-01, 5.4149e-01, 8.0869e-01, 6.5103e-01,
-                      2.7217e-01, 7.0732e-01, 5.5532e-01, 9.9150e-01,
-                      7.5543e-01, 2.6834e-01, 2.8447e-01, 3.5912e-01,
-                      4.5601e-01, 7.0765e-01, 6.6949e-01, 5.9725e-01,
-                      4.8923e-01, 9.9235e-01, 7.6412e-02, 4.1164e-02,
-                      4.3938e-01, 9.1861e-01, 8.8739e-01, 9.4972e-01,
-                      4.9408e-01, 4.9347e-01, 3.4149e-01, 5.0322e-01,
-                      3.1901e-02, 5.2875e-01, 3.3499e-01, 9.5821e-01,
-                      5.2956e-01, 4.7216e-01, 2.0353e-01, 3.0726e-02,
-                      5.1848e-01, 2.6131e-01, 8.5289e-02, 4.9542e-01,
-                      1.5835e-01, 6.7945e-01, 7.8119e-01, 3.4856e-01,
-                      7.3888e-01, 4.3503e-01, 4.8394e-01, 1.0914e-01,
-                      5.9027e-01, 7.1288e-01, 9.8329e-01, 5.5542e-02,
-                      1.2536e-01, 1.9606e-01, 5.4455e-01, 4.3811e-01,
-                      5.8744e-01, 3.2588e-01, 6.3981e-02, 1.1337e-01,
-                      5.4324e-01, 8.4644e-01, 5.6165e-02, 5.0125e-01,
-                      1.5973e-01, 1.8614e-01, 7.8747e-01, 9.1964e-01,
-                      9.1086e-01, 5.6162e-01, 9.8390e-01, 1.9761e-01,
-                      4.5863e-01, 7.9353e-01, 3.8658e-02, 1.4135e-01,
-                      8.1843e-01, 3.0910e-01, 1.5630e-01, 6.8785e-01,
-                      4.2323e-01, 9.6230e-02, 7.4216e-01, 2.9855e-02,
-                      3.1890e-01, 2.8569e-01, 1.1579e-01, 7.3771e-01,
-                      8.3701e-01, 7.5848e-01]), size=(5000, 5000), nnz=250,
-       layout=torch.sparse_csr)
-tensor([0.7970, 0.8043, 0.6125,  ..., 0.7108, 0.2175, 0.0136])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-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}
+[39.69, 38.64, 38.76, 38.66, 38.74, 43.79, 39.13, 39.04, 39.27, 38.98]
+[94.85]
+12.54550838470459
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 475914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.308423519134521, 'TIME_S_1KI': 0.021660265340239036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.9414702892302, 'W': 94.85}
+[39.69, 38.64, 38.76, 38.66, 38.74, 43.79, 39.13, 39.04, 39.27, 38.98, 39.59, 39.06, 44.18, 38.71, 38.85, 39.28, 38.92, 38.81, 38.79, 40.52]
+712.02
+35.601
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 475914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.308423519134521, 'TIME_S_1KI': 0.021660265340239036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.9414702892302, 'W': 94.85, 'J_1KI': 2.5003287784961783, 'W_1KI': 0.19930071399454524, 'W_D': 59.248999999999995, 'J_D': 743.3088262853622, 'W_D_1KI': 0.12449518190261265, 'J_D_1KI': 0.000261591762172604}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
index b474ab7..ae25247 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 471062, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.751366138458252, "TIME_S_1KI": 0.022823675309106343, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1247.6856990122794, "W": 95.07, "J_1KI": 2.6486655663421788, "W_1KI": 0.20182056714402774, "W_D": 59.887249999999995, "J_D": 785.9520919130443, "W_D_1KI": 0.12713241569050357, "J_D_1KI": 0.0002698846769438069}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 463602, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329545974731445, "TIME_S_1KI": 0.02228106430673605, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1198.2176897239685, "W": 95.11, "J_1KI": 2.5845826586683587, "W_1KI": 0.20515442124926123, "W_D": 59.724, "J_D": 752.4167101364135, "W_D_1KI": 0.12882601886963388, "J_D_1KI": 0.00027788063655815524}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
index b89ee01..3c48d35 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +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', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.06770849227905273}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01933002471923828}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([ 631, 2604, 2210,  ..., 3865,  405, 4638]),
-       values=tensor([0.5383, 0.4516, 0.3480,  ..., 0.7821, 0.4439, 0.3322]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 393, 4092, 1605,  ..., 4543,  205, 1898]),
+       values=tensor([0.0363, 0.1593, 0.8850,  ..., 0.0884, 0.4054, 0.0261]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.7026, 0.4971, 0.6548,  ..., 0.0098, 0.8140, 0.3691])
+tensor([0.7170, 0.2316, 0.8921,  ..., 0.0306, 0.1187, 0.4918])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.06770849227905273 seconds
+Time: 0.01933002471923828 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '155076', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.4566495418548584}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54319', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.230255126953125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([2690, 1150, 3956,  ...,  712, 3582,  778]),
-       values=tensor([0.8228, 0.9297, 0.1263,  ..., 0.8170, 0.9056, 0.4551]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4735, 1903, 2985,  ..., 3889, 4420, 4686]),
+       values=tensor([0.8501, 0.7899, 0.6223,  ..., 0.9437, 0.2014, 0.9727]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8564, 0.8640, 0.2790,  ..., 0.6413, 0.1958, 0.4583])
+tensor([0.5741, 0.3449, 0.6519,  ..., 0.7953, 0.3519, 0.0286])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 3.4566495418548584 seconds
+Time: 1.230255126953125 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '471062', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.751366138458252}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '463602', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329545974731445}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([1001, 2591,  823,  ...,  591, 3447, 2958]),
-       values=tensor([0.9383, 0.1843, 0.8614,  ..., 0.3843, 0.5733, 0.2218]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([1727, 4803, 1040,  ..., 3710, 1053, 4648]),
+       values=tensor([0.0640, 0.8338, 0.2393,  ..., 0.0278, 0.9877, 0.3687]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8666, 0.6997, 0.8565,  ..., 0.1580, 0.8946, 0.5984])
+tensor([0.8593, 0.3881, 0.7226,  ..., 0.2122, 0.1433, 0.4534])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.751366138458252 seconds
+Time: 10.329545974731445 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([1001, 2591,  823,  ...,  591, 3447, 2958]),
-       values=tensor([0.9383, 0.1843, 0.8614,  ..., 0.3843, 0.5733, 0.2218]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([1727, 4803, 1040,  ..., 3710, 1053, 4648]),
+       values=tensor([0.0640, 0.8338, 0.2393,  ..., 0.0278, 0.9877, 0.3687]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8666, 0.6997, 0.8565,  ..., 0.1580, 0.8946, 0.5984])
+tensor([0.8593, 0.3881, 0.7226,  ..., 0.2122, 0.1433, 0.4534])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.751366138458252 seconds
+Time: 10.329545974731445 seconds
 
-[39.99, 39.18, 38.82, 38.83, 39.11, 38.71, 39.17, 38.68, 38.75, 38.85]
-[95.07]
-13.123863458633423
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 471062, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.751366138458252, 'TIME_S_1KI': 0.022823675309106343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1247.6856990122794, 'W': 95.07}
-[39.99, 39.18, 38.82, 38.83, 39.11, 38.71, 39.17, 38.68, 38.75, 38.85, 39.95, 39.47, 39.27, 39.32, 39.37, 38.76, 39.37, 39.0, 39.09, 38.72]
-703.655
-35.18275
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 471062, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.751366138458252, 'TIME_S_1KI': 0.022823675309106343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1247.6856990122794, 'W': 95.07, 'J_1KI': 2.6486655663421788, 'W_1KI': 0.20182056714402774, 'W_D': 59.887249999999995, 'J_D': 785.9520919130443, 'W_D_1KI': 0.12713241569050357, 'J_D_1KI': 0.0002698846769438069}
+[40.79, 39.42, 39.16, 38.83, 39.21, 39.28, 39.68, 38.8, 39.35, 39.35]
+[95.11]
+12.598230361938477
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 463602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329545974731445, 'TIME_S_1KI': 0.02228106430673605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.2176897239685, 'W': 95.11}
+[40.79, 39.42, 39.16, 38.83, 39.21, 39.28, 39.68, 38.8, 39.35, 39.35, 40.35, 38.9, 38.98, 39.04, 38.98, 40.01, 39.46, 39.09, 39.7, 39.17]
+707.72
+35.386
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 463602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329545974731445, 'TIME_S_1KI': 0.02228106430673605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.2176897239685, 'W': 95.11, 'J_1KI': 2.5845826586683587, 'W_1KI': 0.20515442124926123, 'W_D': 59.724, 'J_D': 752.4167101364135, 'W_D_1KI': 0.12882601886963388, 'J_D_1KI': 0.00027788063655815524}
diff --git a/pytorch/output_synthetic_16core/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 174f326..c7b5a2b 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33105, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.270436525344849, "TIME_S_1KI": 0.31023822761953934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.494291753769, "W": 88.28, "J_1KI": 37.56212933858236, "W_1KI": 2.6666666666666665, "W_D": 71.58725, "J_D": 1008.3635788100362, "W_D_1KI": 2.1624301465035494, "J_D_1KI": 0.0653203487842788}
diff --git a/pytorch/output_synthetic_16core/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 48c062d..31fde2c 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": 0.3259446620941162}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.047393798828125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      22,  ...,  999982,
-                             999991, 1000000]),
-       col_indices=tensor([10285, 14477, 16251,  ..., 79839, 98536, 99886]),
-       values=tensor([0.0755, 0.8469, 0.4749,  ..., 0.2250, 0.2555, 0.2499]),
+tensor(crow_indices=tensor([      0,      11,      25,  ...,  999973,
+                             999987, 1000000]),
+       col_indices=tensor([ 2538, 10020, 11588,  ..., 84720, 92719, 95287]),
+       values=tensor([0.8172, 0.5815, 0.2513,  ..., 0.2819, 0.8178, 0.1271]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5289, 0.3805, 0.4649,  ..., 0.7570, 0.9550, 0.1372])
+tensor([0.9323, 0.3660, 0.2073,  ..., 0.7127, 0.8566, 0.0523])
 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: 0.3259446620941162 seconds
+Time: 0.047393798828125 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22154', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.0265889167785645}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,      11,      20,  ...,  999976,
+                             999985, 1000000]),
+       col_indices=tensor([10428, 14843, 15503,  ..., 86013, 91025, 96391]),
+       values=tensor([0.0148, 0.3731, 0.6426,  ..., 0.4125, 0.5086, 0.3848]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8754, 0.9877, 0.9510,  ..., 0.4555, 0.1143, 0.3690])
+tensor([0.0982, 0.0147, 0.0440,  ..., 0.9267, 0.0489, 0.6248])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.624319791793823 seconds
+Time: 7.0265889167785645 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33105', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.270436525344849}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,      12,      18,  ...,  999974,
+                             999988, 1000000]),
+       col_indices=tensor([  697, 33076, 59577,  ..., 88840, 91058, 94574]),
+       values=tensor([0.8969, 0.3012, 0.5025,  ..., 0.5812, 0.6517, 0.5598]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8754, 0.9877, 0.9510,  ..., 0.4555, 0.1143, 0.3690])
+tensor([0.0089, 0.5150, 0.8606,  ..., 0.9603, 0.9290, 0.1786])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.624319791793823 seconds
+Time: 10.270436525344849 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      12,      18,  ...,  999974,
+                             999988, 1000000]),
+       col_indices=tensor([  697, 33076, 59577,  ..., 88840, 91058, 94574]),
+       values=tensor([0.8969, 0.3012, 0.5025,  ..., 0.5812, 0.6517, 0.5598]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0089, 0.5150, 0.8606,  ..., 0.9603, 0.9290, 0.1786])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.270436525344849 seconds
+
+[18.44, 17.49, 17.8, 17.92, 17.7, 20.94, 18.06, 17.75, 21.44, 18.07]
+[88.28]
+14.085798501968384
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33105, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.270436525344849, 'TIME_S_1KI': 0.31023822761953934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.494291753769, 'W': 88.28}
+[18.44, 17.49, 17.8, 17.92, 17.7, 20.94, 18.06, 17.75, 21.44, 18.07, 18.18, 18.15, 17.95, 18.02, 21.88, 18.26, 18.15, 17.92, 17.92, 18.32]
+333.855
+16.69275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33105, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.270436525344849, 'TIME_S_1KI': 0.31023822761953934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.494291753769, 'W': 88.28, 'J_1KI': 37.56212933858236, 'W_1KI': 2.6666666666666665, 'W_D': 71.58725, 'J_D': 1008.3635788100362, 'W_D_1KI': 2.1624301465035494, 'J_D_1KI': 0.0653203487842788}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
index be7a2cb..8d42109 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2748, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.477163553237915, "TIME_S_1KI": 3.8126504924446563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1424.6626942634582, "W": 81.1, "J_1KI": 518.4362060638495, "W_1KI": 29.512372634643373, "W_D": 64.67649999999999, "J_D": 1136.1553236193656, "W_D_1KI": 23.535844250363898, "J_D_1KI": 8.564717703916994}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
index d59582d..280d994 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.4098339080810547}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,      105,      217,  ...,  9999774,
+                             9999878, 10000000]),
+       col_indices=tensor([ 2925,  3045,  3251,  ..., 98848, 99298, 99703]),
+       values=tensor([0.4813, 0.4380, 0.0490,  ..., 0.5758, 0.0326, 0.9259]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.5874, 0.0844, 0.8298,  ..., 0.9009, 0.0712, 0.0168])
+tensor([0.0648, 0.1204, 0.6207,  ..., 0.1724, 0.6764, 0.4459])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 3.892810106277466 seconds
+Time: 0.4098339080810547 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2562', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.787212610244751}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,       90,      201,  ...,  9999780,
+                             9999895, 10000000]),
+       col_indices=tensor([ 1242,  4056,  4707,  ..., 96589, 97728, 98727]),
+       values=tensor([0.8897, 0.2716, 0.4760,  ..., 0.6356, 0.3047, 0.7796]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3536, 0.8501, 0.0907,  ..., 0.0431, 0.6064, 0.5575])
+tensor([0.4134, 0.7359, 0.5031,  ..., 0.9568, 0.9528, 0.4063])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.332640647888184 seconds
+Time: 9.787212610244751 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2748', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.477163553237915}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,       82,      171,  ...,  9999801,
+                             9999897, 10000000]),
+       col_indices=tensor([ 1661,  2279,  2856,  ..., 99449, 99691, 99739]),
+       values=tensor([0.1663, 0.0376, 0.1009,  ..., 0.7118, 0.9261, 0.1836]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3536, 0.8501, 0.0907,  ..., 0.0431, 0.6064, 0.5575])
+tensor([0.7724, 0.0559, 0.5235,  ..., 0.7708, 0.2517, 0.0642])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.332640647888184 seconds
+Time: 10.477163553237915 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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       82,      171,  ...,  9999801,
+                             9999897, 10000000]),
+       col_indices=tensor([ 1661,  2279,  2856,  ..., 99449, 99691, 99739]),
+       values=tensor([0.1663, 0.0376, 0.1009,  ..., 0.7118, 0.9261, 0.1836]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7724, 0.0559, 0.5235,  ..., 0.7708, 0.2517, 0.0642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.477163553237915 seconds
+
+[18.37, 17.8, 17.96, 17.86, 22.53, 18.03, 17.75, 18.1, 18.23, 17.82]
+[81.1]
+17.56674098968506
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.477163553237915, 'TIME_S_1KI': 3.8126504924446563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.6626942634582, 'W': 81.1}
+[18.37, 17.8, 17.96, 17.86, 22.53, 18.03, 17.75, 18.1, 18.23, 17.82, 18.37, 17.83, 17.94, 18.39, 17.8, 17.92, 17.99, 17.94, 18.19, 17.86]
+328.47
+16.4235
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.477163553237915, 'TIME_S_1KI': 3.8126504924446563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.6626942634582, 'W': 81.1, 'J_1KI': 518.4362060638495, 'W_1KI': 29.512372634643373, 'W_D': 64.67649999999999, 'J_D': 1136.1553236193656, 'W_D_1KI': 23.535844250363898, 'J_D_1KI': 8.564717703916994}
diff --git a/pytorch/output_synthetic_16core/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 60a31fb..d39bedd 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 65044, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.857811450958252, "TIME_S_1KI": 0.1669302541503944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1160.4457891416548, "W": 82.65999999999998, "J_1KI": 17.840935199890147, "W_1KI": 1.2708320521493142, "W_D": 66.37149999999998, "J_D": 931.7750749336478, "W_D_1KI": 1.0204092614230365, "J_D_1KI": 0.015687984463179334}
diff --git a/pytorch/output_synthetic_16core/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 14be47f..a139e8c 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": 0.17937397956848145}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03411436080932617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      4,  ...,  99996, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([ 6463, 19403, 32975,  ..., 50312, 73566, 75866]),
-       values=tensor([0.6504, 0.4570, 0.8704,  ..., 0.7277, 0.1675, 0.6048]),
+       col_indices=tensor([61814, 31861, 93735,  ..., 37976, 26709, 88923]),
+       values=tensor([0.4964, 0.9275, 0.0463,  ..., 0.6388, 0.5613, 0.1901]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7096, 0.4020, 0.6001,  ..., 0.3911, 0.2531, 0.2591])
+tensor([0.9466, 0.9805, 0.4146,  ..., 0.4981, 0.9805, 0.0095])
 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: 0.17937397956848145 seconds
+Time: 0.03411436080932617 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '30778', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.968464136123657}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99998,
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99993,  99997,
                             100000]),
-       col_indices=tensor([64186, 21974, 57698,  ..., 75952, 18460, 38945]),
-       values=tensor([0.5668, 0.1226, 0.0967,  ..., 0.2541, 0.6343, 0.4356]),
+       col_indices=tensor([86302, 87189, 44148,  ...,  4090, 44893, 91495]),
+       values=tensor([0.5947, 0.5243, 0.4325,  ..., 0.8552, 0.8488, 0.7980]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9872, 0.9595, 0.0420,  ..., 0.0153, 0.9518, 0.5571])
+tensor([0.2800, 0.7786, 0.6115,  ..., 0.5946, 0.9897, 0.5537])
 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: 9.751020431518555 seconds
+Time: 4.968464136123657 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '65044', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.857811450958252}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([35835, 88904, 80345,  ..., 79801,  8127, 81515]),
-       values=tensor([0.8153, 0.8474, 0.9328,  ..., 0.8046, 0.4857, 0.5161]),
+       col_indices=tensor([27172, 43192, 23755,  ..., 52370, 88374,  3897]),
+       values=tensor([0.0211, 0.7600, 0.3262,  ..., 0.1220, 0.7210, 0.9662]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1493, 0.1613, 0.9905,  ..., 0.3209, 0.7704, 0.3686])
+tensor([0.8254, 0.5550, 0.3634,  ..., 0.5298, 0.8710, 0.0274])
 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: 10.23182988166809 seconds
+Time: 10.857811450958252 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([35835, 88904, 80345,  ..., 79801,  8127, 81515]),
-       values=tensor([0.8153, 0.8474, 0.9328,  ..., 0.8046, 0.4857, 0.5161]),
+       col_indices=tensor([27172, 43192, 23755,  ..., 52370, 88374,  3897]),
+       values=tensor([0.0211, 0.7600, 0.3262,  ..., 0.1220, 0.7210, 0.9662]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1493, 0.1613, 0.9905,  ..., 0.3209, 0.7704, 0.3686])
+tensor([0.8254, 0.5550, 0.3634,  ..., 0.5298, 0.8710, 0.0274])
 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: 10.23182988166809 seconds
+Time: 10.857811450958252 seconds
 
-[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}
+[18.23, 17.9, 18.0, 17.99, 18.15, 18.02, 18.22, 19.12, 18.05, 18.53]
+[82.66]
+14.038782835006714
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65044, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.857811450958252, 'TIME_S_1KI': 0.1669302541503944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.4457891416548, 'W': 82.65999999999998}
+[18.23, 17.9, 18.0, 17.99, 18.15, 18.02, 18.22, 19.12, 18.05, 18.53, 18.28, 17.9, 18.17, 17.79, 18.21, 17.83, 17.84, 17.79, 18.22, 18.1]
+325.77
+16.2885
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65044, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.857811450958252, 'TIME_S_1KI': 0.1669302541503944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.4457891416548, 'W': 82.65999999999998, 'J_1KI': 17.840935199890147, 'W_1KI': 1.2708320521493142, 'W_D': 66.37149999999998, 'J_D': 931.7750749336478, 'W_D_1KI': 1.0204092614230365, 'J_D_1KI': 0.015687984463179334}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
index b14656f..4c80d3b 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 43635, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050816535949707, "TIME_S_1KI": 0.23033841035750444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.4814233660697, "W": 89.55, "J_1KI": 28.06190955347931, "W_1KI": 2.052251632863527, "W_D": 73.275, "J_D": 1001.9416671931745, "W_D_1KI": 1.679271227225851, "J_D_1KI": 0.03848450159793402}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 45510, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.42077898979187, "TIME_S_1KI": 0.22897778487787013, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1214.5695349788666, "W": 87.39, "J_1KI": 26.687970445591443, "W_1KI": 1.920237310481213, "W_D": 71.04925, "J_D": 987.4614318926334, "W_D_1KI": 1.561178861788618, "J_D_1KI": 0.03430408397689778}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
index fe4dfe5..d883b1c 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.24062752723693848}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.039800167083740234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      8,  ..., 499990, 499993,
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 499993, 499997,
                             500000]),
-       col_indices=tensor([25845, 82264, 90566,  ..., 92820, 97145, 99590]),
-       values=tensor([0.6382, 0.4794, 0.9065,  ..., 0.0565, 0.2096, 0.7456]),
+       col_indices=tensor([ 4828, 48889, 52503,  ..., 31911, 36084, 76746]),
+       values=tensor([0.4793, 0.0828, 0.1169,  ..., 0.5530, 0.3033, 0.0718]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.3202, 0.7109, 0.9868,  ..., 0.4243, 0.8639, 0.9226])
+tensor([0.5059, 0.6298, 0.1664,  ..., 0.1879, 0.5431, 0.8952])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.24062752723693848 seconds
+Time: 0.039800167083740234 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43635', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050816535949707}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '26381', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.086489200592041}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     11,  ..., 499985, 499991,
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499989, 499994,
                             500000]),
-       col_indices=tensor([ 2943, 35530, 40183,  ..., 77324, 82017, 92181]),
-       values=tensor([0.7176, 0.7408, 0.8709,  ..., 0.2236, 0.1509, 0.6788]),
+       col_indices=tensor([14519, 22983, 80951,  ..., 84187, 95762, 97051]),
+       values=tensor([0.1542, 0.8524, 0.3039,  ..., 0.4189, 0.6409, 0.4295]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.0661, 0.4747, 0.8915,  ..., 0.6458, 0.7762, 0.4694])
+tensor([0.0352, 0.2725, 0.4170,  ..., 0.1491, 0.4370, 0.8032])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.050816535949707 seconds
+Time: 6.086489200592041 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45510', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.42077898979187}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     11,  ..., 499985, 499991,
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499990, 499995,
                             500000]),
-       col_indices=tensor([ 2943, 35530, 40183,  ..., 77324, 82017, 92181]),
-       values=tensor([0.7176, 0.7408, 0.8709,  ..., 0.2236, 0.1509, 0.6788]),
+       col_indices=tensor([14786, 31808, 59751,  ..., 39791, 89593, 95677]),
+       values=tensor([0.6756, 0.3891, 0.0863,  ..., 0.6881, 0.4209, 0.2818]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.0661, 0.4747, 0.8915,  ..., 0.6458, 0.7762, 0.4694])
+tensor([0.4948, 0.2234, 0.2049,  ..., 0.0447, 0.7948, 0.3022])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.050816535949707 seconds
+Time: 10.42077898979187 seconds
 
-[18.28, 18.77, 17.89, 17.54, 18.06, 17.6, 17.83, 17.74, 18.0, 17.55]
-[89.55]
-13.673717737197876
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.050816535949707, 'TIME_S_1KI': 0.23033841035750444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.4814233660697, 'W': 89.55}
-[18.28, 18.77, 17.89, 17.54, 18.06, 17.6, 17.83, 17.74, 18.0, 17.55, 18.31, 17.61, 17.7, 17.7, 17.86, 17.61, 17.69, 21.85, 18.07, 17.82]
-325.5
-16.275
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.050816535949707, 'TIME_S_1KI': 0.23033841035750444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.4814233660697, 'W': 89.55, 'J_1KI': 28.06190955347931, 'W_1KI': 2.052251632863527, 'W_D': 73.275, 'J_D': 1001.9416671931745, 'W_D_1KI': 1.679271227225851, 'J_D_1KI': 0.03848450159793402}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499990, 499995,
+                            500000]),
+       col_indices=tensor([14786, 31808, 59751,  ..., 39791, 89593, 95677]),
+       values=tensor([0.6756, 0.3891, 0.0863,  ..., 0.6881, 0.4209, 0.2818]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4948, 0.2234, 0.2049,  ..., 0.0447, 0.7948, 0.3022])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.42077898979187 seconds
+
+[19.52, 19.27, 18.28, 17.93, 17.92, 18.18, 17.97, 18.17, 18.27, 18.17]
+[87.39]
+13.898266792297363
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.42077898979187, 'TIME_S_1KI': 0.22897778487787013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1214.5695349788666, 'W': 87.39}
+[19.52, 19.27, 18.28, 17.93, 17.92, 18.18, 17.97, 18.17, 18.27, 18.17, 18.18, 17.95, 17.9, 18.1, 18.19, 18.03, 17.88, 17.96, 18.05, 17.66]
+326.815
+16.34075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.42077898979187, 'TIME_S_1KI': 0.22897778487787013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1214.5695349788666, 'W': 87.39, 'J_1KI': 26.687970445591443, 'W_1KI': 1.920237310481213, 'W_D': 71.04925, 'J_D': 987.4614318926334, 'W_D_1KI': 1.561178861788618, 'J_D_1KI': 0.03430408397689778}
diff --git a/pytorch/output_synthetic_16core/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 05a227c..df907c2 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 251263, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.831458568572998, "TIME_S_1KI": 0.04310805239359952, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1035.0438996100424, "W": 74.32999999999998, "J_1KI": 4.119364568639403, "W_1KI": 0.2958254896264073, "W_D": 58.06174999999998, "J_D": 808.5088139134047, "W_D_1KI": 0.2310795859318721, "J_D_1KI": 0.0009196721599752933}
diff --git a/pytorch/output_synthetic_16core/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 04a16aa..bb6d734 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": 0.05977439880371094}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019912242889404297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([1836, 4826, 4334,  ..., 9720, 1658, 3253]),
+       values=tensor([0.6220, 0.1290, 0.9015,  ..., 0.3260, 0.3650, 0.7979]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1595, 0.0624, 0.6993,  ..., 0.5987, 0.7271, 0.9533])
+tensor([0.5720, 0.7293, 0.6280,  ..., 0.0388, 0.6575, 0.1842])
 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: 0.05977439880371094 seconds
+Time: 0.019912242889404297 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52731', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.203561544418335}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([1066, 3027, 5018,  ...,  516, 4404, 8191]),
+       values=tensor([0.9408, 0.5840, 0.0232,  ..., 0.8231, 0.8506, 0.7636]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2429, 0.7570, 0.9101,  ..., 0.6676, 0.5300, 0.9328])
+tensor([0.2181, 0.5672, 0.6634,  ..., 0.2110, 0.3174, 0.6218])
 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: 7.265056371688843 seconds
+Time: 2.203561544418335 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '251263', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.831458568572998}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([6180, 2035, 3071,  ...,  490, 6496, 2315]),
+       values=tensor([0.2073, 0.5439, 0.2551,  ..., 0.7953, 0.4550, 0.0057]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.7710, 0.0750, 0.1717,  ..., 0.8123, 0.4992, 0.1144])
+tensor([0.4967, 0.8117, 0.4603,  ..., 0.8210, 0.9832, 0.0501])
 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: 10.530851364135742 seconds
+Time: 10.831458568572998 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([6180, 2035, 3071,  ...,  490, 6496, 2315]),
+       values=tensor([0.2073, 0.5439, 0.2551,  ..., 0.7953, 0.4550, 0.0057]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.7710, 0.0750, 0.1717,  ..., 0.8123, 0.4992, 0.1144])
+tensor([0.4967, 0.8117, 0.4603,  ..., 0.8210, 0.9832, 0.0501])
 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: 10.530851364135742 seconds
+Time: 10.831458568572998 seconds
 
-[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}
+[18.25, 18.21, 18.06, 17.61, 18.2, 17.92, 18.19, 17.85, 18.22, 17.78]
+[74.33]
+13.924981832504272
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 251263, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.831458568572998, 'TIME_S_1KI': 0.04310805239359952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1035.0438996100424, 'W': 74.32999999999998}
+[18.25, 18.21, 18.06, 17.61, 18.2, 17.92, 18.19, 17.85, 18.22, 17.78, 19.64, 17.79, 18.1, 18.15, 18.23, 17.78, 18.02, 18.14, 18.11, 17.9]
+325.365
+16.268250000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 251263, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.831458568572998, 'TIME_S_1KI': 0.04310805239359952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1035.0438996100424, 'W': 74.32999999999998, 'J_1KI': 4.119364568639403, 'W_1KI': 0.2958254896264073, 'W_D': 58.06174999999998, 'J_D': 808.5088139134047, 'W_D_1KI': 0.2310795859318721, 'J_D_1KI': 0.0009196721599752933}
diff --git a/pytorch/output_synthetic_16core/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 fdd9d92..610ea48 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 195195, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.530660390853882, "TIME_S_1KI": 0.05394943718258092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1115.5897302937508, "W": 79.81, "J_1KI": 5.7152577181472415, "W_1KI": 0.40887317810394735, "W_D": 63.323, "J_D": 885.1332977244854, "W_D_1KI": 0.3244089244089244, "J_D_1KI": 0.0016619735362531027}
diff --git a/pytorch/output_synthetic_16core/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 5d9701a..d29559f 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": 0.06870174407958984}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02070331573486328}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     25,  ...,  99981,  99995,
+tensor(crow_indices=tensor([     0,     12,     18,  ...,  99984,  99988,
                             100000]),
-       col_indices=tensor([   3,  150,  370,  ..., 2691, 9535, 9749]),
-       values=tensor([0.2561, 0.9230, 0.8831,  ..., 0.2203, 0.7623, 0.4185]),
+       col_indices=tensor([ 729,  732,  881,  ..., 6002, 8211, 9107]),
+       values=tensor([0.1473, 0.0535, 0.1985,  ..., 0.7529, 0.2592, 0.5040]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1427, 0.1860, 0.4972,  ..., 0.5058, 0.8744, 0.6551])
+tensor([0.7970, 0.9066, 0.2901,  ..., 0.5249, 0.8444, 0.0204])
 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: 0.06870174407958984 seconds
+Time: 0.02070331573486328 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50716', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.728132724761963}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     17,  ...,  99982,  99990,
+tensor(crow_indices=tensor([     0,     15,     22,  ...,  99985,  99992,
                             100000]),
-       col_indices=tensor([ 560, 3215, 3961,  ..., 6911, 7414, 7504]),
-       values=tensor([0.0904, 0.0706, 0.8224,  ..., 0.0963, 0.3127, 0.0052]),
+       col_indices=tensor([ 992, 2241, 2699,  ..., 7485, 9702, 9755]),
+       values=tensor([0.1587, 0.3354, 0.8907,  ..., 0.7458, 0.3952, 0.4445]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8141, 0.4563, 0.6350,  ..., 0.0924, 0.8861, 0.1694])
+tensor([0.7531, 0.7793, 0.1410,  ..., 0.7186, 0.3031, 0.2892])
 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: 8.226486682891846 seconds
+Time: 2.728132724761963 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '195195', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.530660390853882}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     10,  ...,  99988,  99994,
+tensor(crow_indices=tensor([     0,      6,     11,  ...,  99979,  99991,
                             100000]),
-       col_indices=tensor([1742, 3653, 4110,  ..., 7414, 9186, 9217]),
-       values=tensor([0.4393, 0.0633, 0.6988,  ..., 0.9636, 0.3600, 0.6461]),
+       col_indices=tensor([  74,  913, 1678,  ..., 8042, 8094, 8596]),
+       values=tensor([0.3009, 0.6152, 0.9919,  ..., 0.0065, 0.7111, 0.2350]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5362, 0.5145, 0.3988,  ..., 0.1543, 0.7121, 0.2032])
+tensor([0.1186, 0.1112, 0.0471,  ..., 0.5653, 0.6270, 0.7376])
 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: 10.382015228271484 seconds
+Time: 10.530660390853882 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     10,  ...,  99988,  99994,
+tensor(crow_indices=tensor([     0,      6,     11,  ...,  99979,  99991,
                             100000]),
-       col_indices=tensor([1742, 3653, 4110,  ..., 7414, 9186, 9217]),
-       values=tensor([0.4393, 0.0633, 0.6988,  ..., 0.9636, 0.3600, 0.6461]),
+       col_indices=tensor([  74,  913, 1678,  ..., 8042, 8094, 8596]),
+       values=tensor([0.3009, 0.6152, 0.9919,  ..., 0.0065, 0.7111, 0.2350]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5362, 0.5145, 0.3988,  ..., 0.1543, 0.7121, 0.2032])
+tensor([0.1186, 0.1112, 0.0471,  ..., 0.5653, 0.6270, 0.7376])
 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: 10.382015228271484 seconds
+Time: 10.530660390853882 seconds
 
-[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}
+[18.85, 21.66, 18.61, 18.04, 18.26, 18.19, 18.11, 17.83, 18.59, 17.86]
+[79.81]
+13.978069543838501
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.530660390853882, 'TIME_S_1KI': 0.05394943718258092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.5897302937508, 'W': 79.81}
+[18.85, 21.66, 18.61, 18.04, 18.26, 18.19, 18.11, 17.83, 18.59, 17.86, 18.27, 17.86, 18.11, 17.94, 18.2, 17.78, 18.07, 17.82, 17.85, 18.66]
+329.74
+16.487000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.530660390853882, 'TIME_S_1KI': 0.05394943718258092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.5897302937508, 'W': 79.81, 'J_1KI': 5.7152577181472415, 'W_1KI': 0.40887317810394735, 'W_D': 63.323, 'J_D': 885.1332977244854, 'W_D_1KI': 0.3244089244089244, 'J_D_1KI': 0.0016619735362531027}
diff --git a/pytorch/output_synthetic_16core/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 5933eb4..9a2b891 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58200, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524534702301025, "TIME_S_1KI": 0.18083392959280112, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.7857535743713, "W": 87.63, "J_1KI": 21.147521539078543, "W_1KI": 1.5056701030927835, "W_D": 71.18625, "J_D": 999.8290807986259, "W_D_1KI": 1.223131443298969, "J_D_1KI": 0.02101600418039466}
diff --git a/pytorch/output_synthetic_16core/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 d725fe1..b0a27e2 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": 0.19623374938964844}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.032953500747680664}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     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]),
+tensor(crow_indices=tensor([      0,      87,     187,  ...,  999811,
+                             999895, 1000000]),
+       col_indices=tensor([  60,  162,  170,  ..., 9440, 9828, 9931]),
+       values=tensor([0.9691, 0.2545, 0.9233,  ..., 0.5616, 0.3084, 0.5234]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2953, 0.0740, 0.7231,  ..., 0.2507, 0.0704, 0.5422])
+tensor([0.0498, 0.1923, 0.6628,  ..., 0.9993, 0.6267, 0.7810])
 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: 0.19623374938964844 seconds
+Time: 0.032953500747680664 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31863', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.748403549194336}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     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]),
+tensor(crow_indices=tensor([      0,     110,     213,  ...,  999801,
+                             999901, 1000000]),
+       col_indices=tensor([  77,  119,  129,  ..., 9737, 9950, 9990]),
+       values=tensor([0.4475, 0.9311, 0.1906,  ..., 0.1630, 0.9417, 0.6731]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1666, 0.0462, 0.0015,  ..., 0.3047, 0.2438, 0.6174])
+tensor([0.2219, 0.3377, 0.8817,  ..., 0.6372, 0.3631, 0.6898])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.015070676803589 seconds
+Time: 5.748403549194336 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58200', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524534702301025}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     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]),
+tensor(crow_indices=tensor([      0,     116,     213,  ...,  999804,
+                             999907, 1000000]),
+       col_indices=tensor([  96,  100,  135,  ..., 9713, 9783, 9969]),
+       values=tensor([0.2374, 0.5111, 0.2281,  ..., 0.8006, 0.1634, 0.0785]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1666, 0.0462, 0.0015,  ..., 0.3047, 0.2438, 0.6174])
+tensor([0.4187, 0.6286, 0.6485,  ..., 0.1996, 0.5955, 0.8769])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.015070676803589 seconds
+Time: 10.524534702301025 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     116,     213,  ...,  999804,
+                             999907, 1000000]),
+       col_indices=tensor([  96,  100,  135,  ..., 9713, 9783, 9969]),
+       values=tensor([0.2374, 0.5111, 0.2281,  ..., 0.8006, 0.1634, 0.0785]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4187, 0.6286, 0.6485,  ..., 0.1996, 0.5955, 0.8769])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.524534702301025 seconds
+
+[18.15, 17.92, 18.14, 18.3, 18.24, 17.98, 17.94, 18.28, 17.96, 17.99]
+[87.63]
+14.045255661010742
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524534702301025, 'TIME_S_1KI': 0.18083392959280112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7857535743713, 'W': 87.63}
+[18.15, 17.92, 18.14, 18.3, 18.24, 17.98, 17.94, 18.28, 17.96, 17.99, 18.28, 18.07, 17.88, 17.88, 17.9, 18.02, 21.74, 18.02, 18.41, 17.97]
+328.875
+16.44375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524534702301025, 'TIME_S_1KI': 0.18083392959280112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7857535743713, 'W': 87.63, 'J_1KI': 21.147521539078543, 'W_1KI': 1.5056701030927835, 'W_D': 71.18625, 'J_D': 999.8290807986259, 'W_D_1KI': 1.223131443298969, 'J_D_1KI': 0.02101600418039466}
diff --git a/pytorch/output_synthetic_16core/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 91f897d..6f3f4c5 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": 8596, "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": 10.36475419998169, "TIME_S_1KI": 1.2057647975781398, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.4265056419372, "W": 84.46, "J_1KI": 159.77507045625143, "W_1KI": 9.825500232666355, "W_D": 68.352, "J_D": 1111.4900368652345, "W_D_1KI": 7.95160539785947, "J_D_1KI": 0.9250355279036144}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8756, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44620156288147, "TIME_S_1KI": 1.193033527053617, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.577045211792, "W": 83.47, "J_1KI": 152.30436788622566, "W_1KI": 9.532891731384193, "W_D": 66.97425, "J_D": 1070.0290214481354, "W_D_1KI": 7.648955002284148, "J_D_1KI": 0.8735672684198433}
diff --git a/pytorch/output_synthetic_16core/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 a70ca9f..e9fd20d 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": 1.2214851379394531}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14447855949401855}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     508,    1039,  ..., 4998996,
-                            4999496, 5000000]),
-       col_indices=tensor([  26,   30,   52,  ..., 9932, 9973, 9998]),
-       values=tensor([0.9442, 0.4994, 0.3456,  ..., 0.3930, 0.0474, 0.1408]),
+tensor(crow_indices=tensor([      0,     507,    1042,  ..., 4998981,
+                            4999514, 5000000]),
+       col_indices=tensor([   8,   38,   72,  ..., 9951, 9971, 9980]),
+       values=tensor([0.6058, 0.5976, 0.8000,  ..., 0.1658, 0.6430, 0.8003]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1466, 0.3658, 0.5068,  ..., 0.5229, 0.0306, 0.8484])
+tensor([0.9071, 0.4156, 0.9536,  ..., 0.8291, 0.1377, 0.0392])
 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: 1.2214851379394531 seconds
+Time: 0.14447855949401855 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8596', '-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": 10.36475419998169}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7267', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.714413404464722}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     466,     997,  ..., 4998966,
-                            4999476, 5000000]),
-       col_indices=tensor([   0,    1,    5,  ..., 9962, 9965, 9989]),
-       values=tensor([0.4765, 0.8415, 0.0752,  ..., 0.8745, 0.5765, 0.8508]),
+tensor(crow_indices=tensor([      0,     473,     945,  ..., 4999048,
+                            4999511, 5000000]),
+       col_indices=tensor([   4,   25,   47,  ..., 9937, 9967, 9993]),
+       values=tensor([0.2180, 0.8351, 0.6646,  ..., 0.1409, 0.2302, 0.7325]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2258, 0.4418, 0.4951,  ..., 0.4808, 0.0990, 0.6508])
+tensor([0.9797, 0.6380, 0.6196,  ..., 0.0914, 0.5364, 0.9534])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.36475419998169 seconds
+Time: 8.714413404464722 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8756', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44620156288147}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     466,     997,  ..., 4998966,
-                            4999476, 5000000]),
-       col_indices=tensor([   0,    1,    5,  ..., 9962, 9965, 9989]),
-       values=tensor([0.4765, 0.8415, 0.0752,  ..., 0.8745, 0.5765, 0.8508]),
+tensor(crow_indices=tensor([      0,     484,    1014,  ..., 4998952,
+                            4999472, 5000000]),
+       col_indices=tensor([  28,   62,   89,  ..., 9928, 9935, 9940]),
+       values=tensor([0.3908, 0.2484, 0.4500,  ..., 0.3668, 0.3711, 0.8718]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2258, 0.4418, 0.4951,  ..., 0.4808, 0.0990, 0.6508])
+tensor([0.8601, 0.7620, 0.0732,  ..., 0.0545, 0.3750, 0.1934])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.36475419998169 seconds
+Time: 10.44620156288147 seconds
 
-[18.03, 17.93, 17.79, 17.69, 17.86, 17.92, 18.02, 17.88, 17.82, 18.11]
-[84.46]
-16.261265754699707
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8596, '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': 10.36475419998169, 'TIME_S_1KI': 1.2057647975781398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.4265056419372, 'W': 84.46}
-[18.03, 17.93, 17.79, 17.69, 17.86, 17.92, 18.02, 17.88, 17.82, 18.11, 18.58, 18.42, 17.91, 17.71, 17.91, 17.76, 17.61, 17.59, 17.81, 18.34]
-322.15999999999997
-16.107999999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8596, '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': 10.36475419998169, 'TIME_S_1KI': 1.2057647975781398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.4265056419372, 'W': 84.46, 'J_1KI': 159.77507045625143, 'W_1KI': 9.825500232666355, 'W_D': 68.352, 'J_D': 1111.4900368652345, 'W_D_1KI': 7.95160539785947, 'J_D_1KI': 0.9250355279036144}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,    1014,  ..., 4998952,
+                            4999472, 5000000]),
+       col_indices=tensor([  28,   62,   89,  ..., 9928, 9935, 9940]),
+       values=tensor([0.3908, 0.2484, 0.4500,  ..., 0.3668, 0.3711, 0.8718]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8601, 0.7620, 0.0732,  ..., 0.0545, 0.3750, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.44620156288147 seconds
+
+[18.13, 18.04, 18.48, 17.86, 18.0, 18.3, 18.04, 17.84, 17.94, 18.14]
+[83.47]
+15.976722717285156
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8756, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44620156288147, 'TIME_S_1KI': 1.193033527053617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.577045211792, 'W': 83.47}
+[18.13, 18.04, 18.48, 17.86, 18.0, 18.3, 18.04, 17.84, 17.94, 18.14, 18.29, 18.21, 17.99, 18.15, 21.9, 18.49, 18.31, 17.94, 18.08, 18.13]
+329.91499999999996
+16.495749999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8756, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44620156288147, 'TIME_S_1KI': 1.193033527053617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.577045211792, 'W': 83.47, 'J_1KI': 152.30436788622566, 'W_1KI': 9.532891731384193, 'W_D': 66.97425, 'J_D': 1070.0290214481354, 'W_D_1KI': 7.648955002284148, 'J_D_1KI': 0.8735672684198433}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
index 06b2e69..646ac12 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2682, "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": 10.339906692504883, "TIME_S_1KI": 3.855297051642387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1411.3430524110793, "W": 81.1, "J_1KI": 526.227834605175, "W_1KI": 30.2386278896346, "W_D": 64.94024999999999, "J_D": 1130.1229427785277, "W_D_1KI": 24.21336689038031, "J_D_1KI": 9.02810100312465}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2958, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.525683641433716, "TIME_S_1KI": 3.558378512993143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.5020074129104, "W": 79.35, "J_1KI": 507.94523577177495, "W_1KI": 26.825557809330626, "W_D": 62.803999999999995, "J_D": 1189.2014628047941, "W_D_1KI": 21.231913455037187, "J_D_1KI": 7.177793595347258}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
index fee56d8..cd10b80 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 3.9144444465637207}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.38048672676086426}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1047,     2059,  ...,  9998021,
-                             9999010, 10000000]),
-       col_indices=tensor([   5,    8,   10,  ..., 9982, 9985, 9996]),
-       values=tensor([0.6280, 0.3451, 0.1654,  ..., 0.9055, 0.0155, 0.3514]),
+tensor(crow_indices=tensor([       0,      978,     2001,  ...,  9997917,
+                             9998993, 10000000]),
+       col_indices=tensor([  10,   12,   31,  ..., 9968, 9976, 9993]),
+       values=tensor([0.1521, 0.7718, 0.5784,  ..., 0.3138, 0.0420, 0.0283]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4839, 0.5284, 0.4128,  ..., 0.3450, 0.9191, 0.1662])
+tensor([0.8445, 0.5776, 0.6277,  ..., 0.5230, 0.9454, 0.0151])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 3.9144444465637207 seconds
+Time: 0.38048672676086426 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2682', '-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": 10.339906692504883}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2759', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.790999174118042}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1046,     1985,  ...,  9998014,
-                             9998990, 10000000]),
-       col_indices=tensor([  17,   31,   38,  ..., 9973, 9978, 9985]),
-       values=tensor([0.0174, 0.0178, 0.9247,  ..., 0.4423, 0.5021, 0.4230]),
+tensor(crow_indices=tensor([       0,      961,     1957,  ...,  9997943,
+                             9998964, 10000000]),
+       col_indices=tensor([   0,    2,   15,  ..., 9987, 9990, 9997]),
+       values=tensor([0.8878, 0.8149, 0.0468,  ..., 0.0944, 0.2051, 0.2941]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8246, 0.4652, 0.0479,  ..., 0.0955, 0.8235, 0.1184])
+tensor([0.8720, 0.1434, 0.3774,  ..., 0.9472, 0.6076, 0.2537])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.339906692504883 seconds
+Time: 9.790999174118042 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2958', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.525683641433716}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1046,     1985,  ...,  9998014,
-                             9998990, 10000000]),
-       col_indices=tensor([  17,   31,   38,  ..., 9973, 9978, 9985]),
-       values=tensor([0.0174, 0.0178, 0.9247,  ..., 0.4423, 0.5021, 0.4230]),
+tensor(crow_indices=tensor([       0,     1002,     2060,  ...,  9998101,
+                             9999084, 10000000]),
+       col_indices=tensor([   6,   12,   22,  ..., 9993, 9996, 9999]),
+       values=tensor([0.4647, 0.6377, 0.7581,  ..., 0.1422, 0.4549, 0.7257]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8246, 0.4652, 0.0479,  ..., 0.0955, 0.8235, 0.1184])
+tensor([0.2726, 0.9874, 0.6365,  ..., 0.6635, 0.0461, 0.2273])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.339906692504883 seconds
+Time: 10.525683641433716 seconds
 
-[18.32, 17.68, 17.65, 18.12, 17.79, 17.54, 17.66, 17.93, 19.1, 17.92]
-[81.1]
-17.402503728866577
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2682, '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': 10.339906692504883, 'TIME_S_1KI': 3.855297051642387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1411.3430524110793, 'W': 81.1}
-[18.32, 17.68, 17.65, 18.12, 17.79, 17.54, 17.66, 17.93, 19.1, 17.92, 18.28, 17.68, 17.82, 17.91, 18.12, 17.75, 18.95, 17.54, 17.87, 17.65]
-323.195
-16.15975
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2682, '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': 10.339906692504883, 'TIME_S_1KI': 3.855297051642387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1411.3430524110793, 'W': 81.1, 'J_1KI': 526.227834605175, 'W_1KI': 30.2386278896346, 'W_D': 64.94024999999999, 'J_D': 1130.1229427785277, 'W_D_1KI': 24.21336689038031, 'J_D_1KI': 9.02810100312465}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1002,     2060,  ...,  9998101,
+                             9999084, 10000000]),
+       col_indices=tensor([   6,   12,   22,  ..., 9993, 9996, 9999]),
+       values=tensor([0.4647, 0.6377, 0.7581,  ..., 0.1422, 0.4549, 0.7257]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2726, 0.9874, 0.6365,  ..., 0.6635, 0.0461, 0.2273])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.525683641433716 seconds
+
+[18.46, 22.29, 18.08, 17.85, 18.08, 18.14, 17.95, 17.97, 18.17, 18.1]
+[79.35]
+18.935122966766357
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2958, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.525683641433716, 'TIME_S_1KI': 3.558378512993143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.5020074129104, 'W': 79.35}
+[18.46, 22.29, 18.08, 17.85, 18.08, 18.14, 17.95, 17.97, 18.17, 18.1, 18.4, 17.86, 18.25, 18.05, 17.97, 17.82, 20.01, 18.07, 17.96, 17.84]
+330.91999999999996
+16.546
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2958, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.525683641433716, 'TIME_S_1KI': 3.558378512993143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.5020074129104, 'W': 79.35, 'J_1KI': 507.94523577177495, 'W_1KI': 26.825557809330626, 'W_D': 62.803999999999995, 'J_D': 1189.2014628047941, 'W_D_1KI': 21.231913455037187, 'J_D_1KI': 7.177793595347258}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
index bd7f556..600b3de 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1445, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.100359916687012, "TIME_S_1KI": 6.98986845445468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2160.905471148491, "W": 62.46, "J_1KI": 1495.4363122134887, "W_1KI": 43.22491349480969, "W_D": 46.34675, "J_D": 1603.4413327721954, "W_D_1KI": 32.07387543252595, "J_D_1KI": 22.196453586523152}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1431, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.125213623046875, "TIME_S_1KI": 7.07562098046602, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1986.3526240086555, "W": 63.01, "J_1KI": 1388.087088755175, "W_1KI": 44.032145352900066, "W_D": 46.759, "J_D": 1474.0495531823635, "W_D_1KI": 32.67575122292103, "J_D_1KI": 22.83420770294971}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
index 345e6fc..b3d4b08 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.263561964035034}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7335808277130127}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1926,     3961,  ..., 19995971,
-                            19997972, 20000000]),
-       col_indices=tensor([   0,    6,   15,  ..., 9987, 9993, 9997]),
-       values=tensor([0.0314, 0.6999, 0.8853,  ..., 0.3076, 0.0508, 0.1862]),
+tensor(crow_indices=tensor([       0,     1938,     3929,  ..., 19996007,
+                            19998018, 20000000]),
+       col_indices=tensor([  10,   23,   25,  ..., 9992, 9994, 9995]),
+       values=tensor([0.0730, 0.5628, 0.7699,  ..., 0.2806, 0.9097, 0.3889]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.4423, 0.2673, 0.1161,  ..., 0.1117, 0.7670, 0.7166])
+tensor([0.2309, 0.6404, 0.8370,  ..., 0.6670, 0.0943, 0.6898])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 7.263561964035034 seconds
+Time: 0.7335808277130127 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1445', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.100359916687012}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1431', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.125213623046875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2031,     4064,  ..., 19995881,
-                            19997886, 20000000]),
-       col_indices=tensor([   6,    7,   12,  ..., 9992, 9994, 9996]),
-       values=tensor([0.1267, 0.2771, 0.5074,  ..., 0.4122, 0.4435, 0.1814]),
+tensor(crow_indices=tensor([       0,     2012,     4019,  ..., 19995967,
+                            19998000, 20000000]),
+       col_indices=tensor([   4,    7,    8,  ..., 9979, 9980, 9988]),
+       values=tensor([0.3444, 0.9651, 0.7506,  ..., 0.6074, 0.5252, 0.1862]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.2940, 0.6612, 0.2260,  ..., 0.5354, 0.3344, 0.1796])
+tensor([0.7493, 0.9404, 0.6976,  ..., 0.3307, 0.3774, 0.7329])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.100359916687012 seconds
+Time: 10.125213623046875 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2031,     4064,  ..., 19995881,
-                            19997886, 20000000]),
-       col_indices=tensor([   6,    7,   12,  ..., 9992, 9994, 9996]),
-       values=tensor([0.1267, 0.2771, 0.5074,  ..., 0.4122, 0.4435, 0.1814]),
+tensor(crow_indices=tensor([       0,     2012,     4019,  ..., 19995967,
+                            19998000, 20000000]),
+       col_indices=tensor([   4,    7,    8,  ..., 9979, 9980, 9988]),
+       values=tensor([0.3444, 0.9651, 0.7506,  ..., 0.6074, 0.5252, 0.1862]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.2940, 0.6612, 0.2260,  ..., 0.5354, 0.3344, 0.1796])
+tensor([0.7493, 0.9404, 0.6976,  ..., 0.3307, 0.3774, 0.7329])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.100359916687012 seconds
+Time: 10.125213623046875 seconds
 
-[18.37, 18.02, 17.98, 17.77, 18.08, 17.81, 18.57, 18.23, 17.73, 17.95]
-[62.46]
-34.59662938117981
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1445, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.100359916687012, 'TIME_S_1KI': 6.98986845445468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2160.905471148491, 'W': 62.46}
-[18.37, 18.02, 17.98, 17.77, 18.08, 17.81, 18.57, 18.23, 17.73, 17.95, 18.39, 17.61, 17.65, 17.9, 17.98, 17.61, 17.59, 17.93, 17.63, 17.64]
-322.265
-16.11325
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1445, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.100359916687012, 'TIME_S_1KI': 6.98986845445468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2160.905471148491, 'W': 62.46, 'J_1KI': 1495.4363122134887, 'W_1KI': 43.22491349480969, 'W_D': 46.34675, 'J_D': 1603.4413327721954, 'W_D_1KI': 32.07387543252595, 'J_D_1KI': 22.196453586523152}
+[18.03, 17.87, 18.44, 17.79, 18.02, 17.71, 17.95, 17.85, 17.83, 17.74]
+[63.01]
+31.524402856826782
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.125213623046875, 'TIME_S_1KI': 7.07562098046602, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.3526240086555, 'W': 63.01}
+[18.03, 17.87, 18.44, 17.79, 18.02, 17.71, 17.95, 17.85, 17.83, 17.74, 18.68, 17.68, 18.59, 19.66, 17.91, 17.67, 18.09, 18.04, 17.79, 17.81]
+325.02
+16.250999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.125213623046875, 'TIME_S_1KI': 7.07562098046602, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.3526240086555, 'W': 63.01, 'J_1KI': 1388.087088755175, 'W_1KI': 44.032145352900066, 'W_D': 46.759, 'J_D': 1474.0495531823635, 'W_D_1KI': 32.67575122292103, 'J_D_1KI': 22.83420770294971}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
index a3c7052..913ab31 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 11.531069993972778, "TIME_S_1KI": 11.531069993972778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4119.280698208809, "W": 53.23, "J_1KI": 4119.280698208809, "W_1KI": 53.23, "W_D": 37.1785, "J_D": 2877.1121066758633, "W_D_1KI": 37.1785, "J_D_1KI": 37.1785}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.198672533035278, "TIME_S_1KI": 11.497939721573031, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3521.5318365383146, "W": 51.94, "J_1KI": 3970.1599059056534, "W_1KI": 58.55693348365276, "W_D": 35.649249999999995, "J_D": 2417.019037807345, "W_D_1KI": 40.19081172491544, "J_D_1KI": 45.31094895706363}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
index b999f8b..697edf6 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 11.531069993972778}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9959039688110352}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3017,     6060,  ..., 29994067,
-                            29997064, 30000000]),
-       col_indices=tensor([   8,    9,   10,  ..., 9986, 9991, 9993]),
-       values=tensor([0.4919, 0.2111, 0.3595,  ..., 0.1115, 0.4648, 0.4893]),
+tensor(crow_indices=tensor([       0,     2964,     5964,  ..., 29994054,
+                            29997049, 30000000]),
+       col_indices=tensor([   0,    6,    7,  ..., 9989, 9993, 9996]),
+       values=tensor([0.3352, 0.3012, 0.1376,  ..., 0.4634, 0.9038, 0.2157]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.2808, 0.4380, 0.4720,  ..., 0.7949, 0.9847, 0.6708])
+tensor([0.3407, 0.2089, 0.1462,  ..., 0.7488, 0.0030, 0.5159])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 11.531069993972778 seconds
+Time: 1.9959039688110352 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '526', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.3105573654174805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3017,     6060,  ..., 29994067,
-                            29997064, 30000000]),
-       col_indices=tensor([   8,    9,   10,  ..., 9986, 9991, 9993]),
-       values=tensor([0.4919, 0.2111, 0.3595,  ..., 0.1115, 0.4648, 0.4893]),
+tensor(crow_indices=tensor([       0,     2980,     5935,  ..., 29993957,
+                            29996987, 30000000]),
+       col_indices=tensor([   2,    4,    5,  ..., 9985, 9987, 9990]),
+       values=tensor([0.7275, 0.2529, 0.2202,  ..., 0.8048, 0.1786, 0.5578]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.2808, 0.4380, 0.4720,  ..., 0.7949, 0.9847, 0.6708])
+tensor([0.3722, 0.8340, 0.1775,  ..., 0.2787, 0.3419, 0.3614])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,70 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 11.531069993972778 seconds
+Time: 7.3105573654174805 seconds
 
-[18.81, 17.57, 18.39, 17.6, 17.66, 17.56, 18.03, 17.8, 17.96, 17.55]
-[53.23]
-77.38644933700562
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 11.531069993972778, 'TIME_S_1KI': 11.531069993972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4119.280698208809, 'W': 53.23}
-[18.81, 17.57, 18.39, 17.6, 17.66, 17.56, 18.03, 17.8, 17.96, 17.55, 18.93, 17.87, 17.97, 17.66, 17.7, 17.69, 17.69, 17.51, 17.83, 17.79]
-321.03
-16.051499999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 11.531069993972778, 'TIME_S_1KI': 11.531069993972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4119.280698208809, 'W': 53.23, 'J_1KI': 4119.280698208809, 'W_1KI': 53.23, 'W_D': 37.1785, 'J_D': 2877.1121066758633, 'W_D_1KI': 37.1785, 'J_D_1KI': 37.1785}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '755', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 8.937042713165283}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3082,     6089,  ..., 29994069,
+                            29996991, 30000000]),
+       col_indices=tensor([   3,    5,    7,  ..., 9989, 9990, 9999]),
+       values=tensor([0.1400, 0.4959, 0.3565,  ..., 0.5786, 0.8662, 0.8079]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3795, 0.0549, 0.6386,  ..., 0.9156, 0.7490, 0.5099])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 8.937042713165283 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '887', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.198672533035278}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3035,     5994,  ..., 29994113,
+                            29997092, 30000000]),
+       col_indices=tensor([   5,   10,   14,  ..., 9987, 9996, 9999]),
+       values=tensor([0.8098, 0.4554, 0.6671,  ..., 0.4349, 0.8044, 0.2223]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3423, 0.6279, 0.0055,  ..., 0.7400, 0.5417, 0.1422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.198672533035278 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3035,     5994,  ..., 29994113,
+                            29997092, 30000000]),
+       col_indices=tensor([   5,   10,   14,  ..., 9987, 9996, 9999]),
+       values=tensor([0.8098, 0.4554, 0.6671,  ..., 0.4349, 0.8044, 0.2223]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3423, 0.6279, 0.0055,  ..., 0.7400, 0.5417, 0.1422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.198672533035278 seconds
+
+[18.62, 17.81, 18.39, 18.03, 17.98, 18.26, 18.01, 18.0, 17.9, 17.68]
+[51.94]
+67.79999685287476
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.198672533035278, 'TIME_S_1KI': 11.497939721573031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3521.5318365383146, 'W': 51.94}
+[18.62, 17.81, 18.39, 18.03, 17.98, 18.26, 18.01, 18.0, 17.9, 17.68, 18.52, 17.91, 18.3, 18.31, 18.44, 18.04, 17.96, 18.17, 17.99, 17.81]
+325.815
+16.29075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.198672533035278, 'TIME_S_1KI': 11.497939721573031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3521.5318365383146, 'W': 51.94, 'J_1KI': 3970.1599059056534, 'W_1KI': 58.55693348365276, 'W_D': 35.649249999999995, 'J_D': 2417.019037807345, 'W_D_1KI': 40.19081172491544, 'J_D_1KI': 45.31094895706363}
diff --git a/pytorch/output_synthetic_16core/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 a7f50f8..4a5c029 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 285101, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.263301372528076, "TIME_S_1KI": 0.03599882628446788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1023.6102543663977, "W": 73.21, "J_1KI": 3.5903425605886956, "W_1KI": 0.2567861915601839, "W_D": 56.916999999999994, "J_D": 795.8041913368701, "W_D_1KI": 0.1996380230164047, "J_D_1KI": 0.0007002361374264022}
diff --git a/pytorch/output_synthetic_16core/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 03249c3..aea26a1 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 +1,752 @@
-['apptainer', 'run', '--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": 0.05496048927307129}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019815444946289062}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 147,  628, 1125, 5287, 8823, 6934, 2121, 9045, 1741,
+                           1008,  777, 1781, 8765, 5338, 5590, 4011, 9135, 3712,
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+       values=tensor([5.2560e-01, 4.5182e-01, 2.3298e-01, 1.2745e-01,
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+                      1.2981e-01, 3.4897e-01, 4.9289e-01, 4.9671e-01,
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+                      3.0281e-01, 7.1212e-01, 7.5584e-01, 8.5415e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7030, 0.4856, 0.8302,  ..., 0.1030, 0.0064, 0.5187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.019815444946289062 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52988', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9514954090118408}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      7.5053e-02, 7.9242e-01, 2.9142e-01, 5.4645e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0435, 0.9300, 0.8297,  ..., 0.3222, 0.7823, 0.3267])
+tensor([0.6534, 0.1395, 0.5723,  ..., 0.2846, 0.6527, 0.4839])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,378 +754,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.05496048927307129 seconds
+Time: 1.9514954090118408 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '285101', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.263301372528076}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      0.5084, 0.2519, 0.6662, 0.9841, 0.4845, 0.3961]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7926, 0.8036, 0.9264,  ..., 0.8315, 0.2335, 0.7753])
+tensor([0.4905, 0.9858, 0.4305,  ..., 0.2183, 0.4525, 0.0992])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,378 +1026,268 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-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', '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}
+Time: 10.263301372528076 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1121, 0.3867, 0.9452,  ..., 0.7986, 0.0788, 0.8344])
+tensor([0.4905, 0.9858, 0.4305,  ..., 0.2183, 0.4525, 0.0992])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,389 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.441989183425903 seconds
+Time: 10.263301372528076 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
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-                      1.9079e-01, 8.4728e-01, 5.7285e-01, 4.6834e-01,
-                      2.6392e-01, 8.2958e-01, 9.0930e-01, 7.7450e-01,
-                      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,
-                      6.3065e-01, 5.0110e-02, 8.6206e-01, 1.5593e-01,
-                      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([0.1121, 0.3867, 0.9452,  ..., 0.7986, 0.0788, 0.8344])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 10.441989183425903 seconds
-
-[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}
+[18.52, 17.73, 17.82, 17.87, 17.98, 18.03, 17.91, 19.05, 18.64, 18.02]
+[73.21]
+13.981836557388306
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 285101, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.263301372528076, 'TIME_S_1KI': 0.03599882628446788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1023.6102543663977, 'W': 73.21}
+[18.52, 17.73, 17.82, 17.87, 17.98, 18.03, 17.91, 19.05, 18.64, 18.02, 18.85, 18.33, 18.07, 17.99, 17.95, 17.9, 17.98, 18.04, 17.93, 17.89]
+325.86
+16.293
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 285101, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.263301372528076, 'TIME_S_1KI': 0.03599882628446788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1023.6102543663977, 'W': 73.21, 'J_1KI': 3.5903425605886956, 'W_1KI': 0.2567861915601839, 'W_D': 56.916999999999994, 'J_D': 795.8041913368701, 'W_D_1KI': 0.1996380230164047, 'J_D_1KI': 0.0007002361374264022}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
index 91b8311..0ec114e 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 269593, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.567216634750366, "TIME_S_1KI": 0.03919692512324269, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1116.8250253772735, "W": 79.97999999999999, "J_1KI": 4.142633619482974, "W_1KI": 0.29666942390937445, "W_D": 63.59324999999999, "J_D": 888.003663979411, "W_D_1KI": 0.23588613205832493, "J_D_1KI": 0.0008749712791442097}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 259324, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.257395267486572, "TIME_S_1KI": 0.03955436159972302, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1007.3763615226745, "W": 73.52, "J_1KI": 3.8846244910716883, "W_1KI": 0.28350634727213836, "W_D": 56.727999999999994, "J_D": 777.2911620845794, "W_D_1KI": 0.21875337415742466, "J_D_1KI": 0.000843552367530289}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
index 271dd36..838ccfc 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.056284427642822266}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.021072864532470703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([8618, 2157,  180,  ..., 6301, 3776, 9276]),
-       values=tensor([0.6370, 0.4514, 0.1911,  ..., 0.9917, 0.3039, 0.3760]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([6834, 1931, 4346,  ..., 6725, 2972, 1681]),
+       values=tensor([0.7465, 0.7749, 0.3553,  ..., 0.3449, 0.2710, 0.4644]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.3110, 0.1501, 0.3783,  ..., 0.1317, 0.1435, 0.1761])
+tensor([0.4472, 0.0239, 0.4773,  ..., 0.7523, 0.1836, 0.2389])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.056284427642822266 seconds
+Time: 0.021072864532470703 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '186552', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.265737533569336}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49827', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.0174834728240967}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4999, 5000]),
-       col_indices=tensor([3104,  929, 8564,  ..., 6717, 8359,  928]),
-       values=tensor([0.4370, 0.1053, 0.0742,  ..., 0.7004, 0.4944, 0.4492]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([6306, 9296, 8522,  ..., 5641, 7164, 5943]),
+       values=tensor([0.9605, 0.0866, 0.1892,  ..., 0.4816, 0.2836, 0.0365]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.3493, 0.4826, 0.4715,  ..., 0.3349, 0.2581, 0.7669])
+tensor([0.7120, 0.8205, 0.0862,  ..., 0.5109, 0.7192, 0.7608])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 7.265737533569336 seconds
+Time: 2.0174834728240967 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '269593', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.567216634750366}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '259324', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.257395267486572}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([2457,  434, 2201,  ..., 5356,   91, 9583]),
-       values=tensor([0.6200, 0.7893, 0.7607,  ..., 0.6419, 0.6044, 0.8766]),
+       col_indices=tensor([2130, 3883, 5256,  ..., 7000, 8219, 5132]),
+       values=tensor([0.8393, 0.8650, 0.4056,  ..., 0.4895, 0.0562, 0.7603]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5913, 0.4546, 0.3506,  ..., 0.4687, 0.7353, 0.8006])
+tensor([0.9495, 0.1374, 0.1837,  ..., 0.6231, 0.7099, 0.0387])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.567216634750366 seconds
+Time: 10.257395267486572 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([2457,  434, 2201,  ..., 5356,   91, 9583]),
-       values=tensor([0.6200, 0.7893, 0.7607,  ..., 0.6419, 0.6044, 0.8766]),
+       col_indices=tensor([2130, 3883, 5256,  ..., 7000, 8219, 5132]),
+       values=tensor([0.8393, 0.8650, 0.4056,  ..., 0.4895, 0.0562, 0.7603]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5913, 0.4546, 0.3506,  ..., 0.4687, 0.7353, 0.8006])
+tensor([0.9495, 0.1374, 0.1837,  ..., 0.6231, 0.7099, 0.0387])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.567216634750366 seconds
+Time: 10.257395267486572 seconds
 
-[17.89, 17.35, 17.61, 17.72, 17.83, 17.6, 17.61, 17.87, 22.45, 17.59]
-[79.98]
-13.963803768157959
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 269593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.567216634750366, 'TIME_S_1KI': 0.03919692512324269, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1116.8250253772735, 'W': 79.97999999999999}
-[17.89, 17.35, 17.61, 17.72, 17.83, 17.6, 17.61, 17.87, 22.45, 17.59, 18.03, 17.7, 17.74, 17.72, 21.48, 17.84, 17.81, 17.57, 18.13, 17.9]
-327.735
-16.38675
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 269593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.567216634750366, 'TIME_S_1KI': 0.03919692512324269, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1116.8250253772735, 'W': 79.97999999999999, 'J_1KI': 4.142633619482974, 'W_1KI': 0.29666942390937445, 'W_D': 63.59324999999999, 'J_D': 888.003663979411, 'W_D_1KI': 0.23588613205832493, 'J_D_1KI': 0.0008749712791442097}
+[18.17, 18.06, 17.92, 18.22, 18.12, 18.42, 22.11, 19.11, 18.46, 18.0]
+[73.52]
+13.702072381973267
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 259324, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.257395267486572, 'TIME_S_1KI': 0.03955436159972302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.3763615226745, 'W': 73.52}
+[18.17, 18.06, 17.92, 18.22, 18.12, 18.42, 22.11, 19.11, 18.46, 18.0, 18.1, 18.11, 18.16, 22.77, 18.06, 18.3, 17.88, 17.93, 18.0, 18.15]
+335.84000000000003
+16.792
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 259324, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.257395267486572, 'TIME_S_1KI': 0.03955436159972302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.3763615226745, 'W': 73.52, 'J_1KI': 3.8846244910716883, 'W_1KI': 0.28350634727213836, 'W_D': 56.727999999999994, 'J_D': 777.2911620845794, 'W_D_1KI': 0.21875337415742466, 'J_D_1KI': 0.000843552367530289}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
index 250b22c..da0856a 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186, "TIME_S_1KI": 15.963828563690184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3837.0465870952603, "W": 57.08, "J_1KI": 3837.0465870952603, "W_1KI": 57.08, "W_D": 41.032999999999994, "J_D": 2758.3309847280975, "W_D_1KI": 41.032999999999994, "J_D_1KI": 41.032999999999994}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 665, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.126577615737915, "TIME_S_1KI": 15.22793626426754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3144.6024104833605, "W": 46.7, "J_1KI": 4728.725429298286, "W_1KI": 70.22556390977444, "W_D": 20.655250000000002, "J_D": 1390.846872358382, "W_D_1KI": 31.060526315789478, "J_D_1KI": 46.707558369608236}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
index 8f6ef0a..4beed36 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +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', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.0304152965545654}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       49,      110,  ..., 24999910,
-                            24999948, 25000000]),
-       col_indices=tensor([  6869,   7642,  11671,  ..., 455502, 470939,
-                           478512]),
-       values=tensor([0.8757, 0.6946, 0.8023,  ..., 0.8472, 0.7183, 0.5606]),
+tensor(crow_indices=tensor([       0,       59,       94,  ..., 24999900,
+                            24999951, 25000000]),
+       col_indices=tensor([  1276,  15885,  34398,  ..., 446460, 484343,
+                           488114]),
+       values=tensor([0.3408, 0.7505, 0.4683,  ..., 0.6426, 0.2990, 0.1628]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4126, 0.9616, 0.6093,  ..., 0.3863, 0.8636, 0.0433])
+tensor([0.4984, 0.2348, 0.7546,  ..., 0.4897, 0.9555, 0.5266])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 15.963828563690186 seconds
+Time: 2.0304152965545654 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '517', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.159608364105225}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       49,      110,  ..., 24999910,
-                            24999948, 25000000]),
-       col_indices=tensor([  6869,   7642,  11671,  ..., 455502, 470939,
-                           478512]),
-       values=tensor([0.8757, 0.6946, 0.8023,  ..., 0.8472, 0.7183, 0.5606]),
+tensor(crow_indices=tensor([       0,       46,       98,  ..., 24999895,
+                            24999952, 25000000]),
+       col_indices=tensor([  1859,  10480,  11583,  ..., 471819, 483100,
+                           486034]),
+       values=tensor([0.5566, 0.4872, 0.1210,  ..., 0.2476, 0.9480, 0.3070]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4126, 0.9616, 0.6093,  ..., 0.3863, 0.8636, 0.0433])
+tensor([0.1091, 0.0853, 0.9295,  ..., 0.2076, 0.8766, 0.1664])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,52 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 15.963828563690186 seconds
+Time: 8.159608364105225 seconds
 
-[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59]
-[57.08]
-67.22225975990295
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08}
-[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59, 18.37, 17.79, 17.89, 17.88, 18.0, 17.73, 17.87, 17.96, 17.67, 17.76]
-320.94000000000005
-16.047000000000004
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08, 'J_1KI': 3837.0465870952603, 'W_1KI': 57.08, 'W_D': 41.032999999999994, 'J_D': 2758.3309847280975, 'W_D_1KI': 41.032999999999994, 'J_D_1KI': 41.032999999999994}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '665', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.126577615737915}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       66,      119,  ..., 24999902,
+                            24999949, 25000000]),
+       col_indices=tensor([  3829,  12709,  24306,  ..., 491038, 494248,
+                           495364]),
+       values=tensor([0.8354, 0.9747, 0.5569,  ..., 0.5257, 0.7884, 0.2877]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8209, 0.7651, 0.4978,  ..., 0.6892, 0.4643, 0.4864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.126577615737915 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       66,      119,  ..., 24999902,
+                            24999949, 25000000]),
+       col_indices=tensor([  3829,  12709,  24306,  ..., 491038, 494248,
+                           495364]),
+       values=tensor([0.8354, 0.9747, 0.5569,  ..., 0.5257, 0.7884, 0.2877]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8209, 0.7651, 0.4978,  ..., 0.6892, 0.4643, 0.4864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.126577615737915 seconds
+
+[18.36, 18.09, 18.07, 17.97, 17.94, 18.13, 18.13, 17.77, 17.92, 18.01]
+[46.7]
+67.33624005317688
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 665, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.126577615737915, 'TIME_S_1KI': 15.22793626426754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3144.6024104833605, 'W': 46.7}
+[18.36, 18.09, 18.07, 17.97, 17.94, 18.13, 18.13, 17.77, 17.92, 18.01, 39.86, 40.69, 39.54, 40.28, 39.05, 39.85, 39.62, 39.36, 40.17, 40.4]
+520.895
+26.04475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 665, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.126577615737915, 'TIME_S_1KI': 15.22793626426754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3144.6024104833605, 'W': 46.7, 'J_1KI': 4728.725429298286, 'W_1KI': 70.22556390977444, 'W_D': 20.655250000000002, 'J_D': 1390.846872358382, 'W_D_1KI': 31.060526315789478, 'J_D_1KI': 46.707558369608236}
diff --git a/pytorch/output_synthetic_16core/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 d6eca5a..ac8e083 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8088, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521591186523438, "TIME_S_1KI": 1.30088911801724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1272.5733924508095, "W": 87.25, "J_1KI": 157.34092389352244, "W_1KI": 10.787586547972305, "W_D": 70.764, "J_D": 1032.1190090932846, "W_D_1KI": 8.749258160237389, "J_D_1KI": 1.0817579327692124}
diff --git a/pytorch/output_synthetic_16core/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 2715834..a65a620 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": 1.3224358558654785}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14249539375305176}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,       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]),
+tensor(crow_indices=tensor([      0,       5,       6,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([159074, 199303, 338786,  ..., 336877, 376694,
+                           404714]),
+       values=tensor([0.7251, 0.9700, 0.9965,  ..., 0.4798, 0.8363, 0.5285]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0977, 0.7761, 0.5514,  ..., 0.9913, 0.3768, 0.8332])
+tensor([0.5063, 0.7490, 0.8579,  ..., 0.3117, 0.7674, 0.7165])
 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: 1.3224358558654785 seconds
+Time: 0.14249539375305176 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7368', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.564131021499634}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       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]),
+tensor(crow_indices=tensor([      0,       7,      11,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([ 45977,  46883, 132654,  ..., 283974, 337716,
+                           438050]),
+       values=tensor([0.6941, 0.4659, 0.2903,  ..., 0.1328, 0.8033, 0.9427]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5701, 0.8906, 0.4066,  ..., 0.2438, 0.9359, 0.5479])
+tensor([0.8274, 0.6187, 0.7071,  ..., 0.9433, 0.5745, 0.3570])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.723366260528564 seconds
+Time: 9.564131021499634 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8088', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521591186523438}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       1,       9,  ..., 2499990,
+tensor(crow_indices=tensor([      0,       6,      11,  ..., 2499988,
                             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]),
+       col_indices=tensor([ 55151,  55855, 262240,  ..., 129037, 280325,
+                           497898]),
+       values=tensor([0.5548, 0.3291, 0.4545,  ..., 0.6191, 0.2200, 0.6842]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5701, 0.8906, 0.4066,  ..., 0.2438, 0.9359, 0.5479])
+tensor([0.8740, 0.8697, 0.8262,  ..., 0.4420, 0.1114, 0.8177])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +59,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.723366260528564 seconds
+Time: 10.521591186523438 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      11,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([ 55151,  55855, 262240,  ..., 129037, 280325,
+                           497898]),
+       values=tensor([0.5548, 0.3291, 0.4545,  ..., 0.6191, 0.2200, 0.6842]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8740, 0.8697, 0.8262,  ..., 0.4420, 0.1114, 0.8177])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.521591186523438 seconds
+
+[19.45, 17.88, 18.22, 21.5, 18.64, 18.15, 18.16, 17.8, 17.97, 17.95]
+[87.25]
+14.585368394851685
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8088, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521591186523438, 'TIME_S_1KI': 1.30088911801724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.5733924508095, 'W': 87.25}
+[19.45, 17.88, 18.22, 21.5, 18.64, 18.15, 18.16, 17.8, 17.97, 17.95, 18.5, 17.88, 17.95, 18.22, 18.0, 18.18, 18.21, 18.1, 18.01, 17.8]
+329.72
+16.486
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8088, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521591186523438, 'TIME_S_1KI': 1.30088911801724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.5733924508095, 'W': 87.25, 'J_1KI': 157.34092389352244, 'W_1KI': 10.787586547972305, 'W_D': 70.764, 'J_D': 1032.1190090932846, 'W_D_1KI': 8.749258160237389, 'J_D_1KI': 1.0817579327692124}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
index 3709697..17825c9 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.266479253768921, "TIME_S_1KI": 7.748286229259563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1511.6237173461914, "W": 78.72, "J_1KI": 1140.8480885631634, "W_1KI": 59.41132075471698, "W_D": 51.030249999999995, "J_D": 979.9102667950391, "W_D_1KI": 38.5133962264151, "J_D_1KI": 29.06671413314347}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1356, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.575294256210327, "TIME_S_1KI": 7.798889569476643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.6908141350748, "W": 76.43, "J_1KI": 1108.179066471294, "W_1KI": 56.36430678466077, "W_D": 60.11625000000001, "J_D": 1181.9460507032277, "W_D_1KI": 44.333517699115056, "J_D_1KI": 32.694334586368036}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
index 4090da6..b27b9d0 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
-['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', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.920783042907715}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.7741575241088867}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       27,       58,  ..., 12499955,
-                            12499976, 12500000]),
-       col_indices=tensor([  1130,  30184,  52843,  ..., 432238, 460389,
-                           464098]),
-       values=tensor([0.9711, 0.9391, 0.1931,  ..., 0.2077, 0.5139, 0.7168]),
+tensor(crow_indices=tensor([       0,       27,       54,  ..., 12499941,
+                            12499975, 12500000]),
+       col_indices=tensor([ 19879,  19996,  22547,  ..., 457855, 459779,
+                           462945]),
+       values=tensor([0.0262, 0.3741, 0.0922,  ..., 0.5524, 0.1014, 0.8276]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.7084, 0.4119, 0.9069,  ..., 0.7058, 0.3504, 0.1364])
+tensor([0.7209, 0.9008, 0.7814,  ..., 0.2206, 0.4926, 0.1534])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 7.920783042907715 seconds
+Time: 0.7741575241088867 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1325', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.266479253768921}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1356', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.575294256210327}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       28,       47,  ..., 12499960,
-                            12499984, 12500000]),
-       col_indices=tensor([105223, 111339, 112839,  ..., 478264, 484121,
-                           494514]),
-       values=tensor([0.5455, 0.8774, 0.0623,  ..., 0.6447, 0.0740, 0.9564]),
+tensor(crow_indices=tensor([       0,       30,       59,  ..., 12499949,
+                            12499973, 12500000]),
+       col_indices=tensor([ 13892,  45461,  46784,  ..., 469557, 488276,
+                           489508]),
+       values=tensor([0.8469, 0.7554, 0.2394,  ..., 0.6309, 0.5261, 0.2516]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8058, 0.5348, 0.2222,  ..., 0.5938, 0.1996, 0.3404])
+tensor([0.9763, 0.3043, 0.0965,  ..., 0.1822, 0.5455, 0.4604])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.266479253768921 seconds
+Time: 10.575294256210327 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       28,       47,  ..., 12499960,
-                            12499984, 12500000]),
-       col_indices=tensor([105223, 111339, 112839,  ..., 478264, 484121,
-                           494514]),
-       values=tensor([0.5455, 0.8774, 0.0623,  ..., 0.6447, 0.0740, 0.9564]),
+tensor(crow_indices=tensor([       0,       30,       59,  ..., 12499949,
+                            12499973, 12500000]),
+       col_indices=tensor([ 13892,  45461,  46784,  ..., 469557, 488276,
+                           489508]),
+       values=tensor([0.8469, 0.7554, 0.2394,  ..., 0.6309, 0.5261, 0.2516]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8058, 0.5348, 0.2222,  ..., 0.5938, 0.1996, 0.3404])
+tensor([0.9763, 0.3043, 0.0965,  ..., 0.1822, 0.5455, 0.4604])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.266479253768921 seconds
+Time: 10.575294256210327 seconds
 
-[18.04, 18.12, 17.82, 17.66, 17.96, 17.84, 17.85, 17.56, 17.9, 17.9]
-[78.72]
-19.202537059783936
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.266479253768921, 'TIME_S_1KI': 7.748286229259563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1511.6237173461914, 'W': 78.72}
-[18.04, 18.12, 17.82, 17.66, 17.96, 17.84, 17.85, 17.56, 17.9, 17.9, 42.26, 43.36, 45.35, 45.57, 48.14, 42.56, 42.0, 41.9, 42.22, 41.77]
-553.7950000000001
-27.689750000000004
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.266479253768921, 'TIME_S_1KI': 7.748286229259563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1511.6237173461914, 'W': 78.72, 'J_1KI': 1140.8480885631634, 'W_1KI': 59.41132075471698, 'W_D': 51.030249999999995, 'J_D': 979.9102667950391, 'W_D_1KI': 38.5133962264151, 'J_D_1KI': 29.06671413314347}
+[18.28, 17.9, 18.0, 17.69, 18.19, 17.78, 17.99, 17.71, 17.86, 17.72]
+[76.43]
+19.66100764274597
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1356, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.575294256210327, 'TIME_S_1KI': 7.798889569476643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.6908141350748, 'W': 76.43}
+[18.28, 17.9, 18.0, 17.69, 18.19, 17.78, 17.99, 17.71, 17.86, 17.72, 22.73, 18.23, 18.33, 18.05, 18.19, 17.82, 18.25, 18.16, 17.83, 17.86]
+326.275
+16.31375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1356, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.575294256210327, 'TIME_S_1KI': 7.798889569476643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.6908141350748, 'W': 76.43, 'J_1KI': 1108.179066471294, 'W_1KI': 56.36430678466077, 'W_D': 60.11625000000001, 'J_D': 1181.9460507032277, 'W_D_1KI': 44.333517699115056, 'J_D_1KI': 32.694334586368036}
diff --git a/pytorch/output_synthetic_16core/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 c8d8a61..8609bc2 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 80207, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.527606010437012, "TIME_S_1KI": 0.13125545164932004, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1181.3196375966072, "W": 83.35, "J_1KI": 14.728385771773127, "W_1KI": 1.039186105950852, "W_D": 67.15325, "J_D": 951.7630828246474, "W_D_1KI": 0.8372492425848118, "J_D_1KI": 0.010438605640216089}
diff --git a/pytorch/output_synthetic_16core/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 e0ea23e..381203d 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": 0.14957594871520996}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03568005561828613}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     15,  ..., 249987, 249991,
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 249987, 249994,
                             250000]),
-       col_indices=tensor([ 3249, 11393, 14942,  ..., 33826, 38027, 48849]),
-       values=tensor([0.4435, 0.3887, 0.6766,  ..., 0.7020, 0.9117, 0.7998]),
+       col_indices=tensor([ 1312, 19953, 25282,  ..., 26652, 33001, 38879]),
+       values=tensor([0.3658, 0.9367, 0.4335,  ..., 0.7027, 0.8564, 0.9906]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4072, 0.0290, 0.9610,  ..., 0.4695, 0.4913, 0.1254])
+tensor([0.4942, 0.6881, 0.2872,  ..., 0.3001, 0.6556, 0.7300])
 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: 0.14957594871520996 seconds
+Time: 0.03568005561828613 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '29428', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.8524389266967773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     12,  ..., 249984, 249993,
+tensor(crow_indices=tensor([     0,      6,      8,  ..., 249998, 250000,
                             250000]),
-       col_indices=tensor([  257,   837, 13772,  ..., 26625, 34572, 42693]),
-       values=tensor([0.6771, 0.0630, 0.4952,  ..., 0.2009, 0.3453, 0.0186]),
+       col_indices=tensor([ 4346,  9215, 13661,  ..., 37674, 16332, 22572]),
+       values=tensor([0.5552, 0.4398, 0.7001,  ..., 0.6234, 0.7005, 0.0878]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1005, 0.4396, 0.3760,  ..., 0.8175, 0.2613, 0.1136])
+tensor([0.5304, 0.8900, 0.0447,  ..., 0.3418, 0.1958, 0.2486])
 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: 9.41176462173462 seconds
+Time: 3.8524389266967773 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '80207', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.527606010437012}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     13,  ..., 249994, 249998,
+tensor(crow_indices=tensor([     0,      9,     19,  ..., 249993, 249998,
                             250000]),
-       col_indices=tensor([  417,  3050, 28352,  ..., 48782,  1625, 48386]),
-       values=tensor([0.9216, 0.4652, 0.6011,  ..., 0.6170, 0.6564, 0.4691]),
+       col_indices=tensor([ 8045, 12111, 14477,  ..., 47402, 12160, 19361]),
+       values=tensor([0.9649, 0.3819, 0.5636,  ..., 0.2633, 0.1370, 0.0196]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8482, 0.9835, 0.6846,  ..., 0.7970, 0.3559, 0.9710])
+tensor([0.0927, 0.4142, 0.0895,  ..., 0.8219, 0.5339, 0.8064])
 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: 10.500975370407104 seconds
+Time: 10.527606010437012 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     13,  ..., 249994, 249998,
+tensor(crow_indices=tensor([     0,      9,     19,  ..., 249993, 249998,
                             250000]),
-       col_indices=tensor([  417,  3050, 28352,  ..., 48782,  1625, 48386]),
-       values=tensor([0.9216, 0.4652, 0.6011,  ..., 0.6170, 0.6564, 0.4691]),
+       col_indices=tensor([ 8045, 12111, 14477,  ..., 47402, 12160, 19361]),
+       values=tensor([0.9649, 0.3819, 0.5636,  ..., 0.2633, 0.1370, 0.0196]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8482, 0.9835, 0.6846,  ..., 0.7970, 0.3559, 0.9710])
+tensor([0.0927, 0.4142, 0.0895,  ..., 0.8219, 0.5339, 0.8064])
 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: 10.500975370407104 seconds
+Time: 10.527606010437012 seconds
 
-[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}
+[18.32, 17.98, 17.86, 18.86, 18.68, 17.81, 17.68, 18.11, 17.76, 17.85]
+[83.35]
+14.173001050949097
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80207, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.527606010437012, 'TIME_S_1KI': 0.13125545164932004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.3196375966072, 'W': 83.35}
+[18.32, 17.98, 17.86, 18.86, 18.68, 17.81, 17.68, 18.11, 17.76, 17.85, 18.01, 18.02, 17.88, 17.81, 17.97, 17.93, 17.96, 17.74, 17.82, 17.95]
+323.93499999999995
+16.196749999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80207, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.527606010437012, 'TIME_S_1KI': 0.13125545164932004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.3196375966072, 'W': 83.35, 'J_1KI': 14.728385771773127, 'W_1KI': 1.039186105950852, 'W_D': 67.15325, 'J_D': 951.7630828246474, 'W_D_1KI': 0.8372492425848118, 'J_D_1KI': 0.010438605640216089}
diff --git a/pytorch/output_synthetic_16core/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 2dcd593..1ef0389 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17086, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.693793773651123, "TIME_S_1KI": 0.6258804737007564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.9557392597198, "W": 87.4, "J_1KI": 74.61990748330328, "W_1KI": 5.1152990752663, "W_D": 70.92850000000001, "J_D": 1034.6761802297833, "W_D_1KI": 4.151264192906474, "J_D_1KI": 0.24296290488742092}
diff --git a/pytorch/output_synthetic_16core/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 eb8fa27..168fd00 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 +1,15 @@
-['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', '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": 0.6362464427947998}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07488131523132324}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      64,     108,  ..., 2499910,
+                            2499955, 2500000]),
+       col_indices=tensor([  984,  1625,  1972,  ..., 46651, 48149, 48861]),
+       values=tensor([5.1121e-01, 5.6272e-01, 1.1145e-04,  ...,
+                      9.0355e-01, 4.1789e-01, 4.2355e-01]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3504, 0.0589, 0.7648,  ..., 0.3104, 0.5013, 0.0863])
+tensor([0.3839, 0.9792, 0.8841,  ..., 0.7211, 0.2437, 0.3590])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +17,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.6362464427947998 seconds
+Time: 0.07488131523132324 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '14022', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.616644144058228}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      54,     105,  ..., 2499907,
+                            2499960, 2500000]),
+       col_indices=tensor([  369,  1157,  3425,  ..., 45077, 46820, 49764]),
+       values=tensor([0.6429, 0.4063, 0.5775,  ..., 0.7664, 0.6925, 0.8507]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0922, 0.7073, 0.7429,  ..., 0.1285, 0.2485, 0.0697])
+tensor([0.4381, 0.1550, 0.0791,  ..., 0.0177, 0.9903, 0.0608])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +37,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.453594446182251 seconds
+Time: 8.616644144058228 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17086', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.693793773651123}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      52,      91,  ..., 2499883,
+                            2499936, 2500000]),
+       col_indices=tensor([  188,  2361,  2646,  ..., 48274, 48923, 49377]),
+       values=tensor([0.2734, 0.1056, 0.9298,  ..., 0.4005, 0.9270, 0.9473]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0922, 0.7073, 0.7429,  ..., 0.1285, 0.2485, 0.0697])
+tensor([0.1965, 0.7813, 0.8576,  ..., 0.6695, 0.1581, 0.1443])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +57,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.453594446182251 seconds
+Time: 10.693793773651123 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      52,      91,  ..., 2499883,
+                            2499936, 2500000]),
+       col_indices=tensor([  188,  2361,  2646,  ..., 48274, 48923, 49377]),
+       values=tensor([0.2734, 0.1056, 0.9298,  ..., 0.4005, 0.9270, 0.9473]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1965, 0.7813, 0.8576,  ..., 0.6695, 0.1581, 0.1443])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.693793773651123 seconds
+
+[18.43, 18.16, 17.96, 18.08, 18.33, 18.04, 18.36, 18.18, 18.22, 18.06]
+[87.4]
+14.587594270706177
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17086, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.693793773651123, 'TIME_S_1KI': 0.6258804737007564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.9557392597198, 'W': 87.4}
+[18.43, 18.16, 17.96, 18.08, 18.33, 18.04, 18.36, 18.18, 18.22, 18.06, 18.48, 17.93, 19.19, 18.85, 18.16, 18.04, 18.63, 18.25, 18.43, 18.27]
+329.43
+16.4715
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17086, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.693793773651123, 'TIME_S_1KI': 0.6258804737007564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.9557392597198, 'W': 87.4, 'J_1KI': 74.61990748330328, 'W_1KI': 5.1152990752663, 'W_D': 70.92850000000001, 'J_D': 1034.6761802297833, 'W_D_1KI': 4.151264192906474, 'J_D_1KI': 0.24296290488742092}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
index aff89af..6f4838a 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389, "TIME_S_1KI": 10.122996807098389, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3257.7272432255745, "W": 53.79, "J_1KI": 3257.7272432255745, "W_1KI": 53.79, "W_D": 37.451750000000004, "J_D": 2268.2206038571003, "W_D_1KI": 37.451750000000004, "J_D_1KI": 37.451750000000004}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1159, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524485111236572, "TIME_S_1KI": 9.080660147745101, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2883.053437728882, "W": 54.72, "J_1KI": 2487.5353215952387, "W_1KI": 47.21311475409836, "W_D": 38.2975, "J_D": 2017.7949384397268, "W_D_1KI": 33.043572044866266, "J_D_1KI": 28.51041591446615}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
index bbe283a..c966aa4 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.2965989112854004}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      509,     1001,  ..., 24999037,
-                            24999516, 25000000]),
-       col_indices=tensor([   11,   136,   275,  ..., 49665, 49739, 49958]),
-       values=tensor([0.3161, 0.2173, 0.0956,  ..., 0.2198, 0.0588, 0.5951]),
+tensor(crow_indices=tensor([       0,      462,      963,  ..., 24999009,
+                            24999507, 25000000]),
+       col_indices=tensor([   19,    54,    59,  ..., 49770, 49789, 49840]),
+       values=tensor([0.0062, 0.3047, 0.0339,  ..., 0.6533, 0.8264, 0.4065]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4562, 0.2414, 0.7128,  ..., 0.4854, 0.8312, 0.1880])
+tensor([0.0572, 0.3375, 0.5398,  ..., 0.2388, 0.0349, 0.7555])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.122996807098389 seconds
+Time: 1.2965989112854004 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '809', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.848743677139282}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      509,     1001,  ..., 24999037,
-                            24999516, 25000000]),
-       col_indices=tensor([   11,   136,   275,  ..., 49665, 49739, 49958]),
-       values=tensor([0.3161, 0.2173, 0.0956,  ..., 0.2198, 0.0588, 0.5951]),
+tensor(crow_indices=tensor([       0,      486,      980,  ..., 24998984,
+                            24999490, 25000000]),
+       col_indices=tensor([   15,   197,   386,  ..., 49782, 49793, 49889]),
+       values=tensor([0.3923, 0.3887, 0.8681,  ..., 0.2288, 0.1762, 0.4981]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4562, 0.2414, 0.7128,  ..., 0.4854, 0.8312, 0.1880])
+tensor([0.2965, 0.2935, 0.4053,  ..., 0.9117, 0.1428, 0.4127])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +36,70 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.122996807098389 seconds
+Time: 7.848743677139282 seconds
 
-[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81]
-[53.79]
-60.56380820274353
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79}
-[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81, 18.18, 18.11, 17.85, 17.68, 17.63, 17.92, 17.78, 17.67, 18.05, 18.04]
-326.765
-16.33825
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79, 'J_1KI': 3257.7272432255745, 'W_1KI': 53.79, 'W_D': 37.451750000000004, 'J_D': 2268.2206038571003, 'W_D_1KI': 37.451750000000004, 'J_D_1KI': 37.451750000000004}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1082', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.800410747528076}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      497,      982,  ..., 24999036,
+                            24999517, 25000000]),
+       col_indices=tensor([   46,   129,   426,  ..., 49653, 49766, 49830]),
+       values=tensor([0.6195, 0.6207, 0.9497,  ..., 0.4637, 0.0557, 0.5508]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1589, 0.3084, 0.3696,  ..., 0.3780, 0.7461, 0.4084])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 9.800410747528076 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1159', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524485111236572}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      513,     1013,  ..., 24998968,
+                            24999473, 25000000]),
+       col_indices=tensor([   48,   124,   131,  ..., 49410, 49843, 49893]),
+       values=tensor([0.3835, 0.3241, 0.0409,  ..., 0.5767, 0.7491, 0.8402]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3332, 0.0589, 0.5895,  ..., 0.7200, 0.0490, 0.9504])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.524485111236572 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      513,     1013,  ..., 24998968,
+                            24999473, 25000000]),
+       col_indices=tensor([   48,   124,   131,  ..., 49410, 49843, 49893]),
+       values=tensor([0.3835, 0.3241, 0.0409,  ..., 0.5767, 0.7491, 0.8402]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3332, 0.0589, 0.5895,  ..., 0.7200, 0.0490, 0.9504])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.524485111236572 seconds
+
+[18.54, 17.98, 18.07, 17.85, 18.22, 17.93, 18.48, 18.6, 18.31, 18.9]
+[54.72]
+52.68738007545471
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1159, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524485111236572, 'TIME_S_1KI': 9.080660147745101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2883.053437728882, 'W': 54.72}
+[18.54, 17.98, 18.07, 17.85, 18.22, 17.93, 18.48, 18.6, 18.31, 18.9, 18.48, 18.15, 18.06, 18.06, 17.98, 17.84, 18.37, 17.81, 17.96, 21.64]
+328.45
+16.4225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1159, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524485111236572, 'TIME_S_1KI': 9.080660147745101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2883.053437728882, 'W': 54.72, 'J_1KI': 2487.5353215952387, 'W_1KI': 47.21311475409836, 'W_D': 38.2975, 'J_D': 2017.7949384397268, 'W_D_1KI': 33.043572044866266, 'J_D_1KI': 28.51041591446615}
diff --git a/pytorch/output_synthetic_16core/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 774716c..be4ba65 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": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 113077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.7483811378479, "TIME_S_1KI": 0.09505364608052831, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1073.5031987619402, "W": 76.43000000000002, "J_1KI": 9.49355924513332, "W_1KI": 0.6759111048223778, "W_D": 60.05525000000002, "J_D": 843.5104406312707, "W_D_1KI": 0.5311004890472866, "J_D_1KI": 0.004696803850891751}
diff --git a/pytorch/output_synthetic_16core/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 05a9200..8dbecc1 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": 0.11198759078979492}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02812027931213379}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     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]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([42362, 37248, 40868,  ..., 37764, 10134, 17711]),
+       values=tensor([0.4763, 0.9715, 0.1475,  ..., 0.3126, 0.8815, 0.0115]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9789, 0.0522, 0.6759,  ..., 0.0240, 0.3185, 0.8367])
+tensor([0.9973, 0.6491, 0.6388,  ..., 0.7622, 0.1974, 0.5505])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,37 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.11198759078979492 seconds
+Time: 0.02812027931213379 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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([  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([0.8784, 0.5968, 0.0083,  ..., 0.0039, 0.6938, 0.6481])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-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', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '37339', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.4671623706817627}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([37263, 14810, 49193,  ..., 22299, 19031, 40338]),
-       values=tensor([0.7995, 0.8033, 0.3510,  ..., 0.6585, 0.0621, 0.7519]),
+       col_indices=tensor([  629, 39590,  3531,  ..., 29068, 27842, 31077]),
+       values=tensor([0.8879, 0.6863, 0.1252,  ..., 0.4874, 0.7763, 0.0925]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9318, 0.0252, 0.9296,  ..., 0.2820, 0.1820, 0.1630])
+tensor([0.1798, 0.4085, 0.6464,  ..., 0.4512, 0.1603, 0.8018])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.608571290969849 seconds
+Time: 3.4671623706817627 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '113077', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.7483811378479}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([ 3755, 45041, 41651,  ..., 28239, 26624, 23506]),
+       values=tensor([0.7255, 0.9443, 0.1927,  ..., 0.4549, 0.6422, 0.3790]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9318, 0.0252, 0.9296,  ..., 0.2820, 0.1820, 0.1630])
+tensor([0.1750, 0.3712, 0.8832,  ..., 0.2728, 0.8510, 0.3193])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +53,29 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.608571290969849 seconds
+Time: 10.7483811378479 seconds
 
-[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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([ 3755, 45041, 41651,  ..., 28239, 26624, 23506]),
+       values=tensor([0.7255, 0.9443, 0.1927,  ..., 0.4549, 0.6422, 0.3790]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1750, 0.3712, 0.8832,  ..., 0.2728, 0.8510, 0.3193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.7483811378479 seconds
+
+[18.23, 17.8, 18.68, 17.79, 18.26, 17.97, 18.22, 17.87, 18.18, 18.24]
+[76.43]
+14.045573711395264
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 113077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.7483811378479, 'TIME_S_1KI': 0.09505364608052831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1073.5031987619402, 'W': 76.43000000000002}
+[18.23, 17.8, 18.68, 17.79, 18.26, 17.97, 18.22, 17.87, 18.18, 18.24, 18.73, 17.83, 18.03, 17.81, 18.07, 18.07, 17.89, 17.96, 20.53, 17.87]
+327.495
+16.37475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 113077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.7483811378479, 'TIME_S_1KI': 0.09505364608052831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1073.5031987619402, 'W': 76.43000000000002, 'J_1KI': 9.49355924513332, 'W_1KI': 0.6759111048223778, 'W_D': 60.05525000000002, 'J_D': 843.5104406312707, 'W_D_1KI': 0.5311004890472866, 'J_D_1KI': 0.004696803850891751}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
index cda8c88..e084c9d 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87647, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.566825151443481, "TIME_S_1KI": 0.12056117324544459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.4293744707109, "W": 86.61000000000001, "J_1KI": 13.935780739451559, "W_1KI": 0.9881684484352005, "W_D": 70.62725000000002, "J_D": 996.0304559298756, "W_D_1KI": 0.8058148025602704, "J_D_1KI": 0.009193866333819417}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 88156, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.868364572525024, "TIME_S_1KI": 0.12328559113985461, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1132.9122091054917, "W": 79.86, "J_1KI": 12.851220666834834, "W_1KI": 0.9058940968283498, "W_D": 63.193749999999994, "J_D": 896.4809781387447, "W_D_1KI": 0.716840033576841, "J_D_1KI": 0.00813149455030674}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
index 97cfaa9..a0c02dc 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,54 +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', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.14960765838623047}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/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,  ..., 124995, 124998,
-                            125000]),
-       col_indices=tensor([ 5328,  5758, 21245,  ..., 40217,  2052, 16010]),
-       values=tensor([0.8699, 0.1385, 0.5605,  ..., 0.3914, 0.4912, 0.7839]),
-       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.1699, 0.5973, 0.2081,  ..., 0.0281, 0.7559, 0.8178])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 125000
-Density: 5e-05
-Time: 0.14960765838623047 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', '70183', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.40775990486145}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 124997, 124999,
-                            125000]),
-       col_indices=tensor([ 5449, 14906, 26173,  ..., 30325, 48181,  9186]),
-       values=tensor([0.5897, 0.0944, 0.5204,  ..., 0.1014, 0.9614, 0.5057]),
-       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5323, 0.2435, 0.2265,  ..., 0.3258, 0.3469, 0.7874])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 125000
-Density: 5e-05
-Time: 8.40775990486145 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', '87647', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.566825151443481}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.028945207595825195}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([     0,      2,      5,  ..., 124995, 124996,
                             125000]),
-       col_indices=tensor([14301, 36836,  2496,  ..., 26599, 44389, 45216]),
-       values=tensor([0.0827, 0.6231, 0.3315,  ..., 0.4386, 0.5843, 0.2734]),
+       col_indices=tensor([ 6853, 13029,  2913,  ..., 16543, 38720, 48018]),
+       values=tensor([0.0310, 0.8074, 0.8860,  ..., 0.7640, 0.7803, 0.3703]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.3713, 0.0426, 0.5176,  ..., 0.5970, 0.6758, 0.3745])
+tensor([0.5471, 0.4507, 0.3833,  ..., 0.1733, 0.0716, 0.3889])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.566825151443481 seconds
+Time: 0.028945207595825195 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '36275', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.320595741271973}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      5,  ..., 124995, 124996,
+tensor(crow_indices=tensor([     0,      1,      6,  ..., 124997, 124999,
                             125000]),
-       col_indices=tensor([14301, 36836,  2496,  ..., 26599, 44389, 45216]),
-       values=tensor([0.0827, 0.6231, 0.3315,  ..., 0.4386, 0.5843, 0.2734]),
+       col_indices=tensor([26872,  2155, 12844,  ..., 14460, 31839, 14088]),
+       values=tensor([0.4897, 0.3509, 0.0171,  ..., 0.2036, 0.0300, 0.0283]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.3713, 0.0426, 0.5176,  ..., 0.5970, 0.6758, 0.3745])
+tensor([0.1984, 0.6261, 0.3986,  ..., 0.2975, 0.8868, 0.2739])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +36,50 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.566825151443481 seconds
+Time: 4.320595741271973 seconds
 
-[18.15, 17.58, 18.01, 17.67, 17.78, 17.59, 17.99, 17.46, 17.88, 17.7]
-[86.61]
-14.102636814117432
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.566825151443481, 'TIME_S_1KI': 0.12056117324544459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4293744707109, 'W': 86.61000000000001}
-[18.15, 17.58, 18.01, 17.67, 17.78, 17.59, 17.99, 17.46, 17.88, 17.7, 18.22, 17.6, 17.52, 17.68, 18.1, 17.84, 17.59, 17.63, 17.89, 17.62]
-319.655
-15.98275
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.566825151443481, 'TIME_S_1KI': 0.12056117324544459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4293744707109, 'W': 86.61000000000001, 'J_1KI': 13.935780739451559, 'W_1KI': 0.9881684484352005, 'W_D': 70.62725000000002, 'J_D': 996.0304559298756, 'W_D_1KI': 0.8058148025602704, 'J_D_1KI': 0.009193866333819417}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '88156', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.868364572525024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124997, 124998,
+                            125000]),
+       col_indices=tensor([17427, 27540, 29335,  ..., 48451,  4975, 32778]),
+       values=tensor([0.3820, 0.7973, 0.1142,  ..., 0.5863, 0.3733, 0.5873]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7507, 0.0731, 0.4946,  ..., 0.5316, 0.6298, 0.9971])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.868364572525024 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124997, 124998,
+                            125000]),
+       col_indices=tensor([17427, 27540, 29335,  ..., 48451,  4975, 32778]),
+       values=tensor([0.3820, 0.7973, 0.1142,  ..., 0.5863, 0.3733, 0.5873]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7507, 0.0731, 0.4946,  ..., 0.5316, 0.6298, 0.9971])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.868364572525024 seconds
+
+[18.49, 17.88, 18.37, 17.92, 18.86, 17.84, 18.14, 20.87, 18.57, 18.13]
+[79.86]
+14.186228513717651
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 88156, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.868364572525024, 'TIME_S_1KI': 0.12328559113985461, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.9122091054917, 'W': 79.86}
+[18.49, 17.88, 18.37, 17.92, 18.86, 17.84, 18.14, 20.87, 18.57, 18.13, 18.45, 17.94, 18.01, 21.62, 18.32, 18.14, 18.03, 18.0, 18.36, 17.84]
+333.32500000000005
+16.66625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 88156, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.868364572525024, 'TIME_S_1KI': 0.12328559113985461, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.9122091054917, 'W': 79.86, 'J_1KI': 12.851220666834834, 'W_1KI': 0.9058940968283498, 'W_D': 63.193749999999994, 'J_D': 896.4809781387447, 'W_D_1KI': 0.716840033576841, 'J_D_1KI': 0.00813149455030674}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
index 0ba39e0..63a6b21 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 339415, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661308526992798, "TIME_S_1KI": 0.031410834898259646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1042.263754928112, "W": 73.63, "J_1KI": 3.0707651545397576, "W_1KI": 0.2169320743043177, "W_D": 56.882, "J_D": 805.1887397503853, "W_D_1KI": 0.1675883505443189, "J_D_1KI": 0.0004937564649303033}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
index 9a09fc6..4acadea 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,33 @@
-['apptainer', 'run', '--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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02093958854675293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 145,  181,  299,  ..., 1340, 4416,  166]),
+       values=tensor([0.2713, 0.7441, 0.5681,  ..., 0.3863, 0.3329, 0.3299]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([6.8436e-01, 7.7662e-01, 5.3826e-02,  ..., 2.4557e-01, 1.4818e-01,
+        6.5619e-04])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.02093958854675293 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50144', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.5512330532073975}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([2014, 2333, 4073,  ..., 1117, 3505, 2207]),
-       values=tensor([0.1339, 0.9980, 0.7024,  ..., 0.3782, 0.0544, 0.2308]),
+       col_indices=tensor([ 261, 2124, 2825,  ..., 2342, 1684, 3815]),
+       values=tensor([0.1132, 0.9807, 0.3410,  ..., 0.0783, 0.3569, 0.0713]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2751, 0.2895, 0.5101,  ..., 0.3933, 0.2935, 0.0678])
+tensor([0.8182, 0.0907, 0.1359,  ..., 0.4059, 0.0754, 0.0727])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +35,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.04866957664489746 seconds
+Time: 1.5512330532073975 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '339415', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661308526992798}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
-       col_indices=tensor([3059, 3492, 4969,  ..., 3863, 1265, 1575]),
-       values=tensor([0.7839, 0.7068, 0.1359,  ..., 0.6765, 0.7179, 0.7182]),
+       col_indices=tensor([  91, 2131, 2855,  ..., 2446,  470, 1581]),
+       values=tensor([0.9229, 0.3729, 0.6792,  ..., 0.1416, 0.2267, 0.3921]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2637, 0.1133, 0.2354,  ..., 0.5397, 0.9545, 0.7707])
+tensor([0.5750, 0.0749, 0.6665,  ..., 0.8045, 0.0578, 0.3106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +54,15 @@ 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}
+Time: 10.661308526992798 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([  91, 2131, 2855,  ..., 2446,  470, 1581]),
+       values=tensor([0.9229, 0.3729, 0.6792,  ..., 0.1416, 0.2267, 0.3921]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2085, 0.7612, 0.9816,  ..., 0.7337, 0.6921, 0.5494])
+tensor([0.5750, 0.0749, 0.6665,  ..., 0.8045, 0.0578, 0.3106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,29 +70,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.660384178161621 seconds
+Time: 10.661308526992798 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    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}
+[18.68, 17.92, 18.02, 18.26, 18.14, 18.26, 18.3, 17.84, 18.15, 17.87]
+[73.63]
+14.155422449111938
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 339415, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661308526992798, 'TIME_S_1KI': 0.031410834898259646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.263754928112, 'W': 73.63}
+[18.68, 17.92, 18.02, 18.26, 18.14, 18.26, 18.3, 17.84, 18.15, 17.87, 18.33, 18.37, 18.02, 18.02, 22.34, 18.22, 18.25, 22.38, 17.9, 18.26]
+334.9599999999999
+16.747999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 339415, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661308526992798, 'TIME_S_1KI': 0.031410834898259646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.263754928112, 'W': 73.63, 'J_1KI': 3.0707651545397576, 'W_1KI': 0.2169320743043177, 'W_D': 56.882, 'J_D': 805.1887397503853, 'W_D_1KI': 0.1675883505443189, 'J_D_1KI': 0.0004937564649303033}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
index 9b486c5..4f7c98c 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 242735, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6920804977417, "TIME_S_1KI": 0.044048367552028754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1022.2163880014419, "W": 74.91, "J_1KI": 4.211244311703883, "W_1KI": 0.30860815292397054, "W_D": 58.500499999999995, "J_D": 798.2935496766567, "W_D_1KI": 0.241005623416483, "J_D_1KI": 0.000992875454369922}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
index a34514c..6818997 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.019707918167114258}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,    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]),
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24992, 24998, 25000]),
+       col_indices=tensor([2443, 3271, 4233,  ..., 3520, 3792, 4350]),
+       values=tensor([0.8426, 0.5824, 0.3389,  ..., 0.9840, 0.1147, 0.8239]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3917, 0.0968, 0.9015,  ..., 0.9180, 0.2586, 0.0822])
+tensor([0.5471, 0.6226, 0.3278,  ..., 0.2451, 0.7959, 0.6112])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.05913829803466797 seconds
+Time: 0.019707918167114258 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53278', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.304640293121338}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24990, 24994, 25000]),
+       col_indices=tensor([ 876,  897, 2274,  ..., 2103, 3712, 4740]),
+       values=tensor([0.9508, 0.2626, 0.2379,  ..., 0.2341, 0.9066, 0.6182]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7930, 0.9227, 0.2342,  ..., 0.4335, 0.3949, 0.6803])
+tensor([0.8511, 0.0306, 0.7639,  ..., 0.5025, 0.8599, 0.9690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 7.6236653327941895 seconds
+Time: 2.304640293121338 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '242735', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6920804977417}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     6,    10,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([1152, 1177, 2929,  ..., 1264, 2609, 4571]),
+       values=tensor([0.4587, 0.9835, 0.0653,  ..., 0.8655, 0.2110, 0.7343]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5474, 0.2694, 0.2646,  ..., 0.5254, 0.5763, 0.9998])
+tensor([0.5612, 0.6517, 0.4101,  ..., 0.1691, 0.2414, 0.3717])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.47565221786499 seconds
+Time: 10.6920804977417 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     6,    10,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([1152, 1177, 2929,  ..., 1264, 2609, 4571]),
+       values=tensor([0.4587, 0.9835, 0.0653,  ..., 0.8655, 0.2110, 0.7343]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5474, 0.2694, 0.2646,  ..., 0.5254, 0.5763, 0.9998])
+tensor([0.5612, 0.6517, 0.4101,  ..., 0.1691, 0.2414, 0.3717])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.47565221786499 seconds
+Time: 10.6920804977417 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}
+[18.79, 17.83, 18.25, 17.86, 18.02, 18.39, 18.09, 17.82, 18.07, 17.75]
+[74.91]
+13.64592695236206
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6920804977417, 'TIME_S_1KI': 0.044048367552028754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.2163880014419, 'W': 74.91}
+[18.79, 17.83, 18.25, 17.86, 18.02, 18.39, 18.09, 17.82, 18.07, 17.75, 18.54, 18.01, 18.05, 19.12, 19.51, 18.12, 18.41, 17.83, 18.29, 17.96]
+328.19
+16.4095
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6920804977417, 'TIME_S_1KI': 0.044048367552028754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.2163880014419, 'W': 74.91, 'J_1KI': 4.211244311703883, 'W_1KI': 0.30860815292397054, 'W_D': 58.500499999999995, 'J_D': 798.2935496766567, 'W_D_1KI': 0.241005623416483, 'J_D_1KI': 0.000992875454369922}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
index 6d5d9d5..64ab37a 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 161950, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.493181943893433, "TIME_S_1KI": 0.06479272580360255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1161.5640621185303, "W": 84.0, "J_1KI": 7.172362223640199, "W_1KI": 0.5186786045075641, "W_D": 67.18725, "J_D": 929.0749408639671, "W_D_1KI": 0.41486415560358136, "J_D_1KI": 0.0025616804915318393}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
index 548b49c..f4d043e 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.022989749908447266}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     56,     99,  ..., 249898, 249957,
+tensor(crow_indices=tensor([     0,     53,    112,  ..., 249913, 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]),
+       col_indices=tensor([  48,   54,  234,  ..., 4368, 4853, 4864]),
+       values=tensor([0.3452, 0.1008, 0.0125,  ..., 0.4983, 0.8936, 0.7126]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9446, 0.5109, 0.8342,  ..., 0.1182, 0.7217, 0.5335])
+tensor([0.5619, 0.6285, 0.8433,  ..., 0.7149, 0.5039, 0.6932])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.08090949058532715 seconds
+Time: 0.022989749908447266 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45672', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.961124897003174}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     58,    109,  ..., 249903, 249955,
+tensor(crow_indices=tensor([     0,     53,     98,  ..., 249913, 249948,
                             250000]),
-       col_indices=tensor([ 168,  371,  372,  ..., 4708, 4876, 4879]),
-       values=tensor([0.3469, 0.2972, 0.5901,  ..., 0.0640, 0.2331, 0.9267]),
+       col_indices=tensor([ 202,  295,  369,  ..., 4836, 4929, 4943]),
+       values=tensor([0.0950, 0.5576, 0.0327,  ..., 0.3192, 0.9052, 0.1110]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6978, 0.0250, 0.3323,  ..., 0.6356, 0.0847, 0.1678])
+tensor([0.6070, 0.1874, 0.1819,  ..., 0.4756, 0.1999, 0.3064])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 8.36373782157898 seconds
+Time: 2.961124897003174 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '161950', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.493181943893433}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     50,    103,  ..., 249909, 249962,
+tensor(crow_indices=tensor([     0,     48,    111,  ..., 249911, 249964,
                             250000]),
-       col_indices=tensor([  86,  107,  119,  ..., 4571, 4629, 4973]),
-       values=tensor([0.3206, 0.5923, 0.4852,  ..., 0.3807, 0.1641, 0.9581]),
+       col_indices=tensor([  19,   43,  144,  ..., 4843, 4924, 4947]),
+       values=tensor([0.0652, 0.5238, 0.9360,  ..., 0.5118, 0.2782, 0.1343]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7369, 0.6267, 0.7979,  ..., 0.0231, 0.0899, 0.6643])
+tensor([0.4213, 0.5109, 0.7737,  ..., 0.0670, 0.5810, 0.9899])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.414216756820679 seconds
+Time: 10.493181943893433 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     50,    103,  ..., 249909, 249962,
+tensor(crow_indices=tensor([     0,     48,    111,  ..., 249911, 249964,
                             250000]),
-       col_indices=tensor([  86,  107,  119,  ..., 4571, 4629, 4973]),
-       values=tensor([0.3206, 0.5923, 0.4852,  ..., 0.3807, 0.1641, 0.9581]),
+       col_indices=tensor([  19,   43,  144,  ..., 4843, 4924, 4947]),
+       values=tensor([0.0652, 0.5238, 0.9360,  ..., 0.5118, 0.2782, 0.1343]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7369, 0.6267, 0.7979,  ..., 0.0231, 0.0899, 0.6643])
+tensor([0.4213, 0.5109, 0.7737,  ..., 0.0670, 0.5810, 0.9899])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.414216756820679 seconds
+Time: 10.493181943893433 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}
+[18.86, 18.02, 19.18, 18.0, 18.43, 21.89, 19.39, 17.96, 18.27, 17.81]
+[84.0]
+13.82814359664917
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 161950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.493181943893433, 'TIME_S_1KI': 0.06479272580360255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.5640621185303, 'W': 84.0}
+[18.86, 18.02, 19.18, 18.0, 18.43, 21.89, 19.39, 17.96, 18.27, 17.81, 18.1, 20.23, 20.02, 18.19, 17.98, 17.88, 18.2, 17.87, 18.32, 18.08]
+336.255
+16.81275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 161950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.493181943893433, 'TIME_S_1KI': 0.06479272580360255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.5640621185303, 'W': 84.0, 'J_1KI': 7.172362223640199, 'W_1KI': 0.5186786045075641, 'W_D': 67.18725, 'J_D': 929.0749408639671, 'W_D_1KI': 0.41486415560358136, 'J_D_1KI': 0.0025616804915318393}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
index 373bd0d..c0ca085 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 44182, "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": 10.514305114746094, "TIME_S_1KI": 0.2379771199752409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1269.0750387310982, "W": 89.25, "J_1KI": 28.723802424767964, "W_1KI": 2.020053415418044, "W_D": 73.10575, "J_D": 1039.51465000242, "W_D_1KI": 1.6546500837445115, "J_D_1KI": 0.03745077370296753}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46969, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.723698854446411, "TIME_S_1KI": 0.22831439575989293, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.0827813720703, "W": 87.97, "J_1KI": 26.87054826315379, "W_1KI": 1.8729374693947072, "W_D": 71.6635, "J_D": 1028.1376537780761, "W_D_1KI": 1.5257616725925611, "J_D_1KI": 0.03248444021785776}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
index f73f661..62668bd 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 0.25171899795532227}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03811240196228027}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     257,     510,  ..., 1249508,
-                            1249742, 1250000]),
-       col_indices=tensor([  10,   14,   18,  ..., 4971, 4976, 4993]),
-       values=tensor([0.7215, 0.2673, 0.1887,  ..., 0.2021, 0.2524, 0.6553]),
+tensor(crow_indices=tensor([      0,     252,     486,  ..., 1249499,
+                            1249768, 1250000]),
+       col_indices=tensor([  15,   65,   81,  ..., 4947, 4948, 4952]),
+       values=tensor([0.2497, 0.0794, 0.3182,  ..., 0.5399, 0.7483, 0.2341]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6580, 0.5682, 0.5133,  ..., 0.8598, 0.8673, 0.3117])
+tensor([0.6929, 0.1024, 0.7145,  ..., 0.7803, 0.6014, 0.5585])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.25171899795532227 seconds
+Time: 0.03811240196228027 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '41713', '-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": 9.913089752197266}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27550', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.158770799636841}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     230,     476,  ..., 1249535,
-                            1249754, 1250000]),
-       col_indices=tensor([  26,   32,   49,  ..., 4901, 4965, 4968]),
-       values=tensor([0.6798, 0.0159, 0.6379,  ..., 0.7230, 0.8415, 0.2703]),
+tensor(crow_indices=tensor([      0,     250,     496,  ..., 1249504,
+                            1249751, 1250000]),
+       col_indices=tensor([  13,   88,   93,  ..., 4888, 4919, 4936]),
+       values=tensor([0.3293, 0.4307, 0.3782,  ..., 0.2045, 0.2965, 0.3765]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.9905, 0.3660, 0.2565,  ..., 0.2843, 0.2598, 0.4388])
+tensor([0.2742, 0.5184, 0.9225,  ..., 0.0830, 0.2762, 0.4744])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 9.913089752197266 seconds
+Time: 6.158770799636841 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44182', '-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": 10.514305114746094}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46969', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.723698854446411}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     234,     486,  ..., 1249518,
-                            1249775, 1250000]),
-       col_indices=tensor([   2,   39,   64,  ..., 4915, 4964, 4987]),
-       values=tensor([0.0513, 0.5642, 0.6511,  ..., 0.0332, 0.5293, 0.6294]),
+tensor(crow_indices=tensor([      0,     267,     518,  ..., 1249533,
+                            1249757, 1250000]),
+       col_indices=tensor([   4,   12,   26,  ..., 4948, 4976, 4977]),
+       values=tensor([0.8262, 0.2304, 0.8718,  ..., 0.9371, 0.9418, 0.0811]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8655, 0.6659, 0.2976,  ..., 0.0599, 0.2467, 0.0329])
+tensor([0.8290, 0.5732, 0.0178,  ..., 0.9134, 0.7238, 0.4621])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.514305114746094 seconds
+Time: 10.723698854446411 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     234,     486,  ..., 1249518,
-                            1249775, 1250000]),
-       col_indices=tensor([   2,   39,   64,  ..., 4915, 4964, 4987]),
-       values=tensor([0.0513, 0.5642, 0.6511,  ..., 0.0332, 0.5293, 0.6294]),
+tensor(crow_indices=tensor([      0,     267,     518,  ..., 1249533,
+                            1249757, 1250000]),
+       col_indices=tensor([   4,   12,   26,  ..., 4948, 4976, 4977]),
+       values=tensor([0.8262, 0.2304, 0.8718,  ..., 0.9371, 0.9418, 0.0811]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8655, 0.6659, 0.2976,  ..., 0.0599, 0.2467, 0.0329])
+tensor([0.8290, 0.5732, 0.0178,  ..., 0.9134, 0.7238, 0.4621])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.514305114746094 seconds
+Time: 10.723698854446411 seconds
 
-[18.16, 17.71, 17.62, 17.44, 17.74, 17.67, 17.83, 17.6, 17.55, 17.84]
-[89.25]
-14.219328165054321
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44182, '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': 10.514305114746094, 'TIME_S_1KI': 0.2379771199752409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.0750387310982, 'W': 89.25}
-[18.16, 17.71, 17.62, 17.44, 17.74, 17.67, 17.83, 17.6, 17.55, 17.84, 18.13, 17.61, 17.91, 17.95, 20.68, 17.51, 17.88, 18.51, 17.79, 17.64]
-322.885
-16.14425
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44182, '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': 10.514305114746094, 'TIME_S_1KI': 0.2379771199752409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.0750387310982, 'W': 89.25, 'J_1KI': 28.723802424767964, 'W_1KI': 2.020053415418044, 'W_D': 73.10575, 'J_D': 1039.51465000242, 'W_D_1KI': 1.6546500837445115, 'J_D_1KI': 0.03745077370296753}
+[18.22, 17.77, 18.1, 17.81, 18.08, 18.08, 18.29, 17.93, 18.14, 17.97]
+[87.97]
+14.34674072265625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.723698854446411, 'TIME_S_1KI': 0.22831439575989293, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.0827813720703, 'W': 87.97}
+[18.22, 17.77, 18.1, 17.81, 18.08, 18.08, 18.29, 17.93, 18.14, 17.97, 18.49, 18.0, 18.1, 18.25, 18.1, 17.98, 17.87, 18.07, 17.92, 20.6]
+326.13
+16.3065
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.723698854446411, 'TIME_S_1KI': 0.22831439575989293, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.0827813720703, 'W': 87.97, 'J_1KI': 26.87054826315379, 'W_1KI': 1.8729374693947072, 'W_D': 71.6635, 'J_D': 1028.1376537780761, 'W_D_1KI': 1.5257616725925611, 'J_D_1KI': 0.03248444021785776}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
index 6e5c014..fe585f9 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19098, "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": 10.554213762283325, "TIME_S_1KI": 0.552634504256117, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.880751209259, "W": 88.77, "J_1KI": 68.06371092309452, "W_1KI": 4.64813069431354, "W_D": 72.8915, "J_D": 1067.3680046949385, "W_D_1KI": 3.8167085558697242, "J_D_1KI": 0.19984859963712034}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19220, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.306657314300537, "TIME_S_1KI": 0.536246478371516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1258.2980075454711, "W": 87.22999999999999, "J_1KI": 65.46815856115875, "W_1KI": 4.538501560874089, "W_D": 70.87349999999999, "J_D": 1022.354509202957, "W_D_1KI": 3.6874869927159204, "J_D_1KI": 0.19185676340873678}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
index a64ec1d..2db63b3 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 0.5497848987579346}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06937050819396973}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     487,    1016,  ..., 2499032,
-                            2499521, 2500000]),
-       col_indices=tensor([   8,   18,   46,  ..., 4955, 4960, 4970]),
-       values=tensor([0.9231, 0.4693, 0.9835,  ..., 0.3329, 0.5539, 0.9296]),
+tensor(crow_indices=tensor([      0,     517,    1013,  ..., 2498965,
+                            2499518, 2500000]),
+       col_indices=tensor([  16,   31,   39,  ..., 4973, 4987, 4996]),
+       values=tensor([0.8092, 0.6907, 0.1859,  ..., 0.6156, 0.1820, 0.0827]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7808, 0.5215, 0.8582,  ..., 0.9627, 0.6165, 0.7692])
+tensor([0.0341, 0.1297, 0.9553,  ..., 0.9560, 0.1422, 0.0438])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.5497848987579346 seconds
+Time: 0.06937050819396973 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19098', '-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": 10.554213762283325}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15136', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.268742084503174}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     480,     948,  ..., 2499011,
-                            2499537, 2500000]),
-       col_indices=tensor([   1,    8,   11,  ..., 4950, 4969, 4990]),
-       values=tensor([0.1158, 0.8497, 0.7920,  ..., 0.6315, 0.2224, 0.2676]),
+tensor(crow_indices=tensor([      0,     498,    1015,  ..., 2498995,
+                            2499510, 2500000]),
+       col_indices=tensor([   1,    5,   12,  ..., 4987, 4992, 4994]),
+       values=tensor([0.2706, 0.5291, 0.0606,  ..., 0.9998, 0.6766, 0.8077]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4700, 0.1718, 0.6444,  ..., 0.1980, 0.7458, 0.7705])
+tensor([0.5238, 0.3873, 0.9372,  ..., 0.7751, 0.3587, 0.1743])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.554213762283325 seconds
+Time: 8.268742084503174 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19220', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.306657314300537}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     480,     948,  ..., 2499011,
-                            2499537, 2500000]),
-       col_indices=tensor([   1,    8,   11,  ..., 4950, 4969, 4990]),
-       values=tensor([0.1158, 0.8497, 0.7920,  ..., 0.6315, 0.2224, 0.2676]),
+tensor(crow_indices=tensor([      0,     478,     976,  ..., 2498986,
+                            2499483, 2500000]),
+       col_indices=tensor([   0,   15,   20,  ..., 4988, 4995, 4997]),
+       values=tensor([0.9696, 0.2544, 0.3304,  ..., 0.5139, 0.4686, 0.4850]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4700, 0.1718, 0.6444,  ..., 0.1980, 0.7458, 0.7705])
+tensor([0.8873, 0.6521, 0.3260,  ..., 0.9177, 0.3863, 0.5956])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.554213762283325 seconds
+Time: 10.306657314300537 seconds
 
-[18.14, 17.83, 17.44, 17.66, 17.78, 17.6, 17.85, 17.79, 17.38, 17.41]
-[88.77]
-14.643243789672852
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19098, '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': 10.554213762283325, 'TIME_S_1KI': 0.552634504256117, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.880751209259, 'W': 88.77}
-[18.14, 17.83, 17.44, 17.66, 17.78, 17.6, 17.85, 17.79, 17.38, 17.41, 18.1, 17.72, 17.57, 17.35, 17.6, 17.54, 17.53, 17.37, 18.02, 17.43]
-317.57000000000005
-15.878500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19098, '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': 10.554213762283325, 'TIME_S_1KI': 0.552634504256117, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.880751209259, 'W': 88.77, 'J_1KI': 68.06371092309452, 'W_1KI': 4.64813069431354, 'W_D': 72.8915, 'J_D': 1067.3680046949385, 'W_D_1KI': 3.8167085558697242, 'J_D_1KI': 0.19984859963712034}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     976,  ..., 2498986,
+                            2499483, 2500000]),
+       col_indices=tensor([   0,   15,   20,  ..., 4988, 4995, 4997]),
+       values=tensor([0.9696, 0.2544, 0.3304,  ..., 0.5139, 0.4686, 0.4850]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8873, 0.6521, 0.3260,  ..., 0.9177, 0.3863, 0.5956])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.306657314300537 seconds
+
+[18.67, 18.15, 17.98, 18.09, 18.08, 18.12, 18.09, 17.98, 17.86, 18.03]
+[87.23]
+14.425060272216797
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.306657314300537, 'TIME_S_1KI': 0.536246478371516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.2980075454711, 'W': 87.22999999999999}
+[18.67, 18.15, 17.98, 18.09, 18.08, 18.12, 18.09, 17.98, 17.86, 18.03, 18.76, 17.96, 18.22, 17.88, 18.31, 18.19, 18.22, 17.8, 18.27, 20.4]
+327.13
+16.3565
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.306657314300537, 'TIME_S_1KI': 0.536246478371516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.2980075454711, 'W': 87.22999999999999, 'J_1KI': 65.46815856115875, 'W_1KI': 4.538501560874089, 'W_D': 70.87349999999999, 'J_D': 1022.354509202957, 'W_D_1KI': 3.6874869927159204, 'J_D_1KI': 0.19185676340873678}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
index ae3e053..5da43df 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.657127857208252, "TIME_S_1KI": 1.175245683415114, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1354.9685973644257, "W": 87.3, "J_1KI": 149.42309190167904, "W_1KI": 9.62726069695633, "W_D": 71.26675, "J_D": 1106.11922435534, "W_D_1KI": 7.859147551830612, "J_D_1KI": 0.8666902902327539}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9074, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.487424612045288, "TIME_S_1KI": 1.1557664328901573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.6191907930374, "W": 85.83, "J_1KI": 143.44491853571054, "W_1KI": 9.458893541988099, "W_D": 69.53375, "J_D": 1054.4851847583054, "W_D_1KI": 7.662965616045845, "J_D_1KI": 0.8444969821518454}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
index 5903d4b..4c63e35 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.157815933227539}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.12977051734924316}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     951,    1952,  ..., 4997990,
-                            4999010, 5000000]),
-       col_indices=tensor([   1,    2,    7,  ..., 4975, 4984, 4994]),
-       values=tensor([0.2978, 0.3383, 0.4211,  ..., 0.6173, 0.3619, 0.1875]),
+tensor(crow_indices=tensor([      0,    1037,    2033,  ..., 4997947,
+                            4998969, 5000000]),
+       col_indices=tensor([   0,    3,   11,  ..., 4985, 4988, 4990]),
+       values=tensor([0.1539, 0.2882, 0.0917,  ..., 0.8336, 0.9260, 0.3814]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3712, 0.9173, 0.1615,  ..., 0.0466, 0.6664, 0.8295])
+tensor([0.9778, 0.3097, 0.5480,  ..., 0.9590, 0.3024, 0.0294])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,20 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 1.157815933227539 seconds
+Time: 0.12977051734924316 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9068', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.657127857208252}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8091', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.36232042312622}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1038,    2022,  ..., 4998028,
-                            4999030, 5000000]),
-       col_indices=tensor([   5,    8,   18,  ..., 4988, 4996, 4999]),
-       values=tensor([0.3556, 0.0118, 0.2581,  ..., 0.9541, 0.9641, 0.1138]),
+tensor(crow_indices=tensor([      0,     972,    1989,  ..., 4997978,
+                            4998985, 5000000]),
+       col_indices=tensor([   0,    9,   19,  ..., 4989, 4992, 4995]),
+       values=tensor([0.2585, 0.0110, 0.4823,  ..., 0.4314, 0.8099, 0.9487]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([2.9156e-01, 7.5281e-01, 6.6249e-01,  ..., 5.6286e-01, 2.5839e-04,
-        4.1914e-01])
+tensor([0.7513, 0.6352, 0.8184,  ..., 0.0273, 0.2479, 0.5631])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -37,17 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.657127857208252 seconds
+Time: 9.36232042312622 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9074', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.487424612045288}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1038,    2022,  ..., 4998028,
-                            4999030, 5000000]),
-       col_indices=tensor([   5,    8,   18,  ..., 4988, 4996, 4999]),
-       values=tensor([0.3556, 0.0118, 0.2581,  ..., 0.9541, 0.9641, 0.1138]),
+tensor(crow_indices=tensor([      0,    1061,    2118,  ..., 4997993,
+                            4998997, 5000000]),
+       col_indices=tensor([   3,    6,   10,  ..., 4996, 4998, 4999]),
+       values=tensor([0.5066, 0.7039, 0.5374,  ..., 0.1064, 0.9581, 0.5937]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([2.9156e-01, 7.5281e-01, 6.6249e-01,  ..., 5.6286e-01, 2.5839e-04,
-        4.1914e-01])
+tensor([0.1982, 0.1257, 0.6934,  ..., 0.0401, 0.8872, 0.0311])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -55,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.657127857208252 seconds
+Time: 10.487424612045288 seconds
 
-[18.19, 17.62, 17.78, 17.94, 17.96, 17.76, 17.78, 17.85, 17.63, 17.71]
-[87.3]
-15.52083158493042
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.657127857208252, 'TIME_S_1KI': 1.175245683415114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1354.9685973644257, 'W': 87.3}
-[18.19, 17.62, 17.78, 17.94, 17.96, 17.76, 17.78, 17.85, 17.63, 17.71, 18.05, 17.78, 17.91, 17.54, 18.13, 17.46, 18.2, 17.86, 17.68, 17.62]
-320.6650000000001
-16.033250000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.657127857208252, 'TIME_S_1KI': 1.175245683415114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1354.9685973644257, 'W': 87.3, 'J_1KI': 149.42309190167904, 'W_1KI': 9.62726069695633, 'W_D': 71.26675, 'J_D': 1106.11922435534, 'W_D_1KI': 7.859147551830612, 'J_D_1KI': 0.8666902902327539}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1061,    2118,  ..., 4997993,
+                            4998997, 5000000]),
+       col_indices=tensor([   3,    6,   10,  ..., 4996, 4998, 4999]),
+       values=tensor([0.5066, 0.7039, 0.5374,  ..., 0.1064, 0.9581, 0.5937]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1982, 0.1257, 0.6934,  ..., 0.0401, 0.8872, 0.0311])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.487424612045288 seconds
+
+[18.43, 18.11, 17.89, 18.52, 18.92, 18.0, 18.0, 17.79, 18.16, 17.76]
+[85.83]
+15.16508436203003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9074, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.487424612045288, 'TIME_S_1KI': 1.1557664328901573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.6191907930374, 'W': 85.83}
+[18.43, 18.11, 17.89, 18.52, 18.92, 18.0, 18.0, 17.79, 18.16, 17.76, 18.23, 17.97, 18.42, 17.87, 17.92, 18.08, 18.11, 17.82, 17.87, 18.53]
+325.925
+16.29625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9074, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.487424612045288, 'TIME_S_1KI': 1.1557664328901573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.6191907930374, 'W': 85.83, 'J_1KI': 143.44491853571054, 'W_1KI': 9.458893541988099, 'W_D': 69.53375, 'J_D': 1054.4851847583054, 'W_D_1KI': 7.662965616045845, 'J_D_1KI': 0.8444969821518454}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
index fca72d3..c0e5e12 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5664, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.680659770965576, "TIME_S_1KI": 1.8857097053258434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1382.186832485199, "W": 86.06, "J_1KI": 244.0301611026128, "W_1KI": 15.194209039548022, "W_D": 70.05975000000001, "J_D": 1125.2110613200666, "W_D_1KI": 12.369306144067798, "J_D_1KI": 2.1838464237407833}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5607, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.746073484420776, "TIME_S_1KI": 1.9165460111326513, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.0484231472017, "W": 84.51, "J_1KI": 237.7471773046552, "W_1KI": 15.07223113964687, "W_D": 68.30000000000001, "J_D": 1077.3542456626894, "W_D_1KI": 12.18120206884252, "J_D_1KI": 2.1724990313612484}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
index e9cfac6..4501c06 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.8537497520446777}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.20109891891479492}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1463,    2978,  ..., 7496989,
-                            7498439, 7500000]),
-       col_indices=tensor([   1,    2,   10,  ..., 4991, 4992, 4997]),
-       values=tensor([0.5985, 0.8707, 0.1470,  ..., 0.1515, 0.9789, 0.5190]),
+tensor(crow_indices=tensor([      0,    1526,    3035,  ..., 7496941,
+                            7498453, 7500000]),
+       col_indices=tensor([   2,    6,   10,  ..., 4993, 4994, 4996]),
+       values=tensor([0.3579, 0.5981, 0.5931,  ..., 0.3837, 0.5123, 0.1240]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.7595, 0.9851, 0.8459,  ..., 0.8698, 0.1337, 0.7899])
+tensor([0.0391, 0.3406, 0.5361,  ..., 0.5039, 0.0205, 0.2852])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 1.8537497520446777 seconds
+Time: 0.20109891891479492 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5664', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.680659770965576}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5221', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.77708387374878}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1531,    3042,  ..., 7497046,
-                            7498567, 7500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4993, 4996, 4997]),
-       values=tensor([0.2133, 0.5634, 0.4796,  ..., 0.0244, 0.1739, 0.8367]),
+tensor(crow_indices=tensor([      0,    1505,    2964,  ..., 7496939,
+                            7498442, 7500000]),
+       col_indices=tensor([   2,    4,    5,  ..., 4996, 4997, 4999]),
+       values=tensor([0.1729, 0.9195, 0.5163,  ..., 0.0436, 0.1803, 0.9350]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6801, 0.4978, 0.3141,  ..., 0.6717, 0.6784, 0.5569])
+tensor([0.3955, 0.7196, 0.4505,  ..., 0.8904, 0.4770, 0.0844])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.680659770965576 seconds
+Time: 9.77708387374878 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5607', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.746073484420776}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1531,    3042,  ..., 7497046,
-                            7498567, 7500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4993, 4996, 4997]),
-       values=tensor([0.2133, 0.5634, 0.4796,  ..., 0.0244, 0.1739, 0.8367]),
+tensor(crow_indices=tensor([      0,    1451,    2905,  ..., 7497061,
+                            7498533, 7500000]),
+       col_indices=tensor([   4,    9,   12,  ..., 4994, 4998, 4999]),
+       values=tensor([0.3703, 0.3823, 0.6108,  ..., 0.0984, 0.8524, 0.0373]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6801, 0.4978, 0.3141,  ..., 0.6717, 0.6784, 0.5569])
+tensor([0.6688, 0.5149, 0.3458,  ..., 0.1151, 0.9310, 0.8037])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.680659770965576 seconds
+Time: 10.746073484420776 seconds
 
-[18.09, 17.53, 17.63, 17.45, 17.68, 17.68, 17.63, 17.31, 17.79, 17.45]
-[86.06]
-16.060734748840332
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.680659770965576, 'TIME_S_1KI': 1.8857097053258434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1382.186832485199, 'W': 86.06}
-[18.09, 17.53, 17.63, 17.45, 17.68, 17.68, 17.63, 17.31, 17.79, 17.45, 18.18, 17.48, 18.72, 17.88, 18.28, 17.87, 17.95, 17.58, 17.89, 17.59]
-320.00500000000005
-16.00025
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.680659770965576, 'TIME_S_1KI': 1.8857097053258434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1382.186832485199, 'W': 86.06, 'J_1KI': 244.0301611026128, 'W_1KI': 15.194209039548022, 'W_D': 70.05975000000001, 'J_D': 1125.2110613200666, 'W_D_1KI': 12.369306144067798, 'J_D_1KI': 2.1838464237407833}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1451,    2905,  ..., 7497061,
+                            7498533, 7500000]),
+       col_indices=tensor([   4,    9,   12,  ..., 4994, 4998, 4999]),
+       values=tensor([0.3703, 0.3823, 0.6108,  ..., 0.0984, 0.8524, 0.0373]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6688, 0.5149, 0.3458,  ..., 0.1151, 0.9310, 0.8037])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.746073484420776 seconds
+
+[18.36, 17.95, 18.08, 17.67, 18.17, 17.84, 18.12, 17.84, 18.09, 18.06]
+[84.51]
+15.77385425567627
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5607, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.746073484420776, 'TIME_S_1KI': 1.9165460111326513, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.0484231472017, 'W': 84.51}
+[18.36, 17.95, 18.08, 17.67, 18.17, 17.84, 18.12, 17.84, 18.09, 18.06, 18.35, 17.89, 17.87, 18.07, 18.23, 17.83, 18.12, 17.92, 18.19, 17.87]
+324.20000000000005
+16.21
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5607, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.746073484420776, 'TIME_S_1KI': 1.9165460111326513, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.0484231472017, 'W': 84.51, 'J_1KI': 237.7471773046552, 'W_1KI': 15.07223113964687, 'W_D': 68.30000000000001, 'J_D': 1077.3542456626894, 'W_D_1KI': 12.18120206884252, 'J_D_1KI': 2.1724990313612484}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..363edc0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.510854721069336, "TIME_S_1KI": 3.6970997963662806, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1362.9070807647706, "W": 82.31, "J_1KI": 479.39046104986653, "W_1KI": 28.951811466760464, "W_D": 66.04650000000001, "J_D": 1093.6124712638857, "W_D_1KI": 23.23126978543792, "J_D_1KI": 8.171392819359099}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..26353c7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.3692905902862549}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1942,     3937,  ...,  9996021,
+                             9998014, 10000000]),
+       col_indices=tensor([   1,    6,   11,  ..., 4995, 4996, 4997]),
+       values=tensor([0.9006, 0.6515, 0.7177,  ..., 0.3221, 0.1090, 0.6573]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3731, 0.3806, 0.5183,  ..., 0.1665, 0.0476, 0.3514])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.3692905902862549 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2843', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.510854721069336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2016,     4009,  ...,  9996028,
+                             9998016, 10000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4991, 4992, 4997]),
+       values=tensor([0.1122, 0.1212, 0.4120,  ..., 0.9869, 0.5095, 0.1756]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1243, 0.0753, 0.2426,  ..., 0.5390, 0.1485, 0.6469])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.510854721069336 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2016,     4009,  ...,  9996028,
+                             9998016, 10000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4991, 4992, 4997]),
+       values=tensor([0.1122, 0.1212, 0.4120,  ..., 0.9869, 0.5095, 0.1756]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1243, 0.0753, 0.2426,  ..., 0.5390, 0.1485, 0.6469])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.510854721069336 seconds
+
+[18.34, 18.07, 17.87, 17.95, 18.1, 18.25, 18.13, 17.86, 18.35, 17.99]
+[82.31]
+16.55821990966797
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.510854721069336, 'TIME_S_1KI': 3.6970997963662806, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.9070807647706, 'W': 82.31}
+[18.34, 18.07, 17.87, 17.95, 18.1, 18.25, 18.13, 17.86, 18.35, 17.99, 18.23, 18.01, 18.07, 17.94, 18.3, 17.93, 18.17, 17.9, 18.02, 18.14]
+325.2699999999999
+16.263499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.510854721069336, 'TIME_S_1KI': 3.6970997963662806, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.9070807647706, 'W': 82.31, 'J_1KI': 479.39046104986653, 'W_1KI': 28.951811466760464, 'W_D': 66.04650000000001, 'J_D': 1093.6124712638857, 'W_D_1KI': 23.23126978543792, 'J_D_1KI': 8.171392819359099}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..5cc798d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.31517243385315, "TIME_S_1KI": 4.35790977349098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1435.467025976181, "W": 77.42, "J_1KI": 606.4499476029494, "W_1KI": 32.708069286016055, "W_D": 60.97375, "J_D": 1130.5322600764036, "W_D_1KI": 25.7599281791297, "J_D_1KI": 10.882943886408832}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..9c08cbf
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.4716074466705322}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2514,     4962,  ..., 12495038,
+                            12497495, 12500000]),
+       col_indices=tensor([   0,    2,    3,  ..., 4994, 4998, 4999]),
+       values=tensor([0.6154, 0.9669, 0.2665,  ..., 0.0694, 0.4098, 0.1560]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5763, 0.6030, 0.9940,  ..., 0.2125, 0.0764, 0.1654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.4716074466705322 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2226', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.872556447982788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2494,     5060,  ..., 12495015,
+                            12497540, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4996, 4997, 4998]),
+       values=tensor([0.6641, 0.1400, 0.8724,  ..., 0.1588, 0.2497, 0.0435]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4856, 0.9202, 0.9997,  ..., 0.6539, 0.7245, 0.6538])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.872556447982788 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2367', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.31517243385315}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2524,     5066,  ..., 12495116,
+                            12497547, 12500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4997, 4998, 4999]),
+       values=tensor([0.7920, 0.5049, 0.7981,  ..., 0.7837, 0.6861, 0.6359]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9717, 0.2840, 0.2667,  ..., 0.4259, 0.7903, 0.5279])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.31517243385315 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2524,     5066,  ..., 12495116,
+                            12497547, 12500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4997, 4998, 4999]),
+       values=tensor([0.7920, 0.5049, 0.7981,  ..., 0.7837, 0.6861, 0.6359]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9717, 0.2840, 0.2667,  ..., 0.4259, 0.7903, 0.5279])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.31517243385315 seconds
+
+[18.22, 18.01, 18.1, 18.03, 17.97, 17.83, 18.06, 17.94, 17.8, 18.08]
+[77.42]
+18.54129457473755
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.31517243385315, 'TIME_S_1KI': 4.35790977349098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1435.467025976181, 'W': 77.42}
+[18.22, 18.01, 18.1, 18.03, 17.97, 17.83, 18.06, 17.94, 17.8, 18.08, 18.25, 18.11, 18.19, 18.18, 17.9, 17.94, 21.89, 18.55, 18.24, 17.82]
+328.925
+16.44625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.31517243385315, 'TIME_S_1KI': 4.35790977349098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1435.467025976181, 'W': 77.42, 'J_1KI': 606.4499476029494, 'W_1KI': 32.708069286016055, 'W_D': 60.97375, 'J_D': 1130.5322600764036, 'W_D_1KI': 25.7599281791297, 'J_D_1KI': 10.882943886408832}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
index 5c599da..f03b074 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 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}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 352628, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.273355960845947, "TIME_S_1KI": 0.029133693186150694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.6947084188461, "W": 73.02, "J_1KI": 2.8293122168938547, "W_1KI": 0.20707374343500798, "W_D": 56.76349999999999, "J_D": 775.5771512097119, "W_D_1KI": 0.16097275315630066, "J_D_1KI": 0.00045649453008921774}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
index bd4d211..a48462c 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,102 @@
-['apptainer', 'run', '--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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03506755828857422}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 578,  489, 2035, 2602, 4011, 1806, 4187, 4466, 1701,
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-       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])
+       col_indices=tensor([2913, 1648, 2644, 3381, 1372, 3923, 4637, 4740, 3605,
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+                           3988, 4385,  855, 1469, 4176, 2016, 3588, 1388, 4820,
+                            138, 2359, 3521,  918, 3462, 3885, 3534]),
+       values=tensor([8.7230e-01, 2.2609e-01, 1.4440e-01, 9.6081e-01,
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+                      7.5958e-01, 9.4342e-01, 6.1220e-02, 8.5277e-01,
+                      8.0569e-01, 8.5113e-02, 3.3815e-01, 2.2293e-01,
+                      7.4069e-01, 9.6487e-01, 9.2541e-01, 4.0658e-01,
+                      7.8100e-01, 1.8005e-01, 7.5104e-01, 2.0211e-01,
+                      1.3360e-01, 5.3522e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.6988, 0.9907, 0.6239,  ..., 0.8127, 0.8805, 0.8314])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,161 +104,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.04602789878845215 seconds
+Time: 0.03506755828857422 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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]),
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-       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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '29942', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8915646076202393}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+       col_indices=tensor([3138, 4671, 1014, 2355, 4387, 3668, 1195, 4247,  491,
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+                      0.4823, 0.8933, 0.7206, 0.5757, 0.6875]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8734, 0.8080, 0.1055,  ..., 0.8475, 0.7666, 0.2333])
+tensor([0.4051, 0.4452, 0.8286,  ..., 0.6416, 0.7748, 0.9825])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +185,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.226954936981201 seconds
+Time: 0.8915646076202393 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '352628', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.273355960845947}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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-                           4408, 2688,  366, 1825, 2362, 2165,  528]),
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+       col_indices=tensor([1604,    4,  806,  370, 4659, 2266,  385, 4480, 1740,
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+                      0.0572, 0.8754, 0.4725, 0.5233, 0.0364, 0.1500, 0.2431,
+                      0.3915, 0.8270, 0.2064, 0.5104, 0.9129, 0.4413, 0.8801,
+                      0.9179, 0.9739, 0.2250, 0.5404, 0.9261, 0.5735, 0.2090,
+                      0.7470, 0.4131, 0.1494, 0.0532, 0.6628]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8734, 0.8080, 0.1055,  ..., 0.8475, 0.7666, 0.2333])
+tensor([0.3633, 0.6430, 0.8109,  ..., 0.6589, 0.7112, 0.9999])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +266,91 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.226954936981201 seconds
+Time: 10.273355960845947 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}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1604,    4,  806,  370, 4659, 2266,  385, 4480, 1740,
+                           2477, 1011, 4368, 1436, 1511,  582, 2881, 3146,  679,
+                           1335,  340, 2368, 3531, 2793, 4894, 1704,  800, 4449,
+                           2819, 3830,  944,  715,  291, 1651, 2756, 3425, 4366,
+                           1387, 4461, 4123, 3059,   45,   97,  307, 3123, 2010,
+                           4861, 3262,  819, 2940, 4148, 3668, 1416, 2946,  250,
+                           2020, 1865, 1972, 4176, 4993, 3807,  275, 1852, 2676,
+                           3641, 2214, 2133, 3702, 1587,   40, 2796, 4522, 2611,
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+                           4954, 4072, 1985,  625, 3210, 3147, 4908, 2800, 1924,
+                            230, 2107, 2981, 3816, 1923, 3645, 3133, 4236, 4114,
+                           2851, 2177, 1793, 4717,  666, 1768, 4852]),
+       values=tensor([0.8340, 0.7919, 0.0199, 0.7955, 0.8390, 0.2112, 0.2062,
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+                      0.9179, 0.9739, 0.2250, 0.5404, 0.9261, 0.5735, 0.2090,
+                      0.7470, 0.4131, 0.1494, 0.0532, 0.6628]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3633, 0.6430, 0.8109,  ..., 0.6589, 0.7112, 0.9999])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.273355960845947 seconds
+
+[18.62, 17.97, 17.77, 17.77, 18.2, 18.07, 17.98, 17.9, 18.36, 17.75]
+[73.02]
+13.663307428359985
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 352628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.273355960845947, 'TIME_S_1KI': 0.029133693186150694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.6947084188461, 'W': 73.02}
+[18.62, 17.97, 17.77, 17.77, 18.2, 18.07, 17.98, 17.9, 18.36, 17.75, 18.68, 18.07, 18.34, 18.06, 18.18, 17.85, 18.03, 18.15, 17.94, 17.93]
+325.13
+16.2565
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 352628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.273355960845947, 'TIME_S_1KI': 0.029133693186150694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.6947084188461, 'W': 73.02, 'J_1KI': 2.8293122168938547, 'W_1KI': 0.20707374343500798, 'W_D': 56.76349999999999, 'J_D': 775.5771512097119, 'W_D_1KI': 0.16097275315630066, 'J_D_1KI': 0.00045649453008921774}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
index 4385053..f8ae472 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 355197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.57451844215393, "TIME_S_1KI": 0.029770855165313703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1141.788979816437, "W": 79.9, "J_1KI": 3.214523151424243, "W_1KI": 0.2249455935720178, "W_D": 63.777750000000005, "J_D": 911.3983993427754, "W_D_1KI": 0.17955599287156143, "J_D_1KI": 0.000505511006206588}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 348362, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331048488616943, "TIME_S_1KI": 0.029656071812129176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1019.3632760500908, "W": 73.39, "J_1KI": 2.9261609361815895, "W_1KI": 0.2106716576434858, "W_D": 57.056, "J_D": 792.4893184127807, "W_D_1KI": 0.1637836503407375, "J_D_1KI": 0.0004701536055618509}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
index 5fcc662..7fb17e1 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04642629623413086}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01948690414428711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([1959, 1786,   16,  ..., 4660,  524, 2490]),
-       values=tensor([0.5665, 0.4844, 0.2984,  ..., 0.5218, 0.3017, 0.5058]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3543, 2601, 4811,  ..., 3171, 1181, 2171]),
+       values=tensor([0.9467, 0.6961, 0.1720,  ..., 0.4974, 0.2968, 0.3956]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.6773, 0.1820, 0.1692,  ..., 0.1637, 0.2279, 0.2140])
+tensor([0.9876, 0.5815, 0.6649,  ..., 0.6796, 0.2344, 0.7286])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.04642629623413086 seconds
+Time: 0.01948690414428711 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '226164', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.685633659362793}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53882', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6240589618682861}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4154, 3836, 1179,  ..., 2527, 1259,  153]),
-       values=tensor([0.7262, 0.5127, 0.2351,  ..., 0.4025, 0.3877, 0.1384]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 267,  783, 3915,  ..., 3618, 4520, 1464]),
+       values=tensor([0.2837, 0.8920, 0.5250,  ..., 0.9331, 0.1091, 0.1041]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.5042, 0.5821, 0.3979,  ..., 0.3479, 0.7780, 0.3728])
+tensor([0.6941, 0.6065, 0.9857,  ..., 0.4180, 0.3910, 0.2569])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 6.685633659362793 seconds
+Time: 1.6240589618682861 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355197', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.57451844215393}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '348362', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331048488616943}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2741, 2960, 2565,  ...,  976,  304, 1232]),
-       values=tensor([0.5180, 0.1270, 0.6648,  ..., 0.3095, 0.3853, 0.9447]),
+       col_indices=tensor([2866, 1356, 2436,  ...,  421, 1796, 3666]),
+       values=tensor([0.0261, 0.5356, 0.3907,  ..., 0.0828, 0.6288, 0.2100]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.1571, 0.5465, 0.3582,  ..., 0.1118, 0.4116, 0.5757])
+tensor([0.2739, 0.9422, 0.5483,  ..., 0.7719, 0.1377, 0.3851])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.57451844215393 seconds
+Time: 10.331048488616943 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2741, 2960, 2565,  ...,  976,  304, 1232]),
-       values=tensor([0.5180, 0.1270, 0.6648,  ..., 0.3095, 0.3853, 0.9447]),
+       col_indices=tensor([2866, 1356, 2436,  ...,  421, 1796, 3666]),
+       values=tensor([0.0261, 0.5356, 0.3907,  ..., 0.0828, 0.6288, 0.2100]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.1571, 0.5465, 0.3582,  ..., 0.1118, 0.4116, 0.5757])
+tensor([0.2739, 0.9422, 0.5483,  ..., 0.7719, 0.1377, 0.3851])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.57451844215393 seconds
+Time: 10.331048488616943 seconds
 
-[17.86, 17.95, 18.35, 17.84, 17.76, 17.65, 18.09, 17.61, 17.89, 17.57]
-[79.9]
-14.2902250289917
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.57451844215393, 'TIME_S_1KI': 0.029770855165313703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1141.788979816437, 'W': 79.9}
-[17.86, 17.95, 18.35, 17.84, 17.76, 17.65, 18.09, 17.61, 17.89, 17.57, 18.28, 17.96, 17.74, 18.66, 18.09, 17.53, 17.73, 17.6, 18.05, 18.18]
-322.44500000000005
-16.12225
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.57451844215393, 'TIME_S_1KI': 0.029770855165313703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1141.788979816437, 'W': 79.9, 'J_1KI': 3.214523151424243, 'W_1KI': 0.2249455935720178, 'W_D': 63.777750000000005, 'J_D': 911.3983993427754, 'W_D_1KI': 0.17955599287156143, 'J_D_1KI': 0.000505511006206588}
+[18.79, 18.13, 18.25, 18.09, 18.59, 17.94, 18.05, 18.04, 18.23, 18.09]
+[73.39]
+13.889675378799438
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 348362, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331048488616943, 'TIME_S_1KI': 0.029656071812129176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1019.3632760500908, 'W': 73.39}
+[18.79, 18.13, 18.25, 18.09, 18.59, 17.94, 18.05, 18.04, 18.23, 18.09, 18.49, 18.19, 18.09, 17.88, 18.04, 18.12, 18.21, 18.0, 18.16, 17.97]
+326.68
+16.334
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 348362, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331048488616943, 'TIME_S_1KI': 0.029656071812129176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1019.3632760500908, 'W': 73.39, 'J_1KI': 2.9261609361815895, 'W_1KI': 0.2106716576434858, 'W_D': 57.056, 'J_D': 792.4893184127807, 'W_D_1KI': 0.1637836503407375, 'J_D_1KI': 0.0004701536055618509}
diff --git a/pytorch/synthetic_densities b/pytorch/synthetic_densities
index 4b46d53..45681ce 100644
--- a/pytorch/synthetic_densities
+++ b/pytorch/synthetic_densities
@@ -7,3 +7,5 @@
 0.1
 0.2
 0.3
+0.4
+0.5