From cf56df31145c342df66f3340f5033e3ed95243af Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Sun, 15 Dec 2024 22:56:17 -0500
Subject: [PATCH] Synthetic data

---
 pytorch/batch.py                              |    2 +-
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   65 +
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   81 +
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |   65 +
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   45 +
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   45 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1521 +++++++++++++
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   68 +
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_50000_0.0001.output |   65 +
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_50000_0.001.output |   45 +
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   62 +
 ...6_csr_20_10_10_synthetic_30000_0.0001.json |    1 +
 ...csr_20_10_10_synthetic_30000_0.0001.output |   62 +
 ...16_csr_20_10_10_synthetic_30000_0.001.json |    1 +
 ..._csr_20_10_10_synthetic_30000_0.001.output |   45 +
 ..._16_csr_20_10_10_synthetic_30000_0.01.json |    0
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 ...16_csr_20_10_10_synthetic_30000_1e-05.json |    1 +
 ..._csr_20_10_10_synthetic_30000_1e-05.output |   62 +
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   85 +
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   81 +
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |   85 +
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   85 +
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   85 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1900 +++++++++++++++++
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   89 +
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_50000_0.0001.output |  105 +
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_50000_0.001.output |   85 +
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   81 +
 ...6_csr_20_10_10_synthetic_30000_0.0001.json |    1 +
 ...csr_20_10_10_synthetic_30000_0.0001.output |   81 +
 ...16_csr_20_10_10_synthetic_30000_0.001.json |    0
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 ...16_csr_20_10_10_synthetic_30000_1e-05.json |    1 +
 ..._csr_20_10_10_synthetic_30000_1e-05.output |   81 +
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   65 +
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   81 +
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |   85 +
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    1 +
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 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   65 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1521 +++++++++++++
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
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 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
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 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   45 +
 ...1_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   65 +
 ...1_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   62 +
 ..._1_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |   65 +
 ...a_1_csr_10_10_10_synthetic_10000_0.01.json |    1 +
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 ...a_1_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...1_csr_10_10_10_synthetic_10000_0.05.output |   45 +
 ..._1_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1521 +++++++++++++
 ...1_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   47 +
 ...1_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_50000_0.0001.output |   65 +
 ..._1_csr_10_10_10_synthetic_50000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_50000_0.001.output |   45 +
 ..._1_csr_10_10_10_synthetic_50000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   62 +
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   65 +
 ...1_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   65 +
 ...1_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   81 +
 ..._1_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |   85 +
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 ...p_1_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...1_csr_10_10_10_synthetic_10000_0.05.output |   65 +
 ..._1_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1521 +++++++++++++
 ...1_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   68 +
 ...1_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
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 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
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 ..._1_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1521 +++++++++++++
 ...1_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   47 +
 ...1_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
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 create mode 100644 pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output
 create mode 100644 pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json
 create mode 100644 pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output
 create mode 100644 pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json
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 create mode 100644 pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json
 create mode 100644 pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
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 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
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 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
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 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
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 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
 create mode 100644 pytorch/synthetic_densities
 create mode 100644 pytorch/synthetic_sizes

diff --git a/pytorch/batch.py b/pytorch/batch.py
index 60d9a63..f391f3c 100644
--- a/pytorch/batch.py
+++ b/pytorch/batch.py
@@ -117,7 +117,7 @@ elif args.matrix_type == MatrixType.SYNTHETIC:
     parameter_list = enumerate([(size, density)
         for size in args.synthetic_size
         for density in args.synthetic_density
-        if size ** 2 * density < 100000000])
+        if size ** 2 * density < 10000000])
 
 #for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
 for i, parameter in parameter_list:
diff --git a/pytorch/output_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
new file mode 100644
index 0000000..8bab18f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1770, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.45119595527649, "TIME_S_1KI": 5.904630483207056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 518.2880183124543, "W": 35.449367856062224, "J_1KI": 292.81808944206455, "W_1KI": 20.027891444102952, "W_D": 16.922367856062227, "J_D": 247.4137349045278, "W_D_1KI": 9.560659805684875, "J_D_1KI": 5.401502715076201}
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
new file mode 100644
index 0000000..9bd0023
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.932083368301392}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      10,  ...,  999982,
+                             999993, 1000000]),
+       col_indices=tensor([37897, 46445, 60989,  ..., 76977, 92294, 96477]),
+       values=tensor([0.9469, 0.5853, 0.3833,  ..., 0.6631, 0.6410, 0.8148]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0925, 0.0591, 0.1895,  ..., 0.1208, 0.2736, 0.9441])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 5.932083368301392 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1770 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.45119595527649}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      15,  ...,  999984,
+                             999991, 1000000]),
+       col_indices=tensor([ 6148, 23043, 28153,  ..., 62723, 86562, 96964]),
+       values=tensor([0.4836, 0.5090, 0.9509,  ..., 0.7452, 0.4499, 0.9407]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2481, 0.7173, 0.6398,  ..., 0.9063, 0.5779, 0.5048])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.45119595527649 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      15,  ...,  999984,
+                             999991, 1000000]),
+       col_indices=tensor([ 6148, 23043, 28153,  ..., 62723, 86562, 96964]),
+       values=tensor([0.4836, 0.5090, 0.9509,  ..., 0.7452, 0.4499, 0.9407]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2481, 0.7173, 0.6398,  ..., 0.9063, 0.5779, 0.5048])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.45119595527649 seconds
+
+[20.2, 20.56, 20.52, 20.56, 20.56, 20.6, 20.4, 20.64, 20.8, 20.88]
+[20.88, 21.44, 21.2, 22.44, 23.6, 27.28, 34.76, 40.76, 46.84, 51.32, 53.0, 52.92, 53.08, 52.84]
+14.62051510810852
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1770, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.45119595527649, 'TIME_S_1KI': 5.904630483207056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 518.2880183124543, 'W': 35.449367856062224}
+[20.2, 20.56, 20.52, 20.56, 20.56, 20.6, 20.4, 20.64, 20.8, 20.88, 20.56, 20.52, 20.64, 20.64, 20.52, 20.52, 20.6, 20.68, 20.6, 20.72]
+370.53999999999996
+18.526999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1770, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.45119595527649, 'TIME_S_1KI': 5.904630483207056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 518.2880183124543, 'W': 35.449367856062224, 'J_1KI': 292.81808944206455, 'W_1KI': 20.027891444102952, 'W_D': 16.922367856062227, 'J_D': 247.4137349045278, 'W_D_1KI': 9.560659805684875, 'J_D_1KI': 5.401502715076201}
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
new file mode 100644
index 0000000..f3aace9
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.278456687927246, "TIME_S_1KI": 0.8709818394989616, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 437.5742044067384, "W": 32.19958635623455, "J_1KI": 37.0794173719802, "W_1KI": 2.728547271945983, "W_D": 13.391586356234548, "J_D": 181.9841000671388, "W_D_1KI": 1.1347840315426274, "J_D_1KI": 0.09615998911470446}
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
new file mode 100644
index 0000000..d71dde9
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.063995361328125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99999,
+                            100000]),
+       col_indices=tensor([67343, 31299, 81155,  ..., 33224, 88457, 24576]),
+       values=tensor([0.5842, 0.8218, 0.6188,  ..., 0.3932, 0.6826, 0.0146]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9733, 0.2979, 0.3395,  ..., 0.2786, 0.7488, 0.6423])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 1.063995361328125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9868 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.779469966888428}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      4,  ...,  99997,  99999,
+                            100000]),
+       col_indices=tensor([14435, 22527, 43950,  ...,  8583,  8872, 18967]),
+       values=tensor([0.6873, 0.0224, 0.4938,  ..., 0.6581, 0.7037, 0.6316]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2290, 0.1645, 0.1242,  ..., 0.3445, 0.2954, 0.7059])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 8.779469966888428 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 11801 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.278456687927246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([88946, 66534, 50450,  ..., 63020, 21924, 98776]),
+       values=tensor([0.0165, 0.3102, 0.5959,  ..., 0.2885, 0.2555, 0.6064]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1483, 0.9193, 0.9702,  ..., 0.6151, 0.3023, 0.2526])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.278456687927246 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([88946, 66534, 50450,  ..., 63020, 21924, 98776]),
+       values=tensor([0.0165, 0.3102, 0.5959,  ..., 0.2885, 0.2555, 0.6064]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1483, 0.9193, 0.9702,  ..., 0.6151, 0.3023, 0.2526])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.278456687927246 seconds
+
+[20.52, 20.48, 20.76, 20.96, 20.96, 21.16, 21.32, 21.28, 21.28, 21.2]
+[21.36, 21.64, 21.64, 23.32, 23.96, 29.24, 34.28, 39.64, 43.16, 45.96, 45.88, 46.84, 47.12]
+13.589435577392578
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.278456687927246, 'TIME_S_1KI': 0.8709818394989616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 437.5742044067384, 'W': 32.19958635623455}
+[20.52, 20.48, 20.76, 20.96, 20.96, 21.16, 21.32, 21.28, 21.28, 21.2, 21.04, 20.92, 20.64, 20.52, 20.52, 20.4, 20.72, 20.96, 21.24, 21.32]
+376.16
+18.808
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.278456687927246, 'TIME_S_1KI': 0.8709818394989616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 437.5742044067384, 'W': 32.19958635623455, 'J_1KI': 37.0794173719802, 'W_1KI': 2.728547271945983, 'W_D': 13.391586356234548, 'J_D': 181.9841000671388, 'W_D_1KI': 1.1347840315426274, 'J_D_1KI': 0.09615998911470446}
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
new file mode 100644
index 0000000..ddd2b32
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 33464, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.751937627792358, "TIME_S_1KI": 0.321298638172136, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.9264199829101, "W": 23.099563679174377, "J_1KI": 9.799379033675296, "W_1KI": 0.6902810088206544, "W_D": 4.345563679174376, "J_D": 61.690565237998875, "W_D_1KI": 0.12985786753449605, "J_D_1KI": 0.0038805243705025113}
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
new file mode 100644
index 0000000..c550e8c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.358994722366333}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([4769, 2640, 4731,  ..., 7727, 9096,  344]),
+       values=tensor([0.5549, 0.8764, 0.0270,  ..., 0.0575, 0.5131, 0.9423]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2724, 0.3491, 0.1026,  ..., 0.4580, 0.8295, 0.5142])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.358994722366333 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 29248 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.177036046981812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([8143, 7461, 5162,  ..., 7740, 5053, 9684]),
+       values=tensor([0.7267, 0.3238, 0.0105,  ..., 0.5150, 0.5465, 0.0983]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8883, 0.6326, 0.2674,  ..., 0.1564, 0.2088, 0.8392])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 9.177036046981812 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 33464 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.751937627792358}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9994,  9997, 10000]),
+       col_indices=tensor([1608, 4931, 8613,  ..., 2107, 3637, 7054]),
+       values=tensor([0.4097, 0.1049, 0.8257,  ..., 0.2263, 0.1754, 0.1229]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9092, 0.1064, 0.7261,  ..., 0.1695, 0.8231, 0.3389])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.751937627792358 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9994,  9997, 10000]),
+       col_indices=tensor([1608, 4931, 8613,  ..., 2107, 3637, 7054]),
+       values=tensor([0.4097, 0.1049, 0.8257,  ..., 0.2263, 0.1754, 0.1229]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9092, 0.1064, 0.7261,  ..., 0.1695, 0.8231, 0.3389])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.751937627792358 seconds
+
+[20.16, 20.16, 20.16, 20.32, 20.36, 20.88, 21.6, 22.28, 22.28, 22.28]
+[21.52, 20.68, 23.48, 24.56, 27.0, 27.0, 27.6, 28.4, 25.44, 25.08, 23.88, 23.84, 23.72, 23.68]
+14.196217060089111
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 33464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.751937627792358, 'TIME_S_1KI': 0.321298638172136, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.9264199829101, 'W': 23.099563679174377}
+[20.16, 20.16, 20.16, 20.32, 20.36, 20.88, 21.6, 22.28, 22.28, 22.28, 20.28, 20.68, 20.64, 20.84, 20.84, 20.88, 20.6, 20.6, 20.48, 20.24]
+375.08000000000004
+18.754
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 33464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.751937627792358, 'TIME_S_1KI': 0.321298638172136, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.9264199829101, 'W': 23.099563679174377, 'J_1KI': 9.799379033675296, 'W_1KI': 0.6902810088206544, 'W_D': 4.345563679174376, 'J_D': 61.690565237998875, 'W_D_1KI': 0.12985786753449605, 'J_D_1KI': 0.0038805243705025113}
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
new file mode 100644
index 0000000..91eebfb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.608984231948853, "TIME_S_1KI": 2.260597535041307, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.61959093093867, "W": 23.443356307834602, "J_1KI": 71.08876857680346, "W_1KI": 4.995388090312082, "W_D": 4.929356307834599, "J_D": 70.14907820272437, "W_D_1KI": 1.0503635857307905, "J_D_1KI": 0.223814955408223}
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
new file mode 100644
index 0000000..bec4359
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2371175289154053}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     13,  ...,  99981,  99991,
+                            100000]),
+       col_indices=tensor([  11,  880, 2486,  ..., 7621, 8410, 9572]),
+       values=tensor([0.7919, 0.7111, 0.9252,  ..., 0.0051, 0.9566, 0.6694]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8227, 0.5043, 0.0669,  ..., 0.5765, 0.9663, 0.4234])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.2371175289154053 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4693 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.608984231948853}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     27,  ...,  99982,  99994,
+                            100000]),
+       col_indices=tensor([ 135, 2132, 2413,  ..., 7244, 7277, 8789]),
+       values=tensor([0.8089, 0.0016, 0.7063,  ..., 0.2204, 0.7876, 0.4440]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2483, 0.7850, 0.0043,  ..., 0.4009, 0.1492, 0.4510])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.608984231948853 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     27,  ...,  99982,  99994,
+                            100000]),
+       col_indices=tensor([ 135, 2132, 2413,  ..., 7244, 7277, 8789]),
+       values=tensor([0.8089, 0.0016, 0.7063,  ..., 0.2204, 0.7876, 0.4440]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2483, 0.7850, 0.0043,  ..., 0.4009, 0.1492, 0.4510])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.608984231948853 seconds
+
+[20.32, 20.32, 20.36, 20.6, 20.68, 20.44, 20.64, 20.8, 20.88, 20.84]
+[20.84, 20.52, 23.32, 24.96, 27.48, 27.48, 28.36, 28.96, 25.92, 25.2, 24.36, 24.56, 24.48, 24.08]
+14.23088002204895
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.608984231948853, 'TIME_S_1KI': 2.260597535041307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.61959093093867, 'W': 23.443356307834602}
+[20.32, 20.32, 20.36, 20.6, 20.68, 20.44, 20.64, 20.8, 20.88, 20.84, 20.68, 20.8, 20.52, 20.64, 20.64, 20.68, 20.4, 20.48, 20.36, 20.24]
+370.28000000000003
+18.514000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.608984231948853, 'TIME_S_1KI': 2.260597535041307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.61959093093867, 'W': 23.443356307834602, 'J_1KI': 71.08876857680346, 'W_1KI': 4.995388090312082, 'W_D': 4.929356307834599, 'J_D': 70.14907820272437, 'W_D_1KI': 1.0503635857307905, 'J_D_1KI': 0.223814955408223}
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
new file mode 100644
index 0000000..dd61d28
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.223905086517334, "TIME_S_1KI": 21.223905086517334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 606.5871645927429, "W": 23.902485146880146, "J_1KI": 606.5871645927429, "W_1KI": 23.902485146880146, "W_D": 5.469485146880146, "J_D": 138.80228213262555, "W_D_1KI": 5.469485146880146, "J_D_1KI": 5.469485146880146}
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
new file mode 100644
index 0000000..dbf2821
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.223905086517334}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     100,     193,  ...,  999807,
+                             999898, 1000000]),
+       col_indices=tensor([  45,   67,   78,  ..., 9873, 9905, 9941]),
+       values=tensor([0.2793, 0.5501, 0.9236,  ..., 0.0106, 0.8963, 0.7259]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2312, 0.2281, 0.2895,  ..., 0.4123, 0.5947, 0.5960])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.223905086517334 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     100,     193,  ...,  999807,
+                             999898, 1000000]),
+       col_indices=tensor([  45,   67,   78,  ..., 9873, 9905, 9941]),
+       values=tensor([0.2793, 0.5501, 0.9236,  ..., 0.0106, 0.8963, 0.7259]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2312, 0.2281, 0.2895,  ..., 0.4123, 0.5947, 0.5960])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.223905086517334 seconds
+
+[20.16, 20.16, 20.16, 20.04, 20.28, 20.72, 20.6, 20.64, 20.6, 20.44]
+[20.44, 20.64, 23.68, 24.76, 27.96, 27.96, 29.28, 30.08, 27.32, 27.04, 23.96, 23.92, 23.72, 23.6, 23.72, 23.92, 24.08, 24.24, 24.24, 24.36, 24.24, 24.12, 24.4, 23.96, 24.12]
+25.377577304840088
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.223905086517334, 'TIME_S_1KI': 21.223905086517334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.5871645927429, 'W': 23.902485146880146}
+[20.16, 20.16, 20.16, 20.04, 20.28, 20.72, 20.6, 20.64, 20.6, 20.44, 20.2, 20.32, 20.32, 20.52, 20.52, 20.8, 20.8, 20.72, 20.68, 20.76]
+368.65999999999997
+18.433
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.223905086517334, 'TIME_S_1KI': 21.223905086517334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.5871645927429, 'W': 23.902485146880146, 'J_1KI': 606.5871645927429, 'W_1KI': 23.902485146880146, 'W_D': 5.469485146880146, 'J_D': 138.80228213262555, 'W_D_1KI': 5.469485146880146, 'J_D_1KI': 5.469485146880146}
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
new file mode 100644
index 0000000..af8ffc0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 112.2527105808258, "TIME_S_1KI": 112.2527105808258, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2847.1031341934195, "W": 24.02975891792854, "J_1KI": 2847.1031341934195, "W_1KI": 24.02975891792854, "W_D": 5.456758917928539, "J_D": 646.5298079283226, "W_D_1KI": 5.456758917928539, "J_D_1KI": 5.456758917928539}
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
new file mode 100644
index 0000000..d49413b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 112.2527105808258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     493,     999,  ..., 4999078,
+                            4999538, 5000000]),
+       col_indices=tensor([   9,   32,   79,  ..., 9948, 9954, 9975]),
+       values=tensor([0.7230, 0.3394, 0.4856,  ..., 0.5860, 0.3031, 0.1676]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5227, 0.7065, 0.1059,  ..., 0.0574, 0.9985, 0.1783])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 112.2527105808258 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     493,     999,  ..., 4999078,
+                            4999538, 5000000]),
+       col_indices=tensor([   9,   32,   79,  ..., 9948, 9954, 9975]),
+       values=tensor([0.7230, 0.3394, 0.4856,  ..., 0.5860, 0.3031, 0.1676]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5227, 0.7065, 0.1059,  ..., 0.0574, 0.9985, 0.1783])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 112.2527105808258 seconds
+
+[20.36, 20.76, 20.76, 20.64, 20.76, 20.64, 20.44, 20.2, 20.64, 20.52]
+[20.84, 20.72, 21.32, 21.96, 24.12, 27.04, 27.04, 28.68, 28.56, 28.24, 25.72, 24.44, 24.36, 24.24, 24.24, 24.56, 24.28, 24.4, 24.4, 24.44, 24.56, 24.2, 24.24, 24.04, 24.28, 24.12, 24.12, 24.28, 24.32, 24.24, 24.56, 24.56, 24.6, 24.44, 24.6, 24.6, 24.44, 24.44, 24.44, 24.4, 24.36, 24.36, 24.28, 24.28, 24.32, 24.24, 24.28, 24.08, 24.04, 24.04, 24.2, 24.24, 24.32, 24.6, 24.68, 24.36, 24.36, 24.28, 24.24, 24.08, 24.24, 24.32, 24.36, 24.6, 24.6, 24.64, 24.68, 24.6, 24.6, 24.4, 24.28, 24.4, 24.4, 24.2, 24.32, 24.36, 24.4, 24.44, 24.56, 24.44, 24.44, 24.4, 24.28, 24.4, 24.56, 24.56, 24.64, 24.76, 24.68, 24.44, 24.44, 24.36, 24.32, 24.32, 24.16, 24.24, 24.2, 24.12, 23.8, 23.88, 23.88, 23.76, 24.08, 24.24, 24.4, 24.4, 24.6, 24.52, 24.4, 24.56, 24.48, 24.4, 24.68, 24.72, 24.68, 24.8, 24.8]
+118.48238444328308
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 112.2527105808258, 'TIME_S_1KI': 112.2527105808258, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2847.1031341934195, 'W': 24.02975891792854}
+[20.36, 20.76, 20.76, 20.64, 20.76, 20.64, 20.44, 20.2, 20.64, 20.52, 20.52, 20.56, 20.56, 20.56, 20.8, 20.88, 20.8, 20.8, 20.68, 20.56]
+371.46000000000004
+18.573
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 112.2527105808258, 'TIME_S_1KI': 112.2527105808258, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2847.1031341934195, 'W': 24.02975891792854, 'J_1KI': 2847.1031341934195, 'W_1KI': 24.02975891792854, 'W_D': 5.456758917928539, 'J_D': 646.5298079283226, 'W_D_1KI': 5.456758917928539, 'J_D_1KI': 5.456758917928539}
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
new file mode 100644
index 0000000..b09b822
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 141369, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.808244943618774, "TIME_S_1KI": 0.0764541373541496, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.87872554779057, "W": 22.096174468711904, "J_1KI": 2.2273534194044706, "W_1KI": 0.15630141310125914, "W_D": 3.7551744687119033, "J_D": 53.51263643360139, "W_D_1KI": 0.02656292729461129, "J_D_1KI": 0.00018789782268114857}
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
new file mode 100644
index 0000000..ea3d934
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09768295288085938}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([9792, 3011, 8315, 6730, 9843, 4902, 9114,  233,  327,
+                           1077, 5110, 4341, 9618, 1148, 4296, 9363, 2120, 5266,
+                           1510, 7695, 5476, 9179, 3305,   25, 5170, 9334, 9520,
+                             18, 2410, 8122, 6722, 5458, 1189, 9940,  135,  139,
+                           2746, 4302,  817, 8119, 9183, 5557, 7078, 7532, 5204,
+                           9640, 2857, 2903, 8250, 2446, 4645, 4964, 6111, 2787,
+                           4305, 8541, 2087, 6834, 9039, 5610,  449, 6263, 9809,
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+                           5716, 4182, 7270, 9111, 2105, 2273, 7055, 6308, 4091,
+                           9837, 2327, 5713, 7469, 8593, 3004, 1329, 1982, 6739,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1182, 0.0531, 0.2619,  ..., 0.1829, 0.3048, 0.7863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.09768295288085938 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 107490 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.983633518218994}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      7.3581e-01, 7.8623e-01, 2.7676e-01, 6.2277e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8535, 0.9437, 0.0129,  ..., 0.9247, 0.1049, 0.8510])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.983633518218994 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141369 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.808244943618774}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([7315, 1858, 1670, 5364, 1184, 3689, 9574, 1136, 5558,
+                           3491,  589, 9091,  159,  766,  502, 9929, 4846, 9650,
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+                      7.2843e-01, 9.0381e-02, 5.7561e-01, 7.3344e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0651, 0.6329, 0.6141,  ..., 0.3243, 0.1158, 0.5219])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.808244943618774 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      3.3857e-01, 2.2407e-02, 7.0282e-01, 1.4713e-01,
+                      6.2629e-01, 6.8243e-01, 9.9627e-01, 5.1013e-01,
+                      8.0536e-02, 9.8552e-01, 6.6502e-01, 6.8200e-02,
+                      8.5999e-02, 5.8414e-01, 8.9296e-01, 3.6543e-01,
+                      6.5870e-01, 7.9749e-01, 1.3448e-01, 7.5013e-01,
+                      2.0267e-01, 4.7561e-01, 3.3676e-01, 8.5537e-01,
+                      2.8565e-01, 3.5750e-01, 6.0110e-01, 4.8385e-01,
+                      6.7175e-01, 2.8373e-02, 2.9667e-01, 1.5980e-01,
+                      6.0663e-01, 5.6893e-01, 8.6187e-01, 3.8426e-01,
+                      9.4313e-01, 1.5700e-01, 6.4374e-01, 6.7882e-01,
+                      8.1698e-02, 8.7238e-01, 6.1915e-01, 2.4451e-01,
+                      8.6242e-01, 6.4213e-01, 9.1978e-01, 5.7440e-01,
+                      6.5066e-02, 1.3295e-01, 6.8781e-01, 6.0675e-01,
+                      2.5313e-01, 2.2555e-02, 1.8691e-01, 8.1480e-01,
+                      8.6104e-01, 2.0666e-01, 2.1914e-01, 6.0359e-01,
+                      5.1611e-01, 5.6270e-01, 2.2858e-01, 7.0563e-02,
+                      7.3900e-02, 9.0084e-01, 4.8907e-01, 2.6475e-01,
+                      7.2082e-01, 6.8751e-01, 1.3829e-01, 9.7521e-02,
+                      9.9126e-01, 6.0189e-01, 7.9516e-01, 2.1625e-01,
+                      5.7626e-01, 1.6852e-01, 9.5043e-01, 3.1655e-01,
+                      2.5323e-01, 5.6154e-01, 5.6857e-01, 5.2077e-01,
+                      9.3389e-01, 8.0069e-01, 1.4968e-01, 8.4212e-01,
+                      3.1001e-01, 2.2924e-01, 6.2681e-01, 2.6673e-01,
+                      9.8161e-01, 4.5832e-01, 3.3629e-01, 6.6850e-01,
+                      7.7463e-01, 6.5327e-02, 8.0080e-01, 4.1266e-01,
+                      7.2843e-01, 9.0381e-02, 5.7561e-01, 7.3344e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0651, 0.6329, 0.6141,  ..., 0.3243, 0.1158, 0.5219])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.808244943618774 seconds
+
+[20.16, 20.24, 20.2, 20.4, 20.4, 20.4, 20.44, 20.32, 20.16, 20.08]
+[20.0, 19.96, 20.64, 22.76, 24.36, 26.04, 26.44, 25.96, 25.96, 24.84, 23.4, 23.52, 23.44, 23.4]
+14.250372886657715
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141369, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.808244943618774, 'TIME_S_1KI': 0.0764541373541496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.87872554779057, 'W': 22.096174468711904}
+[20.16, 20.24, 20.2, 20.4, 20.4, 20.4, 20.44, 20.32, 20.16, 20.08, 20.64, 20.44, 20.4, 20.6, 20.64, 20.48, 20.36, 20.32, 20.32, 20.52]
+366.82000000000005
+18.341
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 141369, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.808244943618774, 'TIME_S_1KI': 0.0764541373541496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.87872554779057, 'W': 22.096174468711904, 'J_1KI': 2.2273534194044706, 'W_1KI': 0.15630141310125914, 'W_D': 3.7551744687119033, 'J_D': 53.51263643360139, 'W_D_1KI': 0.02656292729461129, 'J_D_1KI': 0.00018789782268114857}
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
new file mode 100644
index 0000000..3cf2612
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1458, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.73922610282898, "TIME_S_1KI": 7.365724350362812, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 524.544666223526, "W": 35.90986855086994, "J_1KI": 359.77000426853635, "W_1KI": 24.629539472475955, "W_D": 17.579868550869936, "J_D": 256.7936518120765, "W_D_1KI": 12.0575230115706, "J_D_1KI": 8.269906043601233}
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
new file mode 100644
index 0000000..ac4d6c8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.201478004455566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,       6,  ..., 2499994,
+                            2499998, 2500000]),
+       col_indices=tensor([111852, 327751, 365150,  ..., 493517,  11445,
+                           207886]),
+       values=tensor([0.9407, 0.2669, 0.8671,  ..., 0.7942, 0.4760, 0.2816]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4423, 0.4635, 0.1741,  ..., 0.0346, 0.7600, 0.4318])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 7.201478004455566 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1458 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.73922610282898}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       2,       4,  ..., 2499994,
+                            2500000, 2500000]),
+       col_indices=tensor([198857, 399888, 193187,  ..., 179513, 216653,
+                           450880]),
+       values=tensor([0.4554, 0.7901, 0.7135,  ..., 0.0158, 0.9399, 0.2709]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8645, 0.3649, 0.9819,  ..., 0.4118, 0.2155, 0.1417])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.73922610282898 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       2,       4,  ..., 2499994,
+                            2500000, 2500000]),
+       col_indices=tensor([198857, 399888, 193187,  ..., 179513, 216653,
+                           450880]),
+       values=tensor([0.4554, 0.7901, 0.7135,  ..., 0.0158, 0.9399, 0.2709]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8645, 0.3649, 0.9819,  ..., 0.4118, 0.2155, 0.1417])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.73922610282898 seconds
+
+[20.76, 20.72, 20.4, 20.4, 20.36, 20.36, 20.12, 20.36, 20.28, 20.32]
+[20.56, 20.48, 21.52, 22.84, 24.72, 30.76, 37.24, 43.6, 43.6, 49.32, 53.6, 53.68, 53.6, 53.56]
+14.607256650924683
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1458, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.73922610282898, 'TIME_S_1KI': 7.365724350362812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 524.544666223526, 'W': 35.90986855086994}
+[20.76, 20.72, 20.4, 20.4, 20.36, 20.36, 20.12, 20.36, 20.28, 20.32, 20.48, 20.4, 20.32, 20.04, 20.2, 20.4, 20.36, 20.36, 20.48, 20.52]
+366.6
+18.330000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1458, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.73922610282898, 'TIME_S_1KI': 7.365724350362812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 524.544666223526, 'W': 35.90986855086994, 'J_1KI': 359.77000426853635, 'W_1KI': 24.629539472475955, 'W_D': 17.579868550869936, 'J_D': 256.7936518120765, 'W_D_1KI': 12.0575230115706, 'J_D_1KI': 8.269906043601233}
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
new file mode 100644
index 0000000..36f1dbe
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3515, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.52223539352417, "TIME_S_1KI": 2.9935235827949276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 470.9832169723511, "W": 32.170385708153674, "J_1KI": 133.99238036197755, "W_1KI": 9.15231456846477, "W_D": 13.629385708153674, "J_D": 199.53792237424858, "W_D_1KI": 3.8774923778531076, "J_D_1KI": 1.1031272767718656}
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
new file mode 100644
index 0000000..60bafaf
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.9865975379943848}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249989, 249995,
+                            250000]),
+       col_indices=tensor([12071, 16957, 24871,  ..., 32088, 41674, 47752]),
+       values=tensor([0.0278, 0.4403, 0.7542,  ..., 0.8727, 0.3256, 0.0294]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9906, 0.0790, 0.7013,  ..., 0.2118, 0.2385, 0.3873])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 2.9865975379943848 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3515 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.52223539352417}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     10,  ..., 249992, 249995,
+                            250000]),
+       col_indices=tensor([ 9701, 11138, 26862,  ..., 20273, 37187, 48197]),
+       values=tensor([0.8537, 0.5403, 0.1220,  ..., 0.0155, 0.7712, 0.8752]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5658, 0.7328, 0.9479,  ..., 0.1014, 0.1582, 0.5663])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.52223539352417 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     10,  ..., 249992, 249995,
+                            250000]),
+       col_indices=tensor([ 9701, 11138, 26862,  ..., 20273, 37187, 48197]),
+       values=tensor([0.8537, 0.5403, 0.1220,  ..., 0.0155, 0.7712, 0.8752]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5658, 0.7328, 0.9479,  ..., 0.1014, 0.1582, 0.5663])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.52223539352417 seconds
+
+[20.72, 20.68, 20.52, 20.64, 20.64, 20.68, 20.68, 20.72, 20.72, 20.72]
+[20.64, 20.72, 21.28, 22.68, 24.8, 29.24, 34.6, 38.2, 42.72, 43.84, 43.84, 44.32, 44.16, 44.08]
+14.640272617340088
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3515, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.52223539352417, 'TIME_S_1KI': 2.9935235827949276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 470.9832169723511, 'W': 32.170385708153674}
+[20.72, 20.68, 20.52, 20.64, 20.64, 20.68, 20.68, 20.72, 20.72, 20.72, 20.32, 20.24, 20.44, 20.32, 20.48, 20.64, 20.52, 20.64, 20.96, 20.84]
+370.82
+18.541
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3515, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.52223539352417, 'TIME_S_1KI': 2.9935235827949276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 470.9832169723511, 'W': 32.170385708153674, 'J_1KI': 133.99238036197755, 'W_1KI': 9.15231456846477, 'W_D': 13.629385708153674, 'J_D': 199.53792237424858, 'W_D_1KI': 3.8774923778531076, 'J_D_1KI': 1.1031272767718656}
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
new file mode 100644
index 0000000..091be62
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.268765687942505, "TIME_S_1KI": 27.268765687942505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1234.21608165741, "W": 36.82999610504039, "J_1KI": 1234.21608165741, "W_1KI": 36.82999610504039, "W_D": 18.278996105040388, "J_D": 612.5504571070677, "W_D_1KI": 18.278996105040388, "J_D_1KI": 18.278996105040388}
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
new file mode 100644
index 0000000..9980b68
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.268765687942505}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,     102,  ..., 2499892,
+                            2499945, 2500000]),
+       col_indices=tensor([  987,  1836,  5791,  ..., 47187, 47558, 49789]),
+       values=tensor([0.1085, 0.8855, 0.3536,  ..., 0.4174, 0.4340, 0.6085]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6397, 0.2759, 0.9232,  ..., 0.5725, 0.2810, 0.6127])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 27.268765687942505 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,     102,  ..., 2499892,
+                            2499945, 2500000]),
+       col_indices=tensor([  987,  1836,  5791,  ..., 47187, 47558, 49789]),
+       values=tensor([0.1085, 0.8855, 0.3536,  ..., 0.4174, 0.4340, 0.6085]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6397, 0.2759, 0.9232,  ..., 0.5725, 0.2810, 0.6127])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 27.268765687942505 seconds
+
+[20.68, 20.56, 20.48, 20.12, 20.08, 20.28, 20.44, 20.68, 20.96, 20.96]
+[20.84, 20.76, 20.6, 24.92, 26.28, 30.48, 35.16, 37.64, 40.0, 42.72, 43.28, 43.52, 43.36, 43.36, 43.52, 42.92, 43.08, 42.76, 42.76, 42.52, 42.68, 42.8, 42.88, 43.04, 43.16, 42.96, 42.88, 42.76, 42.52, 42.72, 42.64, 42.64]
+33.511165142059326
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.268765687942505, 'TIME_S_1KI': 27.268765687942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.21608165741, 'W': 36.82999610504039}
+[20.68, 20.56, 20.48, 20.12, 20.08, 20.28, 20.44, 20.68, 20.96, 20.96, 20.44, 20.76, 20.8, 20.8, 20.72, 20.84, 20.84, 20.56, 20.68, 20.76]
+371.02
+18.551
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.268765687942505, 'TIME_S_1KI': 27.268765687942505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.21608165741, 'W': 36.82999610504039, 'J_1KI': 1234.21608165741, 'W_1KI': 36.82999610504039, 'W_D': 18.278996105040388, 'J_D': 612.5504571070677, 'W_D_1KI': 18.278996105040388, 'J_D_1KI': 18.278996105040388}
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
new file mode 100644
index 0000000..02be475
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 19539, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.163734436035156, "TIME_S_1KI": 0.5201767969719615, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 495.2794138240814, "W": 33.936722825817846, "J_1KI": 25.348247803064712, "W_1KI": 1.7368710182618274, "W_D": 13.302722825817849, "J_D": 194.14263413858416, "W_D_1KI": 0.6808292556332386, "J_D_1KI": 0.03484463153862729}
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
new file mode 100644
index 0000000..4c99396
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5373842716217041}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([13933,   723, 18387,  ..., 22194, 38514,  2158]),
+       values=tensor([0.9124, 0.6353, 0.3193,  ..., 0.0372, 0.2371, 0.8076]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6534, 0.7497, 0.2436,  ..., 0.0965, 0.5741, 0.5754])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.5373842716217041 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 19539 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.163734436035156}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24996, 24998, 25000]),
+       col_indices=tensor([44723, 48345, 32100,  ..., 22467, 28064, 29572]),
+       values=tensor([0.7283, 0.2640, 0.9583,  ..., 0.2460, 0.4237, 0.5300]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7754, 0.9311, 0.4703,  ..., 0.3816, 0.8788, 0.3934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.163734436035156 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24996, 24998, 25000]),
+       col_indices=tensor([44723, 48345, 32100,  ..., 22467, 28064, 29572]),
+       values=tensor([0.7283, 0.2640, 0.9583,  ..., 0.2460, 0.4237, 0.5300]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7754, 0.9311, 0.4703,  ..., 0.3816, 0.8788, 0.3934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.163734436035156 seconds
+
+[25.08, 25.2, 25.2, 25.4, 25.08, 25.4, 25.12, 25.0, 25.28, 25.36]
+[25.56, 25.6, 26.0, 26.32, 28.76, 32.48, 32.48, 37.24, 41.24, 45.24, 45.88, 45.72, 45.6, 45.64]
+14.594202756881714
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19539, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.163734436035156, 'TIME_S_1KI': 0.5201767969719615, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 495.2794138240814, 'W': 33.936722825817846}
+[25.08, 25.2, 25.2, 25.4, 25.08, 25.4, 25.12, 25.0, 25.28, 25.36, 20.68, 20.68, 20.6, 20.6, 20.52, 20.72, 20.52, 20.72, 20.72, 20.72]
+412.67999999999995
+20.633999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19539, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.163734436035156, 'TIME_S_1KI': 0.5201767969719615, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 495.2794138240814, 'W': 33.936722825817846, 'J_1KI': 25.348247803064712, 'W_1KI': 1.7368710182618274, 'W_D': 13.302722825817849, 'J_D': 194.14263413858416, 'W_D_1KI': 0.6808292556332386, 'J_D_1KI': 0.03484463153862729}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json
new file mode 100644
index 0000000..d8def16
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 9519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.79372525215149, "TIME_S_1KI": 2.2894973476364626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 607.9340667724609, "W": 24.009013571692563, "J_1KI": 63.86532900225454, "W_1KI": 2.5222201462015508, "W_D": 5.522013571692561, "J_D": 139.82332749271384, "W_D_1KI": 0.5801043777384768, "J_D_1KI": 0.06094173523883567}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output
new file mode 100644
index 0000000..91dd9e3
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.0001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.2060210704803467}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,     6,  ..., 89996, 89998, 90000]),
+       col_indices=tensor([ 2876,  4713,  6957,  ..., 29701, 15647, 23288]),
+       values=tensor([0.6297, 0.3832, 0.4268,  ..., 0.4020, 0.1713, 0.6526]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.2297, 0.3740, 0.0656,  ..., 0.6156, 0.3028, 0.9303])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 2.2060210704803467 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9519 -ss 30000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.79372525215149}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 89997, 89999, 90000]),
+       col_indices=tensor([15244, 15936,  9998,  ..., 16898, 18863, 20836]),
+       values=tensor([0.4356, 0.9410, 0.0325,  ..., 0.8568, 0.9195, 0.8628]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.3669, 0.2405, 0.0914,  ..., 0.8449, 0.6451, 0.3598])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 21.79372525215149 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 89997, 89999, 90000]),
+       col_indices=tensor([15244, 15936,  9998,  ..., 16898, 18863, 20836]),
+       values=tensor([0.4356, 0.9410, 0.0325,  ..., 0.8568, 0.9195, 0.8628]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.3669, 0.2405, 0.0914,  ..., 0.8449, 0.6451, 0.3598])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 21.79372525215149 seconds
+
+[20.48, 20.6, 20.6, 20.52, 20.52, 20.76, 20.88, 21.04, 21.0, 21.0]
+[20.96, 20.88, 23.92, 25.0, 27.36, 27.36, 28.16, 28.88, 25.92, 25.64, 24.4, 24.24, 24.2, 24.16, 24.24, 24.84, 24.92, 24.92, 24.6, 24.64, 24.64, 24.68, 24.56, 24.8, 24.96]
+25.32107639312744
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 9519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.79372525215149, 'TIME_S_1KI': 2.2894973476364626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 607.9340667724609, 'W': 24.009013571692563}
+[20.48, 20.6, 20.6, 20.52, 20.52, 20.76, 20.88, 21.04, 21.0, 21.0, 20.24, 20.28, 20.44, 20.36, 20.2, 20.24, 20.24, 20.44, 20.52, 20.48]
+369.74
+18.487000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 9519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.79372525215149, 'TIME_S_1KI': 2.2894973476364626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 607.9340667724609, 'W': 24.009013571692563, 'J_1KI': 63.86532900225454, 'W_1KI': 2.5222201462015508, 'W_D': 5.522013571692561, 'J_D': 139.82332749271384, 'W_D_1KI': 0.5801043777384768, 'J_D_1KI': 0.06094173523883567}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json
new file mode 100644
index 0000000..d309fe1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.699798345565796, "TIME_S_1KI": 20.699798345565796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 537.3931253051758, "W": 23.043866328673488, "J_1KI": 537.3931253051758, "W_1KI": 23.043866328673488, "W_D": 4.700866328673488, "J_D": 109.62627590250972, "W_D_1KI": 4.700866328673488, "J_D_1KI": 4.700866328673488}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output
new file mode 100644
index 0000000..67d8d59
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.699798345565796}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     33,     63,  ..., 899945, 899974,
+                            900000]),
+       col_indices=tensor([  547,  1664,  1767,  ..., 28485, 29124, 29514]),
+       values=tensor([0.5453, 0.3696, 0.4974,  ..., 0.8638, 0.8625, 0.2546]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.4458, 0.9665, 0.0852,  ..., 0.0100, 0.1262, 0.9671])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.699798345565796 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     33,     63,  ..., 899945, 899974,
+                            900000]),
+       col_indices=tensor([  547,  1664,  1767,  ..., 28485, 29124, 29514]),
+       values=tensor([0.5453, 0.3696, 0.4974,  ..., 0.8638, 0.8625, 0.2546]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.4458, 0.9665, 0.0852,  ..., 0.0100, 0.1262, 0.9671])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.699798345565796 seconds
+
+[20.6, 20.52, 20.4, 20.52, 20.64, 20.44, 20.4, 20.36, 20.16, 20.16]
+[20.16, 20.08, 20.2, 21.68, 22.88, 25.96, 26.92, 27.04, 26.52, 24.64, 24.28, 23.92, 24.12, 24.12, 24.48, 24.6, 24.4, 24.32, 24.28, 24.36, 24.16, 24.4, 24.32]
+23.320441007614136
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.699798345565796, 'TIME_S_1KI': 20.699798345565796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 537.3931253051758, 'W': 23.043866328673488}
+[20.6, 20.52, 20.4, 20.52, 20.64, 20.44, 20.4, 20.36, 20.16, 20.16, 20.44, 20.2, 20.16, 20.16, 20.4, 20.44, 20.56, 20.52, 20.28, 20.2]
+366.86
+18.343
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.699798345565796, 'TIME_S_1KI': 20.699798345565796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 537.3931253051758, 'W': 23.043866328673488, 'J_1KI': 537.3931253051758, 'W_1KI': 23.043866328673488, 'W_D': 4.700866328673488, 'J_D': 109.62627590250972, 'W_D_1KI': 4.700866328673488, 'J_D_1KI': 4.700866328673488}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output
new file mode 100644
index 0000000..054bc13
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_0.01.output
@@ -0,0 +1 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.01 -c 16']
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json
new file mode 100644
index 0000000..00f9500
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52473, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.229777336120605, "TIME_S_1KI": 0.40458478333849035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 561.8060250091554, "W": 23.114903822014995, "J_1KI": 10.706573380770212, "W_1KI": 0.4405104305455185, "W_D": 4.660903822014994, "J_D": 113.28292210769662, "W_D_1KI": 0.08882480174594543, "J_D_1KI": 0.0016927715538647577}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output
new file mode 100644
index 0000000..a0c66f4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_20_10_10_synthetic_30000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.4002048969268799}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([17165, 27151, 23572,  ..., 25119,  9148,  7528]),
+       values=tensor([0.4884, 0.2785, 0.9649,  ..., 0.5831, 0.3229, 0.8447]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.9734, 0.5614, 0.1566,  ..., 0.4974, 0.8204, 0.0911])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 0.4002048969268799 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52473 -ss 30000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.229777336120605}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 8998, 9000, 9000]),
+       col_indices=tensor([25247, 22356, 16191,  ..., 29211, 15014, 22819]),
+       values=tensor([0.9864, 0.6356, 0.7247,  ..., 0.1858, 0.6120, 0.1833]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.8210, 0.2318, 0.4195,  ..., 0.3881, 0.9911, 0.4380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.229777336120605 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 8998, 9000, 9000]),
+       col_indices=tensor([25247, 22356, 16191,  ..., 29211, 15014, 22819]),
+       values=tensor([0.9864, 0.6356, 0.7247,  ..., 0.1858, 0.6120, 0.1833]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.8210, 0.2318, 0.4195,  ..., 0.3881, 0.9911, 0.4380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.229777336120605 seconds
+
+[20.6, 20.72, 20.72, 20.72, 20.72, 20.6, 20.28, 20.32, 20.44, 20.48]
+[20.48, 20.52, 20.6, 21.72, 23.52, 25.24, 26.32, 26.68, 25.64, 24.84, 24.92, 24.56, 24.64, 24.64, 24.44, 24.12, 24.32, 24.4, 24.32, 24.44, 24.28, 24.08, 24.04, 23.96]
+24.3049259185791
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.229777336120605, 'TIME_S_1KI': 0.40458478333849035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 561.8060250091554, 'W': 23.114903822014995}
+[20.6, 20.72, 20.72, 20.72, 20.72, 20.6, 20.28, 20.32, 20.44, 20.48, 20.76, 20.68, 20.68, 20.44, 20.48, 20.24, 20.24, 20.32, 20.36, 20.4]
+369.08000000000004
+18.454
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.229777336120605, 'TIME_S_1KI': 0.40458478333849035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 561.8060250091554, 'W': 23.114903822014995, 'J_1KI': 10.706573380770212, 'W_1KI': 0.4405104305455185, 'W_D': 4.660903822014994, 'J_D': 113.28292210769662, 'W_D_1KI': 0.08882480174594543, 'J_D_1KI': 0.0016927715538647577}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..9b3aaee
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 66220, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.006447315216064, "TIME_S_1KI": 0.15110914097275843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1856.6265121269225, "W": 145.81, "J_1KI": 28.037247238401125, "W_1KI": 2.2019027484143763, "W_D": 109.5725, "J_D": 1395.2075200605393, "W_D_1KI": 1.6546738145575355, "J_D_1KI": 0.024987523626661668}
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
new file mode 100644
index 0000000..9d496c4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.20569086074829102}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      18,  ...,  999978,
+                             999986, 1000000]),
+       col_indices=tensor([ 4321, 11912, 13631,  ..., 82074, 92560, 99324]),
+       values=tensor([0.9071, 0.2919, 0.8193,  ..., 0.7739, 0.0445, 0.1624]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6567, 0.9688, 0.9697,  ..., 0.6873, 0.4864, 0.9023])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.20569086074829102 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '51047', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.09404468536377}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      17,  ...,  999979,
+                             999988, 1000000]),
+       col_indices=tensor([15686, 48109, 49313,  ..., 51931, 56127, 66767]),
+       values=tensor([0.4545, 0.6496, 0.9508,  ..., 0.7270, 0.9957, 0.0621]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3660, 0.6002, 0.9317,  ..., 0.1977, 0.4107, 0.4541])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 8.09404468536377 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66220', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.006447315216064}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      18,  ...,  999980,
+                             999990, 1000000]),
+       col_indices=tensor([ 4776, 21129, 24622,  ..., 75160, 86654, 97411]),
+       values=tensor([0.8410, 0.1609, 0.8553,  ..., 0.3742, 0.0938, 0.8797]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6086, 0.7634, 0.6649,  ..., 0.3430, 0.9091, 0.5785])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.006447315216064 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      18,  ...,  999980,
+                             999990, 1000000]),
+       col_indices=tensor([ 4776, 21129, 24622,  ..., 75160, 86654, 97411]),
+       values=tensor([0.8410, 0.1609, 0.8553,  ..., 0.3742, 0.0938, 0.8797]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6086, 0.7634, 0.6649,  ..., 0.3430, 0.9091, 0.5785])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.006447315216064 seconds
+
+[41.04, 40.85, 39.33, 39.23, 39.35, 39.32, 44.72, 39.63, 39.87, 39.35]
+[145.81]
+12.733190536499023
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.006447315216064, 'TIME_S_1KI': 0.15110914097275843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1856.6265121269225, 'W': 145.81}
+[41.04, 40.85, 39.33, 39.23, 39.35, 39.32, 44.72, 39.63, 39.87, 39.35, 40.83, 39.21, 39.3, 44.69, 39.3, 39.36, 39.77, 39.25, 41.13, 39.66]
+724.75
+36.2375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.006447315216064, 'TIME_S_1KI': 0.15110914097275843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1856.6265121269225, 'W': 145.81, 'J_1KI': 28.037247238401125, 'W_1KI': 2.2019027484143763, 'W_D': 109.5725, 'J_D': 1395.2075200605393, 'W_D_1KI': 1.6546738145575355, 'J_D_1KI': 0.024987523626661668}
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
new file mode 100644
index 0000000..b72a71c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 101854, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.331042528152466, "TIME_S_1KI": 0.13088383890816724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1504.6270782256126, "W": 115.31, "J_1KI": 14.77239065943029, "W_1KI": 1.1321106682113615, "W_D": 79.84075000000001, "J_D": 1041.8051721085908, "W_D_1KI": 0.7838744673748701, "J_D_1KI": 0.007696059726420858}
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
new file mode 100644
index 0000000..3cc7b50
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1346125602722168}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      3,  ...,  99997,  99999,
+                            100000]),
+       col_indices=tensor([50727, 53996, 86356,  ...,  6143, 63321, 22305]),
+       values=tensor([0.4164, 0.0014, 0.4337,  ..., 0.6487, 0.2549, 0.7487]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9720, 0.1729, 0.4503,  ..., 0.2850, 0.8795, 0.9664])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.1346125602722168 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78001', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.040945768356323}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([16049, 52557, 57673,  ..., 90883, 73385, 65676]),
+       values=tensor([0.2845, 0.3961, 0.0285,  ..., 0.0101, 0.6896, 0.8511]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5851, 0.1832, 0.4128,  ..., 0.6645, 0.1519, 0.8981])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 8.040945768356323 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '101854', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.331042528152466}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([  641, 46150, 85524,  ..., 87101, 55219, 61785]),
+       values=tensor([0.2560, 0.7953, 0.3517,  ..., 0.8505, 0.5170, 0.2719]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1951, 0.6662, 0.1969,  ..., 0.4780, 0.9904, 0.5617])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 13.331042528152466 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([  641, 46150, 85524,  ..., 87101, 55219, 61785]),
+       values=tensor([0.2560, 0.7953, 0.3517,  ..., 0.8505, 0.5170, 0.2719]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1951, 0.6662, 0.1969,  ..., 0.4780, 0.9904, 0.5617])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 13.331042528152466 seconds
+
+[42.16, 39.6, 39.44, 39.17, 39.28, 39.19, 39.17, 39.11, 40.48, 39.44]
+[115.31]
+13.048539400100708
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 101854, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.331042528152466, 'TIME_S_1KI': 0.13088383890816724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1504.6270782256126, 'W': 115.31}
+[42.16, 39.6, 39.44, 39.17, 39.28, 39.19, 39.17, 39.11, 40.48, 39.44, 39.93, 39.1, 39.15, 39.29, 39.15, 39.23, 39.19, 39.11, 39.43, 39.06]
+709.3849999999999
+35.469249999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 101854, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.331042528152466, 'TIME_S_1KI': 0.13088383890816724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1504.6270782256126, 'W': 115.31, 'J_1KI': 14.77239065943029, 'W_1KI': 1.1321106682113615, 'W_D': 79.84075000000001, 'J_D': 1041.8051721085908, 'W_D_1KI': 0.7838744673748701, 'J_D_1KI': 0.007696059726420858}
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
new file mode 100644
index 0000000..9b9bfd5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 282693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.381328821182251, "TIME_S_1KI": 0.0367229780050523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.42685277462, "W": 97.9, "J_1KI": 4.5364648320779795, "W_1KI": 0.3463120770588589, "W_D": 62.39075000000001, "J_D": 817.2785818666817, "W_D_1KI": 0.22070143229581213, "J_D_1KI": 0.00078071063767342}
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
new file mode 100644
index 0000000..b8e2459
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05349230766296387}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([3626, 2250, 5764,  ..., 7539, 8316, 7972]),
+       values=tensor([0.1411, 0.7419, 0.4018,  ..., 0.4202, 0.3955, 0.4235]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9736, 0.6802, 0.3390,  ..., 0.1575, 0.6861, 0.0446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.05349230766296387 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '196289', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.290691137313843}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     6,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([ 763, 7857, 9582,  ..., 1442, 6306, 9133]),
+       values=tensor([0.7701, 0.8887, 0.1796,  ..., 0.1701, 0.0666, 0.3737]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4503, 0.2095, 0.3791,  ..., 0.5528, 0.9269, 0.0093])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.290691137313843 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '282693', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.381328821182251}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([4956,  145,  658,  ..., 4096, 6098, 6574]),
+       values=tensor([0.3279, 0.7076, 0.5307,  ..., 0.3493, 0.0702, 0.3289]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0837, 0.8046, 0.5398,  ..., 0.6704, 0.0489, 0.7610])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.381328821182251 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([4956,  145,  658,  ..., 4096, 6098, 6574]),
+       values=tensor([0.3279, 0.7076, 0.5307,  ..., 0.3493, 0.0702, 0.3289]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0837, 0.8046, 0.5398,  ..., 0.6704, 0.0489, 0.7610])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.381328821182251 seconds
+
+[39.48, 38.98, 44.27, 38.82, 39.35, 38.99, 39.28, 39.0, 38.75, 39.31]
+[97.9]
+13.099354982376099
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 282693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.381328821182251, 'TIME_S_1KI': 0.0367229780050523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.42685277462, 'W': 97.9}
+[39.48, 38.98, 44.27, 38.82, 39.35, 38.99, 39.28, 39.0, 38.75, 39.31, 39.89, 39.14, 38.98, 38.75, 41.57, 38.58, 39.15, 38.62, 39.12, 38.99]
+710.185
+35.509249999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 282693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.381328821182251, 'TIME_S_1KI': 0.0367229780050523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.42685277462, 'W': 97.9, 'J_1KI': 4.5364648320779795, 'W_1KI': 0.3463120770588589, 'W_D': 62.39075000000001, 'J_D': 817.2785818666817, 'W_D_1KI': 0.22070143229581213, 'J_D_1KI': 0.00078071063767342}
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
new file mode 100644
index 0000000..d2eaa5f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 189141, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.465899229049683, "TIME_S_1KI": 0.05533384738924761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1388.9149661660194, "W": 107.77, "J_1KI": 7.343278116146259, "W_1KI": 0.5697865613484121, "W_D": 72.38875, "J_D": 932.9295560643077, "W_D_1KI": 0.3827237352028381, "J_D_1KI": 0.002023483724855204}
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
new file mode 100644
index 0000000..117af51
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06988883018493652}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     21,  ...,  99977,  99988,
+                            100000]),
+       col_indices=tensor([ 768, 2423, 2910,  ..., 9615, 9787, 9788]),
+       values=tensor([0.1330, 0.2030, 0.8709,  ..., 0.6786, 0.0798, 0.8357]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0016, 0.6011, 0.7478,  ..., 0.9565, 0.9755, 0.4110])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.06988883018493652 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '150238', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.34029221534729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     21,  ...,  99979,  99987,
+                            100000]),
+       col_indices=tensor([ 978, 1327, 2112,  ..., 8470, 8534, 8708]),
+       values=tensor([0.4296, 0.3021, 0.5865,  ..., 0.4657, 0.4173, 0.7957]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7639, 0.4914, 0.7736,  ..., 0.7926, 0.8542, 0.7117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.34029221534729 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '189141', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.465899229049683}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     15,  ...,  99985,  99995,
+                            100000]),
+       col_indices=tensor([ 277, 3135, 4455,  ..., 4161, 8684, 9934]),
+       values=tensor([0.4295, 0.8999, 0.7885,  ..., 0.8935, 0.6648, 0.4808]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1477, 0.6711, 0.3568,  ..., 0.3604, 0.6617, 0.9866])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.465899229049683 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     15,  ...,  99985,  99995,
+                            100000]),
+       col_indices=tensor([ 277, 3135, 4455,  ..., 4161, 8684, 9934]),
+       values=tensor([0.4295, 0.8999, 0.7885,  ..., 0.8935, 0.6648, 0.4808]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1477, 0.6711, 0.3568,  ..., 0.3604, 0.6617, 0.9866])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.465899229049683 seconds
+
+[39.55, 39.91, 39.11, 39.43, 38.85, 39.56, 39.33, 39.4, 38.87, 38.91]
+[107.77]
+12.887769937515259
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 189141, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.465899229049683, 'TIME_S_1KI': 0.05533384738924761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1388.9149661660194, 'W': 107.77}
+[39.55, 39.91, 39.11, 39.43, 38.85, 39.56, 39.33, 39.4, 38.87, 38.91, 39.56, 38.88, 39.03, 38.89, 39.75, 39.07, 39.18, 39.11, 38.8, 42.89]
+707.6249999999999
+35.381249999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 189141, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.465899229049683, 'TIME_S_1KI': 0.05533384738924761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1388.9149661660194, 'W': 107.77, 'J_1KI': 7.343278116146259, 'W_1KI': 0.5697865613484121, 'W_D': 72.38875, 'J_D': 932.9295560643077, 'W_D_1KI': 0.3827237352028381, 'J_D_1KI': 0.002023483724855204}
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
new file mode 100644
index 0000000..a1716dd
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 105256, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.995163202285767, "TIME_S_1KI": 0.10446115378017184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1749.1652185320856, "W": 133.05, "J_1KI": 16.61819961362854, "W_1KI": 1.264060956145018, "W_D": 97.78125000000001, "J_D": 1285.4983955249193, "W_D_1KI": 0.928985045983127, "J_D_1KI": 0.008825958101990642}
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
new file mode 100644
index 0000000..a290fe5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.13490986824035645}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     107,     208,  ...,  999789,
+                             999899, 1000000]),
+       col_indices=tensor([ 114,  296,  309,  ..., 9749, 9750, 9977]),
+       values=tensor([0.3507, 0.7412, 0.8612,  ..., 0.2456, 0.4049, 0.8296]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8457, 0.6850, 0.0016,  ..., 0.7234, 0.0569, 0.9899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.13490986824035645 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77829', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.763918876647949}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     200,  ...,  999783,
+                             999885, 1000000]),
+       col_indices=tensor([   5,   70,  184,  ..., 9826, 9903, 9930]),
+       values=tensor([0.4822, 0.0560, 0.4645,  ..., 0.7540, 0.5324, 0.2081]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2499, 0.5119, 0.0857,  ..., 0.6236, 0.3822, 0.7230])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 7.763918876647949 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '105256', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.995163202285767}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     220,  ...,  999823,
+                             999909, 1000000]),
+       col_indices=tensor([  16,   85,  154,  ..., 9645, 9832, 9858]),
+       values=tensor([0.5111, 0.0405, 0.8270,  ..., 0.3072, 0.2885, 0.2472]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6255, 0.9183, 0.8326,  ..., 0.9246, 0.2373, 0.5392])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.995163202285767 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     220,  ...,  999823,
+                             999909, 1000000]),
+       col_indices=tensor([  16,   85,  154,  ..., 9645, 9832, 9858]),
+       values=tensor([0.5111, 0.0405, 0.8270,  ..., 0.3072, 0.2885, 0.2472]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6255, 0.9183, 0.8326,  ..., 0.9246, 0.2373, 0.5392])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.995163202285767 seconds
+
+[40.1, 38.91, 39.01, 39.37, 38.97, 38.99, 39.2, 38.87, 38.93, 38.8]
+[133.05]
+13.146675825119019
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.995163202285767, 'TIME_S_1KI': 0.10446115378017184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1749.1652185320856, 'W': 133.05}
+[40.1, 38.91, 39.01, 39.37, 38.97, 38.99, 39.2, 38.87, 38.93, 38.8, 40.08, 39.28, 39.91, 38.88, 38.99, 38.83, 39.08, 39.35, 39.95, 38.73]
+705.375
+35.26875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.995163202285767, 'TIME_S_1KI': 0.10446115378017184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1749.1652185320856, 'W': 133.05, 'J_1KI': 16.61819961362854, 'W_1KI': 1.264060956145018, 'W_D': 97.78125000000001, 'J_D': 1285.4983955249193, 'W_D_1KI': 0.928985045983127, 'J_D_1KI': 0.008825958101990642}
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
new file mode 100644
index 0000000..0aa6e19
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27486, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.233055591583252, "TIME_S_1KI": 0.3723006472961963, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2077.8737717533113, "W": 151.69, "J_1KI": 75.59753226199925, "W_1KI": 5.518809575784036, "W_D": 115.92275, "J_D": 1587.9282864692211, "W_D_1KI": 4.217519828276212, "J_D_1KI": 0.1534424735602202}
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
new file mode 100644
index 0000000..a8a40a5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.45975399017333984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     512,    1006,  ..., 4999034,
+                            4999489, 5000000]),
+       col_indices=tensor([  23,   40,  103,  ..., 9927, 9976, 9991]),
+       values=tensor([0.6183, 0.2980, 0.3566,  ..., 0.0352, 0.5258, 0.0852]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4623, 0.5953, 0.6862,  ..., 0.1082, 0.6720, 0.4260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.45975399017333984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22838', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.724292278289795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     513,    1006,  ..., 4998953,
+                            4999498, 5000000]),
+       col_indices=tensor([  69,   83,  128,  ..., 9917, 9953, 9972]),
+       values=tensor([0.6637, 0.2623, 0.2360,  ..., 0.3507, 0.8119, 0.6229]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8552, 0.8520, 0.0158,  ..., 0.2551, 0.9127, 0.4905])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 8.724292278289795 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27486', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.233055591583252}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     518,    1031,  ..., 4999018,
+                            4999521, 5000000]),
+       col_indices=tensor([   2,   29,   76,  ..., 9919, 9923, 9942]),
+       values=tensor([0.8327, 0.8899, 0.2406,  ..., 0.0134, 0.9622, 0.2874]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5989, 0.5463, 0.4311,  ..., 0.3886, 0.2295, 0.1764])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.233055591583252 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     518,    1031,  ..., 4999018,
+                            4999521, 5000000]),
+       col_indices=tensor([   2,   29,   76,  ..., 9919, 9923, 9942]),
+       values=tensor([0.8327, 0.8899, 0.2406,  ..., 0.0134, 0.9622, 0.2874]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5989, 0.5463, 0.4311,  ..., 0.3886, 0.2295, 0.1764])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.233055591583252 seconds
+
+[45.15, 39.18, 39.48, 39.74, 39.27, 40.48, 39.56, 39.63, 39.62, 39.49]
+[151.69]
+13.698159217834473
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27486, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.233055591583252, 'TIME_S_1KI': 0.3723006472961963, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2077.8737717533113, 'W': 151.69}
+[45.15, 39.18, 39.48, 39.74, 39.27, 40.48, 39.56, 39.63, 39.62, 39.49, 40.16, 40.86, 39.65, 39.13, 39.26, 39.59, 39.63, 39.11, 39.14, 39.23]
+715.345
+35.767250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27486, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.233055591583252, 'TIME_S_1KI': 0.3723006472961963, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2077.8737717533113, 'W': 151.69, 'J_1KI': 75.59753226199925, 'W_1KI': 5.518809575784036, 'W_D': 115.92275, 'J_D': 1587.9282864692211, 'W_D_1KI': 4.217519828276212, 'J_D_1KI': 0.1534424735602202}
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
new file mode 100644
index 0000000..cda3d72
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 375977, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.999524116516113, "TIME_S_1KI": 0.029255843087518954, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.755545105934, "W": 96.41999999999999, "J_1KI": 3.5208418203930933, "W_1KI": 0.25645185742744897, "W_D": 61.132499999999986, "J_D": 839.2914941006896, "W_D_1KI": 0.16259638222550846, "J_D_1KI": 0.0004324636406628822}
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
new file mode 100644
index 0000000..e9eebf5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1900 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04503059387207031}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([4113,  614, 2519, 8527, 7416, 2734,  949, 6682,  484,
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+                      3.3560e-01, 5.8114e-01, 9.8752e-01, 9.0553e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.9668, 0.9511, 0.4479,  ..., 0.0607, 0.6261, 0.2898])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.04503059387207031 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '233174', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.088708162307739}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (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([6339, 4641, 8767, 9629, 5248, 7593, 5215, 4233, 3907,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.2611, 0.6478, 0.1138,  ..., 0.3633, 0.3210, 0.9692])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.088708162307739 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '345384', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.645604133605957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      7.9441e-01, 1.8102e-01, 1.2417e-01, 7.0777e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7495, 0.5234, 0.3924,  ..., 0.9829, 0.4941, 0.4696])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.645604133605957 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '375977', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.999524116516113}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      4.3737e-01, 4.8044e-02, 6.7611e-01, 9.4603e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7103, 0.5485, 0.7256,  ..., 0.6866, 0.8523, 0.5088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.999524116516113 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      8.2578e-01, 7.0533e-01, 4.9176e-01, 2.1029e-01,
+                      7.9780e-01, 7.8577e-02, 6.7550e-01, 9.6215e-02,
+                      3.7034e-01, 8.4600e-01, 2.1426e-01, 6.8059e-01,
+                      8.9566e-01, 2.0948e-01, 2.5723e-01, 2.1477e-01,
+                      7.0287e-01, 8.8856e-01, 6.8210e-01, 2.1374e-01,
+                      5.9763e-01, 5.0325e-01, 9.7355e-01, 2.5234e-01,
+                      1.4671e-01, 7.4961e-01, 5.0105e-01, 4.1481e-01,
+                      3.3586e-01, 3.2885e-01, 4.4906e-01, 9.4966e-01,
+                      9.7029e-01, 3.6201e-01, 9.9074e-01, 5.6940e-01,
+                      5.3932e-01, 5.4720e-02, 8.8651e-01, 8.6641e-01,
+                      6.7084e-01, 6.3171e-01, 1.7082e-02, 9.7000e-01,
+                      6.0249e-01, 8.5227e-02, 5.1565e-01, 2.8812e-01,
+                      7.6133e-01, 8.8965e-01, 4.3881e-01, 8.9089e-02,
+                      2.0705e-02, 6.0003e-01, 6.0058e-01, 2.3583e-01,
+                      7.0114e-01, 4.6051e-01, 9.3395e-01, 9.1531e-01,
+                      9.8035e-01, 8.6110e-01, 5.0684e-01, 1.4922e-02,
+                      3.5417e-01, 6.3192e-01, 6.2847e-01, 2.1922e-01,
+                      5.2679e-01, 5.0140e-01, 4.1193e-01, 9.3722e-01,
+                      7.2416e-01, 8.2701e-01, 5.8773e-01, 4.5892e-01,
+                      8.6080e-01, 7.0796e-01, 7.3090e-01, 2.3398e-01,
+                      8.2336e-01, 8.1931e-02, 5.6648e-01, 9.2281e-01,
+                      5.8624e-01, 8.3555e-01, 9.2999e-01, 8.9524e-01,
+                      9.7694e-01, 1.0057e-01, 9.1836e-01, 1.8716e-01,
+                      9.1811e-01, 2.5747e-02, 5.2811e-01, 5.9349e-01,
+                      3.1233e-01, 5.9616e-01, 9.2941e-01, 9.2230e-01,
+                      7.5079e-01, 2.7152e-01, 6.8334e-01, 1.8560e-01,
+                      1.9301e-01, 7.6648e-01, 7.1679e-01, 9.8760e-01,
+                      8.5633e-01, 5.0996e-01, 8.6064e-01, 6.3841e-01,
+                      6.3329e-01, 2.8584e-02, 4.5359e-01, 6.6242e-01,
+                      6.9051e-01, 1.3944e-02, 2.4069e-01, 9.0102e-01,
+                      4.0050e-01, 1.3846e-01, 6.7660e-02, 9.8925e-01,
+                      6.0724e-01, 2.5858e-01, 1.3118e-01, 6.4780e-02,
+                      5.2943e-03, 8.5863e-01, 1.0742e-01, 8.2977e-01,
+                      2.6180e-01, 4.9962e-03, 3.6669e-01, 3.7121e-01,
+                      9.4605e-01, 9.0263e-01, 3.6751e-01, 2.8743e-01,
+                      5.2275e-01, 9.3565e-01, 3.4542e-02, 8.3878e-01,
+                      9.8396e-01, 5.7945e-01, 8.3609e-01, 2.4747e-01,
+                      5.0297e-01, 6.4252e-01, 4.6862e-01, 1.5146e-01,
+                      6.8402e-01, 7.2437e-01, 2.6435e-02, 6.6999e-01,
+                      8.2226e-01, 8.3864e-01, 4.0283e-01, 7.2857e-01,
+                      8.9768e-02, 8.3236e-01, 5.2385e-01, 2.0285e-01,
+                      9.6598e-01, 2.8956e-01, 6.8980e-01, 8.3998e-01,
+                      2.5672e-01, 6.8220e-01, 5.4447e-01, 9.8634e-01,
+                      4.3737e-01, 4.8044e-02, 6.7611e-01, 9.4603e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7103, 0.5485, 0.7256,  ..., 0.6866, 0.8523, 0.5088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.999524116516113 seconds
+
+[39.86, 39.2, 39.16, 38.85, 39.2, 39.62, 38.88, 38.71, 38.95, 38.67]
+[96.42]
+13.729055643081665
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 375977, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.999524116516113, 'TIME_S_1KI': 0.029255843087518954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.755545105934, 'W': 96.41999999999999}
+[39.86, 39.2, 39.16, 38.85, 39.2, 39.62, 38.88, 38.71, 38.95, 38.67, 40.17, 39.41, 39.19, 38.69, 38.88, 39.53, 40.39, 39.3, 39.0, 38.88]
+705.75
+35.2875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 375977, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.999524116516113, 'TIME_S_1KI': 0.029255843087518954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.755545105934, 'W': 96.41999999999999, 'J_1KI': 3.5208418203930933, 'W_1KI': 0.25645185742744897, 'W_D': 61.132499999999986, 'J_D': 839.2914941006896, 'W_D_1KI': 0.16259638222550846, 'J_D_1KI': 0.0004324636406628822}
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
new file mode 100644
index 0000000..7e41519
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21375, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.636817216873169, "TIME_S_1KI": 0.4976288756431892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2053.7217113614083, "W": 152.01000000000002, "J_1KI": 96.08054789994893, "W_1KI": 7.111578947368422, "W_D": 116.32275000000001, "J_D": 1571.5713255724313, "W_D_1KI": 5.442000000000001, "J_D_1KI": 0.2545964912280702}
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
new file mode 100644
index 0000000..e90e199
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5442898273468018}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      11,  ..., 2499988,
+                            2499996, 2500000]),
+       col_indices=tensor([ 37839,  98870, 148404,  ..., 161688, 445826,
+                           487462]),
+       values=tensor([0.2708, 0.4230, 0.0396,  ..., 0.5012, 0.9237, 0.4084]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6604, 0.4578, 0.9008,  ..., 0.1692, 0.6250, 0.2013])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.5442898273468018 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19291', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.475887298583984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      11,  ..., 2499990,
+                            2499994, 2500000]),
+       col_indices=tensor([  2997,  16168, 106256,  ..., 284595, 359619,
+                           400100]),
+       values=tensor([0.5956, 0.5098, 0.7367,  ..., 0.1293, 0.8182, 0.3844]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4741, 0.3124, 0.4103,  ..., 0.8230, 0.7925, 0.1055])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.475887298583984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21375', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.636817216873169}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499987,
+                            2499990, 2500000]),
+       col_indices=tensor([ 69634, 109368, 119504,  ..., 397654, 413765,
+                           480494]),
+       values=tensor([0.1977, 0.6347, 0.9236,  ..., 0.5996, 0.0558, 0.7507]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2633, 0.4244, 0.4182,  ..., 0.0717, 0.3446, 0.9616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.636817216873169 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499987,
+                            2499990, 2500000]),
+       col_indices=tensor([ 69634, 109368, 119504,  ..., 397654, 413765,
+                           480494]),
+       values=tensor([0.1977, 0.6347, 0.9236,  ..., 0.5996, 0.0558, 0.7507]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2633, 0.4244, 0.4182,  ..., 0.0717, 0.3446, 0.9616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.636817216873169 seconds
+
+[39.92, 39.18, 39.71, 40.61, 40.15, 39.79, 39.41, 39.75, 39.22, 39.64]
+[152.01]
+13.510438203811646
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21375, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.636817216873169, 'TIME_S_1KI': 0.4976288756431892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2053.7217113614083, 'W': 152.01000000000002}
+[39.92, 39.18, 39.71, 40.61, 40.15, 39.79, 39.41, 39.75, 39.22, 39.64, 39.92, 39.78, 39.31, 39.34, 39.44, 40.15, 39.73, 39.14, 39.7, 39.19]
+713.7450000000001
+35.687250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21375, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.636817216873169, 'TIME_S_1KI': 0.4976288756431892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2053.7217113614083, 'W': 152.01000000000002, 'J_1KI': 96.08054789994893, 'W_1KI': 7.111578947368422, 'W_D': 116.32275000000001, 'J_D': 1571.5713255724313, 'W_D_1KI': 5.442000000000001, 'J_D_1KI': 0.2545964912280702}
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
new file mode 100644
index 0000000..7a8ab5e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 88993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.460110664367676, "TIME_S_1KI": 0.11753857791475371, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1554.5358743476868, "W": 116.24, "J_1KI": 17.468069110465844, "W_1KI": 1.3061701482139043, "W_D": 80.32, "J_D": 1074.1596819305419, "W_D_1KI": 0.9025428966323192, "J_D_1KI": 0.010141729086920537}
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
new file mode 100644
index 0000000..82a28ba
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1613328456878662}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 249987, 249990,
+                            250000]),
+       col_indices=tensor([ 2831, 11435, 18332,  ..., 36257, 39398, 40541]),
+       values=tensor([0.1158, 0.5239, 0.2299,  ..., 0.2166, 0.7808, 0.4412]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7586, 0.4736, 0.7326,  ..., 0.5631, 0.8162, 0.2413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.1613328456878662 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65082', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.079791784286499}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249995,
+                            250000]),
+       col_indices=tensor([ 9506, 10457, 11174,  ..., 14178, 16522, 25750]),
+       values=tensor([0.5729, 0.5279, 0.3744,  ..., 0.1961, 0.5511, 0.6709]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0404, 0.4787, 0.7701,  ..., 0.8815, 0.0868, 0.4305])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 8.079791784286499 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84576', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.978835582733154}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([26217, 28400, 13678,  ..., 15637, 35417, 48424]),
+       values=tensor([0.3837, 0.9571, 0.9616,  ..., 0.3970, 0.1960, 0.8766]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6737, 0.6555, 0.0878,  ..., 0.0726, 0.6482, 0.1469])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.978835582733154 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '88993', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.460110664367676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249992, 249993,
+                            250000]),
+       col_indices=tensor([ 7470, 20811, 24121,  ..., 36968, 38743, 40607]),
+       values=tensor([0.2685, 0.7271, 0.6618,  ..., 0.0403, 0.7886, 0.4035]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0454, 0.0390, 0.3317,  ..., 0.3195, 0.9524, 0.5758])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.460110664367676 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249992, 249993,
+                            250000]),
+       col_indices=tensor([ 7470, 20811, 24121,  ..., 36968, 38743, 40607]),
+       values=tensor([0.2685, 0.7271, 0.6618,  ..., 0.0403, 0.7886, 0.4035]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0454, 0.0390, 0.3317,  ..., 0.3195, 0.9524, 0.5758])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.460110664367676 seconds
+
+[40.38, 39.72, 39.57, 39.21, 40.98, 39.68, 39.65, 39.04, 39.23, 43.6]
+[116.24]
+13.373502016067505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.460110664367676, 'TIME_S_1KI': 0.11753857791475371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1554.5358743476868, 'W': 116.24}
+[40.38, 39.72, 39.57, 39.21, 40.98, 39.68, 39.65, 39.04, 39.23, 43.6, 39.84, 40.48, 39.37, 39.22, 39.24, 38.98, 44.18, 39.09, 39.18, 39.34]
+718.4
+35.92
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.460110664367676, 'TIME_S_1KI': 0.11753857791475371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1554.5358743476868, 'W': 116.24, 'J_1KI': 17.468069110465844, 'W_1KI': 1.3061701482139043, 'W_D': 80.32, 'J_D': 1074.1596819305419, 'W_D_1KI': 0.9025428966323192, 'J_D_1KI': 0.010141729086920537}
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
new file mode 100644
index 0000000..19e233d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46287, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.077528476715088, "TIME_S_1KI": 0.23932267108940064, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2013.3453536748884, "W": 148.1, "J_1KI": 43.49699383573981, "W_1KI": 3.1996024801780196, "W_D": 112.52425, "J_D": 1529.7108434385657, "W_D_1KI": 2.4310119471989973, "J_D_1KI": 0.052520404156652996}
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
new file mode 100644
index 0000000..a9b5d6b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2967829704284668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,     108,  ..., 2499894,
+                            2499951, 2500000]),
+       col_indices=tensor([  368,  1693,  4088,  ..., 44885, 46596, 47442]),
+       values=tensor([0.5982, 0.3592, 0.7042,  ..., 0.6155, 0.2314, 0.2925]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7227, 0.5816, 0.4934,  ..., 0.3583, 0.6407, 0.9822])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.2967829704284668 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35379', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.025555610656738}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499914,
+                            2499959, 2500000]),
+       col_indices=tensor([  123,   723,   909,  ..., 47588, 48779, 49819]),
+       values=tensor([0.6654, 0.3505, 0.8901,  ..., 0.8476, 0.5107, 0.1185]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6658, 0.6242, 0.4020,  ..., 0.5009, 0.1451, 0.6481])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 8.025555610656738 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46287', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.077528476715088}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      45,      94,  ..., 2499903,
+                            2499951, 2500000]),
+       col_indices=tensor([  506,  1320,  4404,  ..., 49283, 49651, 49966]),
+       values=tensor([0.0094, 0.0130, 0.0811,  ..., 0.5846, 0.7695, 0.1584]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8952, 0.2999, 0.6108,  ..., 0.3758, 0.9662, 0.9596])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 11.077528476715088 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      45,      94,  ..., 2499903,
+                            2499951, 2500000]),
+       col_indices=tensor([  506,  1320,  4404,  ..., 49283, 49651, 49966]),
+       values=tensor([0.0094, 0.0130, 0.0811,  ..., 0.5846, 0.7695, 0.1584]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8952, 0.2999, 0.6108,  ..., 0.3758, 0.9662, 0.9596])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 11.077528476715088 seconds
+
+[40.89, 39.29, 39.33, 39.28, 39.26, 39.19, 39.19, 40.42, 39.41, 39.34]
+[148.1]
+13.594499349594116
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46287, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.077528476715088, 'TIME_S_1KI': 0.23932267108940064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2013.3453536748884, 'W': 148.1}
+[40.89, 39.29, 39.33, 39.28, 39.26, 39.19, 39.19, 40.42, 39.41, 39.34, 40.01, 40.0, 39.66, 39.57, 39.25, 39.46, 39.33, 39.3, 39.25, 40.41]
+711.5149999999999
+35.57574999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46287, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.077528476715088, 'TIME_S_1KI': 0.23932267108940064, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2013.3453536748884, 'W': 148.1, 'J_1KI': 43.49699383573981, 'W_1KI': 3.1996024801780196, 'W_D': 112.52425, 'J_D': 1529.7108434385657, 'W_D_1KI': 2.4310119471989973, 'J_D_1KI': 0.052520404156652996}
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
new file mode 100644
index 0000000..1e1cae0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 126164, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.496079683303833, "TIME_S_1KI": 0.08319393553869434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1339.4068178844452, "W": 103.41, "J_1KI": 10.616394675854009, "W_1KI": 0.8196474430106845, "W_D": 67.667, "J_D": 876.4494840517044, "W_D_1KI": 0.5363415871405472, "J_D_1KI": 0.0042511460253364455}
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
new file mode 100644
index 0000000..f5dabe4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.13353276252746582}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([10989,  5739, 28866,  ..., 21823,  4005, 34886]),
+       values=tensor([0.4353, 0.4497, 0.0871,  ..., 0.0925, 0.2903, 0.5435]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7562, 0.8922, 0.4564,  ..., 0.1486, 0.4797, 0.4813])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.13353276252746582 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78632', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.544129848480225}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([34114, 35224, 10296,  ..., 13464,   985,  3770]),
+       values=tensor([0.2384, 0.3975, 0.4000,  ..., 0.4541, 0.7785, 0.5313]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2311, 0.0634, 0.6873,  ..., 0.2883, 0.1765, 0.0650])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 6.544129848480225 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '126164', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.496079683303833}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 3707, 41195, 46820,  ..., 24919, 16438, 24153]),
+       values=tensor([0.4077, 0.2091, 0.6369,  ..., 0.9924, 0.1508, 0.5036]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4831, 0.5861, 0.9166,  ..., 0.7031, 0.1228, 0.1244])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.496079683303833 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 3707, 41195, 46820,  ..., 24919, 16438, 24153]),
+       values=tensor([0.4077, 0.2091, 0.6369,  ..., 0.9924, 0.1508, 0.5036]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4831, 0.5861, 0.9166,  ..., 0.7031, 0.1228, 0.1244])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.496079683303833 seconds
+
+[41.91, 39.37, 39.94, 39.02, 39.81, 39.1, 40.39, 38.98, 39.08, 39.0]
+[103.41]
+12.952391624450684
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126164, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.496079683303833, 'TIME_S_1KI': 0.08319393553869434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.4068178844452, 'W': 103.41}
+[41.91, 39.37, 39.94, 39.02, 39.81, 39.1, 40.39, 38.98, 39.08, 39.0, 39.67, 39.09, 39.31, 39.83, 39.31, 39.25, 39.43, 38.89, 44.16, 39.22]
+714.86
+35.743
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126164, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.496079683303833, 'TIME_S_1KI': 0.08319393553869434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1339.4068178844452, 'W': 103.41, 'J_1KI': 10.616394675854009, 'W_1KI': 0.8196474430106845, 'W_D': 67.667, 'J_D': 876.4494840517044, 'W_D_1KI': 0.5363415871405472, 'J_D_1KI': 0.0042511460253364455}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json
new file mode 100644
index 0000000..62f730b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 250038, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.432795524597168, "TIME_S_1KI": 0.08971754503154387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2606.0890457463265, "W": 109.03, "J_1KI": 10.422771921653215, "W_1KI": 0.4360537198345852, "W_D": 73.72525, "J_D": 1762.2174302477242, "W_D_1KI": 0.2948561818603573, "J_D_1KI": 0.0011792454821281456}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output
new file mode 100644
index 0000000..3701848
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.10453343391418457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     3,  ..., 89993, 89997, 90000]),
+       col_indices=tensor([20651, 24290, 28771,  ..., 10287, 15356, 24487]),
+       values=tensor([0.1253, 0.8320, 0.5079,  ..., 0.2152, 0.2753, 0.6533]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.9310, 0.8886, 0.9050,  ..., 0.7990, 0.2751, 0.5722])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 0.10453343391418457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '200892', '-ss', '30000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 16.872318267822266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     5,  ..., 89991, 89992, 90000]),
+       col_indices=tensor([ 9009, 16842, 24312,  ..., 27764, 28622, 29005]),
+       values=tensor([0.8393, 0.9269, 0.8193,  ..., 0.0379, 0.8842, 0.8625]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.6604, 0.9619, 0.4104,  ..., 0.2632, 0.2079, 0.2105])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 16.872318267822266 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '250038', '-ss', '30000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.432795524597168}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     6,  ..., 89997, 89998, 90000]),
+       col_indices=tensor([12588, 20450, 20704,  ..., 21668, 10676, 12342]),
+       values=tensor([0.6372, 0.0652, 0.9949,  ..., 0.3492, 0.9239, 0.3604]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.0127, 0.8502, 0.1682,  ..., 0.0608, 0.3685, 0.2970])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 22.432795524597168 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     6,  ..., 89997, 89998, 90000]),
+       col_indices=tensor([12588, 20450, 20704,  ..., 21668, 10676, 12342]),
+       values=tensor([0.6372, 0.0652, 0.9949,  ..., 0.3492, 0.9239, 0.3604]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.0127, 0.8502, 0.1682,  ..., 0.0608, 0.3685, 0.2970])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 22.432795524597168 seconds
+
+[40.69, 39.01, 39.44, 38.94, 38.95, 39.35, 38.98, 39.05, 38.97, 38.87]
+[109.03]
+23.90249514579773
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 250038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.432795524597168, 'TIME_S_1KI': 0.08971754503154387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2606.0890457463265, 'W': 109.03}
+[40.69, 39.01, 39.44, 38.94, 38.95, 39.35, 38.98, 39.05, 38.97, 38.87, 40.47, 39.15, 39.52, 39.41, 39.16, 39.78, 39.02, 38.95, 38.92, 38.96]
+706.095
+35.30475
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 250038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.432795524597168, 'TIME_S_1KI': 0.08971754503154387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2606.0890457463265, 'W': 109.03, 'J_1KI': 10.422771921653215, 'W_1KI': 0.4360537198345852, 'W_D': 73.72525, 'J_D': 1762.2174302477242, 'W_D_1KI': 0.2948561818603573, 'J_D_1KI': 0.0011792454821281456}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output
new file mode 100644
index 0000000..8658000
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_0.001.output
@@ -0,0 +1,21 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.1439976692199707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     29,     67,  ..., 899936, 899964,
+                            900000]),
+       col_indices=tensor([   58,   341,  3959,  ..., 27670, 28034, 29816]),
+       values=tensor([0.8286, 0.0691, 0.1730,  ..., 0.2645, 0.7295, 0.5386]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.0558, 0.4553, 0.9674,  ..., 0.2366, 0.6209, 0.6160])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 0.1439976692199707 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '145835', '-ss', '30000', '-sd', '0.001', '-c', '16']
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json
new file mode 100644
index 0000000..9b8b9bd
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 321850, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.594725370407104, "TIME_S_1KI": 0.06398858278827747, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2327.1387594389917, "W": 101.41000000000001, "J_1KI": 7.230507253189348, "W_1KI": 0.3150846667702346, "W_D": 65.9145, "J_D": 1512.5942979887725, "W_D_1KI": 0.20479881932577287, "J_D_1KI": 0.0006363175992722476}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output
new file mode 100644
index 0000000..e9d1ad8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_20_10_10_synthetic_30000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08333611488342285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([13464, 15002, 12998,  ...,  1674,  7890,  9839]),
+       values=tensor([0.3937, 0.5826, 0.6728,  ..., 0.2443, 0.0810, 0.3168]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.3767, 0.3322, 0.0921,  ..., 0.4449, 0.8687, 0.6223])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 0.08333611488342285 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '251991', '-ss', '30000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.441835403442383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 1592, 26221,  2007,  ...,  5499,  7511, 18290]),
+       values=tensor([0.1009, 0.0773, 0.0762,  ..., 0.6540, 0.2265, 0.9524]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.5719, 0.1239, 0.1698,  ..., 0.8424, 0.3509, 0.9636])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 16.441835403442383 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '321850', '-ss', '30000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.594725370407104}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([28655, 14046, 22660,  ..., 19793, 14001, 26576]),
+       values=tensor([0.0604, 0.3035, 0.4856,  ..., 0.8323, 0.7946, 0.0096]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.2670, 0.6630, 0.3861,  ..., 0.4215, 0.9031, 0.7574])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 20.594725370407104 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([28655, 14046, 22660,  ..., 19793, 14001, 26576]),
+       values=tensor([0.0604, 0.3035, 0.4856,  ..., 0.8323, 0.7946, 0.0096]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.2670, 0.6630, 0.3861,  ..., 0.4215, 0.9031, 0.7574])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 20.594725370407104 seconds
+
+[39.56, 39.04, 39.2, 38.58, 39.16, 39.36, 38.83, 40.63, 38.67, 39.16]
+[101.41]
+22.94782328605652
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 321850, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.594725370407104, 'TIME_S_1KI': 0.06398858278827747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2327.1387594389917, 'W': 101.41000000000001}
+[39.56, 39.04, 39.2, 38.58, 39.16, 39.36, 38.83, 40.63, 38.67, 39.16, 39.61, 44.11, 38.89, 39.27, 38.75, 38.81, 40.71, 38.62, 38.81, 38.61]
+709.91
+35.4955
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 321850, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.594725370407104, 'TIME_S_1KI': 0.06398858278827747, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2327.1387594389917, 'W': 101.41000000000001, 'J_1KI': 7.230507253189348, 'W_1KI': 0.3150846667702346, 'W_D': 65.9145, 'J_D': 1512.5942979887725, 'W_D_1KI': 0.20479881932577287, 'J_D_1KI': 0.0006363175992722476}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..092cf4b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33012, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519692420959473, "TIME_S_1KI": 0.318662680872394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.664799463749, "W": 88.39, "J_1KI": 37.09756450574788, "W_1KI": 2.677511208045559, "W_D": 72.108, "J_D": 999.0737567567826, "W_D_1KI": 2.184296619411123, "J_D_1KI": 0.06616674601390778}
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
new file mode 100644
index 0000000..d5ef17d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3180568218231201}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999979,
+                             999991, 1000000]),
+       col_indices=tensor([10691, 12782, 14246,  ..., 70658, 88202, 93324]),
+       values=tensor([0.3844, 0.6658, 0.7124,  ..., 0.3153, 0.8920, 0.6509]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9202, 0.9151, 0.8232,  ..., 0.5628, 0.6151, 0.8368])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.3180568218231201 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33012', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519692420959473}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,      15,  ...,  999984,
+                             999990, 1000000]),
+       col_indices=tensor([ 7405, 49048, 69982,  ..., 87685, 98650, 99933]),
+       values=tensor([0.6053, 0.2022, 0.4562,  ..., 0.3977, 0.5709, 0.7435]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0490, 0.1129, 0.5767,  ..., 0.3037, 0.9982, 0.0194])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.519692420959473 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,      15,  ...,  999984,
+                             999990, 1000000]),
+       col_indices=tensor([ 7405, 49048, 69982,  ..., 87685, 98650, 99933]),
+       values=tensor([0.6053, 0.2022, 0.4562,  ..., 0.3977, 0.5709, 0.7435]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0490, 0.1129, 0.5767,  ..., 0.3037, 0.9982, 0.0194])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.519692420959473 seconds
+
+[18.25, 17.99, 18.13, 17.81, 18.01, 17.82, 18.18, 18.25, 17.98, 18.75]
+[88.39]
+13.855241537094116
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33012, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519692420959473, 'TIME_S_1KI': 0.318662680872394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.664799463749, 'W': 88.39}
+[18.25, 17.99, 18.13, 17.81, 18.01, 17.82, 18.18, 18.25, 17.98, 18.75, 18.55, 17.94, 18.05, 17.81, 18.35, 17.79, 18.28, 18.36, 18.2, 17.83]
+325.64
+16.282
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33012, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519692420959473, 'TIME_S_1KI': 0.318662680872394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.664799463749, 'W': 88.39, 'J_1KI': 37.09756450574788, 'W_1KI': 2.677511208045559, 'W_D': 72.108, 'J_D': 999.0737567567826, 'W_D_1KI': 2.184296619411123, 'J_D_1KI': 0.06616674601390778}
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
new file mode 100644
index 0000000..d7ba913
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64591, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.66994047164917, "TIME_S_1KI": 0.16519237156336286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1159.8280617713929, "W": 82.8, "J_1KI": 17.9564964433341, "W_1KI": 1.2819123407285846, "W_D": 66.57124999999999, "J_D": 932.5024620434641, "W_D_1KI": 1.0306582960474369, "J_D_1KI": 0.015956685854800777}
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
new file mode 100644
index 0000000..ad36eba
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.17906904220581055}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([85471,  5444, 13434,  ..., 17615, 87992, 83918]),
+       values=tensor([0.7119, 0.1219, 0.2242,  ..., 0.7199, 0.3920, 0.9751]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8861, 0.1716, 0.8373,  ..., 0.2826, 0.6276, 0.0027])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.17906904220581055 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58636', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.531909704208374}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([28875, 86601,  1118,  ..., 53659, 98581, 89346]),
+       values=tensor([0.0170, 0.0837, 0.6677,  ..., 0.0775, 0.7543, 0.4196]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4702, 0.4277, 0.7376,  ..., 0.9470, 0.3873, 0.6416])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 9.531909704208374 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64591', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.66994047164917}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([32373, 45973, 94969,  ...,  5823, 12968, 35562]),
+       values=tensor([0.6698, 0.7885, 0.1863,  ..., 0.4943, 0.2796, 0.7613]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4737, 0.5533, 0.8139,  ..., 0.3662, 0.3156, 0.7007])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.66994047164917 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([32373, 45973, 94969,  ...,  5823, 12968, 35562]),
+       values=tensor([0.6698, 0.7885, 0.1863,  ..., 0.4943, 0.2796, 0.7613]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4737, 0.5533, 0.8139,  ..., 0.3662, 0.3156, 0.7007])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.66994047164917 seconds
+
+[18.59, 17.9, 18.34, 17.96, 18.14, 17.94, 18.32, 17.79, 17.82, 17.71]
+[82.8]
+14.007585287094116
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64591, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.66994047164917, 'TIME_S_1KI': 0.16519237156336286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1159.8280617713929, 'W': 82.8}
+[18.59, 17.9, 18.34, 17.96, 18.14, 17.94, 18.32, 17.79, 17.82, 17.71, 18.29, 17.85, 18.6, 17.8, 18.13, 17.74, 17.99, 17.83, 18.16, 17.94]
+324.57500000000005
+16.22875
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64591, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.66994047164917, 'TIME_S_1KI': 0.16519237156336286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1159.8280617713929, 'W': 82.8, 'J_1KI': 17.9564964433341, 'W_1KI': 1.2819123407285846, 'W_D': 66.57124999999999, 'J_D': 932.5024620434641, 'W_D_1KI': 1.0306582960474369, 'J_D_1KI': 0.015956685854800777}
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
new file mode 100644
index 0000000..c1f8ab6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 250193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.988550901412964, "TIME_S_1KI": 0.043920297136262665, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.7657339859009, "W": 74.48, "J_1KI": 4.267768218878628, "W_1KI": 0.29769018317858614, "W_D": 58.048, "J_D": 832.1920693664551, "W_D_1KI": 0.23201288605196788, "J_D_1KI": 0.0009273356410929478}
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
new file mode 100644
index 0000000..15dcb35
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.06029987335205078}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([9584, 2249, 9621,  ...,  267, 2843, 1232]),
+       values=tensor([0.1887, 0.8280, 0.8733,  ..., 0.6422, 0.8241, 0.9503]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2203, 0.8610, 0.9153,  ..., 0.2931, 0.9983, 0.3156])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.06029987335205078 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '174129', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.307769536972046}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([5050, 9096,  467,  ..., 6460, 6547, 2963]),
+       values=tensor([0.3312, 0.9984, 0.8182,  ..., 0.5509, 0.3722, 0.7285]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0543, 0.3720, 0.3677,  ..., 0.5280, 0.6433, 0.3148])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.307769536972046 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '250193', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.988550901412964}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([4233, 4275, 7541,  ..., 2248, 7833,  717]),
+       values=tensor([0.0347, 0.7995, 0.4404,  ..., 0.0217, 0.2651, 0.9390]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4739, 0.4789, 0.6628,  ..., 0.7267, 0.9323, 0.5704])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.988550901412964 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([4233, 4275, 7541,  ..., 2248, 7833,  717]),
+       values=tensor([0.0347, 0.7995, 0.4404,  ..., 0.0217, 0.2651, 0.9390]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4739, 0.4789, 0.6628,  ..., 0.7267, 0.9323, 0.5704])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.988550901412964 seconds
+
+[18.5, 18.04, 18.08, 20.55, 18.03, 18.27, 18.34, 17.92, 18.14, 18.0]
+[74.48]
+14.33627462387085
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.988550901412964, 'TIME_S_1KI': 0.043920297136262665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.7657339859009, 'W': 74.48}
+[18.5, 18.04, 18.08, 20.55, 18.03, 18.27, 18.34, 17.92, 18.14, 18.0, 18.31, 18.29, 18.5, 18.09, 18.0, 17.95, 17.89, 18.08, 18.14, 17.85]
+328.64
+16.432
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.988550901412964, 'TIME_S_1KI': 0.043920297136262665, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.7657339859009, 'W': 74.48, 'J_1KI': 4.267768218878628, 'W_1KI': 0.29769018317858614, 'W_D': 58.048, 'J_D': 832.1920693664551, 'W_D_1KI': 0.23201288605196788, 'J_D_1KI': 0.0009273356410929478}
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
new file mode 100644
index 0000000..dab286f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 186516, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.228839635848999, "TIME_S_1KI": 0.054841620214078145, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1075.814607758522, "W": 79.58, "J_1KI": 5.767948099672533, "W_1KI": 0.4266658088314139, "W_D": 63.054, "J_D": 852.4053063282967, "W_D_1KI": 0.338062150164061, "J_D_1KI": 0.0018125101876732344}
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
new file mode 100644
index 0000000..fad6e92
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0709388256072998}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99974,  99988,
+                            100000]),
+       col_indices=tensor([1106, 1398, 2518,  ..., 6886, 7547, 8173]),
+       values=tensor([0.5902, 0.0057, 0.8492,  ..., 0.2608, 0.7269, 0.6940]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8144, 0.0674, 0.1585,  ..., 0.0850, 0.2846, 0.5370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.0709388256072998 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '148014', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.332475900650024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     17,  ...,  99965,  99978,
+                            100000]),
+       col_indices=tensor([  77,  628, 3642,  ..., 8176, 8481, 9600]),
+       values=tensor([0.7580, 0.3721, 0.0885,  ..., 0.9345, 0.1388, 0.5730]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9678, 0.5744, 0.4262,  ..., 0.2115, 0.3242, 0.5272])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.332475900650024 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '186516', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.228839635848999}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     15,     30,  ...,  99982,  99990,
+                            100000]),
+       col_indices=tensor([ 298,  367, 1190,  ..., 3689, 6850, 7173]),
+       values=tensor([0.7086, 0.6908, 0.8648,  ..., 0.4576, 0.3199, 0.8368]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7366, 0.0593, 0.8663,  ..., 0.2557, 0.4256, 0.5242])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.228839635848999 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     15,     30,  ...,  99982,  99990,
+                            100000]),
+       col_indices=tensor([ 298,  367, 1190,  ..., 3689, 6850, 7173]),
+       values=tensor([0.7086, 0.6908, 0.8648,  ..., 0.4576, 0.3199, 0.8368]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7366, 0.0593, 0.8663,  ..., 0.2557, 0.4256, 0.5242])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.228839635848999 seconds
+
+[18.35, 18.1, 18.16, 18.05, 17.94, 18.34, 18.01, 17.89, 17.93, 17.71]
+[79.58]
+13.51865553855896
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 186516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.228839635848999, 'TIME_S_1KI': 0.054841620214078145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1075.814607758522, 'W': 79.58}
+[18.35, 18.1, 18.16, 18.05, 17.94, 18.34, 18.01, 17.89, 17.93, 17.71, 19.07, 18.27, 18.23, 17.94, 18.27, 18.14, 21.36, 18.53, 18.63, 18.33]
+330.52
+16.526
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 186516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.228839635848999, 'TIME_S_1KI': 0.054841620214078145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1075.814607758522, 'W': 79.58, 'J_1KI': 5.767948099672533, 'W_1KI': 0.4266658088314139, 'W_D': 63.054, 'J_D': 852.4053063282967, 'W_D_1KI': 0.338062150164061, 'J_D_1KI': 0.0018125101876732344}
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
new file mode 100644
index 0000000..27eac6d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57497, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.399010181427002, "TIME_S_1KI": 0.18086178724849997, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1216.1904487371446, "W": 87.17000000000002, "J_1KI": 21.15224183413299, "W_1KI": 1.5160790997791191, "W_D": 70.89300000000001, "J_D": 989.0947514319422, "W_D_1KI": 1.2329860688383745, "J_D_1KI": 0.021444354815701245}
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
new file mode 100644
index 0000000..98e81fe
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.1964414119720459}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     102,     210,  ...,  999804,
+                             999909, 1000000]),
+       col_indices=tensor([   4,  297,  328,  ..., 9417, 9717, 9744]),
+       values=tensor([0.3827, 0.2830, 0.2497,  ..., 0.1291, 0.2102, 0.5312]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7948, 0.9855, 0.6473,  ..., 0.4205, 0.5296, 0.9253])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.1964414119720459 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53451', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.761078357696533}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     220,  ...,  999796,
+                             999898, 1000000]),
+       col_indices=tensor([ 465,  658,  715,  ..., 9500, 9653, 9927]),
+       values=tensor([0.9513, 0.9158, 0.4499,  ..., 0.0775, 0.2496, 0.9759]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5799, 0.5098, 0.6156,  ..., 0.8166, 0.2331, 0.2979])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.761078357696533 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '57497', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.399010181427002}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     104,     198,  ...,  999802,
+                             999905, 1000000]),
+       col_indices=tensor([ 124,  157,  187,  ..., 9539, 9601, 9680]),
+       values=tensor([0.6532, 0.0603, 0.0418,  ..., 0.1935, 0.1125, 0.4778]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5307, 0.8097, 0.3092,  ..., 0.4937, 0.1856, 0.7516])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.399010181427002 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     104,     198,  ...,  999802,
+                             999905, 1000000]),
+       col_indices=tensor([ 124,  157,  187,  ..., 9539, 9601, 9680]),
+       values=tensor([0.6532, 0.0603, 0.0418,  ..., 0.1935, 0.1125, 0.4778]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5307, 0.8097, 0.3092,  ..., 0.4937, 0.1856, 0.7516])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.399010181427002 seconds
+
+[18.23, 18.13, 17.96, 18.03, 17.94, 17.9, 17.91, 17.8, 18.15, 18.93]
+[87.17]
+13.951938152313232
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.399010181427002, 'TIME_S_1KI': 0.18086178724849997, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1216.1904487371446, 'W': 87.17000000000002}
+[18.23, 18.13, 17.96, 18.03, 17.94, 17.9, 17.91, 17.8, 18.15, 18.93, 18.34, 17.87, 18.21, 18.15, 18.42, 17.87, 18.22, 18.25, 18.03, 17.9]
+325.54
+16.277
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.399010181427002, 'TIME_S_1KI': 0.18086178724849997, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1216.1904487371446, 'W': 87.17000000000002, 'J_1KI': 21.15224183413299, 'W_1KI': 1.5160790997791191, 'W_D': 70.89300000000001, 'J_D': 989.0947514319422, 'W_D_1KI': 1.2329860688383745, 'J_D_1KI': 0.021444354815701245}
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
new file mode 100644
index 0000000..ae32267
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9007, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.744792222976685, "TIME_S_1KI": 1.192937961915919, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1359.414791688919, "W": 84.86, "J_1KI": 150.92869897734195, "W_1KI": 9.421561008104806, "W_D": 68.55725000000001, "J_D": 1098.2528838971855, "W_D_1KI": 7.611552126124127, "J_D_1KI": 0.8450707367740786}
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
new file mode 100644
index 0000000..c9ec716
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1656646728515625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     506,     991,  ..., 4998989,
+                            4999492, 5000000]),
+       col_indices=tensor([  25,   30,   53,  ..., 9970, 9993, 9995]),
+       values=tensor([0.0157, 0.5603, 0.3033,  ..., 0.4419, 0.2413, 0.9606]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4291, 0.9468, 0.9558,  ..., 0.3375, 0.0455, 0.9666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 1.1656646728515625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9007', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.744792222976685}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     491,    1013,  ..., 4998981,
+                            4999517, 5000000]),
+       col_indices=tensor([  61,   62,   77,  ..., 9979, 9982, 9988]),
+       values=tensor([0.6511, 0.9070, 0.7175,  ..., 0.4257, 0.4784, 0.0096]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7046, 0.2172, 0.5779,  ..., 0.4690, 0.0165, 0.6122])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.744792222976685 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     491,    1013,  ..., 4998981,
+                            4999517, 5000000]),
+       col_indices=tensor([  61,   62,   77,  ..., 9979, 9982, 9988]),
+       values=tensor([0.6511, 0.9070, 0.7175,  ..., 0.4257, 0.4784, 0.0096]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7046, 0.2172, 0.5779,  ..., 0.4690, 0.0165, 0.6122])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.744792222976685 seconds
+
+[18.37, 18.68, 18.13, 17.9, 18.06, 18.22, 18.04, 18.49, 17.9, 18.1]
+[84.86]
+16.019500255584717
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9007, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.744792222976685, 'TIME_S_1KI': 1.192937961915919, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.414791688919, 'W': 84.86}
+[18.37, 18.68, 18.13, 17.9, 18.06, 18.22, 18.04, 18.49, 17.9, 18.1, 18.25, 17.97, 18.05, 17.84, 17.92, 18.2, 17.96, 17.89, 18.19, 18.51]
+326.05499999999995
+16.302749999999996
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9007, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.744792222976685, 'TIME_S_1KI': 1.192937961915919, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1359.414791688919, 'W': 84.86, 'J_1KI': 150.92869897734195, 'W_1KI': 9.421561008104806, 'W_D': 68.55725000000001, 'J_D': 1098.2528838971855, 'W_D_1KI': 7.611552126124127, 'J_D_1KI': 0.8450707367740786}
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
new file mode 100644
index 0000000..3d91d0f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 279705, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.192691802978516, "TIME_S_1KI": 0.03644086377783206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1002.2923643112182, "W": 73.24, "J_1KI": 3.5833909451429835, "W_1KI": 0.2618473034089487, "W_D": 56.983999999999995, "J_D": 779.8283463668822, "W_D_1KI": 0.20372892869272982, "J_D_1KI": 0.0007283707073263969}
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
new file mode 100644
index 0000000..d0774e1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05549430847167969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([3370, 8033, 9994, 2466, 6901, 8760, 7929, 6009, 6694,
+                           5159, 1569, 4321, 2884, 3593, 7087,  277, 9865,  708,
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+tensor([0.9318, 0.2708, 0.1659,  ..., 0.9519, 0.7638, 0.9831])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.05549430847167969 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '189208', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.102759838104248}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      9.0564e-01, 3.4766e-01, 2.5763e-01, 6.1550e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4272, 0.4478, 0.5565,  ..., 0.4220, 0.4867, 0.3940])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.102759838104248 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '279705', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.192691802978516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3844, 0.6738, 0.8785,  ..., 0.1267, 0.6920, 0.7757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.192691802978516 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      6.6896e-01, 9.4509e-01, 1.4801e-01, 6.1207e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3844, 0.6738, 0.8785,  ..., 0.1267, 0.6920, 0.7757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.192691802978516 seconds
+
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+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 279705, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.192691802978516, 'TIME_S_1KI': 0.03644086377783206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1002.2923643112182, 'W': 73.24, 'J_1KI': 3.5833909451429835, 'W_1KI': 0.2618473034089487, 'W_D': 56.983999999999995, 'J_D': 779.8283463668822, 'W_D_1KI': 0.20372892869272982, 'J_D_1KI': 0.0007283707073263969}
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
new file mode 100644
index 0000000..8eef901
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8355, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.90480637550354, "TIME_S_1KI": 1.305183288510298, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1307.563270778656, "W": 87.44, "J_1KI": 156.5006906976249, "W_1KI": 10.4655894673848, "W_D": 70.932, "J_D": 1060.7053742322921, "W_D_1KI": 8.489766606822261, "J_D_1KI": 1.0161300546765126}
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
new file mode 100644
index 0000000..03135d2
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2567212581634521}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       9,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([164554, 277712, 289036,  ..., 389470, 409865,
+                           491502]),
+       values=tensor([0.0126, 0.9348, 0.8595,  ..., 0.3584, 0.7345, 0.5238]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0175, 0.7668, 0.4852,  ..., 0.2657, 0.5513, 0.9738])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 1.2567212581634521 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8355', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.90480637550354}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      12,  ..., 2499995,
+                            2499997, 2500000]),
+       col_indices=tensor([ 72448,  73110, 121261,  ...,  13350, 176428,
+                           278854]),
+       values=tensor([0.1918, 0.3445, 0.8471,  ..., 0.5873, 0.4603, 0.6922]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8369, 0.9252, 0.2721,  ..., 0.2352, 0.7861, 0.2173])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.90480637550354 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      12,  ..., 2499995,
+                            2499997, 2500000]),
+       col_indices=tensor([ 72448,  73110, 121261,  ...,  13350, 176428,
+                           278854]),
+       values=tensor([0.1918, 0.3445, 0.8471,  ..., 0.5873, 0.4603, 0.6922]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8369, 0.9252, 0.2721,  ..., 0.2352, 0.7861, 0.2173])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.90480637550354 seconds
+
+[18.48, 21.5, 18.34, 18.43, 18.27, 18.03, 18.28, 18.13, 18.03, 18.78]
+[87.44]
+14.953834295272827
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8355, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.90480637550354, 'TIME_S_1KI': 1.305183288510298, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1307.563270778656, 'W': 87.44}
+[18.48, 21.5, 18.34, 18.43, 18.27, 18.03, 18.28, 18.13, 18.03, 18.78, 18.48, 18.09, 18.1, 17.9, 18.11, 18.02, 17.99, 17.84, 18.15, 18.16]
+330.15999999999997
+16.508
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8355, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.90480637550354, 'TIME_S_1KI': 1.305183288510298, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1307.563270778656, 'W': 87.44, 'J_1KI': 156.5006906976249, 'W_1KI': 10.4655894673848, 'W_D': 70.932, 'J_D': 1060.7053742322921, 'W_D_1KI': 8.489766606822261, 'J_D_1KI': 1.0161300546765126}
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
new file mode 100644
index 0000000..035895d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 77922, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570462703704834, "TIME_S_1KI": 0.13565440701861906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.7638923931122, "W": 83.24, "J_1KI": 15.525318811030418, "W_1KI": 1.0682477349144015, "W_D": 66.53899999999999, "J_D": 967.0408413736818, "W_D_1KI": 0.8539180205846871, "J_D_1KI": 0.010958625556129042}
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
new file mode 100644
index 0000000..70de4f5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.14919304847717285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      9,  ..., 249989, 249995,
+                            250000]),
+       col_indices=tensor([ 8787, 10800, 12548,  ..., 22776, 32520, 35593]),
+       values=tensor([0.0395, 0.0216, 0.0459,  ..., 0.9233, 0.0886, 0.1442]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0084, 0.2765, 0.2672,  ..., 0.0856, 0.1416, 0.8826])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.14919304847717285 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70378', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.483437538146973}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 249988, 249997,
+                            250000]),
+       col_indices=tensor([ 1665,  9567,  9654,  ...,  4112, 18670, 38091]),
+       values=tensor([0.4890, 0.0494, 0.7903,  ..., 0.9513, 0.0590, 0.1377]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5003, 0.9747, 0.2176,  ..., 0.9666, 0.4758, 0.9002])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.483437538146973 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '77922', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570462703704834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249995, 249999,
+                            250000]),
+       col_indices=tensor([18420, 40988,  3727,  ..., 33621, 36384, 44487]),
+       values=tensor([0.1861, 0.8144, 0.1628,  ..., 0.4774, 0.5715, 0.3216]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6272, 0.7644, 0.0884,  ..., 0.9496, 0.3089, 0.8679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.570462703704834 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249995, 249999,
+                            250000]),
+       col_indices=tensor([18420, 40988,  3727,  ..., 33621, 36384, 44487]),
+       values=tensor([0.1861, 0.8144, 0.1628,  ..., 0.4774, 0.5715, 0.3216]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6272, 0.7644, 0.0884,  ..., 0.9496, 0.3089, 0.8679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.570462703704834 seconds
+
+[18.59, 17.97, 19.57, 18.3, 18.09, 17.87, 18.02, 21.06, 18.56, 17.89]
+[83.24]
+14.533444166183472
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570462703704834, 'TIME_S_1KI': 0.13565440701861906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.7638923931122, 'W': 83.24}
+[18.59, 17.97, 19.57, 18.3, 18.09, 17.87, 18.02, 21.06, 18.56, 17.89, 18.2, 17.85, 17.83, 21.57, 17.96, 18.06, 18.27, 18.3, 18.38, 18.04]
+334.02
+16.701
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 77922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570462703704834, 'TIME_S_1KI': 0.13565440701861906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.7638923931122, 'W': 83.24, 'J_1KI': 15.525318811030418, 'W_1KI': 1.0682477349144015, 'W_D': 66.53899999999999, 'J_D': 967.0408413736818, 'W_D_1KI': 0.8539180205846871, 'J_D_1KI': 0.010958625556129042}
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
new file mode 100644
index 0000000..f58be3b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.690638303756714, "TIME_S_1KI": 0.6159266177194627, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.2395059108735, "W": 87.56, "J_1KI": 74.62346637730447, "W_1KI": 5.044650573255747, "W_D": 71.326, "J_D": 1055.0965394997595, "W_D_1KI": 4.109350694244396, "J_D_1KI": 0.23675466349279234}
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
new file mode 100644
index 0000000..ec20a71
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6049323081970215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,     103,  ..., 2499893,
+                            2499950, 2500000]),
+       col_indices=tensor([  214,   217,  3424,  ..., 47339, 47927, 48505]),
+       values=tensor([0.8463, 0.5755, 0.1058,  ..., 0.4565, 0.0843, 0.4040]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2070, 0.0126, 0.4112,  ..., 0.3463, 0.8132, 0.3234])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.6049323081970215 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17357', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.690638303756714}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      47,     101,  ..., 2499901,
+                            2499949, 2500000]),
+       col_indices=tensor([  511,   725,   819,  ..., 47217, 48788, 49222]),
+       values=tensor([0.0511, 0.3894, 0.2647,  ..., 0.8233, 0.9615, 0.4045]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7118, 0.9063, 0.7110,  ..., 0.7333, 0.4959, 0.7807])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.690638303756714 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      47,     101,  ..., 2499901,
+                            2499949, 2500000]),
+       col_indices=tensor([  511,   725,   819,  ..., 47217, 48788, 49222]),
+       values=tensor([0.0511, 0.3894, 0.2647,  ..., 0.8233, 0.9615, 0.4045]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7118, 0.9063, 0.7110,  ..., 0.7333, 0.4959, 0.7807])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.690638303756714 seconds
+
+[18.27, 17.74, 18.29, 17.77, 18.01, 17.85, 17.88, 17.79, 18.2, 17.88]
+[87.56]
+14.792593717575073
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.690638303756714, 'TIME_S_1KI': 0.6159266177194627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2395059108735, 'W': 87.56}
+[18.27, 17.74, 18.29, 17.77, 18.01, 17.85, 17.88, 17.79, 18.2, 17.88, 18.2, 18.34, 18.01, 17.89, 18.13, 18.46, 18.22, 17.9, 17.95, 18.15]
+324.68
+16.234
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.690638303756714, 'TIME_S_1KI': 0.6159266177194627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2395059108735, 'W': 87.56, 'J_1KI': 74.62346637730447, 'W_1KI': 5.044650573255747, 'W_D': 71.326, 'J_D': 1055.0965394997595, 'W_D_1KI': 4.109350694244396, 'J_D_1KI': 0.23675466349279234}
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
new file mode 100644
index 0000000..3c6e8be
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 112508, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.720516443252563, "TIME_S_1KI": 0.09528670355221462, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1090.2722121667862, "W": 75.82, "J_1KI": 9.69061944187779, "W_1KI": 0.6739076332349699, "W_D": 59.38549999999999, "J_D": 853.9483046113252, "W_D_1KI": 0.5278335762790201, "J_D_1KI": 0.004691520392141182}
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
new file mode 100644
index 0000000..b66333e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.11333847045898438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 24996, 24999, 25000]),
+       col_indices=tensor([ 9502, 18497,  7204,  ..., 33396, 45910,   109]),
+       values=tensor([0.5325, 0.6011, 0.4727,  ..., 0.6967, 0.0269, 0.7415]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7210, 0.8240, 0.5786,  ..., 0.5702, 0.4441, 0.2533])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.11333847045898438 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '92642', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.645956993103027}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([35285,  1305, 12700,  ...,  6399, 17561, 45264]),
+       values=tensor([0.6896, 0.7157, 0.5414,  ..., 0.3157, 0.2585, 0.8046]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8892, 0.5178, 0.0901,  ..., 0.0600, 0.1718, 0.0275])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 8.645956993103027 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '112508', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.720516443252563}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27684, 39939,  2715,  ..., 47308, 11944, 42221]),
+       values=tensor([0.6561, 0.5911, 0.5622,  ..., 0.2806, 0.4491, 0.6100]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8957, 0.2813, 0.1993,  ..., 0.7019, 0.9944, 0.8970])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.720516443252563 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27684, 39939,  2715,  ..., 47308, 11944, 42221]),
+       values=tensor([0.6561, 0.5911, 0.5622,  ..., 0.2806, 0.4491, 0.6100]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8957, 0.2813, 0.1993,  ..., 0.7019, 0.9944, 0.8970])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.720516443252563 seconds
+
+[18.48, 18.01, 18.17, 19.64, 18.03, 18.42, 18.45, 18.1, 18.26, 18.04]
+[75.82]
+14.379744291305542
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112508, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.720516443252563, 'TIME_S_1KI': 0.09528670355221462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1090.2722121667862, 'W': 75.82}
+[18.48, 18.01, 18.17, 19.64, 18.03, 18.42, 18.45, 18.1, 18.26, 18.04, 18.62, 18.47, 18.04, 18.06, 17.8, 18.08, 18.08, 18.67, 17.82, 18.04]
+328.69000000000005
+16.434500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112508, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.720516443252563, 'TIME_S_1KI': 0.09528670355221462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1090.2722121667862, 'W': 75.82, 'J_1KI': 9.69061944187779, 'W_1KI': 0.6739076332349699, 'W_D': 59.38549999999999, 'J_D': 853.9483046113252, 'W_D_1KI': 0.5278335762790201, 'J_D_1KI': 0.004691520392141182}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json
new file mode 100644
index 0000000..1a2b755
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 234425, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.312235116958618, "TIME_S_1KI": 0.09091280843322434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2054.109435153008, "W": 83.45000000000002, "J_1KI": 8.762330959381499, "W_1KI": 0.3559773914898156, "W_D": 67.21450000000002, "J_D": 1654.4749985511307, "W_D_1KI": 0.2867206995840888, "J_D_1KI": 0.0012230807276702091}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output
new file mode 100644
index 0000000..06a3a12
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.10643196105957031}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,     9,  ..., 89992, 89998, 90000]),
+       col_indices=tensor([ 7924, 12206, 12582,  ..., 21107, 10373, 19571]),
+       values=tensor([0.8274, 0.6462, 0.9289,  ..., 0.2542, 0.4328, 0.6143]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.4141, 0.4229, 0.5665,  ..., 0.1440, 0.7095, 0.1472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 0.10643196105957031 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '197309', '-ss', '30000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 17.675063133239746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 89992, 89999, 90000]),
+       col_indices=tensor([  929,  2315, 11088,  ..., 21381, 23338, 19838]),
+       values=tensor([0.3872, 0.2873, 0.0227,  ..., 0.4746, 0.4839, 0.3522]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.1013, 0.5431, 0.3309,  ..., 0.2751, 0.1147, 0.0007])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 17.675063133239746 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '234425', '-ss', '30000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.312235116958618}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,    10,  ..., 89994, 89997, 90000]),
+       col_indices=tensor([ 6200, 14122, 21980,  ..., 11781, 19689, 21155]),
+       values=tensor([0.5859, 0.7824, 0.3581,  ..., 0.7747, 0.1479, 0.5181]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.3266, 0.0767, 0.6789,  ..., 0.9087, 0.9799, 0.8849])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 21.312235116958618 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,    10,  ..., 89994, 89997, 90000]),
+       col_indices=tensor([ 6200, 14122, 21980,  ..., 11781, 19689, 21155]),
+       values=tensor([0.5859, 0.7824, 0.3581,  ..., 0.7747, 0.1479, 0.5181]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.3266, 0.0767, 0.6789,  ..., 0.9087, 0.9799, 0.8849])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 21.312235116958618 seconds
+
+[18.37, 18.49, 18.06, 17.95, 17.95, 17.71, 18.07, 17.8, 17.94, 17.81]
+[83.45]
+24.61485242843628
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 234425, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.312235116958618, 'TIME_S_1KI': 0.09091280843322434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.109435153008, 'W': 83.45000000000002}
+[18.37, 18.49, 18.06, 17.95, 17.95, 17.71, 18.07, 17.8, 17.94, 17.81, 18.28, 18.06, 18.16, 17.86, 18.06, 17.97, 18.34, 17.89, 18.12, 18.1]
+324.71
+16.2355
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 234425, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.312235116958618, 'TIME_S_1KI': 0.09091280843322434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.109435153008, 'W': 83.45000000000002, 'J_1KI': 8.762330959381499, 'W_1KI': 0.3559773914898156, 'W_D': 67.21450000000002, 'J_D': 1654.4749985511307, 'W_D_1KI': 0.2867206995840888, 'J_D_1KI': 0.0012230807276702091}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output
new file mode 100644
index 0000000..355226c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_0.001.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2140212059020996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     34,     62,  ..., 899934, 899967,
+                            900000]),
+       col_indices=tensor([ 1559,  1711,  3295,  ..., 29804, 29893, 29964]),
+       values=tensor([0.7225, 0.7366, 0.0675,  ..., 0.3495, 0.2204, 0.5611]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.1783, 0.4759, 0.5239,  ..., 0.8363, 0.1566, 0.5506])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 0.2140212059020996 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '98121', '-ss', '30000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 19.3143093585968}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     29,     64,  ..., 899940, 899966,
+                            900000]),
+       col_indices=tensor([  612,   701,  1017,  ..., 29770, 29777, 29834]),
+       values=tensor([0.4034, 0.5977, 0.8788,  ..., 0.6466, 0.3405, 0.9207]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.7678, 0.0123, 0.5496,  ..., 0.4589, 0.2646, 0.8857])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 19.3143093585968 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '106684', '-ss', '30000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.90600872039795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     21,     51,  ..., 899936, 899963,
+                            900000]),
+       col_indices=tensor([  855,  2329,  2453,  ..., 28070, 28293, 29379]),
+       values=tensor([0.2478, 0.6443, 0.3087,  ..., 0.2033, 0.4619, 0.6203]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.3161, 0.0015, 0.4480,  ..., 0.6517, 0.7843, 0.6370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.90600872039795 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     21,     51,  ..., 899936, 899963,
+                            900000]),
+       col_indices=tensor([  855,  2329,  2453,  ..., 28070, 28293, 29379]),
+       values=tensor([0.2478, 0.6443, 0.3087,  ..., 0.2033, 0.4619, 0.6203]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.3161, 0.0015, 0.4480,  ..., 0.6517, 0.7843, 0.6370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.90600872039795 seconds
+
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json
new file mode 100644
index 0000000..21aeca6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 303288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.079484939575195, "TIME_S_1KI": 0.06950319478375404, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1922.1091361045835, "W": 78.6, "J_1KI": 6.337570679039671, "W_1KI": 0.2591596106670887, "W_D": 62.18274999999999, "J_D": 1520.636537953019, "W_D_1KI": 0.20502871857772148, "J_D_1KI": 0.0006760198839971298}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output
new file mode 100644
index 0000000..d8910fb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_20_10_10_synthetic_30000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08539462089538574}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 8168, 26166, 15021,  ...,  3965, 14348,  3180]),
+       values=tensor([0.0414, 0.9204, 0.6909,  ..., 0.5705, 0.2524, 0.4947]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.9721, 0.7014, 0.8881,  ..., 0.4193, 0.5170, 0.9013])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 0.08539462089538574 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '245917', '-ss', '30000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 17.02755308151245}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 9352, 11930, 17471,  ..., 19597, 20552,  1111]),
+       values=tensor([0.4298, 0.4908, 0.5157,  ..., 0.6454, 0.4570, 0.2738]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.3622, 0.2189, 0.3857,  ..., 0.2935, 0.6447, 0.7890])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 17.02755308151245 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '303288', '-ss', '30000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.079484939575195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([20060, 11216, 16521,  ..., 22127, 15786,  9820]),
+       values=tensor([0.3604, 0.7216, 0.9721,  ..., 0.8443, 0.2707, 0.5761]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.4467, 0.9917, 0.2567,  ..., 0.4911, 0.0150, 0.9779])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.079484939575195 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([20060, 11216, 16521,  ..., 22127, 15786,  9820]),
+       values=tensor([0.3604, 0.7216, 0.9721,  ..., 0.8443, 0.2707, 0.5761]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.4467, 0.9917, 0.2567,  ..., 0.4911, 0.0150, 0.9779])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.079484939575195 seconds
+
+[18.69, 17.92, 17.95, 17.83, 18.17, 17.93, 17.96, 17.93, 18.28, 17.85]
+[78.6]
+24.454314708709717
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 303288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.079484939575195, 'TIME_S_1KI': 0.06950319478375404, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1922.1091361045835, 'W': 78.6}
+[18.69, 17.92, 17.95, 17.83, 18.17, 17.93, 17.96, 17.93, 18.28, 17.85, 18.57, 17.96, 18.0, 18.17, 18.14, 18.05, 18.17, 20.58, 18.66, 18.18]
+328.345
+16.417250000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 303288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.079484939575195, 'TIME_S_1KI': 0.06950319478375404, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1922.1091361045835, 'W': 78.6, 'J_1KI': 6.337570679039671, 'W_1KI': 0.2591596106670887, 'W_D': 62.18274999999999, 'J_D': 1520.636537953019, 'W_D_1KI': 0.20502871857772148, 'J_D_1KI': 0.0006760198839971298}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..27d9946
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.691206455230713, "TIME_S_1KI": 24.691206455230713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 647.4072245025635, "W": 22.90104020202067, "J_1KI": 647.4072245025635, "W_1KI": 22.90104020202067, "W_D": 3.140040202020675, "J_D": 88.76822598814977, "W_D_1KI": 3.140040202020675, "J_D_1KI": 3.140040202020675}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..d5446f7
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.691206455230713}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      21,  ...,  999980,
+                             999990, 1000000]),
+       col_indices=tensor([ 5106, 13656, 15471,  ..., 68202, 79637, 95576]),
+       values=tensor([0.9862, 0.5796, 0.7870,  ..., 0.3201, 0.7080, 0.2748]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6105, 0.8083, 0.7150,  ..., 0.7011, 0.0810, 0.6416])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 24.691206455230713 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      21,  ...,  999980,
+                             999990, 1000000]),
+       col_indices=tensor([ 5106, 13656, 15471,  ..., 68202, 79637, 95576]),
+       values=tensor([0.9862, 0.5796, 0.7870,  ..., 0.3201, 0.7080, 0.2748]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6105, 0.8083, 0.7150,  ..., 0.7011, 0.0810, 0.6416])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 24.691206455230713 seconds
+
+[20.64, 20.52, 20.48, 20.68, 20.48, 20.48, 20.52, 20.48, 20.28, 20.36]
+[20.64, 20.84, 21.24, 24.56, 26.2, 27.16, 28.16, 26.08, 25.68, 24.72, 24.72, 24.48, 24.6, 24.6, 24.72, 24.68, 24.6, 24.52, 24.52, 24.8, 24.72, 24.6, 24.48, 24.48, 24.52, 24.44, 24.64]
+28.269773721694946
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.691206455230713, 'TIME_S_1KI': 24.691206455230713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 647.4072245025635, 'W': 22.90104020202067}
+[20.64, 20.52, 20.48, 20.68, 20.48, 20.48, 20.52, 20.48, 20.28, 20.36, 20.6, 20.56, 20.64, 22.68, 24.64, 25.4, 25.4, 25.36, 24.48, 22.68]
+395.2199999999999
+19.760999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.691206455230713, 'TIME_S_1KI': 24.691206455230713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 647.4072245025635, 'W': 22.90104020202067, 'J_1KI': 647.4072245025635, 'W_1KI': 22.90104020202067, 'W_D': 3.140040202020675, 'J_D': 88.76822598814977, 'W_D_1KI': 3.140040202020675, 'J_D_1KI': 3.140040202020675}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..75a4f33
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.487157583236694, "TIME_S_1KI": 3.3082516035446985, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.83638416290285, "W": 22.27932516765204, "J_1KI": 102.78750289050564, "W_1KI": 7.028178286325565, "W_D": 3.710325167652041, "J_D": 54.263714344978354, "W_D_1KI": 1.170449579700959, "J_D_1KI": 0.36922699675109116}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..2086467
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.3119447231292725}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([34080, 20424, 38945,  ..., 64155, 47978, 44736]),
+       values=tensor([0.5824, 0.7466, 0.8758,  ..., 0.8278, 0.8938, 0.7712]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9015, 0.6308, 0.7799,  ..., 0.6045, 0.4908, 0.8218])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 3.3119447231292725 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3170 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.487157583236694}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99999,
+                            100000]),
+       col_indices=tensor([62540, 50524, 43651,  ..., 12394, 59846, 74659]),
+       values=tensor([0.6601, 0.8101, 0.4564,  ..., 0.4320, 0.9061, 0.8749]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2783, 0.8812, 0.6091,  ..., 0.5557, 0.0745, 0.6879])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.487157583236694 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99999,
+                            100000]),
+       col_indices=tensor([62540, 50524, 43651,  ..., 12394, 59846, 74659]),
+       values=tensor([0.6601, 0.8101, 0.4564,  ..., 0.4320, 0.9061, 0.8749]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2783, 0.8812, 0.6091,  ..., 0.5557, 0.0745, 0.6879])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.487157583236694 seconds
+
+[20.76, 20.76, 20.76, 20.8, 20.84, 20.72, 20.6, 20.36, 20.36, 20.32]
+[20.32, 20.36, 20.48, 22.0, 23.24, 25.44, 26.04, 26.48, 26.08, 24.6, 24.44, 24.44, 24.4, 24.6]
+14.625056266784668
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.487157583236694, 'TIME_S_1KI': 3.3082516035446985, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.83638416290285, 'W': 22.27932516765204}
+[20.76, 20.76, 20.76, 20.8, 20.84, 20.72, 20.6, 20.36, 20.36, 20.32, 20.04, 20.16, 20.24, 20.6, 20.72, 20.72, 20.88, 20.72, 21.08, 21.0]
+371.38
+18.569
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.487157583236694, 'TIME_S_1KI': 3.3082516035446985, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.83638416290285, 'W': 22.27932516765204, 'J_1KI': 102.78750289050564, 'W_1KI': 7.028178286325565, 'W_D': 3.710325167652041, 'J_D': 54.263714344978354, 'W_D_1KI': 1.170449579700959, 'J_D_1KI': 0.36922699675109116}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..25fcf70
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.42804479598999, "TIME_S_1KI": 0.32415432999658034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 291.8528582954407, "W": 21.456480473872652, "J_1KI": 9.07220572879828, "W_1KI": 0.666971727506144, "W_D": 3.1474804738726547, "J_D": 42.81229504752161, "W_D_1KI": 0.09783899514680307, "J_D_1KI": 0.0030413116303016183}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..034e4a3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3263826370239258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([1982,  558, 3662,  ...,  629, 5634, 6549]),
+       values=tensor([0.5250, 0.9307, 0.0448,  ..., 0.0150, 0.4421, 0.4831]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5546, 0.0630, 0.8785,  ..., 0.4779, 0.8090, 0.6189])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.3263826370239258 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32170 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.42804479598999}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9996,  9999, 10000]),
+       col_indices=tensor([8155, 9480, 4094,  ..., 6796, 6921, 3902]),
+       values=tensor([0.0915, 0.3699, 0.5728,  ..., 0.9057, 0.8661, 0.7356]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3327, 0.6532, 0.3155,  ..., 0.1421, 0.0155, 0.6755])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.42804479598999 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9996,  9999, 10000]),
+       col_indices=tensor([8155, 9480, 4094,  ..., 6796, 6921, 3902]),
+       values=tensor([0.0915, 0.3699, 0.5728,  ..., 0.9057, 0.8661, 0.7356]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3327, 0.6532, 0.3155,  ..., 0.1421, 0.0155, 0.6755])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.42804479598999 seconds
+
+[20.24, 20.16, 19.96, 20.2, 20.32, 20.28, 20.4, 20.32, 20.32, 20.36]
+[20.36, 20.24, 20.48, 22.52, 23.04, 24.76, 25.6, 25.52, 24.28, 23.12, 23.12, 23.16, 23.44]
+13.602084398269653
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.42804479598999, 'TIME_S_1KI': 0.32415432999658034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.8528582954407, 'W': 21.456480473872652}
+[20.24, 20.16, 19.96, 20.2, 20.32, 20.28, 20.4, 20.32, 20.32, 20.36, 20.52, 20.48, 20.64, 20.48, 20.48, 20.32, 20.48, 20.36, 20.32, 20.2]
+366.17999999999995
+18.308999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.42804479598999, 'TIME_S_1KI': 0.32415432999658034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.8528582954407, 'W': 21.456480473872652, 'J_1KI': 9.07220572879828, 'W_1KI': 0.666971727506144, 'W_D': 3.1474804738726547, 'J_D': 42.81229504752161, 'W_D_1KI': 0.09783899514680307, 'J_D_1KI': 0.0030413116303016183}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..6de6107
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4747, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.586360931396484, "TIME_S_1KI": 2.2301160588574858, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.0044006347656, "W": 22.3162197944069, "J_1KI": 68.67587963656321, "W_1KI": 4.701120664505352, "W_D": 3.9862197944068996, "J_D": 58.23231742858882, "W_D_1KI": 0.8397345258914893, "J_D_1KI": 0.17689794099251935}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..0876b19
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2116076946258545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99984,  99990,
+                            100000]),
+       col_indices=tensor([ 365,  990, 1421,  ..., 6204, 7506, 8345]),
+       values=tensor([0.4012, 0.2163, 0.0214,  ..., 0.4427, 0.7190, 0.8381]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6373, 0.6560, 0.2779,  ..., 0.6662, 0.5919, 0.8676])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.2116076946258545 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4747 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.586360931396484}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     23,  ...,  99976,  99989,
+                            100000]),
+       col_indices=tensor([ 145,  447,  695,  ..., 7955, 8009, 9128]),
+       values=tensor([0.3182, 0.0478, 0.7097,  ..., 0.3986, 0.2793, 0.7202]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3264, 0.5290, 0.7390,  ..., 0.4961, 0.6761, 0.4965])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.586360931396484 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     23,  ...,  99976,  99989,
+                            100000]),
+       col_indices=tensor([ 145,  447,  695,  ..., 7955, 8009, 9128]),
+       values=tensor([0.3182, 0.0478, 0.7097,  ..., 0.3986, 0.2793, 0.7202]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3264, 0.5290, 0.7390,  ..., 0.4961, 0.6761, 0.4965])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.586360931396484 seconds
+
+[20.56, 20.48, 20.36, 20.48, 20.4, 20.2, 20.32, 20.48, 20.52, 20.6]
+[20.44, 20.44, 20.44, 21.8, 24.32, 26.12, 27.12, 27.16, 25.36, 24.28, 24.24, 24.12, 23.96, 23.84]
+14.608406066894531
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4747, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.586360931396484, 'TIME_S_1KI': 2.2301160588574858, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.0044006347656, 'W': 22.3162197944069}
+[20.56, 20.48, 20.36, 20.48, 20.4, 20.2, 20.32, 20.48, 20.52, 20.6, 20.28, 20.08, 20.4, 20.32, 20.2, 20.36, 20.36, 20.4, 20.28, 20.48]
+366.6
+18.330000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4747, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.586360931396484, 'TIME_S_1KI': 2.2301160588574858, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.0044006347656, 'W': 22.3162197944069, 'J_1KI': 68.67587963656321, 'W_1KI': 4.701120664505352, 'W_D': 3.9862197944068996, 'J_D': 58.23231742858882, 'W_D_1KI': 0.8397345258914893, 'J_D_1KI': 0.17689794099251935}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..9af62e4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.214847326278687, "TIME_S_1KI": 21.214847326278687, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 593.507265138626, "W": 22.813207511083125, "J_1KI": 593.507265138626, "W_1KI": 22.813207511083125, "W_D": 4.622207511083129, "J_D": 120.25111933398253, "W_D_1KI": 4.622207511083129, "J_D_1KI": 4.622207511083129}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..a8afaf9
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.214847326278687}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     111,     190,  ...,  999805,
+                             999902, 1000000]),
+       col_indices=tensor([   3,  255,  407,  ..., 9480, 9499, 9966]),
+       values=tensor([0.6179, 0.1045, 0.6429,  ..., 0.5216, 0.7550, 0.7148]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6572, 0.8503, 0.2699,  ..., 0.6176, 0.8577, 0.2518])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.214847326278687 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     111,     190,  ...,  999805,
+                             999902, 1000000]),
+       col_indices=tensor([   3,  255,  407,  ..., 9480, 9499, 9966]),
+       values=tensor([0.6179, 0.1045, 0.6429,  ..., 0.5216, 0.7550, 0.7148]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6572, 0.8503, 0.2699,  ..., 0.6176, 0.8577, 0.2518])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.214847326278687 seconds
+
+[20.56, 20.64, 20.64, 20.52, 20.36, 20.4, 19.92, 19.96, 20.0, 20.08]
+[20.04, 20.08, 23.16, 25.4, 27.72, 28.8, 28.8, 29.48, 25.64, 25.4, 24.0, 23.96, 23.64, 23.72, 23.92, 24.04, 24.32, 24.36, 24.04, 24.0, 23.84, 24.08, 24.28, 24.28, 24.28]
+26.015949964523315
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.214847326278687, 'TIME_S_1KI': 21.214847326278687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 593.507265138626, 'W': 22.813207511083125}
+[20.56, 20.64, 20.64, 20.52, 20.36, 20.4, 19.92, 19.96, 20.0, 20.08, 19.76, 19.76, 19.96, 20.28, 20.4, 20.36, 20.4, 20.0, 19.92, 20.2]
+363.81999999999994
+18.190999999999995
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.214847326278687, 'TIME_S_1KI': 21.214847326278687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 593.507265138626, 'W': 22.813207511083125, 'J_1KI': 593.507265138626, 'W_1KI': 22.813207511083125, 'W_D': 4.622207511083129, 'J_D': 120.25111933398253, 'W_D_1KI': 4.622207511083129, 'J_D_1KI': 4.622207511083129}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..4f5ecb3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.68757820129395, "TIME_S_1KI": 106.68757820129395, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2614.243714923859, "W": 23.06903562044379, "J_1KI": 2614.243714923859, "W_1KI": 23.06903562044379, "W_D": 4.456035620443789, "J_D": 504.9696617529395, "W_D_1KI": 4.456035620443789, "J_D_1KI": 4.456035620443789}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..4e8e799
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.68757820129395}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     529,    1008,  ..., 4999026,
+                            4999478, 5000000]),
+       col_indices=tensor([  75,  122,  128,  ..., 9908, 9909, 9916]),
+       values=tensor([0.8571, 0.2596, 0.0411,  ..., 0.7048, 0.9398, 0.3732]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2354, 0.1436, 0.6485,  ..., 0.5167, 0.9065, 0.2719])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.68757820129395 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     529,    1008,  ..., 4999026,
+                            4999478, 5000000]),
+       col_indices=tensor([  75,  122,  128,  ..., 9908, 9909, 9916]),
+       values=tensor([0.8571, 0.2596, 0.0411,  ..., 0.7048, 0.9398, 0.3732]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2354, 0.1436, 0.6485,  ..., 0.5167, 0.9065, 0.2719])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.68757820129395 seconds
+
+[20.52, 20.6, 20.8, 20.84, 20.8, 20.6, 20.68, 20.68, 20.4, 20.56]
+[20.56, 20.64, 20.92, 22.0, 23.8, 25.56, 26.6, 26.56, 26.4, 25.36, 24.52, 24.6, 24.56, 24.6, 24.32, 24.4, 24.4, 24.32, 24.24, 24.44, 24.6, 24.36, 24.32, 24.36, 24.48, 24.6, 24.76, 24.72, 24.64, 24.6, 24.48, 24.56, 24.64, 24.64, 24.44, 24.48, 24.36, 24.16, 24.16, 24.24, 24.28, 24.16, 24.2, 24.36, 24.44, 24.44, 24.32, 24.0, 24.0, 24.0, 24.28, 24.44, 24.56, 24.48, 24.48, 24.32, 24.52, 24.52, 24.36, 24.4, 24.4, 24.32, 24.36, 24.32, 24.68, 24.72, 24.6, 24.6, 24.64, 24.6, 24.72, 24.64, 24.64, 24.68, 24.68, 24.52, 24.4, 24.32, 24.2, 24.16, 24.24, 24.2, 24.2, 24.4, 24.52, 24.56, 24.8, 24.8, 24.56, 24.44, 24.4, 23.84, 23.76, 23.88, 24.0, 24.0, 24.16, 24.2, 24.36, 24.2, 24.16, 24.2, 24.24, 24.16, 24.16, 24.4, 24.32, 24.56]
+113.32262682914734
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.68757820129395, 'TIME_S_1KI': 106.68757820129395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.243714923859, 'W': 23.06903562044379}
+[20.52, 20.6, 20.8, 20.84, 20.8, 20.6, 20.68, 20.68, 20.4, 20.56, 20.4, 20.52, 20.72, 20.84, 20.96, 20.84, 20.8, 20.52, 20.64, 20.56]
+372.26
+18.613
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.68757820129395, 'TIME_S_1KI': 106.68757820129395, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.243714923859, 'W': 23.06903562044379, 'J_1KI': 2614.243714923859, 'W_1KI': 23.06903562044379, 'W_D': 4.456035620443789, 'J_D': 504.9696617529395, 'W_D_1KI': 4.456035620443789, 'J_D_1KI': 4.456035620443789}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..69bee1d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 145400, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.376285076141357, "TIME_S_1KI": 0.07136372129395707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.9616888427734, "W": 22.159348523127505, "J_1KI": 2.2349497169379187, "W_1KI": 0.15240267209853856, "W_D": 3.711348523127505, "J_D": 54.42606233215331, "W_D_1KI": 0.02552509300637899, "J_D_1KI": 0.00017555084598610036}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..d251499
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08243966102600098}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 252, 7839, 5648, 3465, 7349, 4902, 9434, 7529, 7692,
+                            165, 3611,  104,  550, 6486, 7084, 9069, 7958, 6919,
+                            690, 9278, 3067, 6601, 7528, 1640, 3373, 4102, 2924,
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+                           7425,  800, 8678, 5796, 5760,  120, 6846,  442, 3920,
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+                      5.9912e-01, 8.9877e-01, 9.8527e-01, 4.8430e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8709, 0.3477, 0.9071,  ..., 0.3290, 0.2447, 0.6100])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.08243966102600098 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 127365 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.197555303573608}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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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+                      8.3562e-01, 2.2207e-01, 3.7585e-02, 7.4761e-02]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1139, 0.8384, 0.4762,  ..., 0.6681, 0.9732, 0.3908])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.197555303573608 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 145400 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.376285076141357}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([2248, 4486, 2578, 3740,  621, 6003, 5803, 7740, 8017,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.6283, 0.6554, 0.1926,  ..., 0.5716, 0.9993, 0.6492])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.376285076141357 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered 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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+                      2.7835e-01, 8.3789e-01, 7.1509e-01, 6.6596e-02,
+                      7.0322e-01, 6.7972e-02, 7.9658e-01, 6.6196e-01,
+                      7.4672e-01, 9.2136e-01, 6.6804e-01, 3.4306e-01,
+                      4.8283e-01, 7.4549e-01, 5.5160e-01, 3.8359e-01,
+                      4.5479e-01, 2.3253e-01, 1.2656e-01, 4.0585e-02,
+                      5.6244e-01, 6.4034e-03, 9.0407e-01, 7.9786e-02,
+                      6.0034e-01, 6.5899e-01, 8.2659e-01, 1.3903e-01,
+                      5.4187e-01, 4.8715e-01, 9.9846e-01, 5.8032e-01,
+                      9.2359e-01, 9.8268e-01, 4.4520e-01, 5.6869e-01,
+                      7.0005e-03, 4.7278e-02, 2.7563e-01, 5.8058e-01,
+                      1.5187e-01, 1.5041e-01, 6.7326e-01, 5.1848e-01,
+                      8.4097e-01, 3.3985e-01, 8.7930e-01, 6.0871e-01,
+                      4.7442e-01, 6.2568e-01, 5.9426e-01, 5.8463e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.6283, 0.6554, 0.1926,  ..., 0.5716, 0.9993, 0.6492])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.376285076141357 seconds
+
+[20.4, 20.32, 20.32, 20.44, 20.32, 20.32, 20.52, 20.6, 20.96, 21.36]
+[21.4, 21.52, 21.84, 22.84, 24.24, 24.88, 25.24, 25.24, 24.84, 24.56, 23.72, 23.8, 23.96, 23.68]
+14.664767265319824
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 145400, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.376285076141357, 'TIME_S_1KI': 0.07136372129395707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9616888427734, 'W': 22.159348523127505}
+[20.4, 20.32, 20.32, 20.44, 20.32, 20.32, 20.52, 20.6, 20.96, 21.36, 20.68, 20.64, 20.6, 20.52, 20.32, 20.28, 20.32, 20.32, 20.6, 20.68]
+368.96000000000004
+18.448
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 145400, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.376285076141357, 'TIME_S_1KI': 0.07136372129395707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9616888427734, 'W': 22.159348523127505, 'J_1KI': 2.2349497169379187, 'W_1KI': 0.15240267209853856, 'W_D': 3.711348523127505, 'J_D': 54.42606233215331, 'W_D_1KI': 0.02552509300637899, 'J_D_1KI': 0.00017555084598610036}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..c0e7e6f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 98.37349367141724, "TIME_S_1KI": 98.37349367141724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2530.5661976623533, "W": 23.87864246176097, "J_1KI": 2530.5661976623533, "W_1KI": 23.87864246176097, "W_D": 5.392642461760968, "J_D": 571.491396617889, "W_D_1KI": 5.392642461760968, "J_D_1KI": 5.392642461760968}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..3a0a0f2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 98.37349367141724}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       6,  ..., 2499994,
+                            2499997, 2500000]),
+       col_indices=tensor([ 13104,  56490,  58201,  ...,  30329, 136735,
+                           267614]),
+       values=tensor([0.2415, 0.0022, 0.5702,  ..., 0.5534, 0.4567, 0.6374]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5993, 0.2850, 0.9957,  ..., 0.8791, 0.8991, 0.2848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 98.37349367141724 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       6,  ..., 2499994,
+                            2499997, 2500000]),
+       col_indices=tensor([ 13104,  56490,  58201,  ...,  30329, 136735,
+                           267614]),
+       values=tensor([0.2415, 0.0022, 0.5702,  ..., 0.5534, 0.4567, 0.6374]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5993, 0.2850, 0.9957,  ..., 0.8791, 0.8991, 0.2848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 98.37349367141724 seconds
+
+[20.36, 20.36, 20.48, 20.36, 20.56, 20.68, 20.6, 20.64, 20.6, 20.52]
+[20.56, 20.56, 20.56, 21.88, 23.2, 25.36, 26.72, 27.0, 26.4, 25.72, 25.04, 25.04, 25.2, 25.32, 25.2, 25.0, 25.12, 24.96, 24.88, 25.0, 24.92, 24.92, 24.96, 25.08, 25.24, 25.28, 25.48, 25.28, 25.28, 25.44, 25.44, 25.4, 25.2, 25.12, 24.96, 25.12, 25.32, 25.52, 25.8, 25.72, 25.44, 25.08, 25.0, 25.0, 24.96, 25.0, 25.04, 25.12, 25.12, 25.2, 25.2, 25.16, 25.04, 24.88, 24.96, 25.16, 25.16, 25.24, 25.24, 25.4, 25.2, 25.32, 25.16, 25.16, 25.2, 25.2, 25.0, 25.16, 25.28, 25.28, 25.28, 25.16, 25.2, 25.2, 25.04, 25.2, 25.36, 25.32, 25.32, 25.52, 25.44, 25.4, 25.36, 25.4, 25.32, 25.24, 25.04, 25.04, 24.92, 24.96, 24.96, 25.12, 25.32, 25.24, 25.2, 25.04, 24.92, 25.08, 25.0, 24.96, 24.88]
+105.97613334655762
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 98.37349367141724, 'TIME_S_1KI': 98.37349367141724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2530.5661976623533, 'W': 23.87864246176097}
+[20.36, 20.36, 20.48, 20.36, 20.56, 20.68, 20.6, 20.64, 20.6, 20.52, 20.68, 20.4, 20.36, 20.52, 20.4, 20.6, 20.68, 20.68, 20.64, 20.76]
+369.72
+18.486
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 98.37349367141724, 'TIME_S_1KI': 98.37349367141724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2530.5661976623533, 'W': 23.87864246176097, 'J_1KI': 2530.5661976623533, 'W_1KI': 23.87864246176097, 'W_D': 5.392642461760968, 'J_D': 571.491396617889, 'W_D_1KI': 5.392642461760968, 'J_D_1KI': 5.392642461760968}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..05b637f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1803, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.916261672973633, "TIME_S_1KI": 6.054498986674227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.36130602836613, "W": 23.871230542861202, "J_1KI": 193.21203883991467, "W_1KI": 13.239728531814311, "W_D": 5.286230542861201, "J_D": 77.14383104681971, "W_D_1KI": 2.931908232313478, "J_D_1KI": 1.6261276940174587}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..a1e0ae8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.821210145950317}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 249996, 249998,
+                            250000]),
+       col_indices=tensor([12413, 12946, 15415,  ..., 25881, 14227, 42249]),
+       values=tensor([0.3226, 0.4714, 0.3498,  ..., 0.9478, 0.5271, 0.1593]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8728, 0.8759, 0.3915,  ..., 0.5486, 0.7678, 0.2723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 5.821210145950317 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1803 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.916261672973633}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 249988, 249991,
+                            250000]),
+       col_indices=tensor([ 7415, 12339, 19287,  ..., 32647, 33814, 45500]),
+       values=tensor([0.8370, 0.0969, 0.8316,  ..., 0.1944, 0.4025, 0.6344]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2154, 0.6825, 0.0342,  ..., 0.6227, 0.4225, 0.9397])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.916261672973633 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 249988, 249991,
+                            250000]),
+       col_indices=tensor([ 7415, 12339, 19287,  ..., 32647, 33814, 45500]),
+       values=tensor([0.8370, 0.0969, 0.8316,  ..., 0.1944, 0.4025, 0.6344]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2154, 0.6825, 0.0342,  ..., 0.6227, 0.4225, 0.9397])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.916261672973633 seconds
+
+[20.84, 20.76, 20.76, 20.6, 20.4, 20.28, 20.24, 20.52, 20.56, 20.44]
+[20.44, 20.32, 23.6, 25.4, 27.36, 28.36, 28.36, 29.32, 26.76, 26.0, 25.28, 24.92, 25.04, 25.08]
+14.593353509902954
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1803, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.916261672973633, 'TIME_S_1KI': 6.054498986674227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.36130602836613, 'W': 23.871230542861202}
+[20.84, 20.76, 20.76, 20.6, 20.4, 20.28, 20.24, 20.52, 20.56, 20.44, 20.2, 20.24, 20.32, 20.44, 20.72, 20.92, 20.92, 21.36, 21.28, 21.28]
+371.7
+18.585
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1803, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.916261672973633, 'TIME_S_1KI': 6.054498986674227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.36130602836613, 'W': 23.871230542861202, 'J_1KI': 193.21203883991467, 'W_1KI': 13.239728531814311, 'W_D': 5.286230542861201, 'J_D': 77.14383104681971, 'W_D_1KI': 2.931908232313478, 'J_D_1KI': 1.6261276940174587}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..62a0664
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 53.93572449684143, "TIME_S_1KI": 53.93572449684143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1405.257185211182, "W": 23.49413775375655, "J_1KI": 1405.257185211182, "W_1KI": 23.49413775375655, "W_D": 4.945137753756551, "J_D": 295.78401357650813, "W_D_1KI": 4.945137753756551, "J_D_1KI": 4.945137753756551}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..cbf5bdc
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 53.93572449684143}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      63,     121,  ..., 2499897,
+                            2499959, 2500000]),
+       col_indices=tensor([  158,  1232,  2736,  ..., 48449, 48581, 49575]),
+       values=tensor([0.0263, 0.9327, 0.9651,  ..., 0.1558, 0.2228, 0.0301]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0174, 0.1708, 0.2801,  ..., 0.8892, 0.6468, 0.1800])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 53.93572449684143 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      63,     121,  ..., 2499897,
+                            2499959, 2500000]),
+       col_indices=tensor([  158,  1232,  2736,  ..., 48449, 48581, 49575]),
+       values=tensor([0.0263, 0.9327, 0.9651,  ..., 0.1558, 0.2228, 0.0301]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0174, 0.1708, 0.2801,  ..., 0.8892, 0.6468, 0.1800])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 53.93572449684143 seconds
+
+[20.72, 20.76, 20.76, 20.8, 20.72, 20.72, 20.24, 20.36, 20.72, 20.72]
+[20.76, 21.16, 21.32, 25.56, 26.68, 28.72, 29.48, 27.44, 25.8, 24.96, 24.56, 24.52, 24.56, 24.64, 24.6, 24.52, 24.56, 24.56, 24.6, 24.6, 24.68, 24.68, 24.4, 24.48, 24.4, 24.56, 24.52, 24.8, 24.64, 24.8, 24.68, 24.88, 24.56, 24.56, 24.36, 24.48, 24.64, 24.68, 24.56, 24.72, 24.48, 24.48, 24.44, 24.92, 25.08, 24.92, 24.96, 24.76, 24.76, 24.56, 24.44, 24.36, 24.36, 24.44, 24.36, 24.44, 24.52]
+59.8130989074707
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 53.93572449684143, 'TIME_S_1KI': 53.93572449684143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.257185211182, 'W': 23.49413775375655}
+[20.72, 20.76, 20.76, 20.8, 20.72, 20.72, 20.24, 20.36, 20.72, 20.72, 20.2, 20.6, 20.56, 20.68, 20.8, 20.8, 20.64, 20.56, 20.36, 20.16]
+370.97999999999996
+18.549
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 53.93572449684143, 'TIME_S_1KI': 53.93572449684143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.257185211182, 'W': 23.49413775375655, 'J_1KI': 1405.257185211182, 'W_1KI': 23.49413775375655, 'W_D': 4.945137753756551, 'J_D': 295.78401357650813, 'W_D_1KI': 4.945137753756551, 'J_D_1KI': 4.945137753756551}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..8adab40
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10285, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.171167612075806, "TIME_S_1KI": 0.9889321936874872, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.4937496185303, "W": 23.99041156544644, "J_1KI": 31.647423395092883, "W_1KI": 2.332563107967568, "W_D": 5.591411565446439, "J_D": 75.86237156176564, "W_D_1KI": 0.5436472110302809, "J_D_1KI": 0.052858260673824105}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..12e8b6c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.020900011062622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([43592, 45763, 41730,  ...,  2923, 32227, 39553]),
+       values=tensor([0.0398, 0.4210, 0.0283,  ..., 0.1409, 0.8695, 0.8837]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8554, 0.8486, 0.8747,  ..., 0.5244, 0.7497, 0.0831])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 1.020900011062622 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10285 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.171167612075806}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([14664, 43703, 46520,  ...,  7061, 31497, 43987]),
+       values=tensor([0.1911, 0.5487, 0.9416,  ..., 0.5242, 0.5616, 0.0900]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7733, 0.9528, 0.6124,  ..., 0.0354, 0.2670, 0.0752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.171167612075806 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([14664, 43703, 46520,  ...,  7061, 31497, 43987]),
+       values=tensor([0.1911, 0.5487, 0.9416,  ..., 0.5242, 0.5616, 0.0900]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7733, 0.9528, 0.6124,  ..., 0.0354, 0.2670, 0.0752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.171167612075806 seconds
+
+[20.44, 20.44, 20.36, 20.08, 20.36, 20.36, 20.4, 20.44, 20.6, 20.52]
+[20.52, 20.72, 24.24, 25.88, 27.44, 27.44, 28.48, 29.32, 25.96, 26.28, 25.8, 26.0, 25.92]
+13.567660093307495
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.171167612075806, 'TIME_S_1KI': 0.9889321936874872, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.4937496185303, 'W': 23.99041156544644}
+[20.44, 20.44, 20.36, 20.08, 20.36, 20.36, 20.4, 20.44, 20.6, 20.52, 20.56, 20.36, 20.28, 20.12, 20.2, 20.6, 20.92, 20.8, 20.6, 20.6]
+367.98
+18.399
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.171167612075806, 'TIME_S_1KI': 0.9889321936874872, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.4937496185303, 'W': 23.99041156544644, 'J_1KI': 31.647423395092883, 'W_1KI': 2.332563107967568, 'W_D': 5.591411565446439, 'J_D': 75.86237156176564, 'W_D_1KI': 0.5436472110302809, 'J_D_1KI': 0.052858260673824105}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..e1dfb7e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6038, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.267276763916016, "TIME_S_1KI": 1.7004433196283564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.7928693008422, "W": 66.02, "J_1KI": 141.9001108481024, "W_1KI": 10.934084133819145, "W_D": 30.906499999999994, "J_D": 401.09767971897116, "W_D_1KI": 5.1186651209009595, "J_D_1KI": 0.8477418219445113}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..ba4be77
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.7388477325439453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      13,      28,  ...,  999983,
+                             999988, 1000000]),
+       col_indices=tensor([ 2300,  3196, 10757,  ..., 92248, 95895, 96660]),
+       values=tensor([0.0937, 0.5944, 0.4639,  ..., 0.5292, 0.3684, 0.5963]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9252, 0.4601, 0.1039,  ..., 0.3841, 0.9664, 0.4740])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 1.7388477325439453 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6038', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.267276763916016}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      25,  ...,  999976,
+                             999990, 1000000]),
+       col_indices=tensor([ 7337,  9006, 37341,  ..., 86240, 86867, 93776]),
+       values=tensor([0.1177, 0.4165, 0.7590,  ..., 0.7494, 0.7065, 0.3766]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1410, 0.1591, 0.3967,  ..., 0.8959, 0.7085, 0.3739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.267276763916016 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      25,  ...,  999976,
+                             999990, 1000000]),
+       col_indices=tensor([ 7337,  9006, 37341,  ..., 86240, 86867, 93776]),
+       values=tensor([0.1177, 0.4165, 0.7590,  ..., 0.7494, 0.7065, 0.3766]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1410, 0.1591, 0.3967,  ..., 0.8959, 0.7085, 0.3739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.267276763916016 seconds
+
+[39.74, 38.35, 38.46, 38.36, 38.8, 38.69, 39.25, 38.82, 38.81, 38.76]
+[66.02]
+12.977777481079102
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.267276763916016, 'TIME_S_1KI': 1.7004433196283564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7928693008422, 'W': 66.02}
+[39.74, 38.35, 38.46, 38.36, 38.8, 38.69, 39.25, 38.82, 38.81, 38.76, 39.02, 38.96, 38.6, 38.78, 38.41, 38.79, 38.39, 38.7, 43.94, 38.8]
+702.27
+35.1135
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6038, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.267276763916016, 'TIME_S_1KI': 1.7004433196283564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.7928693008422, 'W': 66.02, 'J_1KI': 141.9001108481024, 'W_1KI': 10.934084133819145, 'W_D': 30.906499999999994, 'J_D': 401.09767971897116, 'W_D_1KI': 5.1186651209009595, 'J_D_1KI': 0.8477418219445113}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..fbd54c6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12169, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.374937295913696, "TIME_S_1KI": 0.8525710654871967, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 840.8859812259674, "W": 64.58, "J_1KI": 69.10066408299511, "W_1KI": 5.306927438573425, "W_D": 29.637499999999996, "J_D": 385.90520700812334, "W_D_1KI": 2.4354918234859064, "J_D_1KI": 0.20013902732236885}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..cc678f8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8628060817718506}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([15542, 51530, 32014,  ..., 17183, 69417, 75150]),
+       values=tensor([0.6948, 0.1030, 0.8530,  ..., 0.6511, 0.2631, 0.7718]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3720, 0.8026, 0.8839,  ..., 0.1725, 0.9607, 0.0788])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.8628060817718506 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12169', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.374937295913696}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([38450, 44184, 11395,  ...,  3206,  2272, 42747]),
+       values=tensor([0.8156, 0.6388, 0.3060,  ..., 0.5932, 0.6977, 0.4008]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7706, 0.5998, 0.9728,  ..., 0.9827, 0.6551, 0.5654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.374937295913696 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([38450, 44184, 11395,  ...,  3206,  2272, 42747]),
+       values=tensor([0.8156, 0.6388, 0.3060,  ..., 0.5932, 0.6977, 0.4008]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7706, 0.5998, 0.9728,  ..., 0.9827, 0.6551, 0.5654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.374937295913696 seconds
+
+[39.65, 38.92, 38.96, 38.86, 38.63, 38.6, 38.47, 38.43, 38.43, 38.83]
+[64.58]
+13.0208420753479
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.374937295913696, 'TIME_S_1KI': 0.8525710654871967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.8859812259674, 'W': 64.58}
+[39.65, 38.92, 38.96, 38.86, 38.63, 38.6, 38.47, 38.43, 38.43, 38.83, 40.06, 39.01, 39.04, 38.86, 38.59, 38.53, 38.46, 38.45, 40.01, 38.66]
+698.85
+34.9425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.374937295913696, 'TIME_S_1KI': 0.8525710654871967, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.8859812259674, 'W': 64.58, 'J_1KI': 69.10066408299511, 'W_1KI': 5.306927438573425, 'W_D': 29.637499999999996, 'J_D': 385.90520700812334, 'W_D_1KI': 2.4354918234859064, 'J_D_1KI': 0.20013902732236885}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..3886b8d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 237950, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.479950666427612, "TIME_S_1KI": 0.044042658820876705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.0966372108459, "W": 64.88, "J_1KI": 3.5599774625377005, "W_1KI": 0.2726623240176507, "W_D": 29.35949999999999, "J_D": 383.32820160591587, "W_D_1KI": 0.12338516495061984, "J_D_1KI": 0.0005185339985317077}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..4b3b779
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.055043935775756836}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([2489, 8082, 1798,  ..., 7687, 8784, 7173]),
+       values=tensor([0.0419, 0.2217, 0.5372,  ..., 0.9380, 0.6037, 0.5878]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.6995, 0.5522, 0.6987,  ..., 0.2479, 0.0646, 0.0677])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.055043935775756836 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '190756', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.417458295822144}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([6369, 8699, 1454,  ..., 3376, 4538, 4463]),
+       values=tensor([0.7752, 0.1565, 0.1050,  ..., 0.8742, 0.0228, 0.3625]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2878, 0.2325, 0.9670,  ..., 0.8581, 0.8156, 0.4801])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 8.417458295822144 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237950', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.479950666427612}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([2568, 4888, 9428,  ..., 1921, 2148, 9872]),
+       values=tensor([0.1473, 0.4194, 0.4025,  ..., 0.4119, 0.3062, 0.3667]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9989, 0.1547, 0.2140,  ..., 0.5569, 0.3690, 0.8580])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.479950666427612 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([2568, 4888, 9428,  ..., 1921, 2148, 9872]),
+       values=tensor([0.1473, 0.4194, 0.4025,  ..., 0.4119, 0.3062, 0.3667]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9989, 0.1547, 0.2140,  ..., 0.5569, 0.3690, 0.8580])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.479950666427612 seconds
+
+[40.47, 39.33, 38.5, 38.74, 38.63, 38.5, 43.74, 38.79, 39.18, 38.53]
+[64.88]
+13.056360006332397
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.479950666427612, 'TIME_S_1KI': 0.044042658820876705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.0966372108459, 'W': 64.88}
+[40.47, 39.33, 38.5, 38.74, 38.63, 38.5, 43.74, 38.79, 39.18, 38.53, 39.54, 38.39, 38.83, 43.63, 38.88, 38.94, 39.01, 39.89, 38.82, 38.68]
+710.4100000000001
+35.520500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.479950666427612, 'TIME_S_1KI': 0.044042658820876705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.0966372108459, 'W': 64.88, 'J_1KI': 3.5599774625377005, 'W_1KI': 0.2726623240176507, 'W_D': 29.35949999999999, 'J_D': 383.32820160591587, 'W_D_1KI': 0.12338516495061984, 'J_D_1KI': 0.0005185339985317077}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..4c5c61e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75505, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.45979928970337, "TIME_S_1KI": 0.15177536970668656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 869.2745451879501, "W": 66.09, "J_1KI": 11.512807697343886, "W_1KI": 0.8753062711078737, "W_D": 31.095250000000007, "J_D": 408.9924239863158, "W_D_1KI": 0.4118303423614331, "J_D_1KI": 0.00545434530642253}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..afe840d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.15292811393737793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     10,  ...,  99983,  99994,
+                            100000]),
+       col_indices=tensor([ 267, 3923, 5616,  ..., 4271, 7755, 9973]),
+       values=tensor([0.9283, 0.7846, 0.2151,  ..., 0.9447, 0.6120, 0.1119]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9192, 0.5849, 0.9579,  ..., 0.9586, 0.7879, 0.6201])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.15292811393737793 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68659', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.547868490219116}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     21,  ...,  99972,  99985,
+                            100000]),
+       col_indices=tensor([ 305,  380,  962,  ..., 8769, 9180, 9915]),
+       values=tensor([0.5782, 0.8638, 0.8069,  ..., 0.1223, 0.7033, 0.9891]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4135, 0.4566, 0.8532,  ..., 0.7837, 0.5944, 0.7679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 9.547868490219116 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75505', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.45979928970337}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     21,  ...,  99982,  99992,
+                            100000]),
+       col_indices=tensor([1022, 1138, 1407,  ..., 6223, 7233, 9402]),
+       values=tensor([0.9484, 0.5958, 0.7782,  ..., 0.0863, 0.6723, 0.0562]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0389, 0.1147, 0.5260,  ..., 0.1033, 0.1694, 0.2810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 11.45979928970337 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     21,  ...,  99982,  99992,
+                            100000]),
+       col_indices=tensor([1022, 1138, 1407,  ..., 6223, 7233, 9402]),
+       values=tensor([0.9484, 0.5958, 0.7782,  ..., 0.0863, 0.6723, 0.0562]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0389, 0.1147, 0.5260,  ..., 0.1033, 0.1694, 0.2810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 11.45979928970337 seconds
+
+[39.42, 38.37, 38.5, 39.45, 39.07, 38.36, 39.56, 38.91, 39.08, 38.45]
+[66.09]
+13.152890682220459
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75505, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.45979928970337, 'TIME_S_1KI': 0.15177536970668656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 869.2745451879501, 'W': 66.09}
+[39.42, 38.37, 38.5, 39.45, 39.07, 38.36, 39.56, 38.91, 39.08, 38.45, 39.1, 38.46, 38.46, 39.62, 38.45, 39.31, 38.8, 38.73, 38.89, 38.78]
+699.895
+34.994749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75505, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.45979928970337, 'TIME_S_1KI': 0.15177536970668656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 869.2745451879501, 'W': 66.09, 'J_1KI': 11.512807697343886, 'W_1KI': 0.8753062711078737, 'W_D': 31.095250000000007, 'J_D': 408.9924239863158, 'W_D_1KI': 0.4118303423614331, 'J_D_1KI': 0.00545434530642253}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..4467311
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10051, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.396085023880005, "TIME_S_1KI": 1.0343334020376087, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 865.9663576483727, "W": 66.05, "J_1KI": 86.1572338720896, "W_1KI": 6.571485424335887, "W_D": 30.772, "J_D": 403.44461404323573, "W_D_1KI": 3.061585911849567, "J_D_1KI": 0.3046051051486983}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..ec9215b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.0446221828460693}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     186,  ...,  999791,
+                             999885, 1000000]),
+       col_indices=tensor([  85,  646,  706,  ..., 9852, 9875, 9886]),
+       values=tensor([0.7433, 0.1282, 0.1316,  ..., 0.9681, 0.9495, 0.6187]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5006, 0.3207, 0.7634,  ..., 0.1693, 0.2023, 0.9705])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 1.0446221828460693 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10051', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.396085023880005}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      82,     194,  ...,  999830,
+                             999924, 1000000]),
+       col_indices=tensor([ 207,  248,  391,  ..., 9735, 9842, 9886]),
+       values=tensor([0.2382, 0.1304, 0.8275,  ..., 0.9132, 0.3101, 0.1677]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0491, 0.4304, 0.0195,  ..., 0.4012, 0.5324, 0.0059])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.396085023880005 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      82,     194,  ...,  999830,
+                             999924, 1000000]),
+       col_indices=tensor([ 207,  248,  391,  ..., 9735, 9842, 9886]),
+       values=tensor([0.2382, 0.1304, 0.8275,  ..., 0.9132, 0.3101, 0.1677]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0491, 0.4304, 0.0195,  ..., 0.4012, 0.5324, 0.0059])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.396085023880005 seconds
+
+[40.88, 38.45, 38.83, 38.72, 44.02, 38.37, 38.57, 38.7, 39.57, 38.48]
+[66.05]
+13.110769987106323
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10051, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.396085023880005, 'TIME_S_1KI': 1.0343334020376087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.9663576483727, 'W': 66.05}
+[40.88, 38.45, 38.83, 38.72, 44.02, 38.37, 38.57, 38.7, 39.57, 38.48, 42.6, 39.8, 38.39, 38.73, 39.03, 38.72, 38.63, 38.42, 38.48, 38.3]
+705.56
+35.278
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10051, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.396085023880005, 'TIME_S_1KI': 1.0343334020376087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.9663576483727, 'W': 66.05, 'J_1KI': 86.1572338720896, 'W_1KI': 6.571485424335887, 'W_D': 30.772, 'J_D': 403.44461404323573, 'W_D_1KI': 3.061585911849567, 'J_D_1KI': 0.3046051051486983}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..9a92bbf
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496549844741821, "TIME_S_1KI": 5.963948775421489, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1063.6957297325134, "W": 75.3, "J_1KI": 604.3725737116553, "W_1KI": 42.784090909090914, "W_D": 40.305749999999996, "J_D": 569.3632690393924, "W_D_1KI": 22.900994318181816, "J_D_1KI": 13.011928589876032}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..f82f04e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.962578296661377}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     507,     983,  ..., 4998985,
+                            4999482, 5000000]),
+       col_indices=tensor([   6,   14,   63,  ..., 9975, 9976, 9988]),
+       values=tensor([0.1343, 0.9147, 0.2964,  ..., 0.8307, 0.6480, 0.1778]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4820, 0.9526, 0.2470,  ..., 0.0414, 0.1724, 0.7388])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 5.962578296661377 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1760', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496549844741821}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     494,    1024,  ..., 4999026,
+                            4999505, 5000000]),
+       col_indices=tensor([  14,   81,  111,  ..., 9976, 9994, 9996]),
+       values=tensor([0.8750, 0.2097, 0.6973,  ..., 0.7142, 0.2835, 0.0523]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6644, 0.2536, 0.5514,  ..., 0.5924, 0.6712, 0.0391])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.496549844741821 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     494,    1024,  ..., 4999026,
+                            4999505, 5000000]),
+       col_indices=tensor([  14,   81,  111,  ..., 9976, 9994, 9996]),
+       values=tensor([0.8750, 0.2097, 0.6973,  ..., 0.7142, 0.2835, 0.0523]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6644, 0.2536, 0.5514,  ..., 0.5924, 0.6712, 0.0391])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.496549844741821 seconds
+
+[39.19, 39.0, 39.0, 38.91, 38.55, 38.97, 38.49, 38.52, 38.9, 38.82]
+[75.3]
+14.126105308532715
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496549844741821, 'TIME_S_1KI': 5.963948775421489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1063.6957297325134, 'W': 75.3}
+[39.19, 39.0, 39.0, 38.91, 38.55, 38.97, 38.49, 38.52, 38.9, 38.82, 39.55, 38.59, 39.72, 38.89, 38.53, 38.82, 38.62, 39.33, 38.84, 38.85]
+699.885
+34.99425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496549844741821, 'TIME_S_1KI': 5.963948775421489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1063.6957297325134, 'W': 75.3, 'J_1KI': 604.3725737116553, 'W_1KI': 42.784090909090914, 'W_D': 40.305749999999996, 'J_D': 569.3632690393924, 'W_D_1KI': 22.900994318181816, 'J_D_1KI': 13.011928589876032}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..5067ef9
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 363782, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.585279941558838, "TIME_S_1KI": 0.029097866143896176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3318356752395, "W": 64.69, "J_1KI": 2.3512208841428097, "W_1KI": 0.17782628057462985, "W_D": 29.81474999999999, "J_D": 394.2109266918896, "W_D_1KI": 0.08195773842576046, "J_D_1KI": 0.00022529355060382441}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..198dd99
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08438587188720703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 654, 6772, 3481, 2535,  125, 3792, 8070, 9757, 6184,
+                           2668,   22, 1611, 8038, 5477, 9185, 5993, 9592, 3939,
+                           8639, 7553,  398, 5715, 4399, 2570, 9973, 3035, 1537,
+                           7004, 5611, 9891, 2622, 9818,  312, 6105, 1848, 1339,
+                           7382, 4214, 8955, 9892, 6474, 3909, 9732,  690, 3371,
+                           4869,  387, 3460, 9149, 5467, 6478, 5618, 6583, 2381,
+                           1542, 8342, 3787, 7463, 3823, 6427,  315, 6985,  523,
+                           5901, 9665, 9643, 5095, 3067, 2951, 9816, 6719, 6640,
+                           4349, 9622, 9227,  394, 8600, 2210, 9007, 6794, 6193,
+                           3591, 3763, 8848,  712, 2600, 6953,  345, 8176, 4284,
+                           2762, 1429, 3052, 9077, 9247, 8084, 9368, 8295, 4882,
+                           1029, 4128, 2221, 4457,  136, 1060, 5650, 2149,  979,
+                            879,  252, 4258, 4991, 6954, 9684, 5762, 3304, 4194,
+                           5738, 4881, 2067, 4630, 3102, 4373, 4364, 3467, 3904,
+                           2703, 9367, 5744, 1003, 7348, 7382, 2923, 6743, 2716,
+                           6005, 8885, 8781, 4688, 3208, 8165, 7655, 2105, 9594,
+                            413, 1022, 9740, 9415, 4440, 7624, 1850, 8157, 8564,
+                           2131, 4847, 5156,  223, 2070, 8741, 4432, 2158, 1202,
+                           3074, 6959, 1256, 8497, 7709, 1898, 7099, 7257, 3557,
+                           1556, 6637, 1724, 6284, 8045, 1698, 1347, 5955, 3324,
+                           2660, 5510, 9500,  461, 4931, 3894, 7627, 3132, 4579,
+                           5941, 9535, 3710, 3467, 3922,   66, 8349, 6253, 7489,
+                           3867, 9837, 4357, 5889, 1429, 2174, 5793, 5103, 9209,
+                           7044, 5165, 1550, 2922, 7404,  724, 8019, 1723, 9892,
+                           8178,  326, 8495, 1261, 5156, 3647,   48, 9679, 8317,
+                            258, 3999, 7172, 9917, 1368, 9356, 8091, 2920, 4194,
+                           9220,  533, 1856, 4184, 9970,  585,  888, 7980,  502,
+                           3237, 7414, 5484, 1198, 5107, 6730, 1701,  778, 9576,
+                           9847, 7139, 5046, 9505, 4273, 3181, 3701, 7837, 6438,
+                           5446, 5489, 3750, 7620, 3013, 2520, 4866, 2256, 1226,
+                           4389,  803, 6623,  207, 8091, 9177, 5502, 7927, 5893,
+                           7286, 4995, 7523, 7718,  638, 1691, 5859, 7604, 7499,
+                           5376, 8321,  497, 2691, 7462, 1832, 7626, 4908, 8743,
+                           2814, 8920, 2170, 2237, 5900, 1192, 1906, 5324, 8567,
+                            433, 7307, 4785, 7781, 8369, 5137, 3139, 6244, 3003,
+                           5146, 5779,  963, 5834, 8307, 1509, 6851, 2879, 9153,
+                           5590, 2199, 7088, 4851, 1689, 3978, 7052, 9728, 7823,
+                           6554, 7099, 3919, 9251, 3170, 4952, 3743,  753, 4966,
+                           8769, 5930, 1536, 3883, 3398, 3809, 4230, 4021, 4625,
+                            911, 3578, 6189,  791, 2646, 9155, 6564, 5024, 7172,
+                           6929, 9364, 1592, 9019, 2813, 8778, 1820,  106, 4324,
+                           1259, 4349, 3192, 4715, 9115,  966, 2560, 8066, 1744,
+                           3149, 6003, 1498, 3413, 7870, 8393, 6128, 5648, 3764,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4435, 0.0773, 0.0249,  ..., 0.0070, 0.7023, 0.2387])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.08438587188720703 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '124428', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.5914108753204346}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+Time: 3.5914108753204346 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '363782', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.585279941558838}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      8.6570e-01, 1.9544e-01, 4.7643e-01, 8.5814e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3920, 0.2913, 0.8672,  ..., 0.9245, 0.8812, 0.1957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.585279941558838 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      3.7054e-01, 6.5171e-01, 8.9851e-01, 7.9938e-01,
+                      7.9593e-01, 4.1200e-01, 7.2346e-01, 8.8886e-01,
+                      7.7392e-01, 3.0164e-01, 4.8903e-01, 7.0959e-01,
+                      2.3055e-04, 5.5567e-01, 3.2476e-02, 4.6940e-01,
+                      9.5992e-01, 3.3002e-01, 1.9968e-01, 3.6916e-01,
+                      9.3384e-01, 4.4547e-01, 8.4545e-01, 7.0652e-01,
+                      2.5484e-01, 6.6093e-01, 1.0142e-01, 4.0036e-01,
+                      8.8027e-02, 7.0693e-01, 6.1292e-01, 3.5279e-02,
+                      1.6464e-01, 7.4166e-01, 7.4512e-01, 3.1187e-01,
+                      9.3827e-01, 8.1291e-01, 6.8774e-01, 1.6535e-01,
+                      8.2353e-01, 4.4462e-01, 6.2390e-01, 2.5665e-01,
+                      2.4823e-01, 6.5084e-01, 2.8892e-01, 8.3557e-01,
+                      2.6616e-01, 8.8256e-01, 3.9677e-01, 7.0336e-01,
+                      8.3626e-01, 8.4230e-01, 1.2696e-01, 5.4260e-01,
+                      4.5014e-01, 1.2684e-01, 4.1101e-01, 4.8496e-01,
+                      7.6136e-01, 5.3988e-01, 4.7573e-01, 1.6785e-01,
+                      8.8662e-01, 6.3235e-01, 2.9589e-01, 2.8836e-01,
+                      9.5162e-01, 5.4258e-01, 2.7719e-01, 7.2819e-01,
+                      4.5646e-02, 7.4687e-01, 6.9970e-01, 6.6663e-01,
+                      4.4353e-01, 9.2703e-01, 1.7143e-01, 9.9752e-01,
+                      1.6120e-01, 9.1760e-02, 7.4539e-02, 6.2124e-01,
+                      3.9172e-01, 8.8093e-01, 8.8278e-01, 9.1996e-01,
+                      9.9713e-01, 4.7916e-01, 8.6919e-01, 9.8249e-01,
+                      8.6668e-01, 3.9161e-01, 6.4609e-01, 5.0516e-01,
+                      6.3812e-02, 4.1956e-01, 4.4304e-01, 5.3391e-01,
+                      9.4156e-01, 4.0458e-01, 2.9747e-01, 1.0297e-01,
+                      2.1881e-01, 5.0346e-01, 5.5620e-01, 1.2242e-01,
+                      9.4646e-01, 4.2415e-01, 1.2648e-01, 8.3879e-01,
+                      2.1895e-01, 7.2594e-01, 9.5334e-01, 4.1030e-01,
+                      6.5805e-01, 6.1371e-01, 6.3829e-01, 6.4855e-01,
+                      1.9689e-01, 7.6433e-01, 1.4868e-01, 1.2936e-01,
+                      1.2645e-01, 8.9930e-01, 1.6824e-01, 9.4670e-01,
+                      4.8028e-01, 6.9663e-01, 4.2572e-01, 8.7528e-01,
+                      8.1359e-02, 9.6745e-01, 2.7830e-01, 7.5427e-01,
+                      5.4086e-02, 9.3150e-01, 6.8940e-01, 8.9446e-01,
+                      4.2269e-01, 1.5823e-02, 6.6296e-01, 8.5098e-01,
+                      8.8125e-01, 8.8539e-01, 6.4149e-01, 9.0541e-01,
+                      8.6570e-01, 1.9544e-01, 4.7643e-01, 8.5814e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3920, 0.2913, 0.8672,  ..., 0.9245, 0.8812, 0.1957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.585279941558838 seconds
+
+[39.18, 40.08, 38.95, 38.39, 39.05, 38.86, 38.48, 38.43, 38.49, 38.42]
+[64.69]
+13.222010135650635
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363782, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.585279941558838, 'TIME_S_1KI': 0.029097866143896176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3318356752395, 'W': 64.69}
+[39.18, 40.08, 38.95, 38.39, 39.05, 38.86, 38.48, 38.43, 38.49, 38.42, 40.2, 38.44, 38.35, 38.82, 38.9, 38.53, 38.83, 38.37, 38.44, 38.39]
+697.5050000000001
+34.87525000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363782, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.585279941558838, 'TIME_S_1KI': 0.029097866143896176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3318356752395, 'W': 64.69, 'J_1KI': 2.3512208841428097, 'W_1KI': 0.17782628057462985, 'W_D': 29.81474999999999, 'J_D': 394.2109266918896, 'W_D_1KI': 0.08195773842576046, 'J_D_1KI': 0.00022529355060382441}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..f3fd74b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1366, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.481968879699707, "TIME_S_1KI": 7.673476485870942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 980.9324438238144, "W": 72.78, "J_1KI": 718.1057421843444, "W_1KI": 53.2796486090776, "W_D": 37.9805, "J_D": 511.9030596681833, "W_D_1KI": 27.804172767203514, "J_D_1KI": 20.35444565681077}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..170f45c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.686005115509033}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,       7,  ..., 2499986,
+                            2499989, 2500000]),
+       col_indices=tensor([176994, 249617, 373837,  ..., 283997, 343168,
+                           447931]),
+       values=tensor([0.4576, 0.5348, 0.2572,  ..., 0.1314, 0.2229, 0.5974]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1382, 0.9782, 0.8741,  ..., 0.2337, 0.6569, 0.8329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 7.686005115509033 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1366', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.481968879699707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([ 13301,  29016, 299078,  ..., 480591, 481476,
+                           496604]),
+       values=tensor([0.4578, 0.5414, 0.1917,  ..., 0.8449, 0.5002, 0.9459]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0252, 0.3938, 0.2908,  ..., 0.4459, 0.5549, 0.8752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.481968879699707 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([ 13301,  29016, 299078,  ..., 480591, 481476,
+                           496604]),
+       values=tensor([0.4578, 0.5414, 0.1917,  ..., 0.8449, 0.5002, 0.9459]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0252, 0.3938, 0.2908,  ..., 0.4459, 0.5549, 0.8752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.481968879699707 seconds
+
+[39.21, 38.47, 38.56, 38.59, 38.63, 38.48, 38.95, 38.74, 38.52, 38.83]
+[72.78]
+13.478049516677856
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1366, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.481968879699707, 'TIME_S_1KI': 7.673476485870942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 980.9324438238144, 'W': 72.78}
+[39.21, 38.47, 38.56, 38.59, 38.63, 38.48, 38.95, 38.74, 38.52, 38.83, 39.07, 38.47, 38.63, 38.57, 38.58, 38.77, 38.83, 38.82, 38.46, 38.73]
+695.99
+34.7995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1366, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.481968879699707, 'TIME_S_1KI': 7.673476485870942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 980.9324438238144, 'W': 72.78, 'J_1KI': 718.1057421843444, 'W_1KI': 53.2796486090776, 'W_D': 37.9805, 'J_D': 511.9030596681833, 'W_D_1KI': 27.804172767203514, 'J_D_1KI': 20.35444565681077}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..3949dab
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15344, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396661281585693, "TIME_S_1KI": 0.6775717727832178, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.4673494148254, "W": 65.08, "J_1KI": 55.166015994188314, "W_1KI": 4.241397288842545, "W_D": 30.18325, "J_D": 392.5804490507841, "W_D_1KI": 1.9671044056308655, "J_D_1KI": 0.1282002349863703}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..4ae3041
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6842620372772217}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249995,
+                            250000]),
+       col_indices=tensor([ 5258, 47122, 48422,  ..., 30033, 41208, 46342]),
+       values=tensor([0.6499, 0.7211, 0.6182,  ..., 0.7244, 0.8782, 0.8107]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4190, 0.1278, 0.1748,  ..., 0.3464, 0.8679, 0.1666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.6842620372772217 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15344', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396661281585693}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,      9,  ..., 249992, 249997,
+                            250000]),
+       col_indices=tensor([10534, 13796, 13942,  ..., 20381, 35132, 47921]),
+       values=tensor([0.7820, 0.3755, 0.2967,  ..., 0.2418, 0.5762, 0.2824]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5105, 0.5604, 0.4598,  ..., 0.4891, 0.0194, 0.7500])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.396661281585693 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,      9,  ..., 249992, 249997,
+                            250000]),
+       col_indices=tensor([10534, 13796, 13942,  ..., 20381, 35132, 47921]),
+       values=tensor([0.7820, 0.3755, 0.2967,  ..., 0.2418, 0.5762, 0.2824]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5105, 0.5604, 0.4598,  ..., 0.4891, 0.0194, 0.7500])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.396661281585693 seconds
+
+[39.48, 38.54, 38.4, 39.05, 39.65, 38.93, 38.49, 38.76, 38.48, 39.28]
+[65.08]
+13.006566524505615
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15344, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396661281585693, 'TIME_S_1KI': 0.6775717727832178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.4673494148254, 'W': 65.08}
+[39.48, 38.54, 38.4, 39.05, 39.65, 38.93, 38.49, 38.76, 38.48, 39.28, 39.19, 38.51, 38.7, 38.61, 39.0, 38.52, 39.05, 38.49, 38.55, 38.46]
+697.935
+34.89675
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15344, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396661281585693, 'TIME_S_1KI': 0.6775717727832178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.4673494148254, 'W': 65.08, 'J_1KI': 55.166015994188314, 'W_1KI': 4.241397288842545, 'W_D': 30.18325, 'J_D': 392.5804490507841, 'W_D_1KI': 1.9671044056308655, 'J_D_1KI': 0.1282002349863703}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..bde3932
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3489, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.437294721603394, "TIME_S_1KI": 2.991486019376152, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 933.3928768968582, "W": 69.22, "J_1KI": 267.52447030577764, "W_1KI": 19.83949555746632, "W_D": 34.06175, "J_D": 459.30359469288595, "W_D_1KI": 9.762611063341934, "J_D_1KI": 2.798111511419299}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..4299e13
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0088562965393066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      34,      93,  ..., 2499916,
+                            2499957, 2500000]),
+       col_indices=tensor([  603,  3952,  4942,  ..., 45684, 45744, 47378]),
+       values=tensor([0.2755, 0.3359, 0.2897,  ..., 0.6537, 0.9903, 0.6398]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0226, 0.9900, 0.4586,  ..., 0.9619, 0.5778, 0.7456])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 3.0088562965393066 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3489', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.437294721603394}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499916,
+                            2499955, 2500000]),
+       col_indices=tensor([   84,    88,  1962,  ..., 43229, 45310, 46070]),
+       values=tensor([0.8625, 0.0720, 0.1202,  ..., 0.4148, 0.7410, 0.3059]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1590, 0.6012, 0.6850,  ..., 0.6120, 0.4384, 0.7195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.437294721603394 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499916,
+                            2499955, 2500000]),
+       col_indices=tensor([   84,    88,  1962,  ..., 43229, 45310, 46070]),
+       values=tensor([0.8625, 0.0720, 0.1202,  ..., 0.4148, 0.7410, 0.3059]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1590, 0.6012, 0.6850,  ..., 0.6120, 0.4384, 0.7195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.437294721603394 seconds
+
+[39.1, 38.45, 44.52, 38.52, 39.08, 38.38, 40.78, 38.54, 38.4, 38.35]
+[69.22]
+13.484439134597778
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3489, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.437294721603394, 'TIME_S_1KI': 2.991486019376152, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 933.3928768968582, 'W': 69.22}
+[39.1, 38.45, 44.52, 38.52, 39.08, 38.38, 40.78, 38.54, 38.4, 38.35, 39.37, 38.65, 38.68, 38.35, 38.4, 38.76, 38.81, 38.76, 38.48, 38.39]
+703.165
+35.158249999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3489, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.437294721603394, 'TIME_S_1KI': 2.991486019376152, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 933.3928768968582, 'W': 69.22, 'J_1KI': 267.52447030577764, 'W_1KI': 19.83949555746632, 'W_D': 34.06175, 'J_D': 459.30359469288595, 'W_D_1KI': 9.762611063341934, 'J_D_1KI': 2.798111511419299}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..eb380f5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35734, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471917629241943, "TIME_S_1KI": 0.2930519289539918, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 841.987050704956, "W": 64.44, "J_1KI": 23.562630847510942, "W_1KI": 1.803324564840208, "W_D": 29.634499999999996, "J_D": 387.2108202066421, "W_D_1KI": 0.8293082218615323, "J_D_1KI": 0.023207819495761246}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..96eed5a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3157460689544678}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 4062,  9525, 48228,  ..., 39550, 26780, 46383]),
+       values=tensor([0.9682, 0.2653, 0.7546,  ..., 0.8059, 0.5876, 0.9597]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6423, 0.4854, 0.6493,  ..., 0.6821, 0.6803, 0.2283])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.3157460689544678 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '33254', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.771223545074463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27980, 12083,  1659,  ..., 17852, 35908, 47898]),
+       values=tensor([0.9789, 0.4410, 0.2389,  ..., 0.6711, 0.3630, 0.6906]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9986, 0.5860, 0.4640,  ..., 0.2646, 0.6800, 0.7666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.771223545074463 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35734', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471917629241943}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([14210,  9782, 13262,  ..., 32699, 48019, 38373]),
+       values=tensor([0.8162, 0.2704, 0.1597,  ..., 0.7469, 0.6704, 0.2691]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5208, 0.6581, 0.5659,  ..., 0.1337, 0.4152, 0.4244])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.471917629241943 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([14210,  9782, 13262,  ..., 32699, 48019, 38373]),
+       values=tensor([0.8162, 0.2704, 0.1597,  ..., 0.7469, 0.6704, 0.2691]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5208, 0.6581, 0.5659,  ..., 0.1337, 0.4152, 0.4244])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.471917629241943 seconds
+
+[39.24, 38.48, 38.44, 38.43, 38.45, 38.48, 38.5, 39.07, 38.96, 38.4]
+[64.44]
+13.066217422485352
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35734, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471917629241943, 'TIME_S_1KI': 0.2930519289539918, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.987050704956, 'W': 64.44}
+[39.24, 38.48, 38.44, 38.43, 38.45, 38.48, 38.5, 39.07, 38.96, 38.4, 39.76, 38.41, 38.52, 38.56, 38.6, 38.96, 38.81, 38.77, 38.7, 38.54]
+696.11
+34.8055
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35734, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471917629241943, 'TIME_S_1KI': 0.2930519289539918, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 841.987050704956, 'W': 64.44, 'J_1KI': 23.562630847510942, 'W_1KI': 1.803324564840208, 'W_D': 29.634499999999996, 'J_D': 387.2108202066421, 'W_D_1KI': 0.8293082218615323, 'J_D_1KI': 0.023207819495761246}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..2a06eb8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.427528619766235, "TIME_S_1KI": 2.8599913932436194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.8558839225768, "W": 47.94, "J_1KI": 190.03178385150215, "W_1KI": 13.14865606143719, "W_D": 31.4125, "J_D": 453.99114421606066, "W_D_1KI": 8.615606143719145, "J_D_1KI": 2.3630296609213235}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..cc7a205
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.879791498184204}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      23,  ...,  999979,
+                             999989, 1000000]),
+       col_indices=tensor([ 5015, 13201, 16372,  ..., 56043, 65196, 77096]),
+       values=tensor([0.8877, 0.8022, 0.3967,  ..., 0.7199, 0.8399, 0.8151]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6595, 0.9245, 0.4951,  ..., 0.4587, 0.0765, 0.0892])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.879791498184204 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3646', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.427528619766235}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      24,  ...,  999984,
+                             999992, 1000000]),
+       col_indices=tensor([12724, 24596, 29019,  ..., 72798, 83516, 98300]),
+       values=tensor([0.5582, 0.8508, 0.8777,  ..., 0.7164, 0.8705, 0.2253]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8849, 0.3552, 0.8045,  ..., 0.9875, 0.5127, 0.0107])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.427528619766235 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      24,  ...,  999984,
+                             999992, 1000000]),
+       col_indices=tensor([12724, 24596, 29019,  ..., 72798, 83516, 98300]),
+       values=tensor([0.5582, 0.8508, 0.8777,  ..., 0.7164, 0.8705, 0.2253]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8849, 0.3552, 0.8045,  ..., 0.9875, 0.5127, 0.0107])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.427528619766235 seconds
+
+[18.35, 17.89, 18.28, 18.14, 17.94, 18.1, 18.09, 18.19, 18.06, 18.09]
+[47.94]
+14.452563285827637
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.427528619766235, 'TIME_S_1KI': 2.8599913932436194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.8558839225768, 'W': 47.94}
+[18.35, 17.89, 18.28, 18.14, 17.94, 18.1, 18.09, 18.19, 18.06, 18.09, 18.01, 18.32, 17.86, 17.98, 18.22, 18.07, 19.34, 21.72, 17.99, 18.27]
+330.54999999999995
+16.527499999999996
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.427528619766235, 'TIME_S_1KI': 2.8599913932436194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.8558839225768, 'W': 47.94, 'J_1KI': 190.03178385150215, 'W_1KI': 13.14865606143719, 'W_D': 31.4125, 'J_D': 453.99114421606066, 'W_D_1KI': 8.615606143719145, 'J_D_1KI': 2.3630296609213235}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..1871367
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8006, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406643390655518, "TIME_S_1KI": 1.2998555321828025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 663.0740174865723, "W": 46.720000000000006, "J_1KI": 82.82213558413343, "W_1KI": 5.835623282538097, "W_D": 30.276250000000005, "J_D": 429.69594867140063, "W_D_1KI": 3.7816949787659264, "J_D_1KI": 0.47235760414263384}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..b29167b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3114714622497559}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([15714, 63018, 47083,  ..., 95898, 11433, 73543]),
+       values=tensor([0.8298, 0.7556, 0.0451,  ..., 0.9622, 0.2125, 0.4932]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8440, 0.1023, 0.7738,  ..., 0.5206, 0.7518, 0.6360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 1.3114714622497559 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8006', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406643390655518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([38549, 23010, 96204,  ..., 15384, 78128, 94145]),
+       values=tensor([0.9276, 0.2040, 0.0329,  ..., 0.0402, 0.0179, 0.0490]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1445, 0.8456, 0.7445,  ..., 0.5274, 0.1855, 0.5940])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.406643390655518 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([38549, 23010, 96204,  ..., 15384, 78128, 94145]),
+       values=tensor([0.9276, 0.2040, 0.0329,  ..., 0.0402, 0.0179, 0.0490]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1445, 0.8456, 0.7445,  ..., 0.5274, 0.1855, 0.5940])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.406643390655518 seconds
+
+[20.45, 19.81, 17.91, 18.51, 18.24, 17.88, 18.26, 18.12, 17.9, 18.59]
+[46.72]
+14.192508935928345
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8006, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406643390655518, 'TIME_S_1KI': 1.2998555321828025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.0740174865723, 'W': 46.720000000000006}
+[20.45, 19.81, 17.91, 18.51, 18.24, 17.88, 18.26, 18.12, 17.9, 18.59, 18.4, 18.11, 18.06, 18.12, 17.9, 18.34, 18.04, 17.94, 17.82, 18.39]
+328.875
+16.44375
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8006, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406643390655518, 'TIME_S_1KI': 1.2998555321828025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.0740174865723, 'W': 46.720000000000006, 'J_1KI': 82.82213558413343, 'W_1KI': 5.835623282538097, 'W_D': 30.276250000000005, 'J_D': 429.69594867140063, 'W_D_1KI': 3.7816949787659264, 'J_D_1KI': 0.47235760414263384}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..eaa436a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 85057, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.436183214187622, "TIME_S_1KI": 0.12269634732223829, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 663.123476600647, "W": 46.56, "J_1KI": 7.796224609387199, "W_1KI": 0.5473976274733414, "W_D": 8.939750000000004, "J_D": 127.32298324614769, "W_D_1KI": 0.10510304854391765, "J_D_1KI": 0.0012356778224475076}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..0f19044
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.138319730758667}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([6848, 9607, 2682,  ..., 9449, 6129, 3470]),
+       values=tensor([0.4694, 0.9529, 0.1463,  ..., 0.1268, 0.1399, 0.3765]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5998, 0.7790, 0.8385,  ..., 0.1561, 0.5420, 0.2267])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.138319730758667 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '75911', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.370872497558594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2414, 5580, 2005,  ..., 9768,  442, 1851]),
+       values=tensor([0.7205, 0.5630, 0.0022,  ..., 0.3635, 0.2630, 0.6566]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5071, 0.1792, 0.6304,  ..., 0.9432, 0.9596, 0.2753])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 9.370872497558594 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '85057', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.436183214187622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2255, 7580, 9802,  ..., 6433, 5292, 8461]),
+       values=tensor([0.3444, 0.5478, 0.9067,  ..., 0.7957, 0.9972, 0.7349]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8453, 0.7973, 0.9010,  ..., 0.7504, 0.8828, 0.5942])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.436183214187622 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2255, 7580, 9802,  ..., 6433, 5292, 8461]),
+       values=tensor([0.3444, 0.5478, 0.9067,  ..., 0.7957, 0.9972, 0.7349]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8453, 0.7973, 0.9010,  ..., 0.7504, 0.8828, 0.5942])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.436183214187622 seconds
+
+[38.19, 37.44, 39.94, 42.89, 39.51, 39.81, 47.03, 47.24, 47.36, 43.53]
+[46.56]
+14.242342710494995
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 85057, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.436183214187622, 'TIME_S_1KI': 0.12269634732223829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.123476600647, 'W': 46.56}
+[38.19, 37.44, 39.94, 42.89, 39.51, 39.81, 47.03, 47.24, 47.36, 43.53, 45.01, 44.41, 42.1, 40.98, 40.92, 39.18, 41.09, 39.73, 39.61, 39.6]
+752.405
+37.62025
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 85057, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.436183214187622, 'TIME_S_1KI': 0.12269634732223829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 663.123476600647, 'W': 46.56, 'J_1KI': 7.796224609387199, 'W_1KI': 0.5473976274733414, 'W_D': 8.939750000000004, 'J_D': 127.32298324614769, 'W_D_1KI': 0.10510304854391765, 'J_D_1KI': 0.0012356778224475076}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..aaafc55
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 34558, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.457140684127808, "TIME_S_1KI": 0.3025968135924477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 669.6752694511413, "W": 47.19, "J_1KI": 19.37829936486895, "W_1KI": 1.3655304126396204, "W_D": 30.795499999999997, "J_D": 437.020232260704, "W_D_1KI": 0.8911250651079344, "J_D_1KI": 0.02578636104832266}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..0fdae4e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3263256549835205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     22,  ...,  99981,  99992,
+                            100000]),
+       col_indices=tensor([  85, 1274, 1422,  ..., 6599, 6784, 7278]),
+       values=tensor([0.2164, 0.2550, 1.0000,  ..., 0.9260, 0.0708, 0.0725]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6027, 0.7133, 0.6416,  ..., 0.5356, 0.1307, 0.5576])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.3263256549835205 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '32176', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.776132822036743}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     20,  ...,  99981,  99994,
+                            100000]),
+       col_indices=tensor([ 544,  706, 2472,  ..., 6055, 7261, 9945]),
+       values=tensor([0.4979, 0.3488, 0.7538,  ..., 0.1989, 0.3068, 0.3191]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5709, 0.1010, 0.9044,  ..., 0.7157, 0.3275, 0.4556])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 9.776132822036743 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '34558', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.457140684127808}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     24,  ...,  99980,  99989,
+                            100000]),
+       col_indices=tensor([  44, 4326, 6855,  ..., 8487, 8731, 9188]),
+       values=tensor([0.5894, 0.7815, 0.8660,  ..., 0.0108, 0.2427, 0.5894]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0531, 0.8679, 0.3068,  ..., 0.5318, 0.1294, 0.3589])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.457140684127808 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     24,  ...,  99980,  99989,
+                            100000]),
+       col_indices=tensor([  44, 4326, 6855,  ..., 8487, 8731, 9188]),
+       values=tensor([0.5894, 0.7815, 0.8660,  ..., 0.0108, 0.2427, 0.5894]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0531, 0.8679, 0.3068,  ..., 0.5318, 0.1294, 0.3589])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.457140684127808 seconds
+
+[19.5, 18.03, 18.32, 17.98, 18.23, 18.17, 18.6, 17.99, 18.33, 18.09]
+[47.19]
+14.191041946411133
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34558, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.457140684127808, 'TIME_S_1KI': 0.3025968135924477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 669.6752694511413, 'W': 47.19}
+[19.5, 18.03, 18.32, 17.98, 18.23, 18.17, 18.6, 17.99, 18.33, 18.09, 18.43, 17.95, 18.1, 18.53, 18.09, 18.56, 18.03, 17.91, 18.04, 18.04]
+327.89
+16.3945
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34558, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.457140684127808, 'TIME_S_1KI': 0.3025968135924477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 669.6752694511413, 'W': 47.19, 'J_1KI': 19.37829936486895, 'W_1KI': 1.3655304126396204, 'W_D': 30.795499999999997, 'J_D': 437.020232260704, 'W_D_1KI': 0.8911250651079344, 'J_D_1KI': 0.02578636104832266}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..b18c025
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5537, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.417654037475586, "TIME_S_1KI": 1.8814618091882944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 687.7580058908463, "W": 47.74000000000001, "J_1KI": 124.21130682514833, "W_1KI": 8.621997471554996, "W_D": 31.48425000000001, "J_D": 453.5723711137177, "W_D_1KI": 5.686156763590393, "J_D_1KI": 1.0269381910042248}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..42d7009
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.8961181640625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      96,     195,  ...,  999806,
+                             999906, 1000000]),
+       col_indices=tensor([  19,  113,  151,  ..., 9681, 9759, 9836]),
+       values=tensor([0.1144, 0.7732, 0.9749,  ..., 0.1321, 0.3947, 0.2714]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1354, 0.8257, 0.6569,  ..., 0.0257, 0.7874, 0.8457])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 1.8961181640625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5537', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.417654037475586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     103,     198,  ...,  999795,
+                             999893, 1000000]),
+       col_indices=tensor([ 194,  313,  451,  ..., 9690, 9776, 9879]),
+       values=tensor([0.2779, 0.8250, 0.2083,  ..., 0.7384, 0.0572, 0.6638]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5010, 0.3969, 0.7780,  ..., 0.5969, 0.2345, 0.7915])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.417654037475586 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     103,     198,  ...,  999795,
+                             999893, 1000000]),
+       col_indices=tensor([ 194,  313,  451,  ..., 9690, 9776, 9879]),
+       values=tensor([0.2779, 0.8250, 0.2083,  ..., 0.7384, 0.0572, 0.6638]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5010, 0.3969, 0.7780,  ..., 0.5969, 0.2345, 0.7915])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.417654037475586 seconds
+
+[18.42, 18.05, 18.03, 18.24, 18.1, 18.01, 17.83, 17.89, 18.16, 18.06]
+[47.74]
+14.406326055526733
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5537, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.417654037475586, 'TIME_S_1KI': 1.8814618091882944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 687.7580058908463, 'W': 47.74000000000001}
+[18.42, 18.05, 18.03, 18.24, 18.1, 18.01, 17.83, 17.89, 18.16, 18.06, 18.38, 17.81, 18.07, 18.25, 18.24, 17.94, 18.02, 18.06, 18.02, 17.93]
+325.115
+16.25575
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5537, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.417654037475586, 'TIME_S_1KI': 1.8814618091882944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 687.7580058908463, 'W': 47.74000000000001, 'J_1KI': 124.21130682514833, 'W_1KI': 8.621997471554996, 'W_D': 31.48425000000001, 'J_D': 453.5723711137177, 'W_D_1KI': 5.686156763590393, 'J_D_1KI': 1.0269381910042248}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..8a616ae
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.884310483932495, "TIME_S_1KI": 10.884310483932495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 825.0359781861306, "W": 47.81, "J_1KI": 825.0359781861306, "W_1KI": 47.81, "W_D": 31.353, "J_D": 541.0448237621785, "W_D_1KI": 31.352999999999998, "J_D_1KI": 31.352999999999998}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..aad2263
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.884310483932495}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     518,    1026,  ..., 4999007,
+                            4999483, 5000000]),
+       col_indices=tensor([   3,   39,   78,  ..., 9968, 9975, 9994]),
+       values=tensor([0.2142, 0.4373, 0.1249,  ..., 0.9529, 0.9095, 0.5518]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2404, 0.3133, 0.0015,  ..., 0.7254, 0.6117, 0.4995])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.884310483932495 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     518,    1026,  ..., 4999007,
+                            4999483, 5000000]),
+       col_indices=tensor([   3,   39,   78,  ..., 9968, 9975, 9994]),
+       values=tensor([0.2142, 0.4373, 0.1249,  ..., 0.9529, 0.9095, 0.5518]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2404, 0.3133, 0.0015,  ..., 0.7254, 0.6117, 0.4995])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.884310483932495 seconds
+
+[18.07, 17.88, 18.1, 21.37, 18.24, 18.16, 18.18, 18.01, 18.03, 17.87]
+[47.81]
+17.256556749343872
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.884310483932495, 'TIME_S_1KI': 10.884310483932495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 825.0359781861306, 'W': 47.81}
+[18.07, 17.88, 18.1, 21.37, 18.24, 18.16, 18.18, 18.01, 18.03, 17.87, 18.49, 18.1, 18.43, 18.06, 18.42, 17.93, 18.03, 17.8, 18.13, 18.11]
+329.14
+16.457
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.884310483932495, 'TIME_S_1KI': 10.884310483932495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 825.0359781861306, 'W': 47.81, 'J_1KI': 825.0359781861306, 'W_1KI': 47.81, 'W_D': 31.353, 'J_D': 541.0448237621785, 'W_D_1KI': 31.352999999999998, 'J_D_1KI': 31.352999999999998}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..a0720a2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 225343, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.386851072311401, "TIME_S_1KI": 0.04609351553991649, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.4945677185059, "W": 46.24, "J_1KI": 2.935500848566434, "W_1KI": 0.20519829770616352, "W_D": 30.072000000000003, "J_D": 430.2003598709107, "W_D_1KI": 0.1334498963801849, "J_D_1KI": 0.0005922078625925141}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..6742334
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06630802154541016}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([6116, 9123, 2230, 4007, 2708, 6506, 8700, 3316, 2761,
+                           1361, 1958, 5168, 9287, 8535, 3319, 5344,  902, 1975,
+                            488, 7509, 3585, 6731, 5003, 3621, 9227,  602, 6973,
+                           8702, 9039, 2485, 4067, 2477, 9061, 2388, 1777, 7081,
+                           5954,  215, 9598, 6942, 5591, 9010, 7196, 7714, 5337,
+                           1993, 6247, 2446, 6356, 9820, 7249, 3274, 1887, 2531,
+                            135, 4906, 4233,  322,  743, 3780, 3850,  995, 6910,
+                           9173, 1426, 5060, 4803, 1325, 8341, 4031, 7649, 3889,
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+                      2.0325e-01, 6.5615e-01, 3.5141e-01, 5.3907e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5197, 0.1343, 0.9407,  ..., 0.1023, 0.5237, 0.0220])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.06630802154541016 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '158351', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.378459692001343}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      1.2285e-01, 6.1279e-01, 7.3482e-01, 6.7204e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4203, 0.6136, 0.5758,  ..., 0.4091, 0.3563, 0.6125])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.378459692001343 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '225343', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.386851072311401}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      8.5361e-01, 1.4133e-01, 1.4430e-01, 7.9132e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0633, 0.2712, 0.1613,  ..., 0.7795, 0.8074, 0.9414])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.386851072311401 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
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+                      1.7994e-01, 1.0582e-01, 7.1936e-01, 9.9727e-01,
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+                      1.2202e-01, 4.2654e-01, 7.4178e-02, 3.6640e-01,
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+                      8.1895e-01, 7.6617e-01, 3.9340e-01, 5.2325e-01,
+                      8.5361e-01, 1.4133e-01, 1.4430e-01, 7.9132e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0633, 0.2712, 0.1613,  ..., 0.7795, 0.8074, 0.9414])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.386851072311401 seconds
+
+[18.39, 17.96, 17.95, 18.06, 18.03, 17.74, 18.17, 18.27, 17.97, 17.88]
+[46.24]
+14.305678367614746
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.386851072311401, 'TIME_S_1KI': 0.04609351553991649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.4945677185059, 'W': 46.24}
+[18.39, 17.96, 17.95, 18.06, 18.03, 17.74, 18.17, 18.27, 17.97, 17.88, 18.01, 17.95, 18.03, 17.68, 17.83, 17.84, 17.98, 17.81, 17.89, 18.12]
+323.35999999999996
+16.168
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.386851072311401, 'TIME_S_1KI': 0.04609351553991649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.4945677185059, 'W': 46.24, 'J_1KI': 2.935500848566434, 'W_1KI': 0.20519829770616352, 'W_D': 30.072000000000003, 'J_D': 430.2003598709107, 'W_D_1KI': 0.1334498963801849, 'J_D_1KI': 0.0005922078625925141}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..d7d5020
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.995913743972778, "TIME_S_1KI": 13.995913743972778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 871.8022893977165, "W": 48.71, "J_1KI": 871.8022893977165, "W_1KI": 48.71, "W_D": 32.347, "J_D": 578.9404363610745, "W_D_1KI": 32.347, "J_D_1KI": 32.347}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..530a6a0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.995913743972778}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499992,
+                            2499997, 2500000]),
+       col_indices=tensor([ 17718, 235055,  35243,  ...,  14166, 348855,
+                           416543]),
+       values=tensor([0.0021, 0.9166, 0.2725,  ..., 0.7498, 0.6792, 0.5299]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0136, 0.8273, 0.9896,  ..., 0.5941, 0.9828, 0.6210])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 13.995913743972778 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499992,
+                            2499997, 2500000]),
+       col_indices=tensor([ 17718, 235055,  35243,  ...,  14166, 348855,
+                           416543]),
+       values=tensor([0.0021, 0.9166, 0.2725,  ..., 0.7498, 0.6792, 0.5299]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0136, 0.8273, 0.9896,  ..., 0.5941, 0.9828, 0.6210])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 13.995913743972778 seconds
+
+[18.5, 17.83, 21.98, 17.84, 18.21, 18.0, 17.97, 17.87, 17.99, 17.99]
+[48.71]
+17.897809267044067
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.995913743972778, 'TIME_S_1KI': 13.995913743972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 871.8022893977165, 'W': 48.71}
+[18.5, 17.83, 21.98, 17.84, 18.21, 18.0, 17.97, 17.87, 17.99, 17.99, 18.27, 17.72, 18.53, 17.6, 18.07, 17.75, 17.78, 17.88, 17.93, 17.86]
+327.26
+16.363
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.995913743972778, 'TIME_S_1KI': 13.995913743972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 871.8022893977165, 'W': 48.71, 'J_1KI': 871.8022893977165, 'W_1KI': 48.71, 'W_D': 32.347, 'J_D': 578.9404363610745, 'W_D_1KI': 32.347, 'J_D_1KI': 32.347}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..d6c035a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8984, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.250720024108887, "TIME_S_1KI": 1.1409973312676855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 667.435348098278, "W": 47.13, "J_1KI": 74.29155700114404, "W_1KI": 5.245992876224399, "W_D": 30.525750000000002, "J_D": 432.29290424805885, "W_D_1KI": 3.3977905164737314, "J_D_1KI": 0.37820464341871457}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..eb4a2d8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1686315536499023}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     12,  ..., 249984, 249988,
+                            250000]),
+       col_indices=tensor([ 9222, 11801, 17371,  ..., 41613, 43396, 49641]),
+       values=tensor([0.5050, 0.7653, 0.0671,  ..., 0.1421, 0.6855, 0.0275]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2330, 0.0304, 0.5518,  ..., 0.1557, 0.6263, 0.0730])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 1.1686315536499023 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8984', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.250720024108887}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 249994, 249997,
+                            250000]),
+       col_indices=tensor([ 1146,  2450, 11327,  ...,   241,  2629, 25085]),
+       values=tensor([0.2696, 0.3732, 0.9366,  ..., 0.5943, 0.0784, 0.3144]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7229, 0.2746, 0.7643,  ..., 0.7812, 0.8470, 0.7243])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.250720024108887 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 249994, 249997,
+                            250000]),
+       col_indices=tensor([ 1146,  2450, 11327,  ...,   241,  2629, 25085]),
+       values=tensor([0.2696, 0.3732, 0.9366,  ..., 0.5943, 0.0784, 0.3144]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7229, 0.2746, 0.7643,  ..., 0.7812, 0.8470, 0.7243])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.250720024108887 seconds
+
+[18.69, 18.1, 18.11, 18.0, 21.83, 17.95, 18.22, 18.09, 18.15, 18.1]
+[47.13]
+14.161581754684448
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8984, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.250720024108887, 'TIME_S_1KI': 1.1409973312676855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 667.435348098278, 'W': 47.13}
+[18.69, 18.1, 18.11, 18.0, 21.83, 17.95, 18.22, 18.09, 18.15, 18.1, 21.63, 17.85, 18.41, 18.24, 17.96, 18.09, 18.1, 17.94, 18.92, 17.83]
+332.08500000000004
+16.60425
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8984, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.250720024108887, 'TIME_S_1KI': 1.1409973312676855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 667.435348098278, 'W': 47.13, 'J_1KI': 74.29155700114404, 'W_1KI': 5.245992876224399, 'W_D': 30.525750000000002, 'J_D': 432.29290424805885, 'W_D_1KI': 3.3977905164737314, 'J_D_1KI': 0.37820464341871457}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..6747ace
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1969, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.35115671157837, "TIME_S_1KI": 5.25706282964874, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.7024494171142, "W": 48.48, "J_1KI": 369.5797102169193, "W_1KI": 24.62163534789233, "W_D": 32.2385, "J_D": 483.9116216075421, "W_D_1KI": 16.373031995937023, "J_D_1KI": 8.315404771933482}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..edef138
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.33142876625061}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      50,     102,  ..., 2499894,
+                            2499942, 2500000]),
+       col_indices=tensor([  362,   476,   734,  ..., 42817, 42901, 48624]),
+       values=tensor([0.1861, 0.1141, 0.9529,  ..., 0.0521, 0.7769, 0.3485]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6800, 0.1652, 0.7606,  ..., 0.1973, 0.6571, 0.7552])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 5.33142876625061 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1969', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.35115671157837}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      59,     106,  ..., 2499882,
+                            2499933, 2500000]),
+       col_indices=tensor([  752,  1386,  1561,  ..., 49182, 49404, 49846]),
+       values=tensor([0.0219, 0.4602, 0.8212,  ..., 0.9720, 0.3228, 0.9373]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3378, 0.8054, 0.7422,  ..., 0.6857, 0.1927, 0.4134])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.35115671157837 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      59,     106,  ..., 2499882,
+                            2499933, 2500000]),
+       col_indices=tensor([  752,  1386,  1561,  ..., 49182, 49404, 49846]),
+       values=tensor([0.0219, 0.4602, 0.8212,  ..., 0.9720, 0.3228, 0.9373]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3378, 0.8054, 0.7422,  ..., 0.6857, 0.1927, 0.4134])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.35115671157837 seconds
+
+[18.43, 18.02, 18.06, 17.85, 18.04, 18.18, 18.02, 17.8, 18.02, 18.51]
+[48.48]
+15.010364055633545
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.35115671157837, 'TIME_S_1KI': 5.25706282964874, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7024494171142, 'W': 48.48}
+[18.43, 18.02, 18.06, 17.85, 18.04, 18.18, 18.02, 17.8, 18.02, 18.51, 18.08, 17.97, 18.18, 18.15, 17.97, 18.08, 18.16, 17.86, 18.05, 17.82]
+324.83
+16.2415
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.35115671157837, 'TIME_S_1KI': 5.25706282964874, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7024494171142, 'W': 48.48, 'J_1KI': 369.5797102169193, 'W_1KI': 24.62163534789233, 'W_D': 32.2385, 'J_D': 483.9116216075421, 'W_D_1KI': 16.373031995937023, 'J_D_1KI': 8.315404771933482}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..91b7ae2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21352, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.102294206619263, "TIME_S_1KI": 0.47313105126542065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 644.554582464695, "W": 46.45, "J_1KI": 30.187082355971103, "W_1KI": 2.1754402397901837, "W_D": 30.18325, "J_D": 418.83212273794413, "W_D_1KI": 1.4136029411764706, "J_D_1KI": 0.066204708747493}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..c1ed6fd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.49173688888549805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([  851, 39596,  1204,  ...,  6262, 34652, 46359]),
+       values=tensor([0.1009, 0.2308, 0.6894,  ..., 0.4766, 0.7010, 0.2687]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2088, 0.1405, 0.6063,  ..., 0.1063, 0.3954, 0.8044])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.49173688888549805 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21352', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.102294206619263}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([15528, 30130, 16433,  ..., 30917, 35420, 44166]),
+       values=tensor([0.6196, 0.0183, 0.2015,  ..., 0.9265, 0.2661, 0.3216]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3139, 0.2113, 0.1225,  ..., 0.3436, 0.4255, 0.1892])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.102294206619263 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([15528, 30130, 16433,  ..., 30917, 35420, 44166]),
+       values=tensor([0.6196, 0.0183, 0.2015,  ..., 0.9265, 0.2661, 0.3216]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3139, 0.2113, 0.1225,  ..., 0.3436, 0.4255, 0.1892])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.102294206619263 seconds
+
+[18.39, 18.05, 18.35, 17.95, 18.07, 17.91, 18.01, 17.84, 17.86, 18.12]
+[46.45]
+13.876309633255005
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21352, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.102294206619263, 'TIME_S_1KI': 0.47313105126542065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.554582464695, 'W': 46.45}
+[18.39, 18.05, 18.35, 17.95, 18.07, 17.91, 18.01, 17.84, 17.86, 18.12, 18.35, 17.9, 17.93, 17.87, 17.99, 17.82, 18.04, 18.01, 19.31, 17.99]
+325.33500000000004
+16.266750000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21352, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.102294206619263, 'TIME_S_1KI': 0.47313105126542065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.554582464695, 'W': 46.45, 'J_1KI': 30.187082355971103, 'W_1KI': 2.1754402397901837, 'W_D': 30.18325, 'J_D': 418.83212273794413, 'W_D_1KI': 1.4136029411764706, 'J_D_1KI': 0.066204708747493}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..024170e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.361413717269897, "TIME_S_1KI": 24.361413717269897, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 676.3945213317871, "W": 23.115576130225318, "J_1KI": 676.3945213317871, "W_1KI": 23.115576130225318, "W_D": 4.813576130225318, "J_D": 140.85206027984617, "W_D_1KI": 4.813576130225318, "J_D_1KI": 4.813576130225318}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..f9e5f4b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.361413717269897}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      21,  ...,  999974,
+                             999988, 1000000]),
+       col_indices=tensor([ 9500,  9994, 42112,  ..., 68909, 84086, 93735]),
+       values=tensor([0.6307, 0.9197, 0.7409,  ..., 0.9841, 0.2812, 0.5553]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0568, 0.5322, 0.2500,  ..., 0.3574, 0.0150, 0.2325])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 24.361413717269897 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      21,  ...,  999974,
+                             999988, 1000000]),
+       col_indices=tensor([ 9500,  9994, 42112,  ..., 68909, 84086, 93735]),
+       values=tensor([0.6307, 0.9197, 0.7409,  ..., 0.9841, 0.2812, 0.5553]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0568, 0.5322, 0.2500,  ..., 0.3574, 0.0150, 0.2325])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 24.361413717269897 seconds
+
+[20.76, 20.76, 20.72, 20.72, 20.52, 20.4, 20.8, 20.84, 20.72, 21.04]
+[20.92, 20.48, 23.92, 23.92, 25.2, 26.52, 27.64, 28.16, 25.08, 24.44, 24.2, 24.28, 24.28, 24.36, 24.6, 24.64, 24.84, 24.92, 25.08, 24.84, 24.8, 25.16, 25.12, 25.12, 24.92, 25.2, 25.24, 24.92]
+29.261417388916016
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.361413717269897, 'TIME_S_1KI': 24.361413717269897, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 676.3945213317871, 'W': 23.115576130225318}
+[20.76, 20.76, 20.72, 20.72, 20.52, 20.4, 20.8, 20.84, 20.72, 21.04, 20.12, 20.08, 20.12, 20.04, 19.88, 19.88, 19.92, 19.88, 19.8, 20.0]
+366.04
+18.302
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 24.361413717269897, 'TIME_S_1KI': 24.361413717269897, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 676.3945213317871, 'W': 23.115576130225318, 'J_1KI': 676.3945213317871, 'W_1KI': 23.115576130225318, 'W_D': 4.813576130225318, 'J_D': 140.85206027984617, 'W_D_1KI': 4.813576130225318, 'J_D_1KI': 4.813576130225318}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json
new file mode 100644
index 0000000..def7a28
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 117.5588891506195, "TIME_S_1KI": 117.5588891506195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2923.375952148438, "W": 23.010596725488792, "J_1KI": 2923.375952148438, "W_1KI": 23.010596725488792, "W_D": 4.534596725488793, "J_D": 576.0967947998054, "W_D_1KI": 4.534596725488793, "J_D_1KI": 4.534596725488793}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output
new file mode 100644
index 0000000..1edb066
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 117.5588891506195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      59,     119,  ..., 4999916,
+                            4999957, 5000000]),
+       col_indices=tensor([ 1403,  2005,  2494,  ..., 97036, 97364, 98409]),
+       values=tensor([0.0186, 0.2433, 0.9960,  ..., 0.9635, 0.2941, 0.9283]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8104, 0.5597, 0.5404,  ..., 0.7369, 0.5622, 0.9637])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 117.5588891506195 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      59,     119,  ..., 4999916,
+                            4999957, 5000000]),
+       col_indices=tensor([ 1403,  2005,  2494,  ..., 97036, 97364, 98409]),
+       values=tensor([0.0186, 0.2433, 0.9960,  ..., 0.9635, 0.2941, 0.9283]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8104, 0.5597, 0.5404,  ..., 0.7369, 0.5622, 0.9637])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 117.5588891506195 seconds
+
+[20.12, 20.28, 20.08, 20.04, 20.04, 20.04, 20.32, 20.4, 20.48, 20.6]
+[20.4, 20.36, 21.2, 22.2, 24.2, 26.08, 27.2, 26.48, 26.32, 25.16, 24.32, 24.48, 24.48, 24.52, 24.56, 24.4, 24.36, 24.28, 24.48, 24.48, 24.48, 24.2, 24.2, 24.32, 24.2, 24.36, 24.48, 24.2, 24.24, 24.2, 24.24, 24.36, 24.36, 24.44, 24.44, 24.56, 24.56, 24.56, 24.64, 24.84, 24.68, 24.72, 24.52, 24.6, 24.64, 24.36, 24.4, 24.36, 24.28, 24.16, 24.2, 24.48, 24.64, 24.56, 24.44, 24.44, 24.44, 24.48, 24.4, 24.68, 24.44, 24.28, 24.2, 24.24, 24.28, 24.32, 24.2, 24.48, 24.32, 24.24, 24.36, 24.32, 24.16, 24.0, 23.92, 23.72, 23.64, 23.68, 23.68, 23.68, 23.76, 24.0, 24.04, 24.16, 24.4, 24.36, 24.48, 24.76, 24.76, 24.76, 24.64, 24.48, 24.28, 24.44, 24.48, 24.6, 24.52, 24.52, 24.44, 24.4, 24.4, 24.32, 24.36, 24.24, 24.2, 24.2, 24.48, 24.64, 24.48, 24.72, 24.52, 24.24, 24.36, 24.24, 24.24, 24.52, 24.6, 24.44, 24.68, 24.6, 24.48]
+127.04476928710938
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 117.5588891506195, 'TIME_S_1KI': 117.5588891506195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2923.375952148438, 'W': 23.010596725488792}
+[20.12, 20.28, 20.08, 20.04, 20.04, 20.04, 20.32, 20.4, 20.48, 20.6, 21.08, 20.92, 20.92, 21.08, 20.84, 20.88, 20.8, 20.84, 20.44, 20.44]
+369.52
+18.476
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 117.5588891506195, 'TIME_S_1KI': 117.5588891506195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2923.375952148438, 'W': 23.010596725488792, 'J_1KI': 2923.375952148438, 'W_1KI': 23.010596725488792, 'W_D': 4.534596725488793, 'J_D': 576.0967947998054, 'W_D_1KI': 4.534596725488793, 'J_D_1KI': 4.534596725488793}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..78b2e2b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 230.9039385318756, "TIME_S_1KI": 230.9039385318756, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5761.600697250372, "W": 23.455047609770336, "J_1KI": 5761.600697250372, "W_1KI": 23.455047609770336, "W_D": 5.099047609770334, "J_D": 1252.5524037532862, "W_D_1KI": 5.099047609770334, "J_D_1KI": 5.099047609770334}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..1a5d909
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 230.9039385318756}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       89,      166,  ...,  9999787,
+                             9999913, 10000000]),
+       col_indices=tensor([  196,   231,   588,  ..., 93210, 94069, 96596]),
+       values=tensor([0.2369, 0.0996, 0.5969,  ..., 0.6003, 0.9136, 0.6152]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9669, 0.8246, 0.8261,  ..., 0.7936, 0.5607, 0.9848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 230.9039385318756 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       89,      166,  ...,  9999787,
+                             9999913, 10000000]),
+       col_indices=tensor([  196,   231,   588,  ..., 93210, 94069, 96596]),
+       values=tensor([0.2369, 0.0996, 0.5969,  ..., 0.6003, 0.9136, 0.6152]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9669, 0.8246, 0.8261,  ..., 0.7936, 0.5607, 0.9848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 230.9039385318756 seconds
+
+[20.44, 20.56, 20.52, 20.52, 20.52, 20.6, 20.56, 20.68, 20.84, 20.88]
+[21.08, 20.84, 21.6, 22.44, 24.12, 25.68, 27.16, 27.72, 27.68, 27.68, 26.88, 25.92, 25.32, 24.84, 24.64, 24.56, 24.56, 24.6, 24.64, 24.64, 24.8, 24.64, 24.44, 24.44, 24.44, 24.28, 24.6, 24.8, 24.68, 24.56, 24.44, 24.44, 24.6, 24.52, 24.72, 24.48, 24.48, 24.36, 24.24, 24.24, 24.4, 24.32, 24.64, 24.68, 24.64, 24.84, 24.4, 24.44, 24.52, 24.32, 24.48, 24.52, 24.6, 24.6, 24.48, 24.72, 24.56, 24.56, 24.64, 24.76, 24.72, 24.84, 25.04, 24.84, 24.92, 24.72, 24.4, 24.4, 24.6, 24.52, 24.4, 24.6, 24.32, 24.4, 24.48, 24.48, 24.6, 24.76, 25.0, 25.04, 25.0, 24.68, 24.32, 24.44, 24.4, 24.4, 24.56, 24.52, 24.56, 24.52, 24.56, 24.6, 24.84, 24.8, 24.92, 24.8, 24.52, 24.52, 24.84, 24.8, 24.8, 24.6, 24.36, 24.36, 24.56, 24.44, 24.72, 24.76, 24.52, 24.56, 24.72, 24.76, 24.84, 24.96, 25.0, 24.76, 24.96, 25.08, 24.72, 24.72, 24.68, 24.6, 24.52, 24.6, 24.68, 24.88, 24.8, 24.84, 24.88, 24.8, 24.88, 24.92, 24.96, 24.96, 24.8, 24.72, 24.88, 24.96, 25.28, 25.12, 25.08, 25.08, 25.0, 24.52, 24.56, 24.48, 24.52, 24.56, 24.6, 24.6, 24.56, 24.56, 24.52, 24.6, 24.8, 24.88, 24.76, 24.64, 24.52, 24.68, 24.6, 24.6, 24.52, 24.52, 24.44, 24.44, 24.24, 24.28, 24.32, 24.28, 24.6, 24.6, 24.76, 24.88, 24.88, 24.72, 24.72, 24.52, 24.6, 24.6, 24.56, 24.68, 24.92, 24.8, 24.84, 24.84, 24.8, 24.8, 24.44, 24.48, 24.48, 24.48, 24.52, 24.64, 24.56, 24.6, 24.52, 24.56, 24.48, 24.56, 24.48, 24.56, 24.48, 24.56, 24.64, 24.84, 24.88, 24.88, 24.72, 24.36, 24.48, 24.56, 24.8, 24.88, 24.96, 24.8, 24.48, 24.32, 24.2, 24.24, 24.2, 24.44, 24.6, 24.56, 24.88, 24.92, 24.84, 24.76, 24.48, 24.48, 24.52, 24.48, 24.72, 24.8]
+245.6443829536438
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 230.9039385318756, 'TIME_S_1KI': 230.9039385318756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5761.600697250372, 'W': 23.455047609770336}
+[20.44, 20.56, 20.52, 20.52, 20.52, 20.6, 20.56, 20.68, 20.84, 20.88, 20.84, 20.68, 20.52, 20.24, 19.96, 20.0, 19.76, 19.84, 20.12, 20.24]
+367.12
+18.356
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 230.9039385318756, 'TIME_S_1KI': 230.9039385318756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5761.600697250372, 'W': 23.455047609770336, 'J_1KI': 5761.600697250372, 'W_1KI': 23.455047609770336, 'W_D': 5.099047609770334, 'J_D': 1252.5524037532862, 'W_D_1KI': 5.099047609770334, 'J_D_1KI': 5.099047609770334}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json
new file mode 100644
index 0000000..19d74a9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 1091.9541580677032, "TIME_S_1KI": 1091.9541580677032, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 27099.232660408, "W": 23.65811028389948, "J_1KI": 27099.232660408, "W_1KI": 23.65811028389948, "W_D": 5.30711028389948, "J_D": 6079.0407438294715, "W_D_1KI": 5.30711028389948, "J_D_1KI": 5.30711028389948}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output
new file mode 100644
index 0000000..a379597
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 1091.9541580677032}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      520,     1009,  ..., 49998961,
+                            49999487, 50000000]),
+       col_indices=tensor([  172,   626,   631,  ..., 99749, 99860, 99985]),
+       values=tensor([0.6669, 0.0843, 0.5498,  ..., 0.0965, 0.4666, 0.0259]),
+       size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.4596, 0.0337, 0.7880,  ..., 0.9862, 0.8105, 0.6593])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 50000000
+Density: 0.005
+Time: 1091.9541580677032 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      520,     1009,  ..., 49998961,
+                            49999487, 50000000]),
+       col_indices=tensor([  172,   626,   631,  ..., 99749, 99860, 99985]),
+       values=tensor([0.6669, 0.0843, 0.5498,  ..., 0.0965, 0.4666, 0.0259]),
+       size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.4596, 0.0337, 0.7880,  ..., 0.9862, 0.8105, 0.6593])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 50000000
+Density: 0.005
+Time: 1091.9541580677032 seconds
+
+[20.56, 20.52, 20.4, 20.4, 20.44, 20.64, 20.52, 20.56, 20.56, 20.48]
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diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..044dbd0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 6242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.78993320465088, "TIME_S_1KI": 3.490857610485562, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 587.0853565216064, "W": 23.400196384120708, "J_1KI": 94.05404622262198, "W_1KI": 3.748829923761728, "W_D": 4.996196384120708, "J_D": 125.34910764312737, "W_D_1KI": 0.8004159538802801, "J_D_1KI": 0.12823068790135855}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..0c82a6f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.3640708923339844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99997,
+                            100000]),
+       col_indices=tensor([49077, 61829, 75773,  ...,  9180, 24382, 73621]),
+       values=tensor([0.5511, 0.7896, 0.6815,  ..., 0.2019, 0.1356, 0.1654]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3810, 0.9981, 0.5438,  ..., 0.4984, 0.5897, 0.5823])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 3.3640708923339844 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6242 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.78993320465088}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      3,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([26056, 65660, 94841,  ..., 43126, 80704,  3094]),
+       values=tensor([0.6558, 0.9729, 0.1332,  ..., 0.9607, 0.6686, 0.0556]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0734, 0.1949, 0.9405,  ..., 0.2088, 0.6775, 0.6290])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 21.78993320465088 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,      3,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([26056, 65660, 94841,  ..., 43126, 80704,  3094]),
+       values=tensor([0.6558, 0.9729, 0.1332,  ..., 0.9607, 0.6686, 0.0556]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0734, 0.1949, 0.9405,  ..., 0.2088, 0.6775, 0.6290])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 21.78993320465088 seconds
+
+[20.36, 20.36, 20.28, 20.28, 20.56, 20.72, 20.88, 20.96, 21.04, 20.96]
+[20.88, 20.92, 24.04, 25.0, 26.56, 27.48, 28.4, 25.72, 25.72, 25.84, 25.12, 25.12, 25.52, 25.44, 25.72, 25.68, 25.6, 25.28, 25.68, 25.68, 25.72, 25.6, 25.8, 25.48]
+25.08890724182129
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.78993320465088, 'TIME_S_1KI': 3.490857610485562, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 587.0853565216064, 'W': 23.400196384120708}
+[20.36, 20.36, 20.28, 20.28, 20.56, 20.72, 20.88, 20.96, 21.04, 20.96, 20.48, 20.2, 20.2, 20.12, 20.12, 20.24, 20.24, 20.32, 20.44, 20.44]
+368.08
+18.404
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.78993320465088, 'TIME_S_1KI': 3.490857610485562, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 587.0853565216064, 'W': 23.400196384120708, 'J_1KI': 94.05404622262198, 'W_1KI': 3.748829923761728, 'W_D': 4.996196384120708, 'J_D': 125.34910764312737, 'W_D_1KI': 0.8004159538802801, 'J_D_1KI': 0.12823068790135855}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output
new file mode 100644
index 0000000..82012e6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output
@@ -0,0 +1 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 300000 -sd 1e-05 -c 1']
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json
new file mode 100644
index 0000000..849e147
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 9462, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.88818907737732, "TIME_S_1KI": 2.2075870933605284, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 574.1408400344848, "W": 22.884833840996915, "J_1KI": 60.678592267436564, "W_1KI": 2.418604295180397, "W_D": 4.553833840996916, "J_D": 114.24780293416971, "W_D_1KI": 0.48127603477033565, "J_D_1KI": 0.05086409160540432}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.output
new file mode 100644
index 0000000..9831ac1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.21927547454834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     6,  ..., 89992, 89995, 90000]),
+       col_indices=tensor([ 4135,  5257,  7346,  ..., 19970, 20460, 23828]),
+       values=tensor([0.3812, 0.3967, 0.4332,  ..., 0.7451, 0.5477, 0.3750]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.1252, 0.8924, 0.9038,  ..., 0.5916, 0.3272, 0.4447])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 2.21927547454834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9462 -ss 30000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.88818907737732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 89998, 89998, 90000]),
+       col_indices=tensor([ 9013,  9207, 23498,  ...,   264,  8481, 27073]),
+       values=tensor([0.3265, 0.9217, 0.2088,  ..., 0.3044, 0.7404, 0.1795]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.1642, 0.6221, 0.4016,  ..., 0.5731, 0.3090, 0.6430])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 20.88818907737732 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 89998, 89998, 90000]),
+       col_indices=tensor([ 9013,  9207, 23498,  ...,   264,  8481, 27073]),
+       values=tensor([0.3265, 0.9217, 0.2088,  ..., 0.3044, 0.7404, 0.1795]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.1642, 0.6221, 0.4016,  ..., 0.5731, 0.3090, 0.6430])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 20.88818907737732 seconds
+
+[20.4, 20.6, 20.6, 20.76, 20.56, 20.6, 20.32, 20.12, 20.12, 20.2]
+[20.04, 20.0, 20.84, 22.2, 23.8, 25.04, 26.28, 26.28, 26.08, 25.8, 25.32, 25.2, 25.16, 25.28, 26.0, 26.2, 25.92, 26.12, 25.76, 25.28, 25.48, 25.32, 25.56, 25.84]
+25.08826780319214
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 9462, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.88818907737732, 'TIME_S_1KI': 2.2075870933605284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 574.1408400344848, 'W': 22.884833840996915}
+[20.4, 20.6, 20.6, 20.76, 20.56, 20.6, 20.32, 20.12, 20.12, 20.2, 20.2, 20.16, 20.0, 19.92, 19.92, 20.12, 20.36, 20.6, 20.96, 21.0]
+366.62
+18.331
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 9462, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.88818907737732, 'TIME_S_1KI': 2.2075870933605284, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 574.1408400344848, 'W': 22.884833840996915, 'J_1KI': 60.678592267436564, 'W_1KI': 2.418604295180397, 'W_D': 4.553833840996916, 'J_D': 114.24780293416971, 'W_D_1KI': 0.48127603477033565, 'J_D_1KI': 0.05086409160540432}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json
new file mode 100644
index 0000000..cc699f5
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1990, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 21.110622882843018, "TIME_S_1KI": 10.608353207458803, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 543.18098487854, "W": 22.61402761523156, "J_1KI": 272.95526878318594, "W_1KI": 11.363832972478171, "W_D": 4.532027615231559, "J_D": 108.8577083845138, "W_D_1KI": 2.27740081167415, "J_D_1KI": 1.1444225184292212}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output
new file mode 100644
index 0000000..e29b500
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.0005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 10.55085802078247}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     24,  ..., 449973, 449985,
+                            450000]),
+       col_indices=tensor([   46,  2006,  2283,  ..., 27547, 29014, 29850]),
+       values=tensor([0.4834, 0.4450, 0.5507,  ..., 0.7876, 0.9956, 0.8691]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.6061, 0.2390, 0.3325,  ..., 0.9801, 0.7580, 0.8339])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 10.55085802078247 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1990 -ss 30000 -sd 0.0005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 21.110622882843018}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     20,     37,  ..., 449971, 449989,
+                            450000]),
+       col_indices=tensor([  278,  1146,  6158,  ..., 22458, 26366, 27217]),
+       values=tensor([0.7620, 0.0882, 0.1659,  ..., 0.8176, 0.5012, 0.2468]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.2139, 0.3171, 0.2720,  ..., 0.8919, 0.1670, 0.7588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 21.110622882843018 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     20,     37,  ..., 449971, 449989,
+                            450000]),
+       col_indices=tensor([  278,  1146,  6158,  ..., 22458, 26366, 27217]),
+       values=tensor([0.7620, 0.0882, 0.1659,  ..., 0.8176, 0.5012, 0.2468]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.2139, 0.3171, 0.2720,  ..., 0.8919, 0.1670, 0.7588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 21.110622882843018 seconds
+
+[20.0, 20.04, 20.08, 20.0, 20.0, 20.0, 20.0, 20.16, 20.16, 20.32]
+[20.52, 20.72, 21.08, 25.24, 27.4, 28.2, 29.0, 26.36, 25.24, 24.04, 24.0, 24.0, 24.12, 24.4, 24.56, 24.72, 24.8, 24.84, 24.44, 24.4, 24.32, 24.32, 24.48]
+24.01964807510376
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 21.110622882843018, 'TIME_S_1KI': 10.608353207458803, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 543.18098487854, 'W': 22.61402761523156}
+[20.0, 20.04, 20.08, 20.0, 20.0, 20.0, 20.0, 20.16, 20.16, 20.32, 20.32, 20.2, 20.16, 19.96, 19.84, 20.04, 20.24, 20.24, 20.16, 20.08]
+361.64
+18.082
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 21.110622882843018, 'TIME_S_1KI': 10.608353207458803, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 543.18098487854, 'W': 22.61402761523156, 'J_1KI': 272.95526878318594, 'W_1KI': 11.363832972478171, 'W_D': 4.532027615231559, 'J_D': 108.8577083845138, 'W_D_1KI': 2.27740081167415, 'J_D_1KI': 1.1444225184292212}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json
new file mode 100644
index 0000000..31f0e20
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1067, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.000109434127808, "TIME_S_1KI": 19.681452140700852, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 559.3280275917053, "W": 22.321542174679774, "J_1KI": 524.2062114261531, "W_1KI": 20.919908317413096, "W_D": 3.8025421746797754, "J_D": 95.2832200281621, "W_D_1KI": 3.5637696107589276, "J_D_1KI": 3.3399902631292666}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output
new file mode 100644
index 0000000..2362809
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 19.667322635650635}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     32,     62,  ..., 899942, 899975,
+                            900000]),
+       col_indices=tensor([  740,  1042,  1045,  ..., 28033, 28173, 29596]),
+       values=tensor([0.2730, 0.0823, 0.1244,  ..., 0.0611, 0.7750, 0.7520]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.9441, 0.9003, 0.6345,  ..., 0.2976, 0.9481, 0.5370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 19.667322635650635 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1067 -ss 30000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.000109434127808}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     31,     61,  ..., 899954, 899977,
+                            900000]),
+       col_indices=tensor([  498,   561,  1389,  ..., 26094, 29069, 29804]),
+       values=tensor([0.7571, 0.4869, 0.1051,  ..., 0.0359, 0.9032, 0.3458]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.6710, 0.3662, 0.6537,  ..., 0.7839, 0.4339, 0.5677])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 21.000109434127808 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     31,     61,  ..., 899954, 899977,
+                            900000]),
+       col_indices=tensor([  498,   561,  1389,  ..., 26094, 29069, 29804]),
+       values=tensor([0.7571, 0.4869, 0.1051,  ..., 0.0359, 0.9032, 0.3458]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.6710, 0.3662, 0.6537,  ..., 0.7839, 0.4339, 0.5677])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 21.000109434127808 seconds
+
+[20.36, 20.36, 20.6, 20.64, 20.68, 20.56, 20.56, 20.64, 20.44, 20.44]
+[20.44, 20.36, 21.72, 22.8, 24.84, 24.84, 25.76, 26.48, 25.64, 24.72, 24.68, 24.32, 24.2, 24.4, 24.4, 24.28, 24.4, 24.36, 24.52, 24.6, 24.68, 24.64, 24.68, 24.72]
+25.05776810646057
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1067, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 21.000109434127808, 'TIME_S_1KI': 19.681452140700852, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 559.3280275917053, 'W': 22.321542174679774}
+[20.36, 20.36, 20.6, 20.64, 20.68, 20.56, 20.56, 20.64, 20.44, 20.44, 20.16, 20.36, 20.36, 20.72, 20.8, 20.96, 20.68, 20.84, 20.56, 20.28]
+370.37999999999994
+18.519
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1067, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 21.000109434127808, 'TIME_S_1KI': 19.681452140700852, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 559.3280275917053, 'W': 22.321542174679774, 'J_1KI': 524.2062114261531, 'W_1KI': 20.919908317413096, 'W_D': 3.8025421746797754, 'J_D': 95.2832200281621, 'W_D_1KI': 3.5637696107589276, 'J_D_1KI': 3.3399902631292666}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json
new file mode 100644
index 0000000..883d2cb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 96.94969439506531, "TIME_S_1KI": 96.94969439506531, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2378.374522399904, "W": 23.364920168223726, "J_1KI": 2378.374522399904, "W_1KI": 23.364920168223726, "W_D": 5.238920168223725, "J_D": 533.2829799237265, "W_D_1KI": 5.238920168223725, "J_D_1KI": 5.238920168223725}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output
new file mode 100644
index 0000000..9061dbb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 96.94969439506531}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     143,     286,  ..., 4499692,
+                            4499854, 4500000]),
+       col_indices=tensor([  245,   443,   986,  ..., 29592, 29945, 29961]),
+       values=tensor([0.6844, 0.8171, 0.9701,  ..., 0.0838, 0.2528, 0.1757]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.4329, 0.8636, 0.1677,  ..., 0.1956, 0.5933, 0.9265])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 96.94969439506531 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     143,     286,  ..., 4499692,
+                            4499854, 4500000]),
+       col_indices=tensor([  245,   443,   986,  ..., 29592, 29945, 29961]),
+       values=tensor([0.6844, 0.8171, 0.9701,  ..., 0.0838, 0.2528, 0.1757]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.4329, 0.8636, 0.1677,  ..., 0.1956, 0.5933, 0.9265])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 96.94969439506531 seconds
+
+[20.28, 20.48, 20.32, 20.24, 20.08, 20.28, 20.2, 20.08, 20.24, 20.04]
+[20.04, 20.12, 20.0, 23.8, 25.0, 27.52, 28.72, 29.16, 26.24, 24.8, 24.4, 24.48, 24.72, 24.6, 24.44, 24.36, 24.28, 24.16, 24.24, 24.24, 24.24, 24.28, 24.4, 24.48, 24.6, 24.8, 24.6, 24.4, 24.48, 24.44, 24.44, 24.56, 24.36, 24.56, 24.84, 24.56, 24.6, 24.4, 24.36, 24.6, 24.8, 25.16, 25.16, 25.04, 24.76, 24.92, 24.68, 24.48, 24.44, 24.44, 24.44, 24.36, 24.48, 24.64, 24.56, 24.72, 24.72, 24.52, 24.64, 24.6, 24.32, 24.64, 24.56, 24.44, 24.44, 24.4, 24.36, 24.28, 24.52, 24.64, 24.68, 24.64, 24.6, 24.4, 24.2, 24.52, 24.64, 24.84, 25.12, 24.92, 24.76, 24.76, 24.68, 24.84, 24.76, 24.76, 24.76, 24.64, 24.52, 24.32, 24.44, 24.4, 24.28, 24.28, 24.48, 24.24, 24.32]
+101.79253792762756
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 96.94969439506531, 'TIME_S_1KI': 96.94969439506531, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2378.374522399904, 'W': 23.364920168223726}
+[20.28, 20.48, 20.32, 20.24, 20.08, 20.28, 20.2, 20.08, 20.24, 20.04, 20.36, 20.44, 20.08, 20.08, 20.0, 19.84, 19.84, 19.96, 19.96, 20.12]
+362.52000000000004
+18.126
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 96.94969439506531, 'TIME_S_1KI': 96.94969439506531, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2378.374522399904, 'W': 23.364920168223726, 'J_1KI': 2378.374522399904, 'W_1KI': 23.364920168223726, 'W_D': 5.238920168223725, 'J_D': 533.2829799237265, 'W_D_1KI': 5.238920168223725, 'J_D_1KI': 5.238920168223725}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json
new file mode 100644
index 0000000..de9aa95
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 192.67980027198792, "TIME_S_1KI": 192.67980027198792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4713.865335979459, "W": 23.38761944190409, "J_1KI": 4713.865335979459, "W_1KI": 23.38761944190409, "W_D": 5.0876194419040885, "J_D": 1025.4294153118105, "W_D_1KI": 5.0876194419040885, "J_D_1KI": 5.0876194419040885}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output
new file mode 100644
index 0000000..111912f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 192.67980027198792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     280,     537,  ..., 8999446,
+                            8999733, 9000000]),
+       col_indices=tensor([  104,   197,   254,  ..., 29816, 29922, 29974]),
+       values=tensor([0.4269, 0.5481, 0.4506,  ..., 0.7600, 0.9930, 0.8353]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.6961, 0.8979, 0.5119,  ..., 0.0794, 0.6244, 0.2452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 192.67980027198792 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     280,     537,  ..., 8999446,
+                            8999733, 9000000]),
+       col_indices=tensor([  104,   197,   254,  ..., 29816, 29922, 29974]),
+       values=tensor([0.4269, 0.5481, 0.4506,  ..., 0.7600, 0.9930, 0.8353]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.6961, 0.8979, 0.5119,  ..., 0.0794, 0.6244, 0.2452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 192.67980027198792 seconds
+
+[20.36, 20.16, 20.08, 20.2, 20.36, 20.44, 20.48, 20.28, 20.28, 20.04]
+[20.04, 20.4, 20.56, 25.0, 25.72, 28.12, 29.56, 28.28, 27.64, 26.76, 25.6, 24.84, 24.28, 24.32, 24.32, 24.32, 24.48, 24.52, 24.52, 24.48, 24.56, 24.88, 24.76, 24.8, 24.84, 24.76, 24.52, 24.68, 24.6, 24.56, 24.6, 24.56, 24.52, 24.52, 24.56, 24.72, 24.76, 24.76, 24.88, 24.84, 24.84, 24.56, 24.56, 24.72, 24.6, 24.52, 24.52, 24.48, 24.36, 24.4, 24.48, 24.6, 24.64, 24.64, 24.48, 24.56, 24.48, 24.56, 24.44, 24.28, 24.04, 23.96, 23.96, 23.92, 24.04, 24.24, 24.52, 24.76, 24.84, 24.72, 24.6, 24.72, 24.8, 24.76, 24.8, 24.64, 24.52, 24.52, 24.56, 24.56, 24.52, 24.56, 24.6, 24.72, 24.72, 24.6, 24.76, 24.88, 24.52, 24.68, 24.8, 24.72, 24.64, 24.72, 24.32, 24.16, 24.24, 24.2, 24.48, 24.68, 24.6, 24.72, 24.68, 24.56, 24.52, 24.8, 24.8, 24.68, 24.88, 24.88, 24.52, 24.56, 24.56, 24.56, 24.72, 24.72, 24.6, 24.56, 24.52, 24.76, 24.92, 24.96, 24.92, 24.88, 24.88, 24.8, 24.76, 24.48, 24.48, 24.68, 24.44, 24.64, 24.68, 24.72, 24.56, 24.6, 24.36, 24.32, 24.2, 24.24, 24.16, 24.24, 24.32, 24.52, 24.52, 24.44, 24.48, 24.12, 23.96, 23.92, 23.92, 24.08, 24.12, 24.44, 24.6, 24.48, 24.44, 24.64, 24.48, 24.4, 24.44, 24.28, 24.24, 24.52, 24.56, 24.6, 24.8, 24.68, 24.68, 24.84, 24.88, 24.84, 24.84, 24.72, 24.64, 24.68, 24.52, 24.16, 24.32, 24.32, 24.32, 24.28, 24.72, 24.84, 24.72, 24.8, 24.92, 24.6, 24.72, 24.68, 24.64, 24.6]
+201.55387544631958
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 192.67980027198792, 'TIME_S_1KI': 192.67980027198792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4713.865335979459, 'W': 23.38761944190409}
+[20.36, 20.16, 20.08, 20.2, 20.36, 20.44, 20.48, 20.28, 20.28, 20.04, 20.44, 20.44, 20.24, 20.24, 20.2, 20.24, 20.56, 20.56, 20.56, 20.52]
+366.0
+18.3
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 192.67980027198792, 'TIME_S_1KI': 192.67980027198792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4713.865335979459, 'W': 23.38761944190409, 'J_1KI': 4713.865335979459, 'W_1KI': 23.38761944190409, 'W_D': 5.0876194419040885, 'J_D': 1025.4294153118105, 'W_D_1KI': 5.0876194419040885, 'J_D_1KI': 5.0876194419040885}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json
new file mode 100644
index 0000000..8e7a90d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 974.439944267273, "TIME_S_1KI": 974.439944267273, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 23454.716241874696, "W": 23.50814234111929, "J_1KI": 23454.716241874696, "W_1KI": 23.50814234111929, "W_D": 4.921142341119289, "J_D": 4909.958240083218, "W_D_1KI": 4.921142341119289, "J_D_1KI": 4.921142341119289}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output
new file mode 100644
index 0000000..129f379
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 974.439944267273}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1561,     3045,  ..., 44996950,
+                            44998508, 45000000]),
+       col_indices=tensor([    5,    11,    69,  ..., 29993, 29995, 29999]),
+       values=tensor([0.1916, 0.3634, 0.6366,  ..., 0.4534, 0.7597, 0.1741]),
+       size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.1350, 0.9680, 0.5489,  ..., 0.5585, 0.8579, 0.6858])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 45000000
+Density: 0.05
+Time: 974.439944267273 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1561,     3045,  ..., 44996950,
+                            44998508, 45000000]),
+       col_indices=tensor([    5,    11,    69,  ..., 29993, 29995, 29999]),
+       values=tensor([0.1916, 0.3634, 0.6366,  ..., 0.4534, 0.7597, 0.1741]),
+       size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.1350, 0.9680, 0.5489,  ..., 0.5585, 0.8579, 0.6858])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 45000000
+Density: 0.05
+Time: 974.439944267273 seconds
+
+[20.36, 20.44, 20.52, 20.6, 20.8, 20.68, 20.76, 20.72, 21.08, 20.84]
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 974.439944267273, 'TIME_S_1KI': 974.439944267273, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 23454.716241874696, 'W': 23.50814234111929}
+[20.36, 20.44, 20.52, 20.6, 20.8, 20.68, 20.76, 20.72, 21.08, 20.84, 22.08, 21.56, 21.0, 20.48, 20.48, 20.2, 20.24, 20.32, 20.12, 20.2]
+371.74
+18.587
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 974.439944267273, 'TIME_S_1KI': 974.439944267273, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 23454.716241874696, 'W': 23.50814234111929, 'J_1KI': 23454.716241874696, 'W_1KI': 23.50814234111929, 'W_D': 4.921142341119289, 'J_D': 4909.958240083218, 'W_D_1KI': 4.921142341119289, 'J_D_1KI': 4.921142341119289}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json
new file mode 100644
index 0000000..32cac99
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1889.6461987495422, "TIME_S_1KI": 1889.6461987495422, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 46014.76951049807, "W": 23.55154793120378, "J_1KI": 46014.76951049807, "W_1KI": 23.55154793120378, "W_D": 5.418547931203779, "J_D": 10586.702617774983, "W_D_1KI": 5.418547931203779, "J_D_1KI": 5.418547931203779}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output
new file mode 100644
index 0000000..3244029
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1889.6461987495422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2919,     6033,  ..., 89993985,
+                            89996951, 90000000]),
+       col_indices=tensor([   15,    22,    25,  ..., 29928, 29955, 29961]),
+       values=tensor([0.1237, 0.9766, 0.2142,  ..., 0.5188, 0.0654, 0.8458]),
+       size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr)
+tensor([0.8545, 0.2049, 0.9446,  ..., 0.6392, 0.0667, 0.5059])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000000
+Density: 0.1
+Time: 1889.6461987495422 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2919,     6033,  ..., 89993985,
+                            89996951, 90000000]),
+       col_indices=tensor([   15,    22,    25,  ..., 29928, 29955, 29961]),
+       values=tensor([0.1237, 0.9766, 0.2142,  ..., 0.5188, 0.0654, 0.8458]),
+       size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr)
+tensor([0.8545, 0.2049, 0.9446,  ..., 0.6392, 0.0667, 0.5059])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000000
+Density: 0.1
+Time: 1889.6461987495422 seconds
+
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diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json
new file mode 100644
index 0000000..e115dec
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 53329, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.33877921104431, "TIME_S_1KI": 0.4001346211450489, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 541.4117899322509, "W": 22.47116935228423, "J_1KI": 10.152295935274447, "W_1KI": 0.42136866155908104, "W_D": 4.265169352284232, "J_D": 102.76336478900909, "W_D_1KI": 0.07997842360224704, "J_D_1KI": 0.0014997172945723158}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.output
new file mode 100644
index 0000000..e2a45bb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 30000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.39377737045288086}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([10394, 25541,  5557,  ..., 25175, 23986, 28004]),
+       values=tensor([0.8334, 0.8462, 0.9277,  ..., 0.8850, 0.0932, 0.4483]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.3238, 0.7549, 0.3676,  ..., 0.0953, 0.4629, 0.1375])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 0.39377737045288086 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 53329 -ss 30000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.33877921104431}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([19970, 26420, 19050,  ..., 11684, 15529, 21908]),
+       values=tensor([0.5268, 0.0229, 0.8842,  ..., 0.2264, 0.1987, 0.6579]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.3153, 0.9988, 0.0667,  ..., 0.8874, 0.8455, 0.6438])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.33877921104431 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([19970, 26420, 19050,  ..., 11684, 15529, 21908]),
+       values=tensor([0.5268, 0.0229, 0.8842,  ..., 0.2264, 0.1987, 0.6579]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.3153, 0.9988, 0.0667,  ..., 0.8874, 0.8455, 0.6438])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.33877921104431 seconds
+
+[20.04, 20.04, 20.12, 20.16, 20.4, 20.32, 20.4, 20.36, 20.2, 20.16]
+[19.76, 19.88, 23.52, 24.72, 26.8, 26.8, 27.72, 28.52, 25.4, 24.2, 24.0, 24.12, 23.96, 24.08, 24.08, 23.92, 24.0, 24.0, 24.16, 24.28, 24.4, 24.44, 24.48]
+24.093618869781494
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 53329, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.33877921104431, 'TIME_S_1KI': 0.4001346211450489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 541.4117899322509, 'W': 22.47116935228423}
+[20.04, 20.04, 20.12, 20.16, 20.4, 20.32, 20.4, 20.36, 20.2, 20.16, 19.84, 19.92, 20.0, 20.12, 20.12, 20.44, 20.48, 20.48, 20.48, 20.12]
+364.12
+18.206
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 53329, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.33877921104431, 'TIME_S_1KI': 0.4001346211450489, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 541.4117899322509, 'W': 22.47116935228423, 'J_1KI': 10.152295935274447, 'W_1KI': 0.42136866155908104, 'W_D': 4.265169352284232, 'J_D': 102.76336478900909, 'W_D_1KI': 0.07997842360224704, 'J_D_1KI': 0.0014997172945723158}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..ddc6fac
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3411, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.881499767303467, "TIME_S_1KI": 6.121811717180729, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 532.9655063343048, "W": 22.190313426181827, "J_1KI": 156.24904905725734, "W_1KI": 6.505515516324194, "W_D": 3.8713134261818247, "J_D": 92.98095438027374, "W_D_1KI": 1.134949699848087, "J_D_1KI": 0.3327322485629103}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..19d6f4f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,66 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.156386375427246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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,     12,  ..., 249983, 249990,
+                            250000]),
+       col_indices=tensor([ 2925,  8906, 11132,  ..., 41372, 46211, 46407]),
+       values=tensor([4.7685e-01, 8.2631e-01, 1.3241e-01,  ...,
+                      9.9306e-01, 3.1562e-01, 2.9121e-04]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6560, 0.4823, 0.8578,  ..., 0.1247, 0.5626, 0.6108])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 6.156386375427246 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3411 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.881499767303467}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     11,  ..., 249987, 249992,
+                            250000]),
+       col_indices=tensor([ 5492, 16093, 20671,  ..., 32727, 38238, 43452]),
+       values=tensor([0.3185, 0.0470, 0.4206,  ..., 0.2062, 0.5185, 0.9595]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4169, 0.1713, 0.4477,  ..., 0.1220, 0.0527, 0.5400])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 20.881499767303467 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     11,  ..., 249987, 249992,
+                            250000]),
+       col_indices=tensor([ 5492, 16093, 20671,  ..., 32727, 38238, 43452]),
+       values=tensor([0.3185, 0.0470, 0.4206,  ..., 0.2062, 0.5185, 0.9595]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4169, 0.1713, 0.4477,  ..., 0.1220, 0.0527, 0.5400])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 20.881499767303467 seconds
+
+[20.32, 20.28, 20.4, 20.4, 20.32, 20.4, 20.72, 20.56, 20.56, 20.56]
+[20.52, 20.56, 20.8, 22.24, 23.32, 25.08, 26.04, 25.96, 25.92, 24.88, 25.0, 24.72, 24.6, 24.56, 24.12, 24.24, 24.24, 24.44, 24.56, 24.76, 24.96, 24.84, 24.84]
+24.017935037612915
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.881499767303467, 'TIME_S_1KI': 6.121811717180729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 532.9655063343048, 'W': 22.190313426181827}
+[20.32, 20.28, 20.4, 20.4, 20.32, 20.4, 20.72, 20.56, 20.56, 20.56, 20.52, 20.32, 20.2, 20.28, 20.36, 20.32, 20.28, 20.28, 20.08, 19.84]
+366.38000000000005
+18.319000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.881499767303467, 'TIME_S_1KI': 6.121811717180729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 532.9655063343048, 'W': 22.190313426181827, 'J_1KI': 156.24904905725734, 'W_1KI': 6.505515516324194, 'W_D': 3.8713134261818247, 'J_D': 92.98095438027374, 'W_D_1KI': 1.134949699848087, 'J_D_1KI': 0.3327322485629103}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json
new file mode 100644
index 0000000..e80940c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 27.37552046775818, "TIME_S_1KI": 27.37552046775818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 709.9588974285126, "W": 22.617473628428268, "J_1KI": 709.9588974285126, "W_1KI": 22.617473628428268, "W_D": 4.155473628428268, "J_D": 130.43965581655505, "W_D_1KI": 4.155473628428268, "J_D_1KI": 4.155473628428268}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output
new file mode 100644
index 0000000..5ee77f1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 27.37552046775818}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      27,      46,  ..., 1249943,
+                            1249977, 1250000]),
+       col_indices=tensor([ 1915,  6358,  8298,  ..., 42036, 43103, 48835]),
+       values=tensor([0.4919, 0.4887, 0.0616,  ..., 0.3370, 0.2927, 0.3892]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0947, 0.0414, 0.5709,  ..., 0.1435, 0.2486, 0.5038])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 27.37552046775818 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      27,      46,  ..., 1249943,
+                            1249977, 1250000]),
+       col_indices=tensor([ 1915,  6358,  8298,  ..., 42036, 43103, 48835]),
+       values=tensor([0.4919, 0.4887, 0.0616,  ..., 0.3370, 0.2927, 0.3892]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0947, 0.0414, 0.5709,  ..., 0.1435, 0.2486, 0.5038])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 27.37552046775818 seconds
+
+[20.24, 20.24, 20.44, 20.44, 20.52, 20.84, 20.8, 20.8, 20.52, 20.48]
+[20.44, 20.44, 21.24, 22.44, 23.88, 24.76, 25.84, 25.32, 25.32, 25.04, 24.24, 24.36, 24.44, 24.64, 24.84, 24.72, 24.52, 24.56, 24.28, 24.44, 24.4, 24.4, 24.44, 24.2, 24.08, 24.16, 24.08, 24.32, 24.52, 24.52]
+31.389840841293335
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 27.37552046775818, 'TIME_S_1KI': 27.37552046775818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 709.9588974285126, 'W': 22.617473628428268}
+[20.24, 20.24, 20.44, 20.44, 20.52, 20.84, 20.8, 20.8, 20.52, 20.48, 20.56, 20.56, 20.64, 20.6, 20.32, 20.36, 20.2, 20.32, 20.72, 20.56]
+369.24
+18.462
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 27.37552046775818, 'TIME_S_1KI': 27.37552046775818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 709.9588974285126, 'W': 22.617473628428268, 'J_1KI': 709.9588974285126, 'W_1KI': 22.617473628428268, 'W_D': 4.155473628428268, 'J_D': 130.43965581655505, 'W_D_1KI': 4.155473628428268, 'J_D_1KI': 4.155473628428268}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..8992275
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.24125528335571, "TIME_S_1KI": 54.24125528335571, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1372.0545160293577, "W": 23.357171683962374, "J_1KI": 1372.0545160293577, "W_1KI": 23.357171683962374, "W_D": 5.006171683962375, "J_D": 294.0741524674891, "W_D_1KI": 5.006171683962375, "J_D_1KI": 5.006171683962375}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..1dd53bb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.24125528335571}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      50,     100,  ..., 2499893,
+                            2499949, 2500000]),
+       col_indices=tensor([ 3726,  3738,  3891,  ..., 47883, 48507, 49636]),
+       values=tensor([0.9449, 0.1440, 0.2391,  ..., 0.6142, 0.1134, 0.3366]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1491, 0.4739, 0.9733,  ..., 0.7895, 0.7265, 0.6840])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 54.24125528335571 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      50,     100,  ..., 2499893,
+                            2499949, 2500000]),
+       col_indices=tensor([ 3726,  3738,  3891,  ..., 47883, 48507, 49636]),
+       values=tensor([0.9449, 0.1440, 0.2391,  ..., 0.6142, 0.1134, 0.3366]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1491, 0.4739, 0.9733,  ..., 0.7895, 0.7265, 0.6840])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 54.24125528335571 seconds
+
+[20.52, 20.48, 20.52, 20.52, 20.48, 20.28, 20.36, 20.08, 20.2, 20.2]
+[20.4, 20.56, 21.16, 25.2, 26.96, 28.36, 29.16, 26.04, 26.0, 26.0, 24.56, 24.48, 24.68, 24.64, 24.76, 24.8, 24.44, 24.48, 24.56, 24.36, 24.44, 24.6, 24.44, 24.44, 24.32, 24.2, 24.16, 24.24, 24.36, 24.44, 24.52, 24.52, 24.52, 24.52, 24.48, 24.44, 24.56, 24.56, 24.68, 24.76, 24.64, 24.64, 24.48, 24.36, 24.28, 24.16, 24.36, 24.48, 24.4, 24.6, 24.68, 24.6, 24.6, 24.6, 24.56, 24.16]
+58.74232268333435
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.24125528335571, 'TIME_S_1KI': 54.24125528335571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1372.0545160293577, 'W': 23.357171683962374}
+[20.52, 20.48, 20.52, 20.52, 20.48, 20.28, 20.36, 20.08, 20.2, 20.2, 20.4, 20.52, 20.32, 20.48, 20.6, 20.4, 20.44, 20.4, 20.24, 20.28]
+367.02
+18.351
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.24125528335571, 'TIME_S_1KI': 54.24125528335571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1372.0545160293577, 'W': 23.357171683962374, 'J_1KI': 1372.0545160293577, 'W_1KI': 23.357171683962374, 'W_D': 5.006171683962375, 'J_D': 294.0741524674891, 'W_D_1KI': 5.006171683962375, 'J_D_1KI': 5.006171683962375}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json
new file mode 100644
index 0000000..5b8cd5a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 267.4636125564575, "TIME_S_1KI": 267.4636125564575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6714.934566097259, "W": 23.431218104999314, "J_1KI": 6714.934566097259, "W_1KI": 23.431218104999314, "W_D": 5.143218104999313, "J_D": 1473.9469744796756, "W_D_1KI": 5.143218104999313, "J_D_1KI": 5.143218104999313}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output
new file mode 100644
index 0000000..d7cdb54
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.005 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 267.4636125564575}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      245,      505,  ..., 12499495,
+                            12499738, 12500000]),
+       col_indices=tensor([  233,   421,   423,  ..., 49587, 49831, 49917]),
+       values=tensor([0.0085, 0.6781, 0.7487,  ..., 0.0311, 0.6051, 0.4921]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9687, 0.6648, 0.3251,  ..., 0.0954, 0.1242, 0.9203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 267.4636125564575 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      245,      505,  ..., 12499495,
+                            12499738, 12500000]),
+       col_indices=tensor([  233,   421,   423,  ..., 49587, 49831, 49917]),
+       values=tensor([0.0085, 0.6781, 0.7487,  ..., 0.0311, 0.6051, 0.4921]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9687, 0.6648, 0.3251,  ..., 0.0954, 0.1242, 0.9203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 267.4636125564575 seconds
+
+[20.32, 20.36, 20.44, 20.44, 20.44, 20.6, 20.6, 20.64, 20.64, 20.8]
+[20.68, 20.84, 21.36, 22.52, 24.16, 25.64, 27.64, 28.36, 28.44, 28.44, 27.88, 26.96, 25.68, 25.0, 24.68, 24.68, 24.88, 25.0, 25.08, 25.0, 24.72, 24.6, 24.68, 24.44, 24.32, 24.4, 24.32, 24.12, 24.36, 24.6, 24.6, 24.6, 24.48, 24.72, 24.76, 24.72, 24.84, 24.92, 24.72, 24.72, 24.6, 24.44, 24.56, 24.6, 24.36, 24.48, 24.56, 24.36, 24.36, 24.32, 24.4, 24.32, 24.44, 24.44, 24.6, 24.6, 24.56, 24.52, 24.28, 24.28, 24.32, 24.28, 24.52, 24.4, 24.44, 24.24, 24.12, 24.12, 24.32, 24.52, 24.8, 25.0, 25.04, 24.72, 24.72, 24.72, 24.56, 24.24, 24.24, 24.16, 24.12, 24.0, 24.16, 24.4, 24.4, 24.56, 24.68, 24.76, 24.68, 24.72, 24.56, 24.6, 24.68, 24.8, 24.84, 24.88, 24.84, 24.84, 24.72, 24.52, 24.48, 24.44, 24.36, 24.4, 24.56, 24.32, 24.28, 24.4, 24.52, 24.36, 24.76, 24.88, 24.8, 24.72, 24.84, 24.88, 24.84, 24.92, 24.88, 24.88, 24.8, 24.76, 24.68, 24.48, 24.52, 24.44, 24.44, 24.56, 24.56, 24.64, 24.52, 24.4, 24.6, 24.64, 24.72, 24.52, 24.36, 24.48, 24.48, 24.68, 24.56, 24.56, 24.88, 24.92, 24.96, 25.12, 25.12, 24.88, 24.6, 24.6, 24.4, 24.6, 24.88, 24.8, 24.6, 24.56, 24.48, 24.52, 24.68, 24.92, 24.96, 24.72, 24.76, 24.76, 24.6, 24.6, 24.64, 24.52, 24.48, 24.48, 24.6, 24.64, 24.6, 24.56, 24.56, 24.28, 24.44, 24.32, 24.24, 24.2, 24.12, 24.2, 24.44, 24.56, 24.48, 24.48, 24.44, 24.48, 24.64, 24.84, 24.84, 24.96, 24.68, 24.72, 24.44, 24.76, 24.72, 24.6, 24.72, 24.76, 24.6, 24.72, 25.08, 24.72, 24.88, 24.92, 25.0, 25.0, 25.04, 25.2, 25.08, 24.92, 24.56, 24.44, 24.48, 24.36, 24.64, 24.64, 24.68, 24.6, 24.44, 24.48, 24.28, 24.24, 24.4, 24.52, 24.4, 24.24, 24.4, 24.4, 24.36, 24.36, 24.56, 24.76, 24.76, 24.72, 24.8, 24.72, 24.64, 24.56, 24.44, 24.24, 24.32, 24.32, 24.6, 24.64, 24.6, 24.64, 24.68, 24.52, 24.8, 24.8, 24.8, 24.88, 24.76, 24.68, 24.64, 24.56, 24.6, 24.72, 24.64, 24.48, 24.56, 24.48, 24.48, 24.36, 24.44, 24.44, 24.56, 24.76, 24.68, 24.68, 24.72]
+286.5806863307953
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 267.4636125564575, 'TIME_S_1KI': 267.4636125564575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6714.934566097259, 'W': 23.431218104999314}
+[20.32, 20.36, 20.44, 20.44, 20.44, 20.6, 20.6, 20.64, 20.64, 20.8, 20.04, 20.04, 20.04, 19.92, 20.12, 20.08, 20.12, 20.16, 20.32, 20.44]
+365.76
+18.288
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 267.4636125564575, 'TIME_S_1KI': 267.4636125564575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6714.934566097259, 'W': 23.431218104999314, 'J_1KI': 6714.934566097259, 'W_1KI': 23.431218104999314, 'W_D': 5.143218104999313, 'J_D': 1473.9469744796756, 'W_D_1KI': 5.143218104999313, 'J_D_1KI': 5.143218104999313}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..789132f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 535.8486552238464, "TIME_S_1KI": 535.8486552238464, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 13280.309822225587, "W": 23.46860300867941, "J_1KI": 13280.309822225587, "W_1KI": 23.46860300867941, "W_D": 4.95160300867941, "J_D": 2801.991326352374, "W_D_1KI": 4.95160300867941, "J_D_1KI": 4.95160300867941}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..209aa36
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 535.8486552238464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      524,     1011,  ..., 24999011,
+                            24999504, 25000000]),
+       col_indices=tensor([  192,   200,   454,  ..., 49935, 49965, 49995]),
+       values=tensor([0.7895, 0.2997, 0.5746,  ..., 0.4223, 0.3918, 0.3456]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4665, 0.6238, 0.5276,  ..., 0.6350, 0.6391, 0.4023])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 535.8486552238464 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      524,     1011,  ..., 24999011,
+                            24999504, 25000000]),
+       col_indices=tensor([  192,   200,   454,  ..., 49935, 49965, 49995]),
+       values=tensor([0.7895, 0.2997, 0.5746,  ..., 0.4223, 0.3918, 0.3456]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4665, 0.6238, 0.5276,  ..., 0.6350, 0.6391, 0.4023])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 535.8486552238464 seconds
+
+[19.88, 20.2, 20.24, 20.52, 20.64, 20.6, 20.68, 20.56, 20.56, 20.64]
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+565.8756005764008
+{'CPU': 'Altra', 'CORES': 1, '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': 535.8486552238464, 'TIME_S_1KI': 535.8486552238464, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13280.309822225587, 'W': 23.46860300867941}
+[19.88, 20.2, 20.24, 20.52, 20.64, 20.6, 20.68, 20.56, 20.56, 20.64, 20.6, 20.6, 20.48, 20.52, 20.32, 20.32, 20.6, 20.96, 21.36, 21.24]
+370.34
+18.517
+{'CPU': 'Altra', 'CORES': 1, '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': 535.8486552238464, 'TIME_S_1KI': 535.8486552238464, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13280.309822225587, 'W': 23.46860300867941, 'J_1KI': 13280.309822225587, 'W_1KI': 23.46860300867941, 'W_D': 4.95160300867941, 'J_D': 2801.991326352374, 'W_D_1KI': 4.95160300867941, 'J_D_1KI': 4.95160300867941}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..918a20d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 21239, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.893531799316406, "TIME_S_1KI": 0.9837342529929096, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 567.132219734192, "W": 22.611941270368835, "J_1KI": 26.7023974638256, "W_1KI": 1.0646424629393492, "W_D": 4.100941270368832, "J_D": 102.85609262180327, "W_D_1KI": 0.19308542164738604, "J_D_1KI": 0.009091078753584728}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..bf1327e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.9887256622314453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([37749,  5687, 46660,  ..., 48444, 47762, 13606]),
+       values=tensor([0.6973, 0.6140, 0.4905,  ..., 0.2540, 0.0834, 0.5554]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8263, 0.0637, 0.7656,  ..., 0.0179, 0.5334, 0.7448])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.9887256622314453 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 21239 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.893531799316406}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([17445, 34363, 49525,  ..., 23738, 42338, 13045]),
+       values=tensor([0.8308, 0.1110, 0.1320,  ..., 0.5346, 0.9645, 0.6427]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4506, 0.3540, 0.2946,  ..., 0.2277, 0.1153, 0.5755])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 20.893531799316406 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([17445, 34363, 49525,  ..., 23738, 42338, 13045]),
+       values=tensor([0.8308, 0.1110, 0.1320,  ..., 0.5346, 0.9645, 0.6427]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4506, 0.3540, 0.2946,  ..., 0.2277, 0.1153, 0.5755])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 20.893531799316406 seconds
+
+[20.28, 20.2, 20.12, 20.32, 20.4, 20.4, 20.52, 20.44, 20.28, 20.28]
+[20.36, 20.36, 20.32, 21.48, 22.48, 24.48, 25.64, 26.08, 25.96, 25.2, 25.16, 25.16, 25.12, 25.12, 25.16, 25.36, 25.32, 25.24, 25.64, 25.64, 25.56, 25.72, 25.76, 25.8]
+25.081093788146973
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 21239, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.893531799316406, 'TIME_S_1KI': 0.9837342529929096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 567.132219734192, 'W': 22.611941270368835}
+[20.28, 20.2, 20.12, 20.32, 20.4, 20.4, 20.52, 20.44, 20.28, 20.28, 20.6, 20.72, 20.84, 20.84, 20.72, 20.8, 20.8, 20.8, 20.96, 20.96]
+370.22
+18.511000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 21239, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.893531799316406, 'TIME_S_1KI': 0.9837342529929096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 567.132219734192, 'W': 22.611941270368835, 'J_1KI': 26.7023974638256, 'W_1KI': 1.0646424629393492, 'W_D': 4.100941270368832, 'J_D': 102.85609262180327, 'W_D_1KI': 0.19308542164738604, 'J_D_1KI': 0.009091078753584728}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json
new file mode 100644
index 0000000..0c0f3d2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 28.845969438552856, "TIME_S_1KI": 28.845969438552856, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2585.6487583732605, "W": 78.16, "J_1KI": 2585.6487583732605, "W_1KI": 78.16, "W_D": 43.0085, "J_D": 1422.784987519145, "W_D_1KI": 43.0085, "J_D_1KI": 43.0085}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output
new file mode 100644
index 0000000..89364a9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '1000000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 28.845969438552856}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,        6,       13,  ...,  9999985,
+                             9999990, 10000000]),
+       col_indices=tensor([129131, 466272, 498291,  ..., 666802, 863606,
+                           946629]),
+       values=tensor([0.5704, 0.0489, 0.8998,  ..., 0.0930, 0.7201, 0.2084]),
+       size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9798, 0.4611, 0.5869,  ..., 0.0442, 0.2383, 0.1498])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([1000000, 1000000])
+Rows: 1000000
+Size: 1000000000000
+NNZ: 10000000
+Density: 1e-05
+Time: 28.845969438552856 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,        6,       13,  ...,  9999985,
+                             9999990, 10000000]),
+       col_indices=tensor([129131, 466272, 498291,  ..., 666802, 863606,
+                           946629]),
+       values=tensor([0.5704, 0.0489, 0.8998,  ..., 0.0930, 0.7201, 0.2084]),
+       size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9798, 0.4611, 0.5869,  ..., 0.0442, 0.2383, 0.1498])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([1000000, 1000000])
+Rows: 1000000
+Size: 1000000000000
+NNZ: 10000000
+Density: 1e-05
+Time: 28.845969438552856 seconds
+
+[40.14, 38.78, 39.11, 39.29, 39.32, 38.69, 39.29, 38.91, 39.05, 39.11]
+[78.16]
+33.081483602523804
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 28.845969438552856, 'TIME_S_1KI': 28.845969438552856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2585.6487583732605, 'W': 78.16}
+[40.14, 38.78, 39.11, 39.29, 39.32, 38.69, 39.29, 38.91, 39.05, 39.11, 40.65, 38.68, 39.17, 39.0, 39.16, 39.21, 38.7, 38.72, 38.68, 38.64]
+703.03
+35.1515
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 28.845969438552856, 'TIME_S_1KI': 28.845969438552856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2585.6487583732605, 'W': 78.16, 'J_1KI': 2585.6487583732605, 'W_1KI': 78.16, 'W_D': 43.0085, 'J_D': 1422.784987519145, 'W_D_1KI': 43.0085, 'J_D_1KI': 43.0085}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..11555aa
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12281, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.43535876274109, "TIME_S_1KI": 1.7454082536227578, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1544.854190015793, "W": 65.9, "J_1KI": 125.79221480464072, "W_1KI": 5.366012539695466, "W_D": 30.747500000000002, "J_D": 720.7952080047131, "W_D_1KI": 2.503664196726651, "J_D_1KI": 0.20386484787286466}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..a35639b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,66 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.7099568843841553}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      20,  ...,  999975,
+                             999991, 1000000]),
+       col_indices=tensor([ 4154, 20798, 21409,  ..., 65320, 83277, 90457]),
+       values=tensor([0.0206, 0.0188, 0.3875,  ..., 0.2566, 0.8734, 0.4713]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([2.2552e-01, 8.2165e-04, 8.9899e-01,  ..., 7.1003e-01, 6.8443e-02,
+        6.7507e-01])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 1.7099568843841553 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12281', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 21.43535876274109}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      20,  ...,  999985,
+                             999994, 1000000]),
+       col_indices=tensor([ 2661, 16984, 17010,  ..., 72407, 79760, 99948]),
+       values=tensor([0.6261, 0.1903, 0.4758,  ..., 0.9266, 0.4335, 0.5751]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1330, 0.4019, 0.6390,  ..., 0.8808, 0.7758, 0.9416])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 21.43535876274109 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      20,  ...,  999985,
+                             999994, 1000000]),
+       col_indices=tensor([ 2661, 16984, 17010,  ..., 72407, 79760, 99948]),
+       values=tensor([0.6261, 0.1903, 0.4758,  ..., 0.9266, 0.4335, 0.5751]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1330, 0.4019, 0.6390,  ..., 0.8808, 0.7758, 0.9416])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 21.43535876274109 seconds
+
+[39.05, 39.08, 38.77, 38.37, 38.41, 38.56, 38.82, 38.92, 43.8, 38.57]
+[65.9]
+23.442400455474854
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12281, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.43535876274109, 'TIME_S_1KI': 1.7454082536227578, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1544.854190015793, 'W': 65.9}
+[39.05, 39.08, 38.77, 38.37, 38.41, 38.56, 38.82, 38.92, 43.8, 38.57, 39.12, 38.73, 38.52, 39.3, 38.76, 38.93, 38.86, 38.9, 38.78, 38.34]
+703.0500000000001
+35.1525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12281, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 21.43535876274109, 'TIME_S_1KI': 1.7454082536227578, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1544.854190015793, 'W': 65.9, 'J_1KI': 125.79221480464072, 'W_1KI': 5.366012539695466, 'W_D': 30.747500000000002, 'J_D': 720.7952080047131, 'W_D_1KI': 2.503664196726651, 'J_D_1KI': 0.20386484787286466}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json
new file mode 100644
index 0000000..0a9c6a6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2942, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.68795394897461, "TIME_S_1KI": 7.03193540073916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1874.2356022763252, "W": 76.49, "J_1KI": 637.0617274902532, "W_1KI": 25.9993201903467, "W_D": 40.7645, "J_D": 998.8531469341516, "W_D_1KI": 13.856050305914344, "J_D_1KI": 4.709738377265243}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output
new file mode 100644
index 0000000..5c997f3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 7.137338638305664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      98,  ..., 4999908,
+                            4999951, 5000000]),
+       col_indices=tensor([  774,  4471,  4915,  ..., 92493, 94807, 99005]),
+       values=tensor([0.1957, 0.1752, 0.4711,  ..., 0.3350, 0.8302, 0.4161]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5056, 0.9806, 0.9907,  ..., 0.9600, 0.9702, 0.1169])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 7.137338638305664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2942', '-ss', '100000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.68795394897461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      45,      79,  ..., 4999902,
+                            4999950, 5000000]),
+       col_indices=tensor([11504, 12222, 12883,  ..., 96456, 97352, 97598]),
+       values=tensor([0.3754, 0.5479, 0.7533,  ..., 0.2937, 0.0115, 0.1659]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4390, 0.1553, 0.7240,  ..., 0.6581, 0.8843, 0.0193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 20.68795394897461 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      45,      79,  ..., 4999902,
+                            4999950, 5000000]),
+       col_indices=tensor([11504, 12222, 12883,  ..., 96456, 97352, 97598]),
+       values=tensor([0.3754, 0.5479, 0.7533,  ..., 0.2937, 0.0115, 0.1659]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4390, 0.1553, 0.7240,  ..., 0.6581, 0.8843, 0.0193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 20.68795394897461 seconds
+
+[40.56, 38.51, 39.43, 38.93, 54.59, 38.38, 38.52, 38.39, 39.6, 38.42]
+[76.49]
+24.50301480293274
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2942, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.68795394897461, 'TIME_S_1KI': 7.03193540073916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1874.2356022763252, 'W': 76.49}
+[40.56, 38.51, 39.43, 38.93, 54.59, 38.38, 38.52, 38.39, 39.6, 38.42, 39.81, 38.45, 38.58, 38.39, 38.5, 38.48, 38.64, 39.27, 39.03, 38.85]
+714.51
+35.7255
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2942, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.68795394897461, 'TIME_S_1KI': 7.03193540073916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1874.2356022763252, 'W': 76.49, 'J_1KI': 637.0617274902532, 'W_1KI': 25.9993201903467, 'W_D': 40.7645, 'J_D': 998.8531469341516, 'W_D_1KI': 13.856050305914344, 'J_D_1KI': 4.709738377265243}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..7cc2d82
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1260, "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": 21.127803564071655, "TIME_S_1KI": 16.768098066723535, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2030.2760949325561, "W": 77.88, "J_1KI": 1611.3302340734572, "W_1KI": 61.80952380952381, "W_D": 42.534, "J_D": 1108.8310660228728, "W_D_1KI": 33.75714285714285, "J_D_1KI": 26.791383219954643}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..56baef1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 16.660033226013184}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  9999825,
+                             9999911, 10000000]),
+       col_indices=tensor([ 2064,  2545,  2770,  ..., 96472, 96974, 97481]),
+       values=tensor([0.9939, 0.7295, 0.6290,  ..., 0.4583, 0.7573, 0.7957]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0307, 0.5740, 0.3084,  ..., 0.9686, 0.7857, 0.7900])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 16.660033226013184 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1260', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 21.127803564071655}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       87,      189,  ...,  9999804,
+                             9999908, 10000000]),
+       col_indices=tensor([ 1134,  1351,  3464,  ..., 96987, 97572, 98330]),
+       values=tensor([0.8017, 0.9469, 0.5440,  ..., 0.1663, 0.6077, 0.2624]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9534, 0.2929, 0.7145,  ..., 0.1886, 0.7155, 0.7573])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 21.127803564071655 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       87,      189,  ...,  9999804,
+                             9999908, 10000000]),
+       col_indices=tensor([ 1134,  1351,  3464,  ..., 96987, 97572, 98330]),
+       values=tensor([0.8017, 0.9469, 0.5440,  ..., 0.1663, 0.6077, 0.2624]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9534, 0.2929, 0.7145,  ..., 0.1886, 0.7155, 0.7573])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 21.127803564071655 seconds
+
+[41.52, 38.75, 38.56, 39.98, 39.02, 38.4, 39.41, 44.36, 39.07, 39.11]
+[77.88]
+26.069287300109863
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1260, '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': 21.127803564071655, 'TIME_S_1KI': 16.768098066723535, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2030.2760949325561, 'W': 77.88}
+[41.52, 38.75, 38.56, 39.98, 39.02, 38.4, 39.41, 44.36, 39.07, 39.11, 39.9, 38.94, 38.5, 38.44, 38.69, 38.62, 38.87, 38.86, 38.81, 38.75]
+706.92
+35.346
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1260, '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': 21.127803564071655, 'TIME_S_1KI': 16.768098066723535, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2030.2760949325561, 'W': 77.88, 'J_1KI': 1611.3302340734572, 'W_1KI': 61.80952380952381, 'W_D': 42.534, 'J_D': 1108.8310660228728, 'W_D_1KI': 33.75714285714285, 'J_D_1KI': 26.791383219954643}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json
new file mode 100644
index 0000000..378f4a9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 80.02073836326599, "TIME_S_1KI": 80.02073836326599, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7205.058165025711, "W": 78.13, "J_1KI": 7205.058165025711, "W_1KI": 78.13, "W_D": 42.671499999999995, "J_D": 3935.116338012218, "W_D_1KI": 42.671499999999995, "J_D_1KI": 42.671499999999995}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output
new file mode 100644
index 0000000..bd8189d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 80.02073836326599}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      526,     1009,  ..., 49999039,
+                            49999525, 50000000]),
+       col_indices=tensor([  783,   789,   851,  ..., 99387, 99562, 99965]),
+       values=tensor([0.0435, 0.6996, 0.0280,  ..., 0.1403, 0.1144, 0.7500]),
+       size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.9356, 0.8803, 0.8700,  ..., 0.3387, 0.6442, 0.0455])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 50000000
+Density: 0.005
+Time: 80.02073836326599 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      526,     1009,  ..., 49999039,
+                            49999525, 50000000]),
+       col_indices=tensor([  783,   789,   851,  ..., 99387, 99562, 99965]),
+       values=tensor([0.0435, 0.6996, 0.0280,  ..., 0.1403, 0.1144, 0.7500]),
+       size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.9356, 0.8803, 0.8700,  ..., 0.3387, 0.6442, 0.0455])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 50000000
+Density: 0.005
+Time: 80.02073836326599 seconds
+
+[39.78, 38.73, 38.83, 38.73, 40.16, 38.51, 39.25, 38.91, 39.2, 38.87]
+[78.13]
+92.21884250640869
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 80.02073836326599, 'TIME_S_1KI': 80.02073836326599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7205.058165025711, 'W': 78.13}
+[39.78, 38.73, 38.83, 38.73, 40.16, 38.51, 39.25, 38.91, 39.2, 38.87, 39.46, 38.64, 39.23, 44.76, 39.46, 39.36, 38.92, 38.68, 39.19, 39.11]
+709.17
+35.4585
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 80.02073836326599, 'TIME_S_1KI': 80.02073836326599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7205.058165025711, 'W': 78.13, 'J_1KI': 7205.058165025711, 'W_1KI': 78.13, 'W_D': 42.671499999999995, 'J_D': 3935.116338012218, 'W_D_1KI': 42.671499999999995, 'J_D_1KI': 42.671499999999995}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..8e3d7eb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 24272, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.77256941795349, "TIME_S_1KI": 0.8558243827436343, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1485.908479528427, "W": 63.94, "J_1KI": 61.21903755473084, "W_1KI": 2.6343111404087014, "W_D": 28.96575, "J_D": 673.1381535955668, "W_D_1KI": 1.193381262359921, "J_D_1KI": 0.049166993340471365}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..1f19b8b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8651721477508545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([90599, 28958, 57214,  ..., 84272, 90301, 79327]),
+       values=tensor([0.9831, 0.6502, 0.8427,  ..., 0.3005, 0.4197, 0.6469]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7674, 0.7013, 0.3294,  ..., 0.7372, 0.8879, 0.9691])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.8651721477508545 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24272', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.77256941795349}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([13062, 27623, 58180,  ..., 66636,  6102, 47055]),
+       values=tensor([0.6006, 0.9692, 0.3277,  ..., 0.8424, 0.3843, 0.6842]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8442, 0.5820, 0.8888,  ..., 0.9824, 0.3648, 0.8783])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 20.77256941795349 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([13062, 27623, 58180,  ..., 66636,  6102, 47055]),
+       values=tensor([0.6006, 0.9692, 0.3277,  ..., 0.8424, 0.3843, 0.6842]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8442, 0.5820, 0.8888,  ..., 0.9824, 0.3648, 0.8783])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 20.77256941795349 seconds
+
+[39.23, 38.93, 39.06, 38.59, 38.47, 39.34, 39.72, 38.43, 38.47, 38.55]
+[63.94]
+23.23910665512085
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 24272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.77256941795349, 'TIME_S_1KI': 0.8558243827436343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1485.908479528427, 'W': 63.94}
+[39.23, 38.93, 39.06, 38.59, 38.47, 39.34, 39.72, 38.43, 38.47, 38.55, 40.18, 38.38, 38.82, 38.77, 39.08, 39.46, 38.9, 38.51, 38.41, 38.33]
+699.4849999999999
+34.97425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 24272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.77256941795349, 'TIME_S_1KI': 0.8558243827436343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1485.908479528427, 'W': 63.94, 'J_1KI': 61.21903755473084, 'W_1KI': 2.6343111404087014, 'W_D': 28.96575, 'J_D': 673.1381535955668, 'W_D_1KI': 1.193381262359921, 'J_D_1KI': 0.049166993340471365}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json
new file mode 100644
index 0000000..39218a0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [3000000, 3000000], "MATRIX_ROWS": 3000000, "MATRIX_SIZE": 9000000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 191.72871232032776, "TIME_S_1KI": 191.72871232032776, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 16829.73738718033, "W": 76.6, "J_1KI": 16829.73738718033, "W_1KI": 76.6, "W_D": 41.25525, "J_D": 9064.164795593619, "W_D_1KI": 41.25525, "J_D_1KI": 41.25525}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output
new file mode 100644
index 0000000..100fb4b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output
@@ -0,0 +1,51 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '3000000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [3000000, 3000000], "MATRIX_ROWS": 3000000, "MATRIX_SIZE": 9000000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 191.72871232032776}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       23,       45,  ..., 89999934,
+                            89999968, 90000000]),
+       col_indices=tensor([ 247582,  664315,  879297,  ..., 2581992, 2759433,
+                           2830895]),
+       values=tensor([2.7185e-04, 1.6444e-01, 5.9424e-01,  ...,
+                      8.6814e-02, 3.3148e-01, 4.3454e-01]),
+       size=(3000000, 3000000), nnz=90000000, layout=torch.sparse_csr)
+tensor([2.7160e-02, 3.7741e-01, 2.6824e-01,  ..., 9.4002e-01, 3.9211e-01,
+        5.3889e-04])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([3000000, 3000000])
+Rows: 3000000
+Size: 9000000000000
+NNZ: 90000000
+Density: 1e-05
+Time: 191.72871232032776 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       23,       45,  ..., 89999934,
+                            89999968, 90000000]),
+       col_indices=tensor([ 247582,  664315,  879297,  ..., 2581992, 2759433,
+                           2830895]),
+       values=tensor([2.7185e-04, 1.6444e-01, 5.9424e-01,  ...,
+                      8.6814e-02, 3.3148e-01, 4.3454e-01]),
+       size=(3000000, 3000000), nnz=90000000, layout=torch.sparse_csr)
+tensor([2.7160e-02, 3.7741e-01, 2.6824e-01,  ..., 9.4002e-01, 3.9211e-01,
+        5.3889e-04])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([3000000, 3000000])
+Rows: 3000000
+Size: 9000000000000
+NNZ: 90000000
+Density: 1e-05
+Time: 191.72871232032776 seconds
+
+[40.3, 39.61, 39.02, 39.13, 38.86, 39.07, 38.9, 39.42, 39.4, 39.23]
+[76.6]
+219.7093653678894
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [3000000, 3000000], 'MATRIX_ROWS': 3000000, 'MATRIX_SIZE': 9000000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 191.72871232032776, 'TIME_S_1KI': 191.72871232032776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 16829.73738718033, 'W': 76.6}
+[40.3, 39.61, 39.02, 39.13, 38.86, 39.07, 38.9, 39.42, 39.4, 39.23, 40.08, 39.53, 39.61, 39.13, 38.92, 38.85, 39.16, 39.29, 39.5, 39.38]
+706.895
+35.34475
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [3000000, 3000000], 'MATRIX_ROWS': 3000000, 'MATRIX_SIZE': 9000000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 191.72871232032776, 'TIME_S_1KI': 191.72871232032776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 16829.73738718033, 'W': 76.6, 'J_1KI': 16829.73738718033, 'W_1KI': 76.6, 'W_D': 41.25525, 'J_D': 9064.164795593619, 'W_D_1KI': 41.25525, 'J_D_1KI': 41.25525}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json
new file mode 100644
index 0000000..4b4dea9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1063, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.793079614639282, "TIME_S_1KI": 19.560752224496035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1982.0967997741698, "W": 77.38, "J_1KI": 1864.625399599407, "W_1KI": 72.793979303857, "W_D": 42.05524999999999, "J_D": 1077.2496308956142, "W_D_1KI": 39.56279397930385, "J_D_1KI": 37.21805642455677}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output
new file mode 100644
index 0000000..b2c76a2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 19.742191314697266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      21,      53,  ..., 8999946,
+                            8999970, 9000000]),
+       col_indices=tensor([  7507,  16267,  30874,  ..., 240828, 243309,
+                           292990]),
+       values=tensor([0.1523, 0.7416, 0.9394,  ..., 0.2210, 0.2823, 0.7452]),
+       size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.6931, 0.4096, 0.6953,  ..., 0.1410, 0.7837, 0.2675])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 9000000
+Density: 0.0001
+Time: 19.742191314697266 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1063', '-ss', '300000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.793079614639282}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      31,      54,  ..., 8999928,
+                            8999960, 9000000]),
+       col_indices=tensor([  1108,   2325,  17171,  ..., 276191, 279985,
+                           288044]),
+       values=tensor([0.7163, 0.0556, 0.2109,  ..., 0.2836, 0.9162, 0.9781]),
+       size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.4023, 0.6004, 0.3990,  ..., 0.2499, 0.0207, 0.8980])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 9000000
+Density: 0.0001
+Time: 20.793079614639282 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      31,      54,  ..., 8999928,
+                            8999960, 9000000]),
+       col_indices=tensor([  1108,   2325,  17171,  ..., 276191, 279985,
+                           288044]),
+       values=tensor([0.7163, 0.0556, 0.2109,  ..., 0.2836, 0.9162, 0.9781]),
+       size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.4023, 0.6004, 0.3990,  ..., 0.2499, 0.0207, 0.8980])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 9000000
+Density: 0.0001
+Time: 20.793079614639282 seconds
+
+[39.41, 38.59, 38.99, 39.22, 38.71, 38.78, 38.62, 38.49, 38.71, 38.61]
+[77.38]
+25.61510467529297
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1063, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.793079614639282, 'TIME_S_1KI': 19.560752224496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.0967997741698, 'W': 77.38}
+[39.41, 38.59, 38.99, 39.22, 38.71, 38.78, 38.62, 38.49, 38.71, 38.61, 40.8, 38.54, 38.59, 45.03, 39.84, 38.78, 38.85, 38.74, 39.11, 38.99]
+706.4950000000001
+35.32475000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1063, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.793079614639282, 'TIME_S_1KI': 19.560752224496035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.0967997741698, 'W': 77.38, 'J_1KI': 1864.625399599407, 'W_1KI': 72.793979303857, 'W_D': 42.05524999999999, 'J_D': 1077.2496308956142, 'W_D_1KI': 39.56279397930385, 'J_D_1KI': 37.21805642455677}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json
new file mode 100644
index 0000000..9826631
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 82.70979857444763, "TIME_S_1KI": 82.70979857444763, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7617.53978612423, "W": 78.7, "J_1KI": 7617.53978612423, "W_1KI": 78.7, "W_D": 43.10925, "J_D": 4172.635667407572, "W_D_1KI": 43.10925, "J_D_1KI": 43.10925}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output
new file mode 100644
index 0000000..1d00751
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 82.70979857444763}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      152,      293,  ..., 44999682,
+                            44999844, 45000000]),
+       col_indices=tensor([  3223,   3275,   5832,  ..., 294123, 295027,
+                           295416]),
+       values=tensor([0.3881, 0.9495, 0.6878,  ..., 0.4195, 0.0754, 0.7743]),
+       size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.1554, 0.9886, 0.7175,  ..., 0.9350, 0.8453, 0.9634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 45000000
+Density: 0.0005
+Time: 82.70979857444763 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      152,      293,  ..., 44999682,
+                            44999844, 45000000]),
+       col_indices=tensor([  3223,   3275,   5832,  ..., 294123, 295027,
+                           295416]),
+       values=tensor([0.3881, 0.9495, 0.6878,  ..., 0.4195, 0.0754, 0.7743]),
+       size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.1554, 0.9886, 0.7175,  ..., 0.9350, 0.8453, 0.9634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 45000000
+Density: 0.0005
+Time: 82.70979857444763 seconds
+
+[40.11, 39.46, 38.73, 39.77, 38.75, 39.19, 44.18, 39.79, 38.69, 39.03]
+[78.7]
+96.79211926460266
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 82.70979857444763, 'TIME_S_1KI': 82.70979857444763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7617.53978612423, 'W': 78.7}
+[40.11, 39.46, 38.73, 39.77, 38.75, 39.19, 44.18, 39.79, 38.69, 39.03, 40.06, 38.93, 39.06, 39.1, 39.39, 38.81, 40.25, 39.15, 38.9, 40.13]
+711.8149999999999
+35.59075
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 82.70979857444763, 'TIME_S_1KI': 82.70979857444763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 7617.53978612423, 'W': 78.7, 'J_1KI': 7617.53978612423, 'W_1KI': 78.7, 'W_D': 43.10925, 'J_D': 4172.635667407572, 'W_D_1KI': 43.10925, 'J_D_1KI': 43.10925}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json
new file mode 100644
index 0000000..b74a472
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.001, "TIME_S": 156.8496162891388, "TIME_S_1KI": 156.8496162891388, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 14006.660120918752, "W": 78.01, "J_1KI": 14006.660120918752, "W_1KI": 78.01, "W_D": 42.747000000000014, "J_D": 7675.204463388207, "W_D_1KI": 42.747000000000014, "J_D_1KI": 42.747000000000014}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output
new file mode 100644
index 0000000..798bebb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.001, "TIME_S": 156.8496162891388}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      283,      569,  ..., 89999415,
+                            89999689, 90000000]),
+       col_indices=tensor([  2602,   2894,   4432,  ..., 298607, 298963,
+                           299275]),
+       values=tensor([0.8641, 0.5339, 0.9185,  ..., 0.5269, 0.1925, 0.1221]),
+       size=(300000, 300000), nnz=90000000, layout=torch.sparse_csr)
+tensor([0.4486, 0.1676, 0.4646,  ..., 0.7992, 0.4354, 0.7205])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 90000000
+Density: 0.001
+Time: 156.8496162891388 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      283,      569,  ..., 89999415,
+                            89999689, 90000000]),
+       col_indices=tensor([  2602,   2894,   4432,  ..., 298607, 298963,
+                           299275]),
+       values=tensor([0.8641, 0.5339, 0.9185,  ..., 0.5269, 0.1925, 0.1221]),
+       size=(300000, 300000), nnz=90000000, layout=torch.sparse_csr)
+tensor([0.4486, 0.1676, 0.4646,  ..., 0.7992, 0.4354, 0.7205])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 90000000
+Density: 0.001
+Time: 156.8496162891388 seconds
+
+[41.21, 38.89, 39.08, 39.22, 38.89, 38.77, 38.88, 39.4, 38.92, 38.72]
+[78.01]
+179.54954648017883
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 156.8496162891388, 'TIME_S_1KI': 156.8496162891388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 14006.660120918752, 'W': 78.01}
+[41.21, 38.89, 39.08, 39.22, 38.89, 38.77, 38.88, 39.4, 38.92, 38.72, 39.91, 38.95, 39.72, 39.54, 39.0, 39.29, 39.46, 38.83, 39.09, 38.82]
+705.2599999999999
+35.26299999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 156.8496162891388, 'TIME_S_1KI': 156.8496162891388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 14006.660120918752, 'W': 78.01, 'J_1KI': 14006.660120918752, 'W_1KI': 78.01, 'W_D': 42.747000000000014, 'J_D': 7675.204463388207, 'W_D_1KI': 42.747000000000014, 'J_D_1KI': 42.747000000000014}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json
new file mode 100644
index 0000000..e1aa60a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 5359, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.92583179473877, "TIME_S_1KI": 3.9048016037952546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1549.9649752044677, "W": 65.36, "J_1KI": 289.2265301743735, "W_1KI": 12.196305280835977, "W_D": 30.458, "J_D": 722.2893698711395, "W_D_1KI": 5.68352304534428, "J_D_1KI": 1.0605566421616497}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output
new file mode 100644
index 0000000..219c53d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.918142080307007}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 899997, 899998,
+                            900000]),
+       col_indices=tensor([ 53227, 167745, 185678,  ...,  77368,  81779,
+                           166650]),
+       values=tensor([0.4014, 0.7044, 0.9681,  ..., 0.8398, 0.4850, 0.3713]),
+       size=(300000, 300000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.1108, 0.9813, 0.9578,  ..., 0.9978, 0.7777, 0.0486])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 900000
+Density: 1e-05
+Time: 3.918142080307007 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5359', '-ss', '300000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.92583179473877}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      5,  ..., 899994, 899995,
+                            900000]),
+       col_indices=tensor([ 27767,  11526,  53261,  ...,  95027, 105010,
+                           203459]),
+       values=tensor([0.0853, 0.1452, 0.4972,  ..., 0.6342, 0.0274, 0.2283]),
+       size=(300000, 300000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.6261, 0.3964, 0.4952,  ..., 0.8021, 0.8822, 0.0136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 900000
+Density: 1e-05
+Time: 20.92583179473877 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      5,  ..., 899994, 899995,
+                            900000]),
+       col_indices=tensor([ 27767,  11526,  53261,  ...,  95027, 105010,
+                           203459]),
+       values=tensor([0.0853, 0.1452, 0.4972,  ..., 0.6342, 0.0274, 0.2283]),
+       size=(300000, 300000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.6261, 0.3964, 0.4952,  ..., 0.8021, 0.8822, 0.0136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 900000
+Density: 1e-05
+Time: 20.92583179473877 seconds
+
+[39.87, 38.56, 38.69, 39.11, 38.77, 38.59, 38.92, 38.89, 38.6, 38.62]
+[65.36]
+23.714274406433105
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 5359, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.92583179473877, 'TIME_S_1KI': 3.9048016037952546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1549.9649752044677, 'W': 65.36}
+[39.87, 38.56, 38.69, 39.11, 38.77, 38.59, 38.92, 38.89, 38.6, 38.62, 39.84, 38.63, 38.77, 38.47, 38.56, 38.46, 38.55, 38.86, 39.14, 38.61]
+698.04
+34.902
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 5359, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.92583179473877, 'TIME_S_1KI': 3.9048016037952546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1549.9649752044677, 'W': 65.36, 'J_1KI': 289.2265301743735, 'W_1KI': 12.196305280835977, 'W_D': 30.458, 'J_D': 722.2893698711395, 'W_D_1KI': 5.68352304534428, 'J_D_1KI': 1.0605566421616497}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json
new file mode 100644
index 0000000..9bbde60
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 55249, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.486661672592163, "TIME_S_1KI": 0.37080601771239596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1479.9529262781143, "W": 64.5, "J_1KI": 26.78696313558823, "W_1KI": 1.167441944650582, "W_D": 29.78750000000001, "J_D": 683.4743843644859, "W_D_1KI": 0.5391500298647941, "J_D_1KI": 0.009758548206570147}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output
new file mode 100644
index 0000000..616b7ef
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3800966739654541}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89996, 89999, 90000]),
+       col_indices=tensor([ 1221, 15892, 17835,  ..., 22172, 27458, 10275]),
+       values=tensor([0.6309, 0.5140, 0.9291,  ..., 0.9679, 0.2956, 0.6723]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.8758, 0.8303, 0.8564,  ..., 0.1623, 0.2512, 0.5347])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 0.3800966739654541 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55249', '-ss', '30000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.486661672592163}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89993, 89996, 90000]),
+       col_indices=tensor([ 3466,  7549,  9181,  ..., 12705, 16674, 29218]),
+       values=tensor([0.0628, 0.6253, 0.0638,  ..., 0.6445, 0.5421, 0.6895]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.0380, 0.3127, 0.8894,  ..., 0.0355, 0.5164, 0.5166])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 20.486661672592163 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 89993, 89996, 90000]),
+       col_indices=tensor([ 3466,  7549,  9181,  ..., 12705, 16674, 29218]),
+       values=tensor([0.0628, 0.6253, 0.0638,  ..., 0.6445, 0.5421, 0.6895]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.0380, 0.3127, 0.8894,  ..., 0.0355, 0.5164, 0.5166])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 20.486661672592163 seconds
+
+[39.55, 38.61, 38.56, 38.2, 38.29, 38.62, 38.69, 39.07, 38.21, 38.58]
+[64.5]
+22.945006608963013
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 55249, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.486661672592163, 'TIME_S_1KI': 0.37080601771239596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.9529262781143, 'W': 64.5}
+[39.55, 38.61, 38.56, 38.2, 38.29, 38.62, 38.69, 39.07, 38.21, 38.58, 39.63, 38.76, 38.33, 38.68, 38.62, 38.18, 38.6, 38.43, 38.25, 38.54]
+694.2499999999999
+34.71249999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 55249, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.486661672592163, 'TIME_S_1KI': 0.37080601771239596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.9529262781143, 'W': 64.5, 'J_1KI': 26.78696313558823, 'W_1KI': 1.167441944650582, 'W_D': 29.78750000000001, 'J_D': 683.4743843644859, 'W_D_1KI': 0.5391500298647941, 'J_D_1KI': 0.009758548206570147}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json
new file mode 100644
index 0000000..80e5199
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 34887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.488590478897095, "TIME_S_1KI": 0.5872843889958177, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1541.8012697553634, "W": 66.46, "J_1KI": 44.194148816331676, "W_1KI": 1.905007595952647, "W_D": 31.609249999999996, "J_D": 733.3009597654938, "W_D_1KI": 0.9060466649468283, "J_D_1KI": 0.025970896464208106}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output
new file mode 100644
index 0000000..ccdbe37
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 0.6019301414489746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     16,     37,  ..., 449975, 449988,
+                            450000]),
+       col_indices=tensor([  264,  1229,  1878,  ..., 24507, 28043, 28225]),
+       values=tensor([0.5098, 0.2540, 0.2183,  ..., 0.6993, 0.8002, 0.9164]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.9266, 0.1174, 0.4919,  ..., 0.2483, 0.0597, 0.7571])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 0.6019301414489746 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '34887', '-ss', '30000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.488590478897095}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     15,     31,  ..., 449970, 449990,
+                            450000]),
+       col_indices=tensor([  170,   825,  5087,  ..., 18453, 22268, 25473]),
+       values=tensor([0.8450, 0.9269, 0.6663,  ..., 0.1685, 0.3198, 0.2341]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.4348, 0.7206, 0.1155,  ..., 0.4036, 0.9348, 0.9025])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 20.488590478897095 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     15,     31,  ..., 449970, 449990,
+                            450000]),
+       col_indices=tensor([  170,   825,  5087,  ..., 18453, 22268, 25473]),
+       values=tensor([0.8450, 0.9269, 0.6663,  ..., 0.1685, 0.3198, 0.2341]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.4348, 0.7206, 0.1155,  ..., 0.4036, 0.9348, 0.9025])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 20.488590478897095 seconds
+
+[39.44, 38.55, 38.49, 38.28, 38.9, 38.59, 38.27, 39.46, 38.91, 38.62]
+[66.46]
+23.198935747146606
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.488590478897095, 'TIME_S_1KI': 0.5872843889958177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1541.8012697553634, 'W': 66.46}
+[39.44, 38.55, 38.49, 38.28, 38.9, 38.59, 38.27, 39.46, 38.91, 38.62, 38.9, 40.17, 38.27, 38.69, 38.64, 38.67, 38.48, 38.59, 38.33, 38.49]
+697.015
+34.85075
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.488590478897095, 'TIME_S_1KI': 0.5872843889958177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1541.8012697553634, 'W': 66.46, 'J_1KI': 44.194148816331676, 'W_1KI': 1.905007595952647, 'W_D': 31.609249999999996, 'J_D': 733.3009597654938, 'W_D_1KI': 0.9060466649468283, 'J_D_1KI': 0.025970896464208106}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json
new file mode 100644
index 0000000..6b2d57a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 20725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.679001569747925, "TIME_S_1KI": 0.9977805341253522, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1558.1207911372185, "W": 66.77, "J_1KI": 75.18073781120475, "W_1KI": 3.2217129071170083, "W_D": 31.95525, "J_D": 745.6962619587779, "W_D_1KI": 1.541869722557298, "J_D_1KI": 0.07439660904980931}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output
new file mode 100644
index 0000000..5dedece
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.0132288932800293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     66,  ..., 899946, 899968,
+                            900000]),
+       col_indices=tensor([ 3782,  4225,  4981,  ..., 28194, 28873, 29915]),
+       values=tensor([0.9172, 0.4074, 0.0680,  ..., 0.0394, 0.1843, 0.7343]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.0798, 0.0011, 0.8799,  ..., 0.7150, 0.2962, 0.9319])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 1.0132288932800293 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20725', '-ss', '30000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.679001569747925}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     26,     62,  ..., 899937, 899971,
+                            900000]),
+       col_indices=tensor([  143,   864,  1272,  ..., 28362, 29224, 29939]),
+       values=tensor([0.4085, 0.1763, 0.0566,  ..., 0.6744, 0.4746, 0.4502]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.4289, 0.5358, 0.7834,  ..., 0.0567, 0.8331, 0.5874])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.679001569747925 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     26,     62,  ..., 899937, 899971,
+                            900000]),
+       col_indices=tensor([  143,   864,  1272,  ..., 28362, 29224, 29939]),
+       values=tensor([0.4085, 0.1763, 0.0566,  ..., 0.6744, 0.4746, 0.4502]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.4289, 0.5358, 0.7834,  ..., 0.0567, 0.8331, 0.5874])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.679001569747925 seconds
+
+[39.66, 38.47, 38.7, 38.29, 38.39, 38.27, 38.73, 40.46, 38.83, 38.77]
+[66.77]
+23.335641622543335
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 20725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.679001569747925, 'TIME_S_1KI': 0.9977805341253522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1558.1207911372185, 'W': 66.77}
+[39.66, 38.47, 38.7, 38.29, 38.39, 38.27, 38.73, 40.46, 38.83, 38.77, 39.05, 38.32, 38.41, 38.47, 38.31, 38.24, 38.86, 38.82, 38.88, 38.21]
+696.295
+34.81475
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 20725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.679001569747925, 'TIME_S_1KI': 0.9977805341253522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1558.1207911372185, 'W': 66.77, 'J_1KI': 75.18073781120475, 'W_1KI': 3.2217129071170083, 'W_D': 31.95525, 'J_D': 745.6962619587779, 'W_D_1KI': 1.541869722557298, 'J_D_1KI': 0.07439660904980931}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json
new file mode 100644
index 0000000..465580a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3915, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.893795013427734, "TIME_S_1KI": 5.336856963838502, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1813.4314341068268, "W": 74.06, "J_1KI": 463.2008771664947, "W_1KI": 18.91698595146871, "W_D": 38.97975, "J_D": 954.4572501164675, "W_D_1KI": 9.956513409961687, "J_D_1KI": 2.543170730513841}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output
new file mode 100644
index 0000000..8f37edd
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 5.36383318901062}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     155,     299,  ..., 4499724,
+                            4499869, 4500000]),
+       col_indices=tensor([  264,   391,   402,  ..., 28982, 29660, 29822]),
+       values=tensor([0.6346, 0.1316, 0.6696,  ..., 0.4424, 0.6280, 0.5777]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.7068, 0.8368, 0.5788,  ..., 0.2748, 0.1756, 0.1861])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 5.36383318901062 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3915', '-ss', '30000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.893795013427734}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     154,     308,  ..., 4499718,
+                            4499857, 4500000]),
+       col_indices=tensor([  610,  1343,  1528,  ..., 29102, 29121, 29420]),
+       values=tensor([0.4309, 0.4085, 0.5621,  ..., 0.6007, 0.1982, 0.6029]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.1648, 0.4135, 0.3012,  ..., 0.9678, 0.7893, 0.8451])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 20.893795013427734 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     154,     308,  ..., 4499718,
+                            4499857, 4500000]),
+       col_indices=tensor([  610,  1343,  1528,  ..., 29102, 29121, 29420]),
+       values=tensor([0.4309, 0.4085, 0.5621,  ..., 0.6007, 0.1982, 0.6029]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.1648, 0.4135, 0.3012,  ..., 0.9678, 0.7893, 0.8451])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 20.893795013427734 seconds
+
+[39.73, 38.52, 38.68, 38.37, 38.44, 38.79, 38.97, 38.82, 38.35, 38.46]
+[74.06]
+24.485976696014404
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3915, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.893795013427734, 'TIME_S_1KI': 5.336856963838502, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1813.4314341068268, 'W': 74.06}
+[39.73, 38.52, 38.68, 38.37, 38.44, 38.79, 38.97, 38.82, 38.35, 38.46, 39.31, 38.45, 39.17, 38.79, 38.86, 38.46, 38.42, 40.19, 42.43, 38.29]
+701.605
+35.08025
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3915, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.893795013427734, 'TIME_S_1KI': 5.336856963838502, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1813.4314341068268, 'W': 74.06, 'J_1KI': 463.2008771664947, 'W_1KI': 18.91698595146871, 'W_D': 38.97975, 'J_D': 954.4572501164675, 'W_D_1KI': 9.956513409961687, 'J_D_1KI': 2.543170730513841}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json
new file mode 100644
index 0000000..a4676a2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1556, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.02338671684265, "TIME_S_1KI": 13.51117398254669, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1995.9884867286682, "W": 77.72, "J_1KI": 1282.768950339761, "W_1KI": 49.948586118251924, "W_D": 42.48775, "J_D": 1091.1613462044, "W_D_1KI": 27.305751928020563, "J_D_1KI": 17.54868375836797}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output
new file mode 100644
index 0000000..d1812b6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 13.49134111404419}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     301,     621,  ..., 8999428,
+                            8999715, 9000000]),
+       col_indices=tensor([  350,   633,   742,  ..., 29783, 29873, 29944]),
+       values=tensor([0.6028, 0.5433, 0.5346,  ..., 0.4728, 0.8732, 0.8469]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.9726, 0.3801, 0.1059,  ..., 0.5337, 0.8863, 0.3497])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 13.49134111404419 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1556', '-ss', '30000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.02338671684265}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     286,     540,  ..., 8999445,
+                            8999721, 9000000]),
+       col_indices=tensor([   47,    65,   169,  ..., 29629, 29825, 29922]),
+       values=tensor([0.0693, 0.5848, 0.3473,  ..., 0.1079, 0.3518, 0.8477]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.4830, 0.8793, 0.7685,  ..., 0.7345, 0.8852, 0.3790])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 21.02338671684265 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     286,     540,  ..., 8999445,
+                            8999721, 9000000]),
+       col_indices=tensor([   47,    65,   169,  ..., 29629, 29825, 29922]),
+       values=tensor([0.0693, 0.5848, 0.3473,  ..., 0.1079, 0.3518, 0.8477]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.4830, 0.8793, 0.7685,  ..., 0.7345, 0.8852, 0.3790])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 21.02338671684265 seconds
+
+[39.69, 38.9, 38.91, 42.35, 40.35, 38.51, 38.87, 38.42, 38.6, 38.45]
+[77.72]
+25.68178701400757
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1556, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.02338671684265, 'TIME_S_1KI': 13.51117398254669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1995.9884867286682, 'W': 77.72}
+[39.69, 38.9, 38.91, 42.35, 40.35, 38.51, 38.87, 38.42, 38.6, 38.45, 39.21, 38.97, 38.49, 40.7, 38.5, 38.41, 38.96, 38.84, 38.98, 38.42]
+704.645
+35.23225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1556, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.02338671684265, 'TIME_S_1KI': 13.51117398254669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1995.9884867286682, 'W': 77.72, 'J_1KI': 1282.768950339761, 'W_1KI': 49.948586118251924, 'W_D': 42.48775, 'J_D': 1091.1613462044, 'W_D_1KI': 27.305751928020563, 'J_D_1KI': 17.54868375836797}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json
new file mode 100644
index 0000000..7f038bb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 67.5497567653656, "TIME_S_1KI": 67.5497567653656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6177.228344964981, "W": 78.3, "J_1KI": 6177.228344964981, "W_1KI": 78.3, "W_D": 42.846000000000004, "J_D": 3380.198284398079, "W_D_1KI": 42.846000000000004, "J_D_1KI": 42.846000000000004}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output
new file mode 100644
index 0000000..509a893
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.05, "TIME_S": 67.5497567653656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1454,     2998,  ..., 44997015,
+                            44998496, 45000000]),
+       col_indices=tensor([   16,    34,    52,  ..., 29923, 29949, 29997]),
+       values=tensor([0.3150, 0.1901, 0.4388,  ..., 0.7749, 0.7841, 0.0957]),
+       size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.5511, 0.2744, 0.0926,  ..., 0.8323, 0.9167, 0.9679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 45000000
+Density: 0.05
+Time: 67.5497567653656 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1454,     2998,  ..., 44997015,
+                            44998496, 45000000]),
+       col_indices=tensor([   16,    34,    52,  ..., 29923, 29949, 29997]),
+       values=tensor([0.3150, 0.1901, 0.4388,  ..., 0.7749, 0.7841, 0.0957]),
+       size=(30000, 30000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.5511, 0.2744, 0.0926,  ..., 0.8323, 0.9167, 0.9679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 45000000
+Density: 0.05
+Time: 67.5497567653656 seconds
+
+[39.5, 38.95, 38.67, 41.05, 38.61, 38.51, 38.69, 44.88, 39.32, 38.97]
+[78.3]
+78.89180517196655
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 67.5497567653656, 'TIME_S_1KI': 67.5497567653656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6177.228344964981, 'W': 78.3}
+[39.5, 38.95, 38.67, 41.05, 38.61, 38.51, 38.69, 44.88, 39.32, 38.97, 39.34, 39.37, 38.66, 38.66, 38.66, 38.61, 39.18, 39.8, 39.03, 39.05]
+709.0799999999999
+35.45399999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 67.5497567653656, 'TIME_S_1KI': 67.5497567653656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6177.228344964981, 'W': 78.3, 'J_1KI': 6177.228344964981, 'W_1KI': 78.3, 'W_D': 42.846000000000004, 'J_D': 3380.198284398079, 'W_D_1KI': 42.846000000000004, 'J_D_1KI': 42.846000000000004}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json
new file mode 100644
index 0000000..9a9fc75
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 133.26440334320068, "TIME_S_1KI": 133.26440334320068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 11974.481567811967, "W": 77.72, "J_1KI": 11974.481567811967, "W_1KI": 77.72, "W_D": 41.982749999999996, "J_D": 6468.369352046549, "W_D_1KI": 41.982749999999996, "J_D_1KI": 41.982749999999996}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output
new file mode 100644
index 0000000..09ee6f8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000000, "MATRIX_DENSITY": 0.1, "TIME_S": 133.26440334320068}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3021,     6099,  ..., 89993867,
+                            89996977, 90000000]),
+       col_indices=tensor([    3,     4,    14,  ..., 29943, 29960, 29964]),
+       values=tensor([0.6910, 0.0984, 0.4875,  ..., 0.7473, 0.5068, 0.5297]),
+       size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr)
+tensor([0.9263, 0.2732, 0.6847,  ..., 0.8727, 0.6781, 0.2367])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000000
+Density: 0.1
+Time: 133.26440334320068 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3021,     6099,  ..., 89993867,
+                            89996977, 90000000]),
+       col_indices=tensor([    3,     4,    14,  ..., 29943, 29960, 29964]),
+       values=tensor([0.6910, 0.0984, 0.4875,  ..., 0.7473, 0.5068, 0.5297]),
+       size=(30000, 30000), nnz=90000000, layout=torch.sparse_csr)
+tensor([0.9263, 0.2732, 0.6847,  ..., 0.8727, 0.6781, 0.2367])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000000
+Density: 0.1
+Time: 133.26440334320068 seconds
+
+[39.6, 39.07, 38.84, 44.59, 38.99, 38.94, 39.08, 38.61, 39.42, 39.07]
+[77.72]
+154.07207369804382
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 133.26440334320068, 'TIME_S_1KI': 133.26440334320068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11974.481567811967, 'W': 77.72}
+[39.6, 39.07, 38.84, 44.59, 38.99, 38.94, 39.08, 38.61, 39.42, 39.07, 40.74, 38.83, 39.58, 40.58, 41.0, 38.76, 38.99, 38.91, 38.77, 44.16]
+714.745
+35.73725
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 133.26440334320068, 'TIME_S_1KI': 133.26440334320068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11974.481567811967, 'W': 77.72, 'J_1KI': 11974.481567811967, 'W_1KI': 77.72, 'W_D': 41.982749999999996, 'J_D': 6468.369352046549, 'W_D_1KI': 41.982749999999996, 'J_D_1KI': 41.982749999999996}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json
new file mode 100644
index 0000000..1c62691
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 175149, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.99797296524048, "TIME_S_1KI": 0.11988634228708402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1522.755680565834, "W": 64.38, "J_1KI": 8.694058661858383, "W_1KI": 0.36757275234229136, "W_D": 29.668999999999997, "J_D": 701.7495850684642, "W_D_1KI": 0.16939291688790686, "J_D_1KI": 0.0009671360777846684}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output
new file mode 100644
index 0000000..3d76ec3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14505624771118164}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 9793, 24410,  9766,  ..., 25093, 22416, 28253]),
+       values=tensor([0.6564, 0.9558, 0.9015,  ..., 0.1425, 0.1152, 0.5551]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.6628, 0.4963, 0.8694,  ..., 0.1058, 0.0731, 0.1152])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 0.14505624771118164 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '144771', '-ss', '30000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 17.35769486427307}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([23214, 17022, 19042,  ..., 25316,  9102,  1076]),
+       values=tensor([0.7231, 0.3079, 0.5530,  ..., 0.2799, 0.6458, 0.4353]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.5188, 0.8305, 0.1941,  ..., 0.8435, 0.1201, 0.3717])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 17.35769486427307 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '175149', '-ss', '30000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.99797296524048}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 8999, 8999, 9000]),
+       col_indices=tensor([ 6289,  7348, 12538,  ...,  3410, 26024,  8619]),
+       values=tensor([0.6330, 0.6673, 0.9719,  ..., 0.9831, 0.5544, 0.1794]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.5417, 0.7572, 0.7994,  ..., 0.0522, 0.2469, 0.9706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 20.99797296524048 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 8999, 8999, 9000]),
+       col_indices=tensor([ 6289,  7348, 12538,  ...,  3410, 26024,  8619]),
+       values=tensor([0.6330, 0.6673, 0.9719,  ..., 0.9831, 0.5544, 0.1794]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.5417, 0.7572, 0.7994,  ..., 0.0522, 0.2469, 0.9706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 20.99797296524048 seconds
+
+[38.91, 38.61, 38.98, 38.21, 38.26, 38.52, 38.56, 39.49, 38.59, 38.29]
+[64.38]
+23.65262007713318
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 175149, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.99797296524048, 'TIME_S_1KI': 0.11988634228708402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1522.755680565834, 'W': 64.38}
+[38.91, 38.61, 38.98, 38.21, 38.26, 38.52, 38.56, 39.49, 38.59, 38.29, 39.46, 38.49, 38.32, 38.33, 38.28, 38.28, 38.38, 38.51, 38.81, 38.54]
+694.22
+34.711
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 175149, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.99797296524048, 'TIME_S_1KI': 0.11988634228708402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1522.755680565834, 'W': 64.38, 'J_1KI': 8.694058661858383, 'W_1KI': 0.36757275234229136, 'W_D': 29.668999999999997, 'J_D': 701.7495850684642, 'W_D_1KI': 0.16939291688790686, 'J_D_1KI': 0.0009671360777846684}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..d3de446
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "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": 53.28069806098938, "TIME_S_1KI": 53.28069806098938, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4799.690837917327, "W": 80.74, "J_1KI": 4799.690837917327, "W_1KI": 80.74, "W_D": 45.05724999999999, "J_D": 2678.484889853238, "W_D_1KI": 45.05724999999999, "J_D_1KI": 45.05724999999999}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..c127bed
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 53.28069806098938}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,      102,  ..., 24999886,
+                            24999937, 25000000]),
+       col_indices=tensor([ 16979,  17933,  30686,  ..., 481834, 490973,
+                           494514]),
+       values=tensor([0.3572, 0.8267, 0.7501,  ..., 0.1157, 0.6125, 0.1407]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1123, 0.0430, 0.5296,  ..., 0.1520, 0.8075, 0.3691])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 53.28069806098938 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,      102,  ..., 24999886,
+                            24999937, 25000000]),
+       col_indices=tensor([ 16979,  17933,  30686,  ..., 481834, 490973,
+                           494514]),
+       values=tensor([0.3572, 0.8267, 0.7501,  ..., 0.1157, 0.6125, 0.1407]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1123, 0.0430, 0.5296,  ..., 0.1520, 0.8075, 0.3691])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 53.28069806098938 seconds
+
+[39.46, 39.23, 39.42, 39.24, 38.85, 39.3, 38.79, 39.04, 38.81, 39.05]
+[80.74]
+59.44625759124756
+{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 53.28069806098938, 'TIME_S_1KI': 53.28069806098938, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4799.690837917327, 'W': 80.74}
+[39.46, 39.23, 39.42, 39.24, 38.85, 39.3, 38.79, 39.04, 38.81, 39.05, 39.8, 38.77, 39.08, 40.74, 39.37, 39.16, 39.3, 47.07, 38.83, 39.0]
+713.6550000000001
+35.682750000000006
+{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 53.28069806098938, 'TIME_S_1KI': 53.28069806098938, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4799.690837917327, 'W': 80.74, 'J_1KI': 4799.690837917327, 'W_1KI': 80.74, 'W_D': 45.05724999999999, 'J_D': 2678.484889853238, 'W_D_1KI': 45.05724999999999, 'J_D_1KI': 45.05724999999999}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..19009e3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 2738, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.017223119735718, "TIME_S_1KI": 7.676122395812899, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1812.0194424438475, "W": 76.16, "J_1KI": 661.804033032815, "W_1KI": 27.81592403214025, "W_D": 40.82625, "J_D": 971.3492484515906, "W_D_1KI": 14.910975164353543, "J_D_1KI": 5.445936875220432}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..b6ef67e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.669674634933472}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      17,  ..., 2499990,
+                            2499993, 2500000]),
+       col_indices=tensor([ 52473,  65771, 123815,  ..., 335848, 435662,
+                           475263]),
+       values=tensor([0.2082, 0.2192, 0.6702,  ..., 0.7546, 0.3364, 0.5504]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6123, 0.6429, 0.4015,  ..., 0.8475, 0.6522, 0.4492])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 7.669674634933472 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2738', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.017223119735718}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([334289, 479579,   4894,  ..., 301830, 313714,
+                           458526]),
+       values=tensor([0.7811, 0.4358, 0.2037,  ..., 0.6487, 0.4394, 0.7955]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3985, 0.6545, 0.8794,  ..., 0.0163, 0.4728, 0.5226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 21.017223119735718 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([334289, 479579,   4894,  ..., 301830, 313714,
+                           458526]),
+       values=tensor([0.7811, 0.4358, 0.2037,  ..., 0.6487, 0.4394, 0.7955]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3985, 0.6545, 0.8794,  ..., 0.0163, 0.4728, 0.5226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 21.017223119735718 seconds
+
+[39.49, 38.83, 40.75, 42.9, 39.3, 39.29, 38.89, 38.88, 38.79, 38.76]
+[76.16]
+23.792272090911865
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.017223119735718, 'TIME_S_1KI': 7.676122395812899, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1812.0194424438475, 'W': 76.16}
+[39.49, 38.83, 40.75, 42.9, 39.3, 39.29, 38.89, 38.88, 38.79, 38.76, 40.02, 38.88, 38.89, 38.66, 38.84, 38.71, 38.92, 38.66, 38.76, 39.18]
+706.675
+35.333749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 2738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.017223119735718, 'TIME_S_1KI': 7.676122395812899, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1812.0194424438475, 'W': 76.16, 'J_1KI': 661.804033032815, 'W_1KI': 27.81592403214025, 'W_D': 40.82625, 'J_D': 971.3492484515906, 'W_D_1KI': 14.910975164353543, 'J_D_1KI': 5.445936875220432}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..b0cdcc0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.60400652885437, "TIME_S_1KI": 0.6715340111092618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1545.1941801166536, "W": 65.18, "J_1KI": 50.36158594995938, "W_1KI": 2.1243725963105407, "W_D": 30.163750000000007, "J_D": 715.0790265494587, "W_D_1KI": 0.9831089889837691, "J_D_1KI": 0.032041880874251}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..114c64b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6844191551208496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249993, 249996,
+                            250000]),
+       col_indices=tensor([ 8529, 23824, 37106,  ..., 11640, 15800, 34725]),
+       values=tensor([0.8073, 0.5844, 0.8147,  ..., 0.3062, 0.9804, 0.2233]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0481, 0.3329, 0.6398,  ..., 0.2932, 0.9523, 0.8115])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.6844191551208496 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '30682', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.60400652885437}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249991, 249996,
+                            250000]),
+       col_indices=tensor([ 1625, 14875, 16966,  ..., 28233, 46165, 49230]),
+       values=tensor([0.7686, 0.4498, 0.3631,  ..., 0.7737, 0.9073, 0.9265]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0406, 0.0267, 0.6076,  ..., 0.5503, 0.5752, 0.3050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 20.60400652885437 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 249991, 249996,
+                            250000]),
+       col_indices=tensor([ 1625, 14875, 16966,  ..., 28233, 46165, 49230]),
+       values=tensor([0.7686, 0.4498, 0.3631,  ..., 0.7737, 0.9073, 0.9265]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0406, 0.0267, 0.6076,  ..., 0.5503, 0.5752, 0.3050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 20.60400652885437 seconds
+
+[43.14, 38.95, 39.67, 38.57, 38.57, 38.52, 38.64, 38.47, 38.93, 38.56]
+[65.18]
+23.7065691947937
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.60400652885437, 'TIME_S_1KI': 0.6715340111092618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.1941801166536, 'W': 65.18}
+[43.14, 38.95, 39.67, 38.57, 38.57, 38.52, 38.64, 38.47, 38.93, 38.56, 40.45, 38.49, 38.69, 38.62, 38.51, 38.64, 38.59, 38.5, 39.32, 39.14]
+700.325
+35.01625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.60400652885437, 'TIME_S_1KI': 0.6715340111092618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.1941801166536, 'W': 65.18, 'J_1KI': 50.36158594995938, 'W_1KI': 2.1243725963105407, 'W_D': 30.163750000000007, 'J_D': 715.0790265494587, 'W_D_1KI': 0.9831089889837691, 'J_D_1KI': 0.032041880874251}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json
new file mode 100644
index 0000000..e7d785e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 14725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.75746774673462, "TIME_S_1KI": 1.4096752289802799, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1579.229201145172, "W": 66.94, "J_1KI": 107.24816306588606, "W_1KI": 4.546010186757215, "W_D": 32.068, "J_D": 756.5390203514098, "W_D_1KI": 2.177792869269949, "J_D_1KI": 0.14789764816773848}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output
new file mode 100644
index 0000000..ac0ade3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 1.4260749816894531}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      22,      46,  ..., 1249956,
+                            1249973, 1250000]),
+       col_indices=tensor([ 3380,  4310,  7517,  ..., 40689, 41242, 47374]),
+       values=tensor([0.8200, 0.1077, 0.6690,  ..., 0.5575, 0.1139, 0.4853]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0332, 0.0813, 0.1673,  ..., 0.7573, 0.1508, 0.1365])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 1.4260749816894531 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14725', '-ss', '50000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.75746774673462}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      52,  ..., 1249951,
+                            1249977, 1250000]),
+       col_indices=tensor([  344,  2189,  2223,  ..., 35575, 37368, 38958]),
+       values=tensor([0.6875, 0.6607, 0.3048,  ..., 0.8392, 0.7333, 0.1352]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9792, 0.5124, 0.4372,  ..., 0.1755, 0.2227, 0.5082])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 20.75746774673462 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      52,  ..., 1249951,
+                            1249977, 1250000]),
+       col_indices=tensor([  344,  2189,  2223,  ..., 35575, 37368, 38958]),
+       values=tensor([0.6875, 0.6607, 0.3048,  ..., 0.8392, 0.7333, 0.1352]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9792, 0.5124, 0.4372,  ..., 0.1755, 0.2227, 0.5082])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 20.75746774673462 seconds
+
+[39.99, 38.86, 39.01, 38.83, 38.77, 38.47, 38.81, 38.72, 39.0, 38.37]
+[66.94]
+23.59171199798584
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 14725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.75746774673462, 'TIME_S_1KI': 1.4096752289802799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1579.229201145172, 'W': 66.94}
+[39.99, 38.86, 39.01, 38.83, 38.77, 38.47, 38.81, 38.72, 39.0, 38.37, 39.4, 38.41, 39.01, 38.37, 38.38, 39.17, 38.41, 38.71, 38.46, 38.34]
+697.4399999999999
+34.872
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 14725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.75746774673462, 'TIME_S_1KI': 1.4096752289802799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1579.229201145172, 'W': 66.94, 'J_1KI': 107.24816306588606, 'W_1KI': 4.546010186757215, 'W_D': 32.068, 'J_D': 756.5390203514098, 'W_D_1KI': 2.177792869269949, 'J_D_1KI': 0.14789764816773848}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..3e8eb72
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6999, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.897379159927368, "TIME_S_1KI": 2.9857664180493457, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1683.2534707903862, "W": 70.47, "J_1KI": 240.49913856127822, "W_1KI": 10.068581225889414, "W_D": 35.496750000000006, "J_D": 847.8789220842721, "W_D_1KI": 5.071688812687528, "J_D_1KI": 0.724630491882773}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..452da36
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0004022121429443}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      47,      90,  ..., 2499903,
+                            2499954, 2500000]),
+       col_indices=tensor([ 2310,  2538,  3521,  ..., 46920, 47069, 48673]),
+       values=tensor([0.5437, 0.3122, 0.8737,  ..., 0.7809, 0.3023, 0.2727]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6467, 0.8674, 0.4268,  ..., 0.4389, 0.3661, 0.7175])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 3.0004022121429443 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6999', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.897379159927368}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,     100,  ..., 2499890,
+                            2499936, 2500000]),
+       col_indices=tensor([ 2219,  5577,  6326,  ..., 48217, 48582, 49573]),
+       values=tensor([0.8831, 0.9308, 0.4380,  ..., 0.8264, 0.2520, 0.0049]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1681, 0.1881, 0.9636,  ..., 0.5462, 0.5477, 0.9460])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 20.897379159927368 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,     100,  ..., 2499890,
+                            2499936, 2500000]),
+       col_indices=tensor([ 2219,  5577,  6326,  ..., 48217, 48582, 49573]),
+       values=tensor([0.8831, 0.9308, 0.4380,  ..., 0.8264, 0.2520, 0.0049]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1681, 0.1881, 0.9636,  ..., 0.5462, 0.5477, 0.9460])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 20.897379159927368 seconds
+
+[39.8, 38.87, 39.22, 38.93, 38.9, 38.99, 38.89, 38.48, 38.43, 39.67]
+[70.47]
+23.88610005378723
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6999, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.897379159927368, 'TIME_S_1KI': 2.9857664180493457, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1683.2534707903862, 'W': 70.47}
+[39.8, 38.87, 39.22, 38.93, 38.9, 38.99, 38.89, 38.48, 38.43, 39.67, 39.45, 38.51, 39.01, 38.47, 38.42, 38.44, 39.43, 38.73, 38.56, 39.45]
+699.4649999999999
+34.97324999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6999, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.897379159927368, 'TIME_S_1KI': 2.9857664180493457, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1683.2534707903862, 'W': 70.47, 'J_1KI': 240.49913856127822, 'W_1KI': 10.068581225889414, 'W_D': 35.496750000000006, 'J_D': 847.8789220842721, 'W_D_1KI': 5.071688812687528, 'J_D_1KI': 0.724630491882773}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json
new file mode 100644
index 0000000..7504f86
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1085, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.97959613800049, "TIME_S_1KI": 19.336033306912892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2081.730987071991, "W": 77.68, "J_1KI": 1918.6460710340932, "W_1KI": 71.59447004608295, "W_D": 42.64725000000001, "J_D": 1142.8952347889544, "W_D_1KI": 39.306221198156685, "J_D_1KI": 36.22693197986791}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output
new file mode 100644
index 0000000..4a5e75c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 19.343926906585693}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      284,      572,  ..., 12499476,
+                            12499735, 12500000]),
+       col_indices=tensor([  166,   205,   430,  ..., 49351, 49645, 49668]),
+       values=tensor([0.0452, 0.2727, 0.3621,  ..., 0.2139, 0.8914, 0.4747]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9835, 0.4490, 0.0492,  ..., 0.9538, 0.9723, 0.7788])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 19.343926906585693 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1085', '-ss', '50000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.97959613800049}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      246,      489,  ..., 12499492,
+                            12499760, 12500000]),
+       col_indices=tensor([   25,   267,   758,  ..., 49421, 49749, 49833]),
+       values=tensor([0.1040, 0.5728, 0.3234,  ..., 0.7341, 0.5414, 0.1257]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8524, 0.8502, 0.2995,  ..., 0.6737, 0.1053, 0.0588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 20.97959613800049 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      246,      489,  ..., 12499492,
+                            12499760, 12500000]),
+       col_indices=tensor([   25,   267,   758,  ..., 49421, 49749, 49833]),
+       values=tensor([0.1040, 0.5728, 0.3234,  ..., 0.7341, 0.5414, 0.1257]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8524, 0.8502, 0.2995,  ..., 0.6737, 0.1053, 0.0588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 20.97959613800049 seconds
+
+[39.14, 39.08, 38.98, 38.87, 38.6, 38.74, 38.49, 39.78, 38.46, 38.7]
+[77.68]
+26.798802614212036
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1085, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.97959613800049, 'TIME_S_1KI': 19.336033306912892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2081.730987071991, 'W': 77.68}
+[39.14, 39.08, 38.98, 38.87, 38.6, 38.74, 38.49, 39.78, 38.46, 38.7, 40.03, 38.43, 38.73, 38.45, 38.99, 38.54, 39.08, 39.02, 40.09, 38.78]
+700.655
+35.03275
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1085, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.97959613800049, 'TIME_S_1KI': 19.336033306912892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2081.730987071991, 'W': 77.68, 'J_1KI': 1918.6460710340932, 'W_1KI': 71.59447004608295, 'W_D': 42.64725000000001, 'J_D': 1142.8952347889544, 'W_D_1KI': 39.306221198156685, 'J_D_1KI': 36.22693197986791}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..446b456
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "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": 37.82172966003418, "TIME_S_1KI": 37.82172966003418, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3731.490132689476, "W": 78.5, "J_1KI": 3731.490132689476, "W_1KI": 78.5, "W_D": 43.14625000000001, "J_D": 2050.9529444274312, "W_D_1KI": 43.14625000000001, "J_D_1KI": 43.14625000000001}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..607e614
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 37.82172966003418}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      967,  ..., 24999023,
+                            24999517, 25000000]),
+       col_indices=tensor([   55,    61,    67,  ..., 49814, 49816, 49912]),
+       values=tensor([0.1124, 0.8573, 0.8758,  ..., 0.9585, 0.5286, 0.9143]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5266, 0.3019, 0.6446,  ..., 0.2615, 0.0113, 0.3544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 37.82172966003418 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      967,  ..., 24999023,
+                            24999517, 25000000]),
+       col_indices=tensor([   55,    61,    67,  ..., 49814, 49816, 49912]),
+       values=tensor([0.1124, 0.8573, 0.8758,  ..., 0.9585, 0.5286, 0.9143]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5266, 0.3019, 0.6446,  ..., 0.2615, 0.0113, 0.3544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 37.82172966003418 seconds
+
+[39.15, 39.92, 38.57, 38.55, 39.15, 44.43, 38.71, 39.58, 38.59, 38.46]
+[78.5]
+47.53490614891052
+{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 37.82172966003418, 'TIME_S_1KI': 37.82172966003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3731.490132689476, 'W': 78.5}
+[39.15, 39.92, 38.57, 38.55, 39.15, 44.43, 38.71, 39.58, 38.59, 38.46, 40.51, 38.75, 38.64, 38.52, 38.8, 39.78, 38.55, 38.77, 39.3, 38.81]
+707.0749999999998
+35.35374999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, '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': 37.82172966003418, 'TIME_S_1KI': 37.82172966003418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3731.490132689476, 'W': 78.5, 'J_1KI': 3731.490132689476, 'W_1KI': 78.5, 'W_D': 43.14625000000001, 'J_D': 2050.9529444274312, 'W_D_1KI': 43.14625000000001, 'J_D_1KI': 43.14625000000001}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..116c34c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 71652, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.077060222625732, "TIME_S_1KI": 0.29415871465731214, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1735.6051402235032, "W": 73.31, "J_1KI": 24.222703347059444, "W_1KI": 1.0231396192709206, "W_D": 28.61775, "J_D": 677.5216751006842, "W_D_1KI": 0.39939917936694025, "J_D_1KI": 0.005574152561923467}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..b01c98d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.31334519386291504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([33825, 38381, 10898,  ..., 16973,  5749, 12690]),
+       values=tensor([0.0927, 0.2822, 0.8971,  ..., 0.4021, 0.1329, 0.8374]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5931, 0.9062, 0.9804,  ..., 0.6131, 0.7776, 0.6003])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.31334519386291504 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67018', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 19.641794443130493}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([16677, 19807, 33770,  ..., 39614,  2095, 28370]),
+       values=tensor([0.9725, 0.0867, 0.0870,  ..., 0.5654, 0.5916, 0.4400]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2944, 0.4597, 0.6320,  ..., 0.6057, 0.1898, 0.1566])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 19.641794443130493 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71652', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.077060222625732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     3,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([32921, 41293, 48516,  ..., 42072,  6133, 17318]),
+       values=tensor([0.8803, 0.8660, 0.8154,  ..., 0.4754, 0.7296, 0.3650]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6139, 0.5193, 0.8626,  ..., 0.9070, 0.3972, 0.6619])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 21.077060222625732 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     3,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([32921, 41293, 48516,  ..., 42072,  6133, 17318]),
+       values=tensor([0.8803, 0.8660, 0.8154,  ..., 0.4754, 0.7296, 0.3650]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6139, 0.5193, 0.8626,  ..., 0.9070, 0.3972, 0.6619])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 21.077060222625732 seconds
+
+[65.71, 63.81, 50.02, 60.96, 68.38, 61.87, 58.96, 64.5, 60.78, 39.15]
+[73.31]
+23.674875736236572
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 71652, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.077060222625732, 'TIME_S_1KI': 0.29415871465731214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1735.6051402235032, 'W': 73.31}
+[65.71, 63.81, 50.02, 60.96, 68.38, 61.87, 58.96, 64.5, 60.78, 39.15, 39.3, 40.05, 38.69, 39.1, 39.07, 38.76, 39.16, 39.36, 38.75, 39.09]
+893.845
+44.69225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 71652, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.077060222625732, 'TIME_S_1KI': 0.29415871465731214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1735.6051402235032, 'W': 73.31, 'J_1KI': 24.222703347059444, 'W_1KI': 1.0231396192709206, 'W_D': 28.61775, 'J_D': 677.5216751006842, 'W_D_1KI': 0.39939917936694025, 'J_D_1KI': 0.005574152561923467}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json
new file mode 100644
index 0000000..72b0c08
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 66.54721975326538, "TIME_S_1KI": 66.54721975326538, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3619.1285343217846, "W": 48.66, "J_1KI": 3619.1285343217846, "W_1KI": 48.66, "W_D": 32.3165, "J_D": 2403.5669395686386, "W_D_1KI": 32.3165, "J_D_1KI": 32.3165}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output
new file mode 100644
index 0000000..88175d1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '1000000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [1000000, 1000000], "MATRIX_ROWS": 1000000, "MATRIX_SIZE": 1000000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 1e-05, "TIME_S": 66.54721975326538}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       21,  ...,  9999976,
+                             9999986, 10000000]),
+       col_indices=tensor([ 54005,  89807, 113734,  ..., 908702, 925766,
+                           933923]),
+       values=tensor([0.9939, 0.7767, 0.0078,  ..., 0.2146, 0.1281, 0.5768]),
+       size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8058, 0.8985, 0.9859,  ..., 0.2784, 0.2654, 0.0031])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([1000000, 1000000])
+Rows: 1000000
+Size: 1000000000000
+NNZ: 10000000
+Density: 1e-05
+Time: 66.54721975326538 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,       21,  ...,  9999976,
+                             9999986, 10000000]),
+       col_indices=tensor([ 54005,  89807, 113734,  ..., 908702, 925766,
+                           933923]),
+       values=tensor([0.9939, 0.7767, 0.0078,  ..., 0.2146, 0.1281, 0.5768]),
+       size=(1000000, 1000000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8058, 0.8985, 0.9859,  ..., 0.2784, 0.2654, 0.0031])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([1000000, 1000000])
+Rows: 1000000
+Size: 1000000000000
+NNZ: 10000000
+Density: 1e-05
+Time: 66.54721975326538 seconds
+
+[18.44, 17.85, 17.91, 17.76, 18.24, 18.72, 18.35, 17.95, 18.1, 18.22]
+[48.66]
+74.37584328651428
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 66.54721975326538, 'TIME_S_1KI': 66.54721975326538, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3619.1285343217846, 'W': 48.66}
+[18.44, 17.85, 17.91, 17.76, 18.24, 18.72, 18.35, 17.95, 18.1, 18.22, 18.26, 17.74, 18.13, 17.82, 17.99, 18.0, 18.47, 17.87, 17.8, 21.42]
+326.87
+16.3435
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [1000000, 1000000], 'MATRIX_ROWS': 1000000, 'MATRIX_SIZE': 1000000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 66.54721975326538, 'TIME_S_1KI': 66.54721975326538, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3619.1285343217846, 'W': 48.66, 'J_1KI': 3619.1285343217846, 'W_1KI': 48.66, 'W_D': 32.3165, 'J_D': 2403.5669395686386, 'W_D_1KI': 32.3165, 'J_D_1KI': 32.3165}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..7aceaa7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 7304, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.99964690208435, "TIME_S_1KI": 2.8750885681933664, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1205.7149387598038, "W": 48.21, "J_1KI": 165.07597737675297, "W_1KI": 6.600492880613363, "W_D": 31.907750000000004, "J_D": 798.001469346881, "W_D_1KI": 4.368530941949617, "J_D_1KI": 0.5981011694892684}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..00d043f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8750712871551514}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      18,  ...,  999987,
+                             999994, 1000000]),
+       col_indices=tensor([   72, 12664, 19832,  ..., 78809, 83339, 93425]),
+       values=tensor([0.5785, 0.6431, 0.7942,  ..., 0.9990, 0.0590, 0.8738]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6025, 0.9571, 0.3756,  ..., 0.4137, 0.6855, 0.5355])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.8750712871551514 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7304', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.99964690208435}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999983,
+                             999991, 1000000]),
+       col_indices=tensor([41096, 50256, 52141,  ..., 67700, 76057, 98450]),
+       values=tensor([0.9809, 0.6280, 0.2788,  ..., 0.6940, 0.2813, 0.2359]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7723, 0.9147, 0.2969,  ..., 0.3023, 0.2205, 0.3351])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 20.99964690208435 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ...,  999983,
+                             999991, 1000000]),
+       col_indices=tensor([41096, 50256, 52141,  ..., 67700, 76057, 98450]),
+       values=tensor([0.9809, 0.6280, 0.2788,  ..., 0.6940, 0.2813, 0.2359]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7723, 0.9147, 0.2969,  ..., 0.3023, 0.2205, 0.3351])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 20.99964690208435 seconds
+
+[19.43, 17.9, 18.08, 18.16, 18.32, 17.98, 18.9, 18.21, 18.25, 17.86]
+[48.21]
+25.009644031524658
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7304, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.99964690208435, 'TIME_S_1KI': 2.8750885681933664, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1205.7149387598038, 'W': 48.21}
+[19.43, 17.9, 18.08, 18.16, 18.32, 17.98, 18.9, 18.21, 18.25, 17.86, 18.22, 17.66, 18.06, 18.08, 17.89, 18.02, 17.74, 17.97, 18.02, 18.1]
+326.04499999999996
+16.302249999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7304, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.99964690208435, 'TIME_S_1KI': 2.8750885681933664, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1205.7149387598038, 'W': 48.21, 'J_1KI': 165.07597737675297, 'W_1KI': 6.600492880613363, 'W_D': 31.907750000000004, 'J_D': 798.001469346881, 'W_D_1KI': 4.368530941949617, 'J_D_1KI': 0.5981011694892684}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json
new file mode 100644
index 0000000..62dfba2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1831, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.986559629440308, "TIME_S_1KI": 11.461802091447463, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.7127650523184, "W": 48.96999999999999, "J_1KI": 710.9299645288469, "W_1KI": 26.74494811578372, "W_D": 32.56024999999999, "J_D": 865.5113959218857, "W_D_1KI": 17.782768978700158, "J_D_1KI": 9.712052964882664}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output
new file mode 100644
index 0000000..1517cae
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 11.463933229446411}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      45,      93,  ..., 4999886,
+                            4999950, 5000000]),
+       col_indices=tensor([  115,  4142,  9033,  ..., 95272, 97957, 99327]),
+       values=tensor([0.3001, 0.5395, 0.4547,  ..., 0.4945, 0.6230, 0.1383]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4447, 0.7495, 0.9824,  ..., 0.5428, 0.2940, 0.4090])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 11.463933229446411 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1831', '-ss', '100000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.986559629440308}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      47,      90,  ..., 4999886,
+                            4999944, 5000000]),
+       col_indices=tensor([ 1153,  2047,  4582,  ..., 97809, 98430, 99156]),
+       values=tensor([0.1070, 0.5860, 0.6550,  ..., 0.5725, 0.6043, 0.9156]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2942, 0.6403, 0.2970,  ..., 0.2270, 0.6172, 0.6041])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 20.986559629440308 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      47,      90,  ..., 4999886,
+                            4999944, 5000000]),
+       col_indices=tensor([ 1153,  2047,  4582,  ..., 97809, 98430, 99156]),
+       values=tensor([0.1070, 0.5860, 0.6550,  ..., 0.5725, 0.6043, 0.9156]),
+       size=(100000, 100000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2942, 0.6403, 0.2970,  ..., 0.2270, 0.6172, 0.6041])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 5000000
+Density: 0.0005
+Time: 20.986559629440308 seconds
+
+[18.11, 17.74, 18.14, 18.38, 18.12, 18.97, 17.92, 18.11, 17.89, 21.28]
+[48.97]
+26.581841230392456
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1831, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.986559629440308, 'TIME_S_1KI': 11.461802091447463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.7127650523184, 'W': 48.96999999999999}
+[18.11, 17.74, 18.14, 18.38, 18.12, 18.97, 17.92, 18.11, 17.89, 21.28, 18.44, 17.94, 18.29, 17.78, 17.91, 18.22, 17.95, 18.14, 18.84, 17.88]
+328.19500000000005
+16.409750000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1831, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.986559629440308, 'TIME_S_1KI': 11.461802091447463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.7127650523184, 'W': 48.96999999999999, 'J_1KI': 710.9299645288469, 'W_1KI': 26.74494811578372, 'W_D': 32.56024999999999, 'J_D': 865.5113959218857, 'W_D_1KI': 17.782768978700158, 'J_D_1KI': 9.712052964882664}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..03d76fb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.55906629562378, "TIME_S_1KI": 27.55906629562378, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1724.2497406768798, "W": 48.51, "J_1KI": 1724.2497406768798, "W_1KI": 48.51, "W_D": 22.645749999999992, "J_D": 804.9253466281888, "W_D_1KI": 22.645749999999992, "J_D_1KI": 22.645749999999992}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..1b57712
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.55906629562378}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      125,      225,  ...,  9999802,
+                             9999889, 10000000]),
+       col_indices=tensor([ 1628,  2146,  2363,  ..., 95541, 97818, 98495]),
+       values=tensor([0.1115, 0.6662, 0.7909,  ..., 0.2161, 0.9828, 0.9922]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3917, 0.2257, 0.3594,  ..., 0.6468, 0.3908, 0.8732])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 27.55906629562378 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      125,      225,  ...,  9999802,
+                             9999889, 10000000]),
+       col_indices=tensor([ 1628,  2146,  2363,  ..., 95541, 97818, 98495]),
+       values=tensor([0.1115, 0.6662, 0.7909,  ..., 0.2161, 0.9828, 0.9922]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3917, 0.2257, 0.3594,  ..., 0.6468, 0.3908, 0.8732])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 27.55906629562378 seconds
+
+[18.54, 17.96, 18.27, 17.87, 17.85, 17.91, 18.31, 18.22, 18.21, 17.84]
+[48.51]
+35.544212341308594
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.55906629562378, 'TIME_S_1KI': 27.55906629562378, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1724.2497406768798, 'W': 48.51}
+[18.54, 17.96, 18.27, 17.87, 17.85, 17.91, 18.31, 18.22, 18.21, 17.84, 39.12, 40.67, 39.37, 39.18, 39.34, 39.09, 38.69, 39.74, 39.23, 39.25]
+517.2850000000001
+25.864250000000006
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.55906629562378, 'TIME_S_1KI': 27.55906629562378, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1724.2497406768798, 'W': 48.51, 'J_1KI': 1724.2497406768798, 'W_1KI': 48.51, 'W_D': 22.645749999999992, 'J_D': 804.9253466281888, 'W_D_1KI': 22.645749999999992, 'J_D_1KI': 22.645749999999992}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json
new file mode 100644
index 0000000..b8a679c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 152.2796802520752, "TIME_S_1KI": 152.2796802520752, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24430.853182520867, "W": 40.51, "J_1KI": 24430.853182520867, "W_1KI": 40.51, "W_D": 24.2635, "J_D": 14632.880923083067, "W_D_1KI": 24.2635, "J_D_1KI": 24.2635}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output
new file mode 100644
index 0000000..3163e2f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.005, "TIME_S": 152.2796802520752}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      501,      992,  ..., 49999019,
+                            49999505, 50000000]),
+       col_indices=tensor([   35,    43,   383,  ..., 99897, 99938, 99967]),
+       values=tensor([0.4513, 0.8581, 0.1042,  ..., 0.5255, 0.8133, 0.9103]),
+       size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.9931, 0.5926, 0.5381,  ..., 0.5378, 0.4212, 0.4881])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 50000000
+Density: 0.005
+Time: 152.2796802520752 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      501,      992,  ..., 49999019,
+                            49999505, 50000000]),
+       col_indices=tensor([   35,    43,   383,  ..., 99897, 99938, 99967]),
+       values=tensor([0.4513, 0.8581, 0.1042,  ..., 0.5255, 0.8133, 0.9103]),
+       size=(100000, 100000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.9931, 0.5926, 0.5381,  ..., 0.5378, 0.4212, 0.4881])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 50000000
+Density: 0.005
+Time: 152.2796802520752 seconds
+
+[18.54, 17.77, 17.99, 18.91, 17.96, 17.85, 17.94, 17.96, 17.92, 17.99]
+[40.51]
+603.0820336341858
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 152.2796802520752, 'TIME_S_1KI': 152.2796802520752, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24430.853182520867, 'W': 40.51}
+[18.54, 17.77, 17.99, 18.91, 17.96, 17.85, 17.94, 17.96, 17.92, 17.99, 18.55, 18.48, 18.08, 18.21, 17.89, 17.61, 18.0, 17.78, 18.03, 18.02]
+324.92999999999995
+16.246499999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 152.2796802520752, 'TIME_S_1KI': 152.2796802520752, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24430.853182520867, 'W': 40.51, 'J_1KI': 24430.853182520867, 'W_1KI': 40.51, 'W_D': 24.2635, 'J_D': 14632.880923083067, 'W_D_1KI': 24.2635, 'J_D_1KI': 24.2635}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..0a18e77
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 15982, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.790293216705322, "TIME_S_1KI": 1.300856789932757, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1154.2612649059295, "W": 46.92999999999999, "J_1KI": 72.22257945851142, "W_1KI": 2.936428482042297, "W_D": 30.483749999999993, "J_D": 749.7594680178164, "W_D_1KI": 1.907380177699912, "J_D_1KI": 0.11934552482166888}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..2e16cf3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3139328956604004}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([39907, 27987, 76798,  ..., 32180, 99907, 17440]),
+       values=tensor([0.1487, 0.6263, 0.1935,  ..., 0.3652, 0.0716, 0.9913]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5233, 0.9086, 0.0476,  ..., 0.5287, 0.8958, 0.9684])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 1.3139328956604004 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15982', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.790293216705322}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 6594, 93201, 43608,  ..., 41278, 68005, 16586]),
+       values=tensor([0.8802, 0.5778, 0.4721,  ..., 0.6728, 0.8802, 0.6767]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6356, 0.8811, 0.4031,  ..., 0.8985, 0.9999, 0.3835])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 20.790293216705322 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 6594, 93201, 43608,  ..., 41278, 68005, 16586]),
+       values=tensor([0.8802, 0.5778, 0.4721,  ..., 0.6728, 0.8802, 0.6767]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6356, 0.8811, 0.4031,  ..., 0.8985, 0.9999, 0.3835])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 20.790293216705322 seconds
+
+[18.44, 18.07, 17.97, 17.94, 21.94, 18.0, 18.46, 18.26, 18.2, 18.27]
+[46.93]
+24.59538173675537
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 15982, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.790293216705322, 'TIME_S_1KI': 1.300856789932757, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1154.2612649059295, 'W': 46.92999999999999}
+[18.44, 18.07, 17.97, 17.94, 21.94, 18.0, 18.46, 18.26, 18.2, 18.27, 18.3, 17.85, 18.0, 18.05, 18.14, 17.86, 17.89, 17.78, 18.15, 17.72]
+328.925
+16.44625
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 15982, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.790293216705322, 'TIME_S_1KI': 1.300856789932757, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1154.2612649059295, 'W': 46.92999999999999, 'J_1KI': 72.22257945851142, 'W_1KI': 2.936428482042297, 'W_D': 30.483749999999993, 'J_D': 749.7594680178164, 'W_D_1KI': 1.907380177699912, 'J_D_1KI': 0.11934552482166888}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output
new file mode 100644
index 0000000..95dcd7c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output
@@ -0,0 +1,10 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '3000000', '-sd', '1e-05', '-c', '1']
+Traceback (most recent call last):
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in <module>
+    program_result = run_program(program(
+                     ^^^^^^^^^^^^^^^^^^^^
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program
+    process.check_returncode()
+  File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode
+    raise CalledProcessError(self.returncode, self.args, self.stdout,
+subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '3000000', '-sd', '1e-05', '-c', '1']' died with <Signals.SIGKILL: 9>.
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json
new file mode 100644
index 0000000..b220cd4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 43.59297776222229, "TIME_S_1KI": 43.59297776222229, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2443.445860421658, "W": 47.95000000000001, "J_1KI": 2443.445860421658, "W_1KI": 47.95000000000001, "W_D": 31.519750000000013, "J_D": 1606.1898364760286, "W_D_1KI": 31.519750000000013, "J_D_1KI": 31.519750000000013}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output
new file mode 100644
index 0000000..48e92e3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 43.59297776222229}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      55,  ..., 8999936,
+                            8999970, 9000000]),
+       col_indices=tensor([  9127,  10614,  42656,  ..., 264952, 278523,
+                           294763]),
+       values=tensor([0.1591, 0.4772, 0.9607,  ..., 0.8861, 0.4140, 0.1211]),
+       size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.3992, 0.8236, 0.1831,  ..., 0.0857, 0.3847, 0.6830])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 9000000
+Density: 0.0001
+Time: 43.59297776222229 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      55,  ..., 8999936,
+                            8999970, 9000000]),
+       col_indices=tensor([  9127,  10614,  42656,  ..., 264952, 278523,
+                           294763]),
+       values=tensor([0.1591, 0.4772, 0.9607,  ..., 0.8861, 0.4140, 0.1211]),
+       size=(300000, 300000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.3992, 0.8236, 0.1831,  ..., 0.0857, 0.3847, 0.6830])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 9000000
+Density: 0.0001
+Time: 43.59297776222229 seconds
+
+[18.34, 18.1, 18.78, 20.56, 17.99, 18.51, 17.93, 18.03, 17.94, 18.04]
+[47.95]
+50.95820355415344
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 43.59297776222229, 'TIME_S_1KI': 43.59297776222229, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2443.445860421658, 'W': 47.95000000000001}
+[18.34, 18.1, 18.78, 20.56, 17.99, 18.51, 17.93, 18.03, 17.94, 18.04, 18.97, 18.18, 18.43, 17.75, 17.93, 17.87, 17.94, 18.08, 17.97, 17.88]
+328.60499999999996
+16.430249999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 43.59297776222229, 'TIME_S_1KI': 43.59297776222229, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2443.445860421658, 'W': 47.95000000000001, 'J_1KI': 2443.445860421658, 'W_1KI': 47.95000000000001, 'W_D': 31.519750000000013, 'J_D': 1606.1898364760286, 'W_D_1KI': 31.519750000000013, 'J_D_1KI': 31.519750000000013}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json
new file mode 100644
index 0000000..efafaea
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 233.5992555618286, "TIME_S_1KI": 233.5992555618286, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24127.435356621743, "W": 44.42, "J_1KI": 24127.435356621743, "W_1KI": 44.42, "W_D": 28.19375, "J_D": 15313.887451277675, "W_D_1KI": 28.19375, "J_D_1KI": 28.19375}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output
new file mode 100644
index 0000000..f20383f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 45000000, "MATRIX_DENSITY": 0.0005, "TIME_S": 233.5992555618286}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      130,      284,  ..., 44999682,
+                            44999844, 45000000]),
+       col_indices=tensor([  1852,   3586,   4765,  ..., 295056, 296384,
+                           297411]),
+       values=tensor([0.2696, 0.5396, 0.2299,  ..., 0.9264, 0.4734, 0.5186]),
+       size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.0972, 0.1995, 0.9087,  ..., 0.4631, 0.8051, 0.0013])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 45000000
+Density: 0.0005
+Time: 233.5992555618286 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      130,      284,  ..., 44999682,
+                            44999844, 45000000]),
+       col_indices=tensor([  1852,   3586,   4765,  ..., 295056, 296384,
+                           297411]),
+       values=tensor([0.2696, 0.5396, 0.2299,  ..., 0.9264, 0.4734, 0.5186]),
+       size=(300000, 300000), nnz=45000000, layout=torch.sparse_csr)
+tensor([0.0972, 0.1995, 0.9087,  ..., 0.4631, 0.8051, 0.0013])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 45000000
+Density: 0.0005
+Time: 233.5992555618286 seconds
+
+[18.63, 18.36, 17.97, 17.94, 18.0, 17.84, 17.92, 17.85, 18.12, 17.92]
+[44.42]
+543.1660368442535
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 233.5992555618286, 'TIME_S_1KI': 233.5992555618286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24127.435356621743, 'W': 44.42}
+[18.63, 18.36, 17.97, 17.94, 18.0, 17.84, 17.92, 17.85, 18.12, 17.92, 18.74, 17.84, 18.23, 17.69, 18.02, 17.95, 18.42, 17.86, 17.99, 17.76]
+324.525
+16.22625
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 45000000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 233.5992555618286, 'TIME_S_1KI': 233.5992555618286, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24127.435356621743, 'W': 44.42, 'J_1KI': 24127.435356621743, 'W_1KI': 44.42, 'W_D': 28.19375, 'J_D': 15313.887451277675, 'W_D_1KI': 28.19375, 'J_D_1KI': 28.19375}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output
new file mode 100644
index 0000000..30ff4c4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output
@@ -0,0 +1,10 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.001', '-c', '1']
+Traceback (most recent call last):
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in <module>
+    program_result = run_program(program(
+                     ^^^^^^^^^^^^^^^^^^^^
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program
+    process.check_returncode()
+  File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode
+    raise CalledProcessError(self.returncode, self.args, self.stdout,
+subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '0.001', '-c', '1']' died with <Signals.SIGKILL: 9>.
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json
new file mode 100644
index 0000000..49d4d09
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3598, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.015570878982544, "TIME_S_1KI": 5.840903523897316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1199.0254331445694, "W": 48.06, "J_1KI": 333.24775796124777, "W_1KI": 13.357420789327406, "W_D": 31.746750000000002, "J_D": 792.0341379459501, "W_D_1KI": 8.823443579766536, "J_D_1KI": 2.4523189493514552}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output
new file mode 100644
index 0000000..46d6c02
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '300000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.835606575012207}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 899996, 899998,
+                            900000]),
+       col_indices=tensor([100602, 129512, 176801,  ...,  48622,  26613,
+                           190176]),
+       values=tensor([0.1487, 0.8854, 0.0841,  ..., 0.8808, 0.2948, 0.6815]),
+       size=(300000, 300000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.5133, 0.8774, 0.0043,  ..., 0.0470, 0.1306, 0.4977])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 900000
+Density: 1e-05
+Time: 5.835606575012207 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3598', '-ss', '300000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [300000, 300000], "MATRIX_ROWS": 300000, "MATRIX_SIZE": 90000000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.015570878982544}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 899988, 899992,
+                            900000]),
+       col_indices=tensor([ 18941,  81855,  33867,  ..., 201457, 255893,
+                           299263]),
+       values=tensor([0.5587, 0.5974, 0.8127,  ..., 0.5995, 0.0776, 0.5594]),
+       size=(300000, 300000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.7686, 0.4534, 0.3324,  ..., 0.2462, 0.7149, 0.9702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 900000
+Density: 1e-05
+Time: 21.015570878982544 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 899988, 899992,
+                            900000]),
+       col_indices=tensor([ 18941,  81855,  33867,  ..., 201457, 255893,
+                           299263]),
+       values=tensor([0.5587, 0.5974, 0.8127,  ..., 0.5995, 0.0776, 0.5594]),
+       size=(300000, 300000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.7686, 0.4534, 0.3324,  ..., 0.2462, 0.7149, 0.9702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([300000, 300000])
+Rows: 300000
+Size: 90000000000
+NNZ: 900000
+Density: 1e-05
+Time: 21.015570878982544 seconds
+
+[18.25, 18.65, 17.9, 17.84, 17.95, 18.05, 18.67, 17.88, 18.3, 18.01]
+[48.06]
+24.948510885238647
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.015570878982544, 'TIME_S_1KI': 5.840903523897316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.0254331445694, 'W': 48.06}
+[18.25, 18.65, 17.9, 17.84, 17.95, 18.05, 18.67, 17.88, 18.3, 18.01, 18.4, 18.0, 17.84, 18.93, 18.16, 17.77, 18.14, 17.83, 18.06, 17.93]
+326.265
+16.31325
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [300000, 300000], 'MATRIX_ROWS': 300000, 'MATRIX_SIZE': 90000000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.015570878982544, 'TIME_S_1KI': 5.840903523897316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.0254331445694, 'W': 48.06, 'J_1KI': 333.24775796124777, 'W_1KI': 13.357420789327406, 'W_D': 31.746750000000002, 'J_D': 792.0341379459501, 'W_D_1KI': 8.823443579766536, 'J_D_1KI': 2.4523189493514552}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json
new file mode 100644
index 0000000..aa132a9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 33277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.4328293800354, "TIME_S_1KI": 0.6140225795605192, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1130.1519185829163, "W": 46.86, "J_1KI": 33.96195325849435, "W_1KI": 1.408179823902395, "W_D": 30.543249999999997, "J_D": 736.6306570050716, "W_D_1KI": 0.9178486642425698, "J_D_1KI": 0.027582073631714693}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.output
new file mode 100644
index 0000000..5969df1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6310491561889648}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     7,  ..., 89994, 89997, 90000]),
+       col_indices=tensor([ 2667,  5647,  6980,  ...,  2168,  3268, 28772]),
+       values=tensor([0.1347, 0.9532, 0.3607,  ..., 0.9962, 0.2520, 0.4682]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.4612, 0.6559, 0.6162,  ..., 0.2529, 0.9562, 0.7602])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 0.6310491561889648 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33277', '-ss', '30000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 90000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.4328293800354}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     6,  ..., 89998, 89999, 90000]),
+       col_indices=tensor([13602, 24899,  9076,  ..., 25653, 15048,  9911]),
+       values=tensor([0.7061, 0.1886, 0.4037,  ..., 0.8692, 0.8414, 0.2535]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.8274, 0.3618, 0.1359,  ..., 0.5733, 0.1118, 0.0977])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 20.4328293800354 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     6,  ..., 89998, 89999, 90000]),
+       col_indices=tensor([13602, 24899,  9076,  ..., 25653, 15048,  9911]),
+       values=tensor([0.7061, 0.1886, 0.4037,  ..., 0.8692, 0.8414, 0.2535]),
+       size=(30000, 30000), nnz=90000, layout=torch.sparse_csr)
+tensor([0.8274, 0.3618, 0.1359,  ..., 0.5733, 0.1118, 0.0977])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 90000
+Density: 0.0001
+Time: 20.4328293800354 seconds
+
+[18.45, 18.04, 18.2, 18.12, 18.16, 18.2, 18.24, 17.92, 17.99, 18.02]
+[46.86]
+24.11762523651123
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.4328293800354, 'TIME_S_1KI': 0.6140225795605192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1130.1519185829163, 'W': 46.86}
+[18.45, 18.04, 18.2, 18.12, 18.16, 18.2, 18.24, 17.92, 17.99, 18.02, 18.46, 18.14, 18.16, 18.26, 17.95, 18.13, 18.04, 18.32, 17.97, 18.06]
+326.33500000000004
+16.316750000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 90000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.4328293800354, 'TIME_S_1KI': 0.6140225795605192, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1130.1519185829163, 'W': 46.86, 'J_1KI': 33.96195325849435, 'W_1KI': 1.408179823902395, 'W_D': 30.543249999999997, 'J_D': 736.6306570050716, 'W_D_1KI': 0.9178486642425698, 'J_D_1KI': 0.027582073631714693}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json
new file mode 100644
index 0000000..e7b3ed3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18733, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.639018058776855, "TIME_S_1KI": 1.1017465466704133, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1175.502190952301, "W": 47.87, "J_1KI": 62.75034382919452, "W_1KI": 2.55538354774996, "W_D": 31.414249999999996, "J_D": 771.4125695033073, "W_D_1KI": 1.6769470987028237, "J_D_1KI": 0.0895183418941346}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output
new file mode 100644
index 0000000..2f47860
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 1.1210110187530518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,     29,  ..., 449963, 449981,
+                            450000]),
+       col_indices=tensor([  792,  5705, 11402,  ..., 28541, 29300, 29723]),
+       values=tensor([0.4108, 0.9785, 0.1600,  ..., 0.6171, 0.0607, 0.2902]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.8871, 0.5462, 0.8388,  ..., 0.2778, 0.5429, 0.3514])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 1.1210110187530518 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '18733', '-ss', '30000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 450000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.639018058776855}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 449977, 449985,
+                            450000]),
+       col_indices=tensor([  220,  1019,  9874,  ..., 23783, 27634, 29111]),
+       values=tensor([0.9174, 0.1323, 0.6653,  ..., 0.3636, 0.2491, 0.8467]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.9099, 0.7629, 0.0246,  ..., 0.7433, 0.9009, 0.8261])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 20.639018058776855 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 449977, 449985,
+                            450000]),
+       col_indices=tensor([  220,  1019,  9874,  ..., 23783, 27634, 29111]),
+       values=tensor([0.9174, 0.1323, 0.6653,  ..., 0.3636, 0.2491, 0.8467]),
+       size=(30000, 30000), nnz=450000, layout=torch.sparse_csr)
+tensor([0.9099, 0.7629, 0.0246,  ..., 0.7433, 0.9009, 0.8261])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 450000
+Density: 0.0005
+Time: 20.639018058776855 seconds
+
+[18.47, 17.86, 18.03, 18.06, 18.18, 18.04, 18.02, 21.6, 18.44, 17.85]
+[47.87]
+24.556135177612305
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.639018058776855, 'TIME_S_1KI': 1.1017465466704133, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.502190952301, 'W': 47.87}
+[18.47, 17.86, 18.03, 18.06, 18.18, 18.04, 18.02, 21.6, 18.44, 17.85, 18.05, 18.82, 17.94, 18.23, 17.84, 17.88, 17.89, 18.05, 18.07, 17.96]
+329.11500000000007
+16.455750000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18733, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 450000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.639018058776855, 'TIME_S_1KI': 1.1017465466704133, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.502190952301, 'W': 47.87, 'J_1KI': 62.75034382919452, 'W_1KI': 2.55538354774996, 'W_D': 31.414249999999996, 'J_D': 771.4125695033073, 'W_D_1KI': 1.6769470987028237, 'J_D_1KI': 0.0895183418941346}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json
new file mode 100644
index 0000000..a79d073
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 11640, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.839291095733643, "TIME_S_1KI": 1.790317104444471, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1197.1045216155053, "W": 48.31, "J_1KI": 102.84403106662417, "W_1KI": 4.150343642611684, "W_D": 31.900750000000002, "J_D": 790.4891754900814, "W_D_1KI": 2.7406142611683855, "J_D_1KI": 0.2354479605814764}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output
new file mode 100644
index 0000000..4ec6b31
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.804121494293213}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     31,     61,  ..., 899944, 899973,
+                            900000]),
+       col_indices=tensor([  214,   468,   621,  ..., 27947, 28785, 29886]),
+       values=tensor([0.5497, 0.2471, 0.3999,  ..., 0.8981, 0.5437, 0.4393]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.3080, 0.4231, 0.6575,  ..., 0.3533, 0.8148, 0.0442])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 1.804121494293213 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '11640', '-ss', '30000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 900000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.839291095733643}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     32,     54,  ..., 899931, 899971,
+                            900000]),
+       col_indices=tensor([ 1264,  2511,  3373,  ..., 24630, 25069, 29984]),
+       values=tensor([0.3611, 0.1242, 0.5465,  ..., 0.3866, 0.2436, 0.7931]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.0647, 0.2891, 0.3015,  ..., 0.6009, 0.0656, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.839291095733643 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     32,     54,  ..., 899931, 899971,
+                            900000]),
+       col_indices=tensor([ 1264,  2511,  3373,  ..., 24630, 25069, 29984]),
+       values=tensor([0.3611, 0.1242, 0.5465,  ..., 0.3866, 0.2436, 0.7931]),
+       size=(30000, 30000), nnz=900000, layout=torch.sparse_csr)
+tensor([0.0647, 0.2891, 0.3015,  ..., 0.6009, 0.0656, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 900000
+Density: 0.001
+Time: 20.839291095733643 seconds
+
+[18.3, 17.96, 21.33, 17.75, 18.29, 18.22, 17.9, 17.9, 18.0, 18.1]
+[48.31]
+24.779642343521118
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.839291095733643, 'TIME_S_1KI': 1.790317104444471, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.1045216155053, 'W': 48.31}
+[18.3, 17.96, 21.33, 17.75, 18.29, 18.22, 17.9, 17.9, 18.0, 18.1, 18.54, 17.81, 18.9, 17.85, 18.01, 17.86, 18.01, 17.93, 18.05, 17.89]
+328.185
+16.40925
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 900000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.839291095733643, 'TIME_S_1KI': 1.790317104444471, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.1045216155053, 'W': 48.31, 'J_1KI': 102.84403106662417, 'W_1KI': 4.150343642611684, 'W_D': 31.900750000000002, 'J_D': 790.4891754900814, 'W_D_1KI': 2.7406142611683855, 'J_D_1KI': 0.2354479605814764}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json
new file mode 100644
index 0000000..4201fbb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2157, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.902870178222656, "TIME_S_1KI": 9.690714037191773, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.0206210327149, "W": 48.72, "J_1KI": 600.3804455413606, "W_1KI": 22.58692628650904, "W_D": 32.4265, "J_D": 861.9250034465789, "W_D_1KI": 15.03314789058878, "J_D_1KI": 6.96947051024051}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output
new file mode 100644
index 0000000..98b9204
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 9.735076665878296}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     157,     297,  ..., 4499702,
+                            4499846, 4500000]),
+       col_indices=tensor([   52,   107,   115,  ..., 29647, 29660, 29851]),
+       values=tensor([0.0696, 0.1442, 0.4515,  ..., 0.9885, 0.1135, 0.9052]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.6712, 0.0504, 0.9004,  ..., 0.5534, 0.8230, 0.9335])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 9.735076665878296 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2157', '-ss', '30000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 4500000, "MATRIX_DENSITY": 0.005, "TIME_S": 20.902870178222656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     143,     285,  ..., 4499715,
+                            4499860, 4500000]),
+       col_indices=tensor([  156,   239,   621,  ..., 29559, 29678, 29713]),
+       values=tensor([0.8567, 0.6051, 0.6450,  ..., 0.7880, 0.1108, 0.6079]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.9332, 0.3072, 0.5823,  ..., 0.4039, 0.3932, 0.8837])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 20.902870178222656 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     143,     285,  ..., 4499715,
+                            4499860, 4500000]),
+       col_indices=tensor([  156,   239,   621,  ..., 29559, 29678, 29713]),
+       values=tensor([0.8567, 0.6051, 0.6450,  ..., 0.7880, 0.1108, 0.6079]),
+       size=(30000, 30000), nnz=4500000, layout=torch.sparse_csr)
+tensor([0.9332, 0.3072, 0.5823,  ..., 0.4039, 0.3932, 0.8837])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 4500000
+Density: 0.005
+Time: 20.902870178222656 seconds
+
+[18.31, 18.87, 18.08, 17.97, 18.03, 17.81, 18.09, 17.94, 17.97, 17.95]
+[48.72]
+26.580883026123047
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2157, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.902870178222656, 'TIME_S_1KI': 9.690714037191773, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.0206210327149, 'W': 48.72}
+[18.31, 18.87, 18.08, 17.97, 18.03, 17.81, 18.09, 17.94, 17.97, 17.95, 18.88, 17.75, 18.5, 17.89, 18.12, 18.19, 18.06, 17.82, 18.14, 18.14]
+325.87
+16.2935
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2157, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 4500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 20.902870178222656, 'TIME_S_1KI': 9.690714037191773, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.0206210327149, 'W': 48.72, 'J_1KI': 600.3804455413606, 'W_1KI': 22.58692628650904, 'W_D': 32.4265, 'J_D': 861.9250034465789, 'W_D_1KI': 15.03314789058878, 'J_D_1KI': 6.96947051024051}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json
new file mode 100644
index 0000000..46b364e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 24.740506410598755, "TIME_S_1KI": 24.740506410598755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1555.6125507831573, "W": 48.26, "J_1KI": 1555.6125507831573, "W_1KI": 48.26, "W_D": 31.86275, "J_D": 1027.063692550063, "W_D_1KI": 31.862749999999995, "J_D_1KI": 31.862749999999995}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output
new file mode 100644
index 0000000..5a27d6d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000000, "MATRIX_DENSITY": 0.01, "TIME_S": 24.740506410598755}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     261,     540,  ..., 8999386,
+                            8999702, 9000000]),
+       col_indices=tensor([   87,    89,   474,  ..., 29936, 29945, 29986]),
+       values=tensor([0.1960, 0.4552, 0.3026,  ..., 0.0541, 0.8647, 0.1885]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.5184, 0.4657, 0.5829,  ..., 0.6124, 0.1016, 0.6788])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 24.740506410598755 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     261,     540,  ..., 8999386,
+                            8999702, 9000000]),
+       col_indices=tensor([   87,    89,   474,  ..., 29936, 29945, 29986]),
+       values=tensor([0.1960, 0.4552, 0.3026,  ..., 0.0541, 0.8647, 0.1885]),
+       size=(30000, 30000), nnz=9000000, layout=torch.sparse_csr)
+tensor([0.5184, 0.4657, 0.5829,  ..., 0.6124, 0.1016, 0.6788])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000000
+Density: 0.01
+Time: 24.740506410598755 seconds
+
+[18.57, 18.03, 17.81, 17.79, 18.07, 17.83, 17.76, 17.79, 22.13, 17.94]
+[48.26]
+32.233994007110596
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 24.740506410598755, 'TIME_S_1KI': 24.740506410598755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1555.6125507831573, 'W': 48.26}
+[18.57, 18.03, 17.81, 17.79, 18.07, 17.83, 17.76, 17.79, 22.13, 17.94, 18.41, 18.28, 18.04, 17.85, 18.14, 17.76, 17.85, 17.98, 18.37, 18.01]
+327.945
+16.39725
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 24.740506410598755, 'TIME_S_1KI': 24.740506410598755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1555.6125507831573, 'W': 48.26, 'J_1KI': 1555.6125507831573, 'W_1KI': 48.26, 'W_D': 31.86275, 'J_D': 1027.063692550063, 'W_D_1KI': 31.862749999999995, 'J_D_1KI': 31.862749999999995}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output
new file mode 100644
index 0000000..1e221b3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output
@@ -0,0 +1,10 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.05', '-c', '1']
+Traceback (most recent call last):
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in <module>
+    program_result = run_program(program(
+                     ^^^^^^^^^^^^^^^^^^^^
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program
+    process.check_returncode()
+  File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode
+    raise CalledProcessError(self.returncode, self.args, self.stdout,
+subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.05', '-c', '1']' died with <Signals.SIGKILL: 9>.
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output
new file mode 100644
index 0000000..3d666e4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output
@@ -0,0 +1,10 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.1', '-c', '1']
+Traceback (most recent call last):
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 129, in <module>
+    program_result = run_program(program(
+                     ^^^^^^^^^^^^^^^^^^^^
+  File "/nfshomes/vut/ampere_research/pytorch/run.py", line 95, in run_program
+    process.check_returncode()
+  File "/usr/lib64/python3.11/subprocess.py", line 502, in check_returncode
+    raise CalledProcessError(self.returncode, self.args, self.stdout,
+subprocess.CalledProcessError: Command '['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '0.1', '-c', '1']' died with <Signals.SIGKILL: 9>.
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json
new file mode 100644
index 0000000..0a79d7f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 107895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.02440118789673, "TIME_S_1KI": 0.1948598284248272, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1153.7907242584229, "W": 46.480000000000004, "J_1KI": 10.69364404521454, "W_1KI": 0.4307891931970898, "W_D": 30.00775, "J_D": 744.893795306921, "W_D_1KI": 0.2781199314148014, "J_D_1KI": 0.002577690638257578}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output
new file mode 100644
index 0000000..b924b07
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '30000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.21645355224609375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 8999, 9000, 9000]),
+       col_indices=tensor([14460, 16831,   822,  ...,  6744,  9809,  7337]),
+       values=tensor([0.8017, 0.3190, 0.3138,  ..., 0.7835, 0.9662, 0.5600]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.9173, 0.3762, 0.0968,  ..., 0.4714, 0.2077, 0.2375])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 0.21645355224609375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '97018', '-ss', '30000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 18.88284707069397}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 9904, 14426, 27453,  ..., 21883, 11984, 20369]),
+       values=tensor([0.0383, 0.4855, 0.7841,  ..., 0.2563, 0.0898, 0.2306]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.3139, 0.0953, 0.3077,  ..., 0.3327, 0.7858, 0.4046])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 18.88284707069397 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '107895', '-ss', '30000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [30000, 30000], "MATRIX_ROWS": 30000, "MATRIX_SIZE": 900000000, "MATRIX_NNZ": 9000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.02440118789673}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 4725,  5479, 22893,  ...,  1358, 17086, 18996]),
+       values=tensor([0.5818, 0.4877, 0.3711,  ..., 0.0217, 0.6305, 0.8996]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.9504, 0.0026, 0.0759,  ..., 0.7751, 0.7432, 0.9903])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.02440118789673 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 9000, 9000, 9000]),
+       col_indices=tensor([ 4725,  5479, 22893,  ...,  1358, 17086, 18996]),
+       values=tensor([0.5818, 0.4877, 0.3711,  ..., 0.0217, 0.6305, 0.8996]),
+       size=(30000, 30000), nnz=9000, layout=torch.sparse_csr)
+tensor([0.9504, 0.0026, 0.0759,  ..., 0.7751, 0.7432, 0.9903])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([30000, 30000])
+Rows: 30000
+Size: 900000000
+NNZ: 9000
+Density: 1e-05
+Time: 21.02440118789673 seconds
+
+[18.53, 17.93, 18.18, 18.25, 18.09, 18.04, 18.17, 18.3, 18.1, 18.0]
+[46.48]
+24.82338047027588
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 107895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.02440118789673, 'TIME_S_1KI': 0.1948598284248272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1153.7907242584229, 'W': 46.480000000000004}
+[18.53, 17.93, 18.18, 18.25, 18.09, 18.04, 18.17, 18.3, 18.1, 18.0, 21.62, 17.95, 18.69, 18.08, 18.06, 18.24, 18.11, 18.56, 18.5, 18.24]
+329.44500000000005
+16.472250000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 107895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [30000, 30000], 'MATRIX_ROWS': 30000, 'MATRIX_SIZE': 900000000, 'MATRIX_NNZ': 9000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.02440118789673, 'TIME_S_1KI': 0.1948598284248272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1153.7907242584229, 'W': 46.480000000000004, 'J_1KI': 10.69364404521454, 'W_1KI': 0.4307891931970898, 'W_D': 30.00775, 'J_D': 744.893795306921, 'W_D_1KI': 0.2781199314148014, 'J_D_1KI': 0.002577690638257578}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..bdc1501
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 145.63775444030762, "TIME_S_1KI": 145.63775444030762, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8970.34398475647, "W": 47.33, "J_1KI": 8970.34398475647, "W_1KI": 47.33, "W_D": 20.887749999999997, "J_D": 3958.806308210372, "W_D_1KI": 20.887749999999997, "J_D_1KI": 20.887749999999997}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..53f20ab
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 145.63775444030762}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      110,  ..., 24999911,
+                            24999950, 25000000]),
+       col_indices=tensor([  8959,  17884,  23107,  ..., 479254, 480973,
+                           488093]),
+       values=tensor([0.9355, 0.2752, 0.4481,  ..., 0.8378, 0.5445, 0.7672]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8528, 0.7383, 0.2866,  ..., 0.1948, 0.0294, 0.9953])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 145.63775444030762 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      110,  ..., 24999911,
+                            24999950, 25000000]),
+       col_indices=tensor([  8959,  17884,  23107,  ..., 479254, 480973,
+                           488093]),
+       values=tensor([0.9355, 0.2752, 0.4481,  ..., 0.8378, 0.5445, 0.7672]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8528, 0.7383, 0.2866,  ..., 0.1948, 0.0294, 0.9953])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 145.63775444030762 seconds
+
+[18.47, 18.19, 18.09, 17.89, 17.96, 17.92, 17.83, 17.75, 17.84, 19.05]
+[47.33]
+189.52765655517578
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 145.63775444030762, 'TIME_S_1KI': 145.63775444030762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8970.34398475647, 'W': 47.33}
+[18.47, 18.19, 18.09, 17.89, 17.96, 17.92, 17.83, 17.75, 17.84, 19.05, 46.69, 47.22, 47.59, 46.72, 46.73, 40.63, 39.32, 36.13, 23.63, 30.6]
+528.845
+26.44225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 145.63775444030762, 'TIME_S_1KI': 145.63775444030762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 8970.34398475647, 'W': 47.33, 'J_1KI': 8970.34398475647, 'W_1KI': 47.33, 'W_D': 20.887749999999997, 'J_D': 3958.806308210372, 'W_D_1KI': 20.887749999999997, 'J_D_1KI': 20.887749999999997}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..4c5f481
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1584, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.39402437210083, "TIME_S_1KI": 13.506328517740423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1269.1197284793855, "W": 49.09, "J_1KI": 801.2119497975918, "W_1KI": 30.99116161616162, "W_D": 32.84400000000001, "J_D": 849.1132279930117, "W_D_1KI": 20.73484848484849, "J_D_1KI": 13.090182124273037}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..fddfb78
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.255852460861206}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499986,
+                            2499994, 2500000]),
+       col_indices=tensor([ 32665, 199892, 257011,  ..., 396065, 419080,
+                           487395]),
+       values=tensor([0.3748, 0.5935, 0.2005,  ..., 0.1065, 0.8464, 0.2707]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0132, 0.8881, 0.5277,  ..., 0.7521, 0.8271, 0.6760])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 13.255852460861206 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1584', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 21.39402437210083}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([ 21234, 111933, 179128,  ..., 123034, 350119,
+                           388488]),
+       values=tensor([0.5221, 0.0977, 0.5310,  ..., 0.7164, 0.7480, 0.4663]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3196, 0.7899, 0.9317,  ..., 0.3730, 0.0273, 0.1855])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 21.39402437210083 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([ 21234, 111933, 179128,  ..., 123034, 350119,
+                           388488]),
+       values=tensor([0.5221, 0.0977, 0.5310,  ..., 0.7164, 0.7480, 0.4663]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3196, 0.7899, 0.9317,  ..., 0.3730, 0.0273, 0.1855])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 21.39402437210083 seconds
+
+[18.22, 18.42, 18.08, 18.01, 17.97, 17.95, 18.16, 18.01, 18.24, 17.86]
+[49.09]
+25.852917671203613
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1584, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.39402437210083, 'TIME_S_1KI': 13.506328517740423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.1197284793855, 'W': 49.09}
+[18.22, 18.42, 18.08, 18.01, 17.97, 17.95, 18.16, 18.01, 18.24, 17.86, 18.17, 18.05, 17.89, 17.96, 18.01, 17.98, 17.96, 17.9, 18.04, 18.33]
+324.91999999999996
+16.246
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1584, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 21.39402437210083, 'TIME_S_1KI': 13.506328517740423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.1197284793855, 'W': 49.09, 'J_1KI': 801.2119497975918, 'W_1KI': 30.99116161616162, 'W_D': 32.84400000000001, 'J_D': 849.1132279930117, 'W_D_1KI': 20.73484848484849, 'J_D_1KI': 13.090182124273037}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..e4cd738
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18145, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.748866319656372, "TIME_S_1KI": 1.1435032416454325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1163.7240363693238, "W": 47.52, "J_1KI": 64.13469475719613, "W_1KI": 2.6189032791402593, "W_D": 31.13875, "J_D": 762.5612760415673, "W_D_1KI": 1.7161063653899147, "J_D_1KI": 0.09457736926921546}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..a874d29
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1573309898376465}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([ 2662,  3637,  9309,  ..., 20434, 25231, 37285]),
+       values=tensor([0.2333, 0.4961, 0.7423,  ..., 0.5095, 0.9257, 0.2343]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2799, 0.1370, 0.8773,  ..., 0.1897, 0.8081, 0.5839])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 1.1573309898376465 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '18145', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 20.748866319656372}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      8,  ..., 249992, 249993,
+                            250000]),
+       col_indices=tensor([ 4160, 33356, 44413,  ..., 34267, 38517, 46233]),
+       values=tensor([0.6958, 0.8946, 0.2330,  ..., 0.2200, 0.1570, 0.6240]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7770, 0.7832, 0.7648,  ..., 0.3539, 0.1104, 0.8005])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 20.748866319656372 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      8,  ..., 249992, 249993,
+                            250000]),
+       col_indices=tensor([ 4160, 33356, 44413,  ..., 34267, 38517, 46233]),
+       values=tensor([0.6958, 0.8946, 0.2330,  ..., 0.2200, 0.1570, 0.6240]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7770, 0.7832, 0.7648,  ..., 0.3539, 0.1104, 0.8005])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 20.748866319656372 seconds
+
+[18.17, 18.03, 18.03, 18.1, 17.95, 18.76, 18.0, 18.04, 18.64, 21.07]
+[47.52]
+24.489142179489136
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18145, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.748866319656372, 'TIME_S_1KI': 1.1435032416454325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1163.7240363693238, 'W': 47.52}
+[18.17, 18.03, 18.03, 18.1, 17.95, 18.76, 18.0, 18.04, 18.64, 21.07, 18.03, 17.85, 18.06, 18.05, 18.34, 17.99, 17.97, 18.26, 17.91, 18.02]
+327.625
+16.38125
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18145, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 20.748866319656372, 'TIME_S_1KI': 1.1435032416454325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1163.7240363693238, 'W': 47.52, 'J_1KI': 64.13469475719613, 'W_1KI': 2.6189032791402593, 'W_D': 31.13875, 'J_D': 762.5612760415673, 'W_D_1KI': 1.7161063653899147, 'J_D_1KI': 0.09457736926921546}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json
new file mode 100644
index 0000000..f35b37b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8093, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.65882921218872, "TIME_S_1KI": 2.5526787609278045, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1204.7929471635819, "W": 48.53, "J_1KI": 148.86852182918346, "W_1KI": 5.996540219943161, "W_D": 32.0905, "J_D": 796.6702672770023, "W_D_1KI": 3.965216854071419, "J_D_1KI": 0.48995636402711223}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output
new file mode 100644
index 0000000..e222138
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 2.594694137573242}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      51,  ..., 1249948,
+                            1249974, 1250000]),
+       col_indices=tensor([ 1900,  3832,  3916,  ..., 43370, 44397, 46024]),
+       values=tensor([0.8523, 0.5318, 0.4293,  ..., 0.0706, 0.5129, 0.4581]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7048, 0.2686, 0.1617,  ..., 0.3130, 0.5850, 0.3952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 2.594694137573242 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8093', '-ss', '50000', '-sd', '0.0005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.0005, "TIME_S": 20.65882921218872}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      31,      51,  ..., 1249949,
+                            1249978, 1250000]),
+       col_indices=tensor([ 2007,  2541,  6490,  ..., 44052, 45524, 48586]),
+       values=tensor([0.7205, 0.3330, 0.8983,  ..., 0.6824, 0.5041, 0.2342]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2974, 0.0968, 0.2938,  ..., 0.7419, 0.3048, 0.3649])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 20.65882921218872 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      31,      51,  ..., 1249949,
+                            1249978, 1250000]),
+       col_indices=tensor([ 2007,  2541,  6490,  ..., 44052, 45524, 48586]),
+       values=tensor([0.7205, 0.3330, 0.8983,  ..., 0.6824, 0.5041, 0.2342]),
+       size=(50000, 50000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2974, 0.0968, 0.2938,  ..., 0.7419, 0.3048, 0.3649])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 1250000
+Density: 0.0005
+Time: 20.65882921218872 seconds
+
+[18.26, 17.93, 19.82, 19.92, 18.14, 18.3, 17.92, 18.17, 18.01, 17.89]
+[48.53]
+24.825735569000244
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8093, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.65882921218872, 'TIME_S_1KI': 2.5526787609278045, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7929471635819, 'W': 48.53}
+[18.26, 17.93, 19.82, 19.92, 18.14, 18.3, 17.92, 18.17, 18.01, 17.89, 18.17, 18.47, 18.36, 17.94, 18.08, 17.71, 18.06, 18.03, 17.85, 17.84]
+328.79
+16.439500000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8093, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.0005, 'TIME_S': 20.65882921218872, 'TIME_S_1KI': 2.5526787609278045, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7929471635819, 'W': 48.53, 'J_1KI': 148.86852182918346, 'W_1KI': 5.996540219943161, 'W_D': 32.0905, 'J_D': 796.6702672770023, 'W_D_1KI': 3.965216854071419, 'J_D_1KI': 0.48995636402711223}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..b01cbb8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.652085304260254, "TIME_S_1KI": 5.276465330674567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1237.7624758052825, "W": 48.73, "J_1KI": 316.2397740943491, "W_1KI": 12.450178845171179, "W_D": 32.29675, "J_D": 820.3510207359792, "W_D_1KI": 8.25159683188554, "J_D_1KI": 2.1082260684429075}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..b99ed97
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.365004062652588}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      43,      96,  ..., 2499913,
+                            2499951, 2500000]),
+       col_indices=tensor([  152,   193,  2640,  ..., 47928, 48233, 48479]),
+       values=tensor([0.0424, 0.9841, 0.8826,  ..., 0.5350, 0.0103, 0.5454]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5585, 0.2658, 0.0418,  ..., 0.1878, 0.1276, 0.9658])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 5.365004062652588 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3914', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 20.652085304260254}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      43,      92,  ..., 2499909,
+                            2499953, 2500000]),
+       col_indices=tensor([  375,   697,   898,  ..., 48167, 48194, 49268]),
+       values=tensor([0.2181, 0.7785, 0.7713,  ..., 0.8896, 0.2915, 0.3579]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4723, 0.0239, 0.8312,  ..., 0.6749, 0.1846, 0.8368])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 20.652085304260254 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      43,      92,  ..., 2499909,
+                            2499953, 2500000]),
+       col_indices=tensor([  375,   697,   898,  ..., 48167, 48194, 49268]),
+       values=tensor([0.2181, 0.7785, 0.7713,  ..., 0.8896, 0.2915, 0.3579]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4723, 0.0239, 0.8312,  ..., 0.6749, 0.1846, 0.8368])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 20.652085304260254 seconds
+
+[18.41, 18.63, 18.36, 18.08, 17.92, 18.1, 17.9, 18.13, 17.86, 17.84]
+[48.73]
+25.40042018890381
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.652085304260254, 'TIME_S_1KI': 5.276465330674567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.7624758052825, 'W': 48.73}
+[18.41, 18.63, 18.36, 18.08, 17.92, 18.1, 17.9, 18.13, 17.86, 17.84, 18.24, 18.29, 17.98, 17.83, 18.07, 17.97, 17.88, 21.28, 17.93, 18.42]
+328.66499999999996
+16.433249999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 20.652085304260254, 'TIME_S_1KI': 5.276465330674567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.7624758052825, 'W': 48.73, 'J_1KI': 316.2397740943491, 'W_1KI': 12.450178845171179, 'W_D': 32.29675, 'J_D': 820.3510207359792, 'W_D_1KI': 8.25159683188554, 'J_D_1KI': 2.1082260684429075}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json
new file mode 100644
index 0000000..b7790eb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 35.204474687576294, "TIME_S_1KI": 35.204474687576294, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2211.0711840987206, "W": 48.25, "J_1KI": 2211.0711840987206, "W_1KI": 48.25, "W_D": 31.797500000000003, "J_D": 1457.1302793031932, "W_D_1KI": 31.797500000000007, "J_D_1KI": 31.797500000000007}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output
new file mode 100644
index 0000000..e6456ba
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.005', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.005, "TIME_S": 35.204474687576294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      483,  ..., 12499493,
+                            12499749, 12500000]),
+       col_indices=tensor([   83,   353,   999,  ..., 49462, 49644, 49677]),
+       values=tensor([0.4021, 0.2117, 0.1170,  ..., 0.4112, 0.6043, 0.8924]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5812, 0.1638, 0.8038,  ..., 0.6848, 0.2982, 0.3371])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 35.204474687576294 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,      483,  ..., 12499493,
+                            12499749, 12500000]),
+       col_indices=tensor([   83,   353,   999,  ..., 49462, 49644, 49677]),
+       values=tensor([0.4021, 0.2117, 0.1170,  ..., 0.4112, 0.6043, 0.8924]),
+       size=(50000, 50000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5812, 0.1638, 0.8038,  ..., 0.6848, 0.2982, 0.3371])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 12500000
+Density: 0.005
+Time: 35.204474687576294 seconds
+
+[18.49, 20.96, 18.34, 17.83, 18.46, 17.92, 17.89, 18.07, 18.14, 17.83]
+[48.25]
+45.82530951499939
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 35.204474687576294, 'TIME_S_1KI': 35.204474687576294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2211.0711840987206, 'W': 48.25}
+[18.49, 20.96, 18.34, 17.83, 18.46, 17.92, 17.89, 18.07, 18.14, 17.83, 18.4, 17.93, 18.23, 18.54, 18.36, 18.08, 18.03, 17.87, 18.07, 17.94]
+329.04999999999995
+16.452499999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.005, 'TIME_S': 35.204474687576294, 'TIME_S_1KI': 35.204474687576294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2211.0711840987206, 'W': 48.25, 'J_1KI': 2211.0711840987206, 'W_1KI': 48.25, 'W_D': 31.797500000000003, 'J_D': 1457.1302793031932, 'W_D_1KI': 31.797500000000007, 'J_D_1KI': 31.797500000000007}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..17bdd0a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "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": 71.75394105911255, "TIME_S_1KI": 71.75394105911255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5446.096093916894, "W": 46.45, "J_1KI": 5446.096093916894, "W_1KI": 46.45, "W_D": 30.238500000000002, "J_D": 3545.3557962520126, "W_D_1KI": 30.238500000000002, "J_D_1KI": 30.238500000000002}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..0c0f915
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 71.75394105911255}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      525,     1034,  ..., 24998963,
+                            24999515, 25000000]),
+       col_indices=tensor([  177,   318,   326,  ..., 49654, 49818, 49958]),
+       values=tensor([0.8680, 0.9679, 0.4484,  ..., 0.6827, 0.9201, 0.6726]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0409, 0.1065, 0.8971,  ..., 0.2398, 0.1614, 0.8383])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 71.75394105911255 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      525,     1034,  ..., 24998963,
+                            24999515, 25000000]),
+       col_indices=tensor([  177,   318,   326,  ..., 49654, 49818, 49958]),
+       values=tensor([0.8680, 0.9679, 0.4484,  ..., 0.6827, 0.9201, 0.6726]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0409, 0.1065, 0.8971,  ..., 0.2398, 0.1614, 0.8383])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 71.75394105911255 seconds
+
+[18.42, 17.83, 17.87, 17.83, 18.36, 17.93, 17.78, 17.97, 18.31, 17.74]
+[46.45]
+117.24641752243042
+{'CPU': 'Xeon 4216', 'CORES': 1, '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': 71.75394105911255, 'TIME_S_1KI': 71.75394105911255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5446.096093916894, 'W': 46.45}
+[18.42, 17.83, 17.87, 17.83, 18.36, 17.93, 17.78, 17.97, 18.31, 17.74, 18.62, 17.9, 18.04, 17.88, 18.17, 17.99, 17.87, 18.14, 17.94, 18.06]
+324.23
+16.2115
+{'CPU': 'Xeon 4216', 'CORES': 1, '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': 71.75394105911255, 'TIME_S_1KI': 71.75394105911255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5446.096093916894, 'W': 46.45, 'J_1KI': 5446.096093916894, 'W_1KI': 46.45, 'W_D': 30.238500000000002, 'J_D': 3545.3557962520126, 'W_D_1KI': 30.238500000000002, 'J_D_1KI': 30.238500000000002}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..cd77447
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 42431, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.11350655555725, "TIME_S_1KI": 0.47402857711478047, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1109.2391641235351, "W": 46.64, "J_1KI": 26.142187648736424, "W_1KI": 1.0991963422969055, "W_D": 30.3375, "J_D": 721.5167912006378, "W_D_1KI": 0.7149843274964058, "J_D_1KI": 0.01685051795848332}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..0874c10
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.49491095542907715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (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([48293, 21867, 31172,  ...,  8085, 31082, 49903]),
+       values=tensor([0.7536, 0.0496, 0.9146,  ..., 0.3335, 0.2529, 0.5168]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5953, 0.1156, 0.8276,  ..., 0.3405, 0.8051, 0.4714])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.49491095542907715 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '42431', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 20.11350655555725}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 5529,  8530, 10143,  ..., 49628,  3004, 27732]),
+       values=tensor([0.0410, 0.4304, 0.2964,  ..., 0.9705, 0.5689, 0.1235]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9110, 0.1233, 0.5259,  ..., 0.7671, 0.0751, 0.3217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 20.11350655555725 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 5529,  8530, 10143,  ..., 49628,  3004, 27732]),
+       values=tensor([0.0410, 0.4304, 0.2964,  ..., 0.9705, 0.5689, 0.1235]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9110, 0.1233, 0.5259,  ..., 0.7671, 0.0751, 0.3217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 20.11350655555725 seconds
+
+[18.38, 18.06, 17.97, 17.82, 18.09, 18.18, 18.18, 18.07, 18.17, 18.01]
+[46.64]
+23.783000946044922
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 42431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.11350655555725, 'TIME_S_1KI': 0.47402857711478047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1109.2391641235351, 'W': 46.64}
+[18.38, 18.06, 17.97, 17.82, 18.09, 18.18, 18.18, 18.07, 18.17, 18.01, 18.57, 18.17, 17.88, 17.88, 17.9, 17.94, 18.68, 18.4, 18.2, 17.96]
+326.05
+16.302500000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 42431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 20.11350655555725, 'TIME_S_1KI': 0.47402857711478047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1109.2391641235351, 'W': 46.64, 'J_1KI': 26.142187648736424, 'W_1KI': 1.0991963422969055, 'W_D': 30.3375, 'J_D': 721.5167912006378, 'W_D_1KI': 0.7149843274964058, 'J_D_1KI': 0.01685051795848332}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..5cee3aa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.577640295028687, "TIME_S_1KI": 22.577640295028687, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.5292521381377, "W": 64.81606725703797, "J_1KI": 1196.5292521381377, "W_1KI": 64.81606725703797, "W_D": 45.67406725703797, "J_D": 843.1606521334647, "W_D_1KI": 45.67406725703797, "J_D_1KI": 45.67406725703797}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..047a391
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.577640295028687}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      13,      26,  ...,  999978,
+                             999989, 1000000]),
+       col_indices=tensor([16134, 16354, 24327,  ..., 64689, 79970, 99510]),
+       values=tensor([0.0032, 0.4253, 0.4412,  ..., 0.5357, 0.1333, 0.2349]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8413, 0.1731, 0.9001,  ..., 0.4021, 0.4850, 0.1983])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 22.577640295028687 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      13,      26,  ...,  999978,
+                             999989, 1000000]),
+       col_indices=tensor([16134, 16354, 24327,  ..., 64689, 79970, 99510]),
+       values=tensor([0.0032, 0.4253, 0.4412,  ..., 0.5357, 0.1333, 0.2349]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8413, 0.1731, 0.9001,  ..., 0.4021, 0.4850, 0.1983])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 22.577640295028687 seconds
+
+[21.64, 21.64, 21.48, 21.36, 21.64, 21.36, 21.36, 21.28, 21.44, 21.36]
+[21.12, 21.24, 21.32, 22.2, 24.84, 39.96, 57.72, 73.16, 89.8, 95.92, 95.4, 95.4, 93.32, 91.16, 90.92, 93.4, 92.2, 91.64]
+18.46038031578064
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.577640295028687, 'TIME_S_1KI': 22.577640295028687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.5292521381377, 'W': 64.81606725703797}
+[21.64, 21.64, 21.48, 21.36, 21.64, 21.36, 21.36, 21.28, 21.44, 21.36, 21.08, 20.96, 20.96, 21.0, 21.28, 21.08, 21.28, 21.12, 21.12, 20.88]
+382.84000000000003
+19.142000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.577640295028687, 'TIME_S_1KI': 22.577640295028687, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.5292521381377, 'W': 64.81606725703797, 'J_1KI': 1196.5292521381377, 'W_1KI': 64.81606725703797, 'W_D': 45.67406725703797, 'J_D': 843.1606521334647, 'W_D_1KI': 45.67406725703797, 'J_D_1KI': 45.67406725703797}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..15d76ee
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5444, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 15.66837453842163, "TIME_S_1KI": 2.878099658049528, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.8989781379698, "W": 61.815457006610345, "J_1KI": 222.24448533026631, "W_1KI": 11.354786371530189, "W_D": 42.416457006610344, "J_D": 830.2070464842318, "W_D_1KI": 7.791413851324457, "J_D_1KI": 1.4311928455776004}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..529e04c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.735213041305542}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998, 100000,
+                            100000]),
+       col_indices=tensor([47108, 85356, 39968,  ..., 81528, 26483, 51109]),
+       values=tensor([0.3148, 0.6992, 0.6314,  ..., 0.5894, 0.0851, 0.0670]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4890, 0.3896, 0.3852,  ..., 0.6786, 0.1828, 0.3984])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 2.735213041305542 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3838 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.402097463607788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 1694, 16648, 92396,  ..., 98787, 30932, 62089]),
+       values=tensor([0.4689, 0.5529, 0.8985,  ..., 0.1212, 0.7499, 0.9985]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8040, 0.7540, 0.7072,  ..., 0.4394, 0.3265, 0.7941])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 7.402097463607788 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5444 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 15.66837453842163}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997, 100000,
+                            100000]),
+       col_indices=tensor([ 8956,  7966, 63353,  ..., 28673, 30724, 93829]),
+       values=tensor([0.9652, 0.8395, 0.8363,  ..., 0.6704, 0.2134, 0.9962]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1215, 0.6198, 0.6986,  ..., 0.9502, 0.5989, 0.9473])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 15.66837453842163 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997, 100000,
+                            100000]),
+       col_indices=tensor([ 8956,  7966, 63353,  ..., 28673, 30724, 93829]),
+       values=tensor([0.9652, 0.8395, 0.8363,  ..., 0.6704, 0.2134, 0.9962]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1215, 0.6198, 0.6986,  ..., 0.9502, 0.5989, 0.9473])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 15.66837453842163 seconds
+
+[21.92, 21.92, 22.08, 21.96, 21.68, 21.6, 21.44, 21.8, 21.6, 21.76]
+[21.64, 21.56, 21.96, 22.76, 26.44, 43.68, 43.68, 58.28, 74.48, 87.48, 92.36, 91.24, 90.2, 88.56, 87.2, 86.36, 85.72, 85.72, 85.2]
+19.572757959365845
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5444, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 15.66837453842163, 'TIME_S_1KI': 2.878099658049528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.8989781379698, 'W': 61.815457006610345}
+[21.92, 21.92, 22.08, 21.96, 21.68, 21.6, 21.44, 21.8, 21.6, 21.76, 21.4, 21.32, 21.48, 21.48, 21.32, 21.52, 21.24, 21.16, 21.2, 21.28]
+387.98
+19.399
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5444, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 15.66837453842163, 'TIME_S_1KI': 2.878099658049528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.8989781379698, 'W': 61.815457006610345, 'J_1KI': 222.24448533026631, 'W_1KI': 11.354786371530189, 'W_D': 42.416457006610344, 'J_D': 830.2070464842318, 'W_D_1KI': 7.791413851324457, 'J_D_1KI': 1.4311928455776004}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..b31b4ad
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 31990, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.162655591964722, "TIME_S_1KI": 0.3176822629560713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 281.0764434432984, "W": 21.267196136821532, "J_1KI": 8.786384602791447, "W_1KI": 0.6648076316605669, "W_D": 2.733196136821533, "J_D": 36.12309984016423, "W_D_1KI": 0.08543907898785662, "J_D_1KI": 0.0026708058451971432}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..3e5c97c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.32822322845458984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([2721, 4826, 6729,  ..., 6567,  802, 8084]),
+       values=tensor([0.9788, 0.8960, 0.9515,  ..., 0.3823, 0.9672, 0.4403]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1442, 0.5021, 0.5745,  ..., 0.9716, 0.6255, 0.3521])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.32822322845458984 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 31990 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.162655591964722}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997, 10000, 10000]),
+       col_indices=tensor([1219, 6055, 1582,  ..., 3506, 4664, 5684]),
+       values=tensor([0.3475, 0.3226, 0.1217,  ..., 0.8742, 0.3097, 0.9052]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3839, 0.3550, 0.5972,  ..., 0.2550, 0.5835, 0.6125])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.162655591964722 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997, 10000, 10000]),
+       col_indices=tensor([1219, 6055, 1582,  ..., 3506, 4664, 5684]),
+       values=tensor([0.3475, 0.3226, 0.1217,  ..., 0.8742, 0.3097, 0.9052]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3839, 0.3550, 0.5972,  ..., 0.2550, 0.5835, 0.6125])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.162655591964722 seconds
+
+[20.68, 20.44, 20.44, 20.76, 20.52, 20.44, 20.48, 20.32, 20.48, 20.48]
+[20.68, 20.88, 21.64, 22.32, 23.4, 23.4, 23.8, 24.24, 23.6, 23.6, 23.44, 23.48, 23.64]
+13.216431617736816
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 31990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.162655591964722, 'TIME_S_1KI': 0.3176822629560713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 281.0764434432984, 'W': 21.267196136821532}
+[20.68, 20.44, 20.44, 20.76, 20.52, 20.44, 20.48, 20.32, 20.48, 20.48, 20.56, 20.64, 20.48, 20.52, 20.48, 20.72, 20.72, 20.8, 20.92, 21.32]
+370.67999999999995
+18.534
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 31990, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.162655591964722, 'TIME_S_1KI': 0.3176822629560713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 281.0764434432984, 'W': 21.267196136821532, 'J_1KI': 8.786384602791447, 'W_1KI': 0.6648076316605669, 'W_D': 2.733196136821533, 'J_D': 36.12309984016423, 'W_D_1KI': 0.08543907898785662, 'J_D_1KI': 0.0026708058451971432}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..44e0916
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4642, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.39481520652771, "TIME_S_1KI": 2.2392966838706827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.1584589004516, "W": 23.611594488388015, "J_1KI": 72.20130523490987, "W_1KI": 5.086513246098236, "W_D": 5.040594488388017, "J_D": 71.54950427007671, "W_D_1KI": 1.0858669729401156, "J_D_1KI": 0.23392222596728038}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..12aea87
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2614803314208984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     19,  ...,  99975,  99988,
+                            100000]),
+       col_indices=tensor([ 662,  710, 3445,  ..., 9576, 9602, 9965]),
+       values=tensor([0.0517, 0.2381, 0.9401,  ..., 0.3987, 0.7682, 0.4070]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1766, 0.1636, 0.7477,  ..., 0.1192, 0.5625, 0.2605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.2614803314208984 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4642 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.39481520652771}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     19,  ...,  99983,  99997,
+                            100000]),
+       col_indices=tensor([  82, 3146, 3840,  ..., 8041, 8695, 8893]),
+       values=tensor([0.8450, 0.6541, 0.7727,  ..., 0.8034, 0.8111, 0.1952]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0486, 0.3621, 0.6684,  ..., 0.7127, 0.4964, 0.1751])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.39481520652771 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     19,  ...,  99983,  99997,
+                            100000]),
+       col_indices=tensor([  82, 3146, 3840,  ..., 8041, 8695, 8893]),
+       values=tensor([0.8450, 0.6541, 0.7727,  ..., 0.8034, 0.8111, 0.1952]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0486, 0.3621, 0.6684,  ..., 0.7127, 0.4964, 0.1751])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.39481520652771 seconds
+
+[20.6, 20.72, 20.92, 21.0, 20.88, 20.88, 20.64, 20.36, 20.08, 19.92]
+[20.16, 20.2, 20.48, 21.88, 25.12, 28.64, 29.48, 29.64, 29.64, 29.56, 24.6, 24.56, 24.44, 24.24]
+14.194655895233154
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.39481520652771, 'TIME_S_1KI': 2.2392966838706827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.1584589004516, 'W': 23.611594488388015}
+[20.6, 20.72, 20.92, 21.0, 20.88, 20.88, 20.64, 20.36, 20.08, 19.92, 20.36, 20.52, 20.6, 20.84, 20.8, 20.56, 20.64, 20.64, 20.64, 20.52]
+371.41999999999996
+18.570999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.39481520652771, 'TIME_S_1KI': 2.2392966838706827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.1584589004516, 'W': 23.611594488388015, 'J_1KI': 72.20130523490987, 'W_1KI': 5.086513246098236, 'W_D': 5.040594488388017, 'J_D': 71.54950427007671, 'W_D_1KI': 1.0858669729401156, 'J_D_1KI': 0.23392222596728038}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..6da6654
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.402220964431763, "TIME_S_1KI": 21.402220964431763, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 598.721107492447, "W": 23.65441632405048, "J_1KI": 598.721107492447, "W_1KI": 23.65441632405048, "W_D": 5.107416324050483, "J_D": 129.27471623349203, "W_D_1KI": 5.107416324050483, "J_D_1KI": 5.107416324050483}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..5b10379
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.402220964431763}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     232,  ...,  999786,
+                             999885, 1000000]),
+       col_indices=tensor([  48,  108,  238,  ..., 9836, 9911, 9942]),
+       values=tensor([0.7065, 0.8335, 0.4165,  ..., 0.0617, 0.0653, 0.1993]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6170, 0.2022, 0.1812,  ..., 0.2173, 0.9754, 0.3705])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.402220964431763 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     232,  ...,  999786,
+                             999885, 1000000]),
+       col_indices=tensor([  48,  108,  238,  ..., 9836, 9911, 9942]),
+       values=tensor([0.7065, 0.8335, 0.4165,  ..., 0.0617, 0.0653, 0.1993]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6170, 0.2022, 0.1812,  ..., 0.2173, 0.9754, 0.3705])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.402220964431763 seconds
+
+[20.56, 20.56, 20.76, 20.64, 20.84, 20.76, 21.0, 20.84, 20.84, 20.8]
+[20.64, 20.52, 20.44, 21.52, 22.84, 29.04, 29.96, 30.44, 29.96, 27.0, 24.4, 24.4, 24.24, 24.08, 24.0, 24.16, 24.04, 24.2, 24.28, 24.16, 24.12, 23.92, 23.72, 23.72, 23.72]
+25.311176538467407
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.402220964431763, 'TIME_S_1KI': 21.402220964431763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.721107492447, 'W': 23.65441632405048}
+[20.56, 20.56, 20.76, 20.64, 20.84, 20.76, 21.0, 20.84, 20.84, 20.8, 20.76, 20.56, 20.4, 20.28, 20.24, 20.28, 20.36, 20.6, 20.6, 20.64]
+370.93999999999994
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+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.402220964431763, 'TIME_S_1KI': 21.402220964431763, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.721107492447, 'W': 23.65441632405048, 'J_1KI': 598.721107492447, 'W_1KI': 23.65441632405048, 'W_D': 5.107416324050483, 'J_D': 129.27471623349203, 'W_D_1KI': 5.107416324050483, 'J_D_1KI': 5.107416324050483}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..23a9329
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 108.30107378959656, "TIME_S_1KI": 108.30107378959656, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2693.844181451797, "W": 24.185192869557547, "J_1KI": 2693.844181451797, "W_1KI": 24.185192869557547, "W_D": 5.823192869557545, "J_D": 648.6106732220641, "W_D_1KI": 5.823192869557545, "J_D_1KI": 5.823192869557545}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..f20f6e0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 108.30107378959656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,    1003,  ..., 4999033,
+                            4999518, 5000000]),
+       col_indices=tensor([  10,   43,   51,  ..., 9955, 9982, 9992]),
+       values=tensor([0.0167, 0.2062, 0.3972,  ..., 0.2194, 0.0680, 0.6916]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1565, 0.8667, 0.6742,  ..., 0.1248, 0.3395, 0.1639])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 108.30107378959656 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,    1003,  ..., 4999033,
+                            4999518, 5000000]),
+       col_indices=tensor([  10,   43,   51,  ..., 9955, 9982, 9992]),
+       values=tensor([0.0167, 0.2062, 0.3972,  ..., 0.2194, 0.0680, 0.6916]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1565, 0.8667, 0.6742,  ..., 0.1248, 0.3395, 0.1639])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 108.30107378959656 seconds
+
+[20.52, 20.36, 20.4, 20.4, 20.48, 20.48, 20.48, 20.12, 20.12, 20.2]
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+111.38402724266052
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 108.30107378959656, 'TIME_S_1KI': 108.30107378959656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2693.844181451797, 'W': 24.185192869557547}
+[20.52, 20.36, 20.4, 20.4, 20.48, 20.48, 20.48, 20.12, 20.12, 20.2, 20.64, 20.72, 20.68, 20.72, 20.36, 20.32, 20.24, 20.16, 20.24, 20.56]
+367.24
+18.362000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 108.30107378959656, 'TIME_S_1KI': 108.30107378959656, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2693.844181451797, 'W': 24.185192869557547, 'J_1KI': 2693.844181451797, 'W_1KI': 24.185192869557547, 'W_D': 5.823192869557545, 'J_D': 648.6106732220641, 'W_D_1KI': 5.823192869557545, 'J_D_1KI': 5.823192869557545}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..c8342c5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 141816, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.164389848709106, "TIME_S_1KI": 0.07167308236524163, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 285.17019953727726, "W": 21.625492932171987, "J_1KI": 2.010846445656888, "W_1KI": 0.1524897961596152, "W_D": 3.365492932171989, "J_D": 44.37994981288918, "W_D_1KI": 0.023731405004879483, "J_D_1KI": 0.00016733940461499043}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..c5a4de0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07915711402893066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([8887, 6657, 7565, 3220, 7011,  960, 7687, 1638, 5195,
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+                      1.3044e-01, 1.4941e-01, 1.8696e-02, 2.4935e-02]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.9198, 0.2486, 0.6139,  ..., 0.7346, 0.8053, 0.7353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.07915711402893066 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 132647 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.821086883544922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      9.6104e-01, 6.8823e-01, 9.8204e-01, 8.2582e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4983, 0.3444, 0.7600,  ..., 0.8482, 0.3104, 0.4836])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.821086883544922 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141816 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.164389848709106}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7176, 0.7209, 0.4421,  ..., 0.7861, 0.0630, 0.0040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.164389848709106 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
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+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.164389848709106 seconds
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..cb5e414
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 39.37885141372681, "TIME_S_1KI": 39.37885141372681, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3668.398367080688, "W": 77.07751960335096, "J_1KI": 3668.398367080688, "W_1KI": 77.07751960335096, "W_D": 57.18851960335097, "J_D": 2721.8088102509973, "W_D_1KI": 57.18851960335097, "J_D_1KI": 57.18851960335097}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..56c2844
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 39.37885141372681}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      10,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([  4222, 120413, 177881,  ..., 234997, 318812,
+                           370543]),
+       values=tensor([0.6429, 0.8175, 0.9231,  ..., 0.8720, 0.9829, 0.6195]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7836, 0.9661, 0.9943,  ..., 0.1995, 0.6325, 0.8613])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 39.37885141372681 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      10,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([  4222, 120413, 177881,  ..., 234997, 318812,
+                           370543]),
+       values=tensor([0.6429, 0.8175, 0.9231,  ..., 0.8720, 0.9829, 0.6195]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7836, 0.9661, 0.9943,  ..., 0.1995, 0.6325, 0.8613])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 39.37885141372681 seconds
+
+[21.56, 21.8, 21.92, 21.8, 21.8, 21.72, 21.76, 21.96, 22.0, 22.0]
+[22.12, 21.92, 21.96, 23.24, 24.64, 30.88, 42.28, 55.2, 68.48, 80.52, 87.0, 88.04, 90.88, 91.16, 92.4, 93.96, 93.96, 94.68, 93.52, 94.04, 94.08, 94.16, 93.04, 90.92, 89.84, 90.08, 89.76, 89.56, 91.4, 89.32, 89.44, 89.84, 89.84, 90.68, 89.76, 89.96, 89.52, 87.76, 87.84, 90.36, 91.92, 93.44, 92.0, 91.96, 92.36, 91.2]
+47.59362244606018
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 39.37885141372681, 'TIME_S_1KI': 39.37885141372681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3668.398367080688, 'W': 77.07751960335096}
+[21.56, 21.8, 21.92, 21.8, 21.8, 21.72, 21.76, 21.96, 22.0, 22.0, 23.68, 23.6, 22.52, 21.8, 21.8, 21.84, 22.16, 22.32, 22.24, 22.24]
+397.78
+19.889
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 39.37885141372681, 'TIME_S_1KI': 39.37885141372681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3668.398367080688, 'W': 77.07751960335096, 'J_1KI': 3668.398367080688, 'W_1KI': 77.07751960335096, 'W_D': 57.18851960335097, 'J_D': 2721.8088102509973, 'W_D_1KI': 57.18851960335097, 'J_D_1KI': 57.18851960335097}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..47ee4ee
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1525, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.375513792037964, "TIME_S_1KI": 6.80361560133637, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1039.7556144714354, "W": 59.41096095809724, "J_1KI": 681.8069603091379, "W_1KI": 38.958007185637534, "W_D": 40.714960958097244, "J_D": 712.5555380096434, "W_D_1KI": 26.698335054489995, "J_D_1KI": 17.507104953763932}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..b2aa093
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.884527921676636}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249988, 249997,
+                            250000]),
+       col_indices=tensor([ 1848, 28763, 31705,  ...,  4981, 22506, 45960]),
+       values=tensor([0.8493, 0.0534, 0.5342,  ..., 0.4299, 0.9704, 0.1142]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1630, 0.3141, 0.8980,  ..., 0.6818, 0.2617, 0.8646])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 6.884527921676636 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1525 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.375513792037964}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249992, 249994,
+                            250000]),
+       col_indices=tensor([ 3205, 25770, 28303,  ..., 16579, 33459, 36956]),
+       values=tensor([0.6871, 0.0301, 0.1880,  ..., 0.0850, 0.6966, 0.8839]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3970, 0.9447, 0.7491,  ..., 0.5145, 0.9554, 0.9707])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.375513792037964 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249992, 249994,
+                            250000]),
+       col_indices=tensor([ 3205, 25770, 28303,  ..., 16579, 33459, 36956]),
+       values=tensor([0.6871, 0.0301, 0.1880,  ..., 0.0850, 0.6966, 0.8839]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3970, 0.9447, 0.7491,  ..., 0.5145, 0.9554, 0.9707])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.375513792037964 seconds
+
+[20.84, 20.72, 20.68, 20.6, 20.64, 20.4, 20.52, 20.48, 20.44, 20.84]
+[21.0, 20.96, 21.88, 23.12, 23.12, 32.08, 50.16, 64.0, 76.16, 91.92, 90.6, 89.16, 89.36, 89.52, 88.8, 89.28, 89.04]
+17.501073837280273
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.375513792037964, 'TIME_S_1KI': 6.80361560133637, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1039.7556144714354, 'W': 59.41096095809724}
+[20.84, 20.72, 20.68, 20.6, 20.64, 20.4, 20.52, 20.48, 20.44, 20.84, 20.92, 20.84, 20.84, 20.72, 21.0, 21.0, 21.28, 21.24, 20.92, 20.6]
+373.91999999999996
+18.695999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.375513792037964, 'TIME_S_1KI': 6.80361560133637, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1039.7556144714354, 'W': 59.41096095809724, 'J_1KI': 681.8069603091379, 'W_1KI': 38.958007185637534, 'W_D': 40.714960958097244, 'J_D': 712.5555380096434, 'W_D_1KI': 26.698335054489995, 'J_D_1KI': 17.507104953763932}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..c30df0a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 60.445369720458984, "TIME_S_1KI": 60.445369720458984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5178.136594352722, "W": 75.0095899661512, "J_1KI": 5178.136594352722, "W_1KI": 75.0095899661512, "W_D": 55.72458996615119, "J_D": 3846.835299849509, "W_D_1KI": 55.72458996615119, "J_D_1KI": 55.72458996615119}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..1aeb456
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 60.445369720458984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      42,      88,  ..., 2499911,
+                            2499959, 2500000]),
+       col_indices=tensor([  784,  2104,  3070,  ..., 44692, 45478, 45799]),
+       values=tensor([0.0569, 0.3731, 0.2156,  ..., 0.1856, 0.5823, 0.7517]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7969, 0.4843, 0.4078,  ..., 0.5644, 0.6126, 0.7864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 60.445369720458984 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      42,      88,  ..., 2499911,
+                            2499959, 2500000]),
+       col_indices=tensor([  784,  2104,  3070,  ..., 44692, 45478, 45799]),
+       values=tensor([0.0569, 0.3731, 0.2156,  ..., 0.1856, 0.5823, 0.7517]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7969, 0.4843, 0.4078,  ..., 0.5644, 0.6126, 0.7864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 60.445369720458984 seconds
+
+[21.52, 21.76, 21.6, 21.68, 21.68, 21.48, 21.52, 21.52, 21.72, 22.04]
+[22.08, 22.0, 22.16, 23.24, 24.16, 35.0, 49.88, 62.44, 76.6, 76.6, 84.16, 86.2, 86.12, 85.64, 84.84, 83.12, 82.96, 81.88, 81.44, 81.36, 81.2, 82.36, 83.24, 83.28, 84.08, 84.08, 84.48, 84.24, 83.64, 83.96, 83.6, 83.68, 83.56, 84.48, 83.84, 84.08, 84.36, 84.48, 84.04, 84.4, 85.08, 85.08, 84.88, 84.76, 84.72, 84.08, 83.2, 83.08, 83.4, 83.56, 83.6, 83.56, 83.72, 83.4, 84.96, 85.84, 86.72, 86.72, 87.6, 88.08, 87.2, 87.32, 87.24, 86.88, 86.4, 85.96, 84.76]
+69.0329942703247
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 60.445369720458984, 'TIME_S_1KI': 60.445369720458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5178.136594352722, 'W': 75.0095899661512}
+[21.52, 21.76, 21.6, 21.68, 21.68, 21.48, 21.52, 21.52, 21.72, 22.04, 21.4, 21.44, 21.4, 21.2, 21.2, 21.2, 21.2, 21.04, 20.96, 21.24]
+385.70000000000005
+19.285000000000004
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 60.445369720458984, 'TIME_S_1KI': 60.445369720458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5178.136594352722, 'W': 75.0095899661512, 'J_1KI': 5178.136594352722, 'W_1KI': 75.0095899661512, 'W_D': 55.72458996615119, 'J_D': 3846.835299849509, 'W_D_1KI': 55.72458996615119, 'J_D_1KI': 55.72458996615119}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..0a48909
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8439, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.30658531188965, "TIME_S_1KI": 1.9322888152493953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 993.3751641559601, "W": 60.56268692312065, "J_1KI": 117.71242613531936, "W_1KI": 7.176524105121538, "W_D": 41.768686923120654, "J_D": 685.1079160590172, "W_D_1KI": 4.949482986505588, "J_D_1KI": 0.5865011241267435}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..ee322a5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.2654190063476562}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([ 6514, 22496, 11789,  ..., 40007,  5149, 28458]),
+       values=tensor([0.4327, 0.6473, 0.1491,  ..., 0.8954, 0.9190, 0.6593]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4414, 0.3955, 0.1417,  ..., 0.3292, 0.0955, 0.0474])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 3.2654190063476562 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3215 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.000005722045898}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27536, 25934, 37963,  ...,  3997, 32688, 28318]),
+       values=tensor([0.1759, 0.2893, 0.0177,  ..., 0.2344, 0.0283, 0.5475]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4720, 0.7633, 0.9347,  ..., 0.8863, 0.6224, 0.2346])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 4.000005722045898 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8439 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.30658531188965}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([22959,  5139, 40799,  ..., 46493,  8579,  7673]),
+       values=tensor([0.4149, 0.3641, 0.9895,  ..., 0.4042, 0.1062, 0.3479]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8723, 0.6408, 0.2457,  ..., 0.3733, 0.2625, 0.6379])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 16.30658531188965 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([22959,  5139, 40799,  ..., 46493,  8579,  7673]),
+       values=tensor([0.4149, 0.3641, 0.9895,  ..., 0.4042, 0.1062, 0.3479]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8723, 0.6408, 0.2457,  ..., 0.3733, 0.2625, 0.6379])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 16.30658531188965 seconds
+
+[21.24, 20.92, 20.92, 21.08, 20.72, 20.88, 21.0, 21.0, 20.68, 20.92]
+[21.0, 20.72, 20.88, 25.2, 27.52, 41.48, 58.76, 70.32, 84.16, 91.04, 91.68, 91.84, 91.84, 92.08, 92.44, 91.88]
+16.40242886543274
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8439, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.30658531188965, 'TIME_S_1KI': 1.9322888152493953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 993.3751641559601, 'W': 60.56268692312065}
+[21.24, 20.92, 20.92, 21.08, 20.72, 20.88, 21.0, 21.0, 20.68, 20.92, 20.8, 20.96, 20.92, 20.92, 20.76, 20.88, 20.8, 20.92, 20.76, 20.56]
+375.88
+18.794
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8439, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.30658531188965, 'TIME_S_1KI': 1.9322888152493953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 993.3751641559601, 'W': 60.56268692312065, 'J_1KI': 117.71242613531936, 'W_1KI': 7.176524105121538, 'W_D': 41.768686923120654, 'J_D': 685.1079160590172, 'W_D_1KI': 4.949482986505588, 'J_D_1KI': 0.5865011241267435}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..87aabb6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 66395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.71402621269226, "TIME_S_1KI": 0.16136796765859268, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1809.3928852605818, "W": 143.23, "J_1KI": 27.251944954598716, "W_1KI": 2.15724075608103, "W_D": 107.452, "J_D": 1357.417330915451, "W_D_1KI": 1.6183748776263271, "J_D_1KI": 0.02437495109008701}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..bc4163e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.2295377254486084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      19,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 2595, 16687, 29551,  ..., 82666, 84305, 92330]),
+       values=tensor([0.2399, 0.6496, 0.1067,  ..., 0.4780, 0.9034, 0.0304]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9784, 0.5709, 0.3671,  ..., 0.6067, 0.7821, 0.8363])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.2295377254486084 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45744', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.2341063022613525}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      18,  ...,  999977,
+                             999994, 1000000]),
+       col_indices=tensor([  464, 33291, 41816,  ..., 39255, 78479, 83666]),
+       values=tensor([0.4695, 0.4859, 0.9230,  ..., 0.6746, 0.1683, 0.8174]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0937, 0.3379, 0.3499,  ..., 0.6520, 0.3862, 0.7030])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 7.2341063022613525 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66395', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.71402621269226}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      21,  ...,  999982,
+                             999990, 1000000]),
+       col_indices=tensor([ 7090, 12502, 14648,  ..., 47720, 74306, 81506]),
+       values=tensor([0.0325, 0.8127, 0.1017,  ..., 0.2993, 0.6676, 0.4101]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9849, 0.0117, 0.6257,  ..., 0.6699, 0.0244, 0.0988])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.71402621269226 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      21,  ...,  999982,
+                             999990, 1000000]),
+       col_indices=tensor([ 7090, 12502, 14648,  ..., 47720, 74306, 81506]),
+       values=tensor([0.0325, 0.8127, 0.1017,  ..., 0.2993, 0.6676, 0.4101]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9849, 0.0117, 0.6257,  ..., 0.6699, 0.0244, 0.0988])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.71402621269226 seconds
+
+[41.74, 39.92, 40.13, 39.36, 40.44, 39.29, 39.29, 39.23, 40.06, 40.03]
+[143.23]
+12.632778644561768
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.71402621269226, 'TIME_S_1KI': 0.16136796765859268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1809.3928852605818, 'W': 143.23}
+[41.74, 39.92, 40.13, 39.36, 40.44, 39.29, 39.29, 39.23, 40.06, 40.03, 39.88, 40.13, 39.21, 40.07, 39.16, 39.33, 39.33, 40.09, 39.55, 40.29]
+715.56
+35.778
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.71402621269226, 'TIME_S_1KI': 0.16136796765859268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1809.3928852605818, 'W': 143.23, 'J_1KI': 27.251944954598716, 'W_1KI': 2.15724075608103, 'W_D': 107.452, 'J_D': 1357.417330915451, 'W_D_1KI': 1.6183748776263271, 'J_D_1KI': 0.02437495109008701}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..9f14449
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102925, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.207890748977661, "TIME_S_1KI": 0.10889376486740501, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1545.566165971756, "W": 113.81, "J_1KI": 15.016431051462288, "W_1KI": 1.1057566188972554, "W_D": 78.15, "J_D": 1061.2951047420502, "W_D_1KI": 0.7592907456886082, "J_D_1KI": 0.007377126506568941}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..73316f3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1461803913116455}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99996,  99998,
+                            100000]),
+       col_indices=tensor([53462, 64739,  8211,  ..., 77032, 12066, 66338]),
+       values=tensor([0.7526, 0.8412, 0.0484,  ..., 0.1652, 0.9362, 0.7970]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2578, 0.3705, 0.8367,  ..., 0.6623, 0.7950, 0.3656])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.1461803913116455 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71829', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.327654123306274}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([53445, 61427, 55256,  ..., 99710, 79743, 76910]),
+       values=tensor([0.2043, 0.7921, 0.3637,  ..., 0.3183, 0.9272, 0.3273]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6989, 0.9157, 0.2952,  ..., 0.1186, 0.5845, 0.8882])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 7.327654123306274 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102925', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.207890748977661}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([13249, 39443, 49972,  ..., 18781, 78628, 93775]),
+       values=tensor([0.7488, 0.1329, 0.0380,  ..., 0.8918, 0.6119, 0.7720]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5554, 0.6245, 0.8914,  ..., 0.6605, 0.7651, 0.7091])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 11.207890748977661 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([13249, 39443, 49972,  ..., 18781, 78628, 93775]),
+       values=tensor([0.7488, 0.1329, 0.0380,  ..., 0.8918, 0.6119, 0.7720]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5554, 0.6245, 0.8914,  ..., 0.6605, 0.7651, 0.7091])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 11.207890748977661 seconds
+
+[41.24, 39.24, 40.12, 39.1, 40.04, 39.21, 39.63, 39.07, 40.14, 39.14]
+[113.81]
+13.580231666564941
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.207890748977661, 'TIME_S_1KI': 0.10889376486740501, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.566165971756, 'W': 113.81}
+[41.24, 39.24, 40.12, 39.1, 40.04, 39.21, 39.63, 39.07, 40.14, 39.14, 41.44, 39.12, 40.01, 39.13, 39.64, 39.12, 40.27, 39.05, 39.93, 38.94]
+713.2
+35.660000000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.207890748977661, 'TIME_S_1KI': 0.10889376486740501, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1545.566165971756, 'W': 113.81, 'J_1KI': 15.016431051462288, 'W_1KI': 1.1057566188972554, 'W_D': 78.15, 'J_D': 1061.2951047420502, 'W_D_1KI': 0.7592907456886082, 'J_D_1KI': 0.007377126506568941}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..5c9115a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 289350, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.562561988830566, "TIME_S_1KI": 0.036504447861864756, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1275.0872325897217, "W": 96.48, "J_1KI": 4.406729678900023, "W_1KI": 0.3334370139968896, "W_D": 61.3225, "J_D": 810.4429604113102, "W_D_1KI": 0.21193191636426473, "J_D_1KI": 0.0007324413905797986}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..1a5d3e8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.052317142486572266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1023, 5622, 6334,  ..., 8476, 7727, 1588]),
+       values=tensor([0.9992, 0.3273, 0.0949,  ..., 0.9070, 0.7782, 0.9129]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3589, 0.4614, 0.1782,  ..., 0.3543, 0.5532, 0.1489])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.052317142486572266 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '200699', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.282996416091919}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([5654, 2010, 6092,  ..., 8357, 4618, 8765]),
+       values=tensor([0.6548, 0.7548, 0.4241,  ..., 0.2252, 0.7987, 0.4358]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1881, 0.6615, 0.7402,  ..., 0.4130, 0.3712, 0.1085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.282996416091919 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289350', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.562561988830566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     3,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([  41, 4057, 4525,  ...,  395, 6429, 4913]),
+       values=tensor([0.6795, 0.3093, 0.3215,  ..., 0.9868, 0.7022, 0.9945]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0893, 0.5810, 0.8251,  ..., 0.0535, 0.5355, 0.1364])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.562561988830566 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     3,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([  41, 4057, 4525,  ...,  395, 6429, 4913]),
+       values=tensor([0.6795, 0.3093, 0.3215,  ..., 0.9868, 0.7022, 0.9945]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0893, 0.5810, 0.8251,  ..., 0.0535, 0.5355, 0.1364])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.562561988830566 seconds
+
+[40.18, 38.78, 39.39, 38.64, 39.47, 38.59, 40.17, 38.45, 39.45, 38.53]
+[96.48]
+13.216078281402588
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.562561988830566, 'TIME_S_1KI': 0.036504447861864756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1275.0872325897217, 'W': 96.48}
+[40.18, 38.78, 39.39, 38.64, 39.47, 38.59, 40.17, 38.45, 39.45, 38.53, 39.33, 38.72, 38.74, 39.53, 38.66, 39.45, 38.52, 39.57, 38.65, 38.7]
+703.1500000000001
+35.157500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.562561988830566, 'TIME_S_1KI': 0.036504447861864756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1275.0872325897217, 'W': 96.48, 'J_1KI': 4.406729678900023, 'W_1KI': 0.3334370139968896, 'W_D': 61.3225, 'J_D': 810.4429604113102, 'W_D_1KI': 0.21193191636426473, 'J_D_1KI': 0.0007324413905797986}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..b5102bb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 187965, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.065882205963135, "TIME_S_1KI": 0.053551896395409436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1344.2036644935608, "W": 106.8, "J_1KI": 7.1513508604982885, "W_1KI": 0.568190886601229, "W_D": 70.53074999999998, "J_D": 887.7124776168464, "W_D_1KI": 0.3752334211156331, "J_D_1KI": 0.001996294103240673}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..81bf5e6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07673120498657227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     16,  ...,  99979,  99991,
+                            100000]),
+       col_indices=tensor([ 168,  470, 1159,  ..., 7824, 8386, 8755]),
+       values=tensor([0.2770, 0.4979, 0.7971,  ..., 0.1786, 0.3153, 0.6794]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8782, 0.5630, 0.5978,  ..., 0.9864, 0.4940, 0.0083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.07673120498657227 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '136841', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.644104957580566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99978,  99990,
+                            100000]),
+       col_indices=tensor([1562, 4109, 4242,  ..., 5789, 5816, 7878]),
+       values=tensor([0.3397, 0.5295, 0.0107,  ..., 0.2250, 0.1834, 0.1775]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8704, 0.9073, 0.5102,  ..., 0.5120, 0.6818, 0.6416])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 7.644104957580566 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '187965', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.065882205963135}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     25,  ...,  99981,  99991,
+                            100000]),
+       col_indices=tensor([ 564, 1289, 1589,  ..., 8514, 9743, 9976]),
+       values=tensor([0.9535, 0.4673, 0.4047,  ..., 0.1356, 0.2907, 0.4698]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6755, 0.5642, 0.0135,  ..., 0.9982, 0.6342, 0.7704])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.065882205963135 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     25,  ...,  99981,  99991,
+                            100000]),
+       col_indices=tensor([ 564, 1289, 1589,  ..., 8514, 9743, 9976]),
+       values=tensor([0.9535, 0.4673, 0.4047,  ..., 0.1356, 0.2907, 0.4698]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6755, 0.5642, 0.0135,  ..., 0.9982, 0.6342, 0.7704])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.065882205963135 seconds
+
+[45.4, 39.59, 38.94, 38.84, 38.95, 40.73, 38.79, 39.87, 38.85, 39.58]
+[106.8]
+12.586176633834839
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 187965, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.065882205963135, 'TIME_S_1KI': 0.053551896395409436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.2036644935608, 'W': 106.8}
+[45.4, 39.59, 38.94, 38.84, 38.95, 40.73, 38.79, 39.87, 38.85, 39.58, 39.31, 39.68, 39.37, 39.04, 45.6, 48.05, 38.82, 39.58, 38.6, 39.88]
+725.3850000000001
+36.26925000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 187965, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.065882205963135, 'TIME_S_1KI': 0.053551896395409436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.2036644935608, 'W': 106.8, 'J_1KI': 7.1513508604982885, 'W_1KI': 0.568190886601229, 'W_D': 70.53074999999998, 'J_D': 887.7124776168464, 'W_D_1KI': 0.3752334211156331, 'J_D_1KI': 0.001996294103240673}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..be0819d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 105478, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5971040725708, "TIME_S_1KI": 0.10046743465529115, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1740.769259557724, "W": 131.98, "J_1KI": 16.503624069073396, "W_1KI": 1.2512561861241205, "W_D": 96.29974999999999, "J_D": 1270.159452213168, "W_D_1KI": 0.912984224198411, "J_D_1KI": 0.008655683879087687}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..1731635
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.16547083854675293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      96,     207,  ...,  999810,
+                             999906, 1000000]),
+       col_indices=tensor([  26,   37,   76,  ..., 9653, 9723, 9999]),
+       values=tensor([0.3241, 0.3803, 0.4811,  ..., 0.7106, 0.6386, 0.1440]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9687, 0.4748, 0.5344,  ..., 0.6395, 0.7779, 0.2708])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.16547083854675293 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63455', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.316709756851196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      90,     176,  ...,  999794,
+                             999890, 1000000]),
+       col_indices=tensor([  23,  147,  291,  ..., 9810, 9851, 9893]),
+       values=tensor([0.8158, 0.9343, 0.8649,  ..., 0.9539, 0.1935, 0.2240]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7787, 0.2300, 0.4854,  ..., 0.5355, 0.5696, 0.8377])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 6.316709756851196 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '105478', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5971040725708}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     216,  ...,  999816,
+                             999921, 1000000]),
+       col_indices=tensor([  50,   64,  228,  ..., 9846, 9935, 9998]),
+       values=tensor([0.2081, 0.8355, 0.6203,  ..., 0.0415, 0.1924, 0.6602]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3579, 0.4434, 0.7372,  ..., 0.2272, 0.7887, 0.7519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.5971040725708 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     216,  ...,  999816,
+                             999921, 1000000]),
+       col_indices=tensor([  50,   64,  228,  ..., 9846, 9935, 9998]),
+       values=tensor([0.2081, 0.8355, 0.6203,  ..., 0.0415, 0.1924, 0.6602]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3579, 0.4434, 0.7372,  ..., 0.2272, 0.7887, 0.7519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.5971040725708 seconds
+
+[41.21, 38.88, 39.8, 38.81, 39.92, 39.04, 39.53, 38.73, 39.74, 38.85]
+[131.98]
+13.18964433670044
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5971040725708, 'TIME_S_1KI': 0.10046743465529115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1740.769259557724, 'W': 131.98}
+[41.21, 38.88, 39.8, 38.81, 39.92, 39.04, 39.53, 38.73, 39.74, 38.85, 39.66, 39.98, 38.99, 40.02, 39.22, 39.28, 39.07, 41.17, 38.93, 45.27]
+713.6050000000001
+35.68025000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 105478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5971040725708, 'TIME_S_1KI': 0.10046743465529115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1740.769259557724, 'W': 131.98, 'J_1KI': 16.503624069073396, 'W_1KI': 1.2512561861241205, 'W_D': 96.29974999999999, 'J_D': 1270.159452213168, 'W_D_1KI': 0.912984224198411, 'J_D_1KI': 0.008655683879087687}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..2c99a83
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28261, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.545162916183472, "TIME_S_1KI": 0.37313481179659147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2163.6921325206754, "W": 147.39, "J_1KI": 76.56106056122131, "W_1KI": 5.2153143908566575, "W_D": 111.76774999999999, "J_D": 1640.7558270204065, "W_D_1KI": 3.9548405930434165, "J_D_1KI": 0.13993986741599437}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..d58d64d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.4614067077636719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     523,    1040,  ..., 4999055,
+                            4999519, 5000000]),
+       col_indices=tensor([   1,    5,   26,  ..., 9948, 9962, 9996]),
+       values=tensor([0.6869, 0.8475, 0.6936,  ..., 0.3132, 0.2618, 0.7215]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5929, 0.6414, 0.0366,  ..., 0.9216, 0.5044, 0.3359])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.4614067077636719 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22756', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.454672574996948}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     518,     995,  ..., 4998951,
+                            4999482, 5000000]),
+       col_indices=tensor([   3,    5,   12,  ..., 9960, 9985, 9990]),
+       values=tensor([0.0194, 0.0116, 0.2988,  ..., 0.0510, 0.2477, 0.0241]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8184, 0.3974, 0.7641,  ..., 0.0303, 0.5906, 0.4265])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 8.454672574996948 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28261', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.545162916183472}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     472,     967,  ..., 4998984,
+                            4999479, 5000000]),
+       col_indices=tensor([  28,   36,   55,  ..., 9923, 9953, 9987]),
+       values=tensor([0.3537, 0.0932, 0.3681,  ..., 0.2268, 0.3044, 0.8997]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5459, 0.4301, 0.8105,  ..., 0.9349, 0.4459, 0.6946])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.545162916183472 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     472,     967,  ..., 4998984,
+                            4999479, 5000000]),
+       col_indices=tensor([  28,   36,   55,  ..., 9923, 9953, 9987]),
+       values=tensor([0.3537, 0.0932, 0.3681,  ..., 0.2268, 0.3044, 0.8997]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5459, 0.4301, 0.8105,  ..., 0.9349, 0.4459, 0.6946])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.545162916183472 seconds
+
+[39.85, 40.19, 39.24, 39.5, 39.23, 40.01, 39.26, 40.01, 39.09, 39.94]
+[147.39]
+14.680047035217285
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.545162916183472, 'TIME_S_1KI': 0.37313481179659147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2163.6921325206754, 'W': 147.39}
+[39.85, 40.19, 39.24, 39.5, 39.23, 40.01, 39.26, 40.01, 39.09, 39.94, 39.9, 39.27, 40.14, 39.15, 40.0, 39.07, 39.99, 39.51, 39.37, 39.14]
+712.4449999999999
+35.622249999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.545162916183472, 'TIME_S_1KI': 0.37313481179659147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2163.6921325206754, 'W': 147.39, 'J_1KI': 76.56106056122131, 'W_1KI': 5.2153143908566575, 'W_D': 111.76774999999999, 'J_D': 1640.7558270204065, 'W_D_1KI': 3.9548405930434165, 'J_D_1KI': 0.13993986741599437}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..e9f145d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 352057, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.363084554672241, "TIME_S_1KI": 0.029435814526262056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.9154741740226, "W": 94.69, "J_1KI": 3.4764696460346554, "W_1KI": 0.2689621282917255, "W_D": 59.05925, "J_D": 763.3702605144381, "W_D_1KI": 0.1677547953882468, "J_D_1KI": 0.0004764989629186376}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..f1c1f4f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1414 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05632638931274414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([6812, 5345, 3814, 3851, 1180, 2370, 9747, 9157, 9309,
+                           1844,  451, 6602, 4443, 8006, 5413, 9948, 6902, 4781,
+                           5273, 3102, 9193, 6090, 9660, 8598, 9786, 3453, 7823,
+                           8095, 5864, 1933, 5014, 3401, 1663, 8599, 9714, 5815,
+                            973, 4504,  306, 2971, 7185,  220, 7724, 5778, 2532,
+                              0, 8277, 8525, 5899, 2513, 5457, 8721, 2772, 4422,
+                            997, 2101, 9163, 4690, 3655,  646, 1228, 2676, 5080,
+                           9204, 4653, 8512,  580, 9554, 3549,  201, 5889, 9262,
+                           3348, 7948, 7695, 1711, 5747, 7743, 1681, 5808, 2747,
+                           7029, 7665, 8165, 7858,  569, 2064, 4739, 7568,  177,
+                           9310, 4386, 8240, 6642, 4389, 3996, 4876, 1054, 4163,
+                           3621, 8213, 1627, 3052, 4037, 3228,   47, 4120, 8716,
+                           1140,  654, 1138, 8841, 9286, 6853, 8247, 7250, 6739,
+                           1808,  169, 5660, 5955, 2424, 5623,  268, 7108, 2287,
+                            739, 2574, 9748, 9883, 2172, 4242, 8003, 2617, 6886,
+                           7295, 7725, 4620,  498, 2580,  442, 5852, 4654, 5268,
+                           6076, 7672, 5783, 3582, 3254, 3994,  929, 1878, 4949,
+                            400, 6765, 9975,  779, 4319,  980, 2110, 2886, 8932,
+                              3, 9221, 5560, 5736, 9363, 3301, 2015, 4960, 9665,
+                           6658, 7513, 8632, 1117, 8631, 3102, 6495, 3285, 5928,
+                           5063, 2953,  415, 9325, 6645, 1813, 4912, 9756, 1834,
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+                           6126, 4560, 5442, 4479,  555, 2007, 6423, 5193, 6710,
+                           6829, 1599, 2342, 3108, 3317, 3816,  713, 4617, 7607,
+                           6987, 4294, 1833, 4504, 3983, 4882, 6215, 2108, 4859,
+                            168, 3488,  619, 9439, 3067, 7601, 4742, 6465, 3039,
+                           9230, 7199, 4541, 3988, 1559, 4055, 8422, 7652, 2090,
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+                            353, 9325, 2381, 8704, 6565, 7086,  807, 7854, 8680,
+                           5552, 8266, 6318,  725, 6560, 2538, 6556, 9098,  566,
+                           7395, 4316, 1599, 6631, 2981, 4986, 5873, 8559, 6556,
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+                           9055, 3143, 2265, 4227, 1735, 9879, 4387, 9710, 7805,
+                           9768, 2516, 6943, 7878, 4239, 2682, 5508, 5275,  427,
+                           5011, 4823, 1510, 3965, 7563,  828, 1786, 3737, 1515,
+                            269, 2676, 8151, 6784, 6882, 1125,  302, 4605, 1945,
+                           6282, 2720, 7960, 9965, 9839, 8018, 2892, 8630, 8988,
+                           8058, 2636, 1587, 7889, 7520, 9635, 2793, 7912, 1905,
+                            631, 3576, 1928, 6739, 9503, 9116, 9690, 2391, 8077,
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+                            600, 2737, 3702, 2860, 2917, 4958, 1280, 1230, 5019,
+                           5065, 5289, 5197, 4954, 2386, 5251,  792, 7396, 8666,
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+                      0.0190, 0.1803, 0.6325, 0.6661, 0.9783, 0.3584]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8769, 0.8902, 0.3074,  ..., 0.5063, 0.6820, 0.8149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.05632638931274414 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '186413', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.5597083568573}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (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([ 875, 2191, 8400, 7819, 1359, 1371, 2712, 1430,  699,
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+                      2.0481e-01, 3.1942e-01, 7.5560e-01, 7.0764e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5451, 0.9325, 0.7234,  ..., 0.9278, 0.0652, 0.2905])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 5.5597083568573 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '352057', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.363084554672241}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (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([0.3721, 0.5043, 0.5568,  ..., 0.8647, 0.9880, 0.8941])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.363084554672241 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+                      1.6669e-01, 8.4050e-01, 3.7991e-01, 6.5276e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3721, 0.5043, 0.5568,  ..., 0.8647, 0.9880, 0.8941])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.363084554672241 seconds
+
+[39.17, 38.6, 39.23, 38.35, 39.37, 38.46, 39.25, 43.56, 39.01, 38.43]
+[94.69]
+12.925498723983765
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+712.615
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..9ddd17e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.323282241821289, "TIME_S_1KI": 0.482509102211792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2021.2716293263436, "W": 152.47, "J_1KI": 94.47401866447038, "W_1KI": 7.1264314092077585, "W_D": 115.044, "J_D": 1525.1208324537276, "W_D_1KI": 5.377144192568356, "J_D_1KI": 0.2513271415082195}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..50f6332
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.532757043838501}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       8,  ..., 2499994,
+                            2499995, 2500000]),
+       col_indices=tensor([298854, 299868, 317882,  ..., 208197, 239895,
+                           321556]),
+       values=tensor([0.0947, 0.1899, 0.7776,  ..., 0.8480, 0.0740, 0.2913]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2732, 0.7262, 0.3001,  ..., 0.8229, 0.3388, 0.7233])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.532757043838501 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19708', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.672011375427246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499994,
+                            2500000, 2500000]),
+       col_indices=tensor([ 49185, 277910, 351023,  ..., 230263, 378248,
+                           487183]),
+       values=tensor([0.7966, 0.8451, 0.5460,  ..., 0.3570, 0.2848, 0.9857]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8196, 0.2368, 0.8865,  ..., 0.6520, 0.2281, 0.7931])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.672011375427246 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21395', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.323282241821289}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       8,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 50735,  77236, 160897,  ..., 492852, 393041,
+                           457835]),
+       values=tensor([0.2461, 0.0110, 0.8932,  ..., 0.0580, 0.2778, 0.4102]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4625, 0.6924, 0.9316,  ..., 0.4127, 0.3248, 0.5422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.323282241821289 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       8,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 50735,  77236, 160897,  ..., 492852, 393041,
+                           457835]),
+       values=tensor([0.2461, 0.0110, 0.8932,  ..., 0.0580, 0.2778, 0.4102]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4625, 0.6924, 0.9316,  ..., 0.4127, 0.3248, 0.5422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.323282241821289 seconds
+
+[41.04, 39.37, 40.41, 40.48, 45.25, 40.28, 40.5, 39.5, 40.46, 39.64]
+[152.47]
+13.256848096847534
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.323282241821289, 'TIME_S_1KI': 0.482509102211792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2021.2716293263436, 'W': 152.47}
+[41.04, 39.37, 40.41, 40.48, 45.25, 40.28, 40.5, 39.5, 40.46, 39.64, 42.68, 45.88, 39.43, 39.56, 39.31, 40.35, 46.4, 49.8, 39.76, 40.2]
+748.52
+37.426
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.323282241821289, 'TIME_S_1KI': 0.482509102211792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2021.2716293263436, 'W': 152.47, 'J_1KI': 94.47401866447038, 'W_1KI': 7.1264314092077585, 'W_D': 115.044, 'J_D': 1525.1208324537276, 'W_D_1KI': 5.377144192568356, 'J_D_1KI': 0.2513271415082195}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..f0cbadb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 97887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.967289686203003, "TIME_S_1KI": 0.11204030858237563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1548.6584901952745, "W": 117.34, "J_1KI": 15.820880098432626, "W_1KI": 1.1987291468734356, "W_D": 81.498, "J_D": 1075.6141949372293, "W_D_1KI": 0.8325722516779552, "J_D_1KI": 0.008505442517167297}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..a9ce226
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1396017074584961}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     11,  ..., 249990, 249993,
+                            250000]),
+       col_indices=tensor([ 1901, 17696, 37644,  ..., 22666, 31352, 38471]),
+       values=tensor([0.6079, 0.0811, 0.7282,  ..., 0.2667, 0.3886, 0.6657]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5204, 0.6126, 0.8277,  ..., 0.7159, 0.4461, 0.9246])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.1396017074584961 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75213', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.06783390045166}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249992, 249996,
+                            250000]),
+       col_indices=tensor([ 3649, 15078, 16220,  ..., 32895, 36388, 49599]),
+       values=tensor([0.6393, 0.2992, 0.9532,  ..., 0.0270, 0.3430, 0.6378]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0844, 0.1224, 0.7905,  ..., 0.3661, 0.3101, 0.4173])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 8.06783390045166 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '97887', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.967289686203003}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 249992, 249996,
+                            250000]),
+       col_indices=tensor([ 9116, 23500, 25241,  ...,  7305, 15035, 46474]),
+       values=tensor([0.8636, 0.6633, 0.2645,  ..., 0.7208, 0.8992, 0.1134]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3603, 0.4772, 0.1653,  ..., 0.3951, 0.3400, 0.6722])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.967289686203003 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 249992, 249996,
+                            250000]),
+       col_indices=tensor([ 9116, 23500, 25241,  ...,  7305, 15035, 46474]),
+       values=tensor([0.8636, 0.6633, 0.2645,  ..., 0.7208, 0.8992, 0.1134]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3603, 0.4772, 0.1653,  ..., 0.3951, 0.3400, 0.6722])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.967289686203003 seconds
+
+[40.69, 39.7, 40.28, 39.02, 40.08, 39.07, 39.32, 38.93, 40.06, 39.15]
+[117.34]
+13.198044061660767
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 97887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.967289686203003, 'TIME_S_1KI': 0.11204030858237563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1548.6584901952745, 'W': 117.34}
+[40.69, 39.7, 40.28, 39.02, 40.08, 39.07, 39.32, 38.93, 40.06, 39.15, 49.08, 39.91, 39.13, 39.81, 39.05, 39.67, 39.05, 40.19, 39.16, 39.9]
+716.84
+35.842
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 97887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.967289686203003, 'TIME_S_1KI': 0.11204030858237563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1548.6584901952745, 'W': 117.34, 'J_1KI': 15.820880098432626, 'W_1KI': 1.1987291468734356, 'W_D': 81.498, 'J_D': 1075.6141949372293, 'W_D_1KI': 0.8325722516779552, 'J_D_1KI': 0.008505442517167297}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..dcf99f8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 47277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432827234268188, "TIME_S_1KI": 0.2206744766856651, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1956.9828476619723, "W": 146.36, "J_1KI": 41.393972706854754, "W_1KI": 3.095797110645769, "W_D": 110.02925000000002, "J_D": 1471.2035733199718, "W_D_1KI": 2.3273314719631117, "J_D_1KI": 0.04922756249260976}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..c946bac
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2981231212615967}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      43,     103,  ..., 2499901,
+                            2499951, 2500000]),
+       col_indices=tensor([  154,  1105,  2164,  ..., 43048, 45641, 46786]),
+       values=tensor([0.5353, 0.9585, 0.2831,  ..., 0.0513, 0.1909, 0.0614]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3993, 0.6905, 0.7348,  ..., 0.6851, 0.9182, 0.5409])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.2981231212615967 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35220', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.822157621383667}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,     100,  ..., 2499912,
+                            2499953, 2500000]),
+       col_indices=tensor([  120,   161,   363,  ..., 47642, 48044, 49939]),
+       values=tensor([0.7949, 0.8676, 0.3054,  ..., 0.9459, 0.0848, 0.8977]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4844, 0.7866, 0.3385,  ..., 0.0837, 0.3382, 0.6328])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 7.822157621383667 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '47277', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432827234268188}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      62,     109,  ..., 2499897,
+                            2499942, 2500000]),
+       col_indices=tensor([ 2040,  2609,  3779,  ..., 46933, 47654, 47998]),
+       values=tensor([0.9101, 0.3119, 0.8580,  ..., 0.1192, 0.4361, 0.9803]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1061, 0.6227, 0.1589,  ..., 0.5507, 0.9975, 0.5119])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.432827234268188 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      62,     109,  ..., 2499897,
+                            2499942, 2500000]),
+       col_indices=tensor([ 2040,  2609,  3779,  ..., 46933, 47654, 47998]),
+       values=tensor([0.9101, 0.3119, 0.8580,  ..., 0.1192, 0.4361, 0.9803]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1061, 0.6227, 0.1589,  ..., 0.5507, 0.9975, 0.5119])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.432827234268188 seconds
+
+[40.03, 40.19, 39.31, 40.07, 40.41, 39.74, 44.15, 41.27, 39.36, 40.06]
+[146.36]
+13.371022462844849
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 47277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432827234268188, 'TIME_S_1KI': 0.2206744766856651, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.9828476619723, 'W': 146.36}
+[40.03, 40.19, 39.31, 40.07, 40.41, 39.74, 44.15, 41.27, 39.36, 40.06, 40.74, 39.25, 45.03, 39.17, 39.46, 39.16, 40.2, 39.66, 39.96, 39.62]
+726.615
+36.33075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 47277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432827234268188, 'TIME_S_1KI': 0.2206744766856651, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.9828476619723, 'W': 146.36, 'J_1KI': 41.393972706854754, 'W_1KI': 3.095797110645769, 'W_D': 110.02925000000002, 'J_D': 1471.2035733199718, 'W_D_1KI': 2.3273314719631117, 'J_D_1KI': 0.04922756249260976}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..a40aca5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129830, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.498366355895996, "TIME_S_1KI": 0.08086240742429328, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1343.8986691188811, "W": 102.66, "J_1KI": 10.351218278663492, "W_1KI": 0.7907263344373412, "W_D": 67.04849999999999, "J_D": 877.7166366298197, "W_D_1KI": 0.5164330278055919, "J_D_1KI": 0.003977763443006947}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..7b7017b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,101 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.11773824691772461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16845,  2751, 33930,  ..., 33536, 38018, 30474]),
+       values=tensor([0.6858, 0.5470, 0.3190,  ..., 0.3110, 0.3011, 0.6040]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7348, 0.5937, 0.8612,  ..., 0.8920, 0.9109, 0.1161])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.11773824691772461 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89180', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.942249059677124}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 5133, 25494,  8495,  ..., 18153, 14682, 27268]),
+       values=tensor([0.7177, 0.6433, 0.0497,  ..., 0.6766, 0.5365, 0.3286]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6426, 0.1118, 0.3197,  ..., 0.9296, 0.1873, 0.3702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 7.942249059677124 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '117899', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.535074234008789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([ 1468,  1704, 43281,  ...,  3197, 24132, 30286]),
+       values=tensor([1.4228e-01, 5.9740e-01, 9.5210e-06,  ...,
+                      2.4125e-01, 6.2955e-01, 4.9169e-01]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8788, 0.8743, 0.0964,  ..., 0.0391, 0.4204, 0.2909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.535074234008789 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '129830', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.498366355895996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16477, 45779, 22583,  ..., 30055, 21515, 45820]),
+       values=tensor([0.8893, 0.7790, 0.5329,  ..., 0.5529, 0.2667, 0.0404]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0469, 0.9963, 0.7558,  ..., 0.9652, 0.6676, 0.7778])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.498366355895996 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16477, 45779, 22583,  ..., 30055, 21515, 45820]),
+       values=tensor([0.8893, 0.7790, 0.5329,  ..., 0.5529, 0.2667, 0.0404]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0469, 0.9963, 0.7558,  ..., 0.9652, 0.6676, 0.7778])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.498366355895996 seconds
+
+[41.64, 39.36, 40.21, 39.07, 40.05, 39.18, 39.19, 39.18, 40.04, 39.2]
+[102.66]
+13.090772151947021
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129830, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.498366355895996, 'TIME_S_1KI': 0.08086240742429328, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1343.8986691188811, 'W': 102.66}
+[41.64, 39.36, 40.21, 39.07, 40.05, 39.18, 39.19, 39.18, 40.04, 39.2, 39.71, 39.93, 38.98, 39.86, 39.33, 39.67, 39.03, 39.93, 39.08, 39.73]
+712.23
+35.6115
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129830, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.498366355895996, 'TIME_S_1KI': 0.08086240742429328, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1343.8986691188811, 'W': 102.66, 'J_1KI': 10.351218278663492, 'W_1KI': 0.7907263344373412, 'W_D': 67.04849999999999, 'J_D': 877.7166366298197, 'W_D_1KI': 0.5164330278055919, 'J_D_1KI': 0.003977763443006947}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..1cb7a6a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33560, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.490610837936401, "TIME_S_1KI": 0.3125926948133612, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1237.3838691329956, "W": 88.17, "J_1KI": 36.87079467023229, "W_1KI": 2.6272348033373065, "W_D": 71.61225, "J_D": 1005.0112621335984, "W_D_1KI": 2.133857270560191, "J_D_1KI": 0.06358335132777686}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..14d045d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3128688335418701}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      17,      23,  ...,  999976,
+                             999990, 1000000]),
+       col_indices=tensor([  283,   794, 12077,  ..., 88041, 96002, 98956]),
+       values=tensor([0.6667, 0.7061, 0.4936,  ..., 0.0020, 0.2226, 0.8107]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3746, 0.1787, 0.3326,  ..., 0.2981, 0.5262, 0.4171])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.3128688335418701 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33560', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.490610837936401}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      12,      24,  ...,  999975,
+                             999989, 1000000]),
+       col_indices=tensor([  291,  3246,  3703,  ..., 78390, 83116, 86469]),
+       values=tensor([0.7026, 0.5046, 0.5818,  ..., 0.3671, 0.4061, 0.2873]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3720, 0.0968, 0.4099,  ..., 0.6733, 0.7032, 0.3728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.490610837936401 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      12,      24,  ...,  999975,
+                             999989, 1000000]),
+       col_indices=tensor([  291,  3246,  3703,  ..., 78390, 83116, 86469]),
+       values=tensor([0.7026, 0.5046, 0.5818,  ..., 0.3671, 0.4061, 0.2873]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3720, 0.0968, 0.4099,  ..., 0.6733, 0.7032, 0.3728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.490610837936401 seconds
+
+[18.34, 17.84, 18.15, 18.01, 17.99, 21.31, 18.71, 18.25, 18.1, 17.94]
+[88.17]
+14.034069061279297
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.490610837936401, 'TIME_S_1KI': 0.3125926948133612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.3838691329956, 'W': 88.17}
+[18.34, 17.84, 18.15, 18.01, 17.99, 21.31, 18.71, 18.25, 18.1, 17.94, 18.37, 20.96, 17.82, 18.03, 18.16, 17.77, 17.89, 17.87, 17.71, 18.52]
+331.155
+16.55775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.490610837936401, 'TIME_S_1KI': 0.3125926948133612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1237.3838691329956, 'W': 88.17, 'J_1KI': 36.87079467023229, 'W_1KI': 2.6272348033373065, 'W_D': 71.61225, 'J_D': 1005.0112621335984, 'W_D_1KI': 2.133857270560191, 'J_D_1KI': 0.06358335132777686}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..0f4c2a6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 65588, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.838059663772583, "TIME_S_1KI": 0.16524455180479025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1179.1915578985215, "W": 82.78, "J_1KI": 17.97876986489177, "W_1KI": 1.2621211197170215, "W_D": 66.50475, "J_D": 947.3524977065921, "W_D_1KI": 1.0139774044032446, "J_D_1KI": 0.015459800640410512}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..7b64a62
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.17682647705078125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      6,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 3198, 22722, 88522,  ..., 47695, 53177, 56584]),
+       values=tensor([0.0931, 0.9110, 0.9063,  ..., 0.1473, 0.7899, 0.0419]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4850, 0.3145, 0.7013,  ..., 0.1298, 0.2149, 0.6470])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.17682647705078125 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '59380', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.506051540374756}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([45126, 76716, 27115,  ..., 82599, 76675, 53817]),
+       values=tensor([0.5870, 0.5895, 0.9992,  ..., 0.5279, 0.4372, 0.6677]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8372, 0.3480, 0.3478,  ..., 0.9164, 0.0517, 0.0932])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 9.506051540374756 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '65588', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.838059663772583}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([69179, 69629, 89362,  ..., 28216, 37414, 39020]),
+       values=tensor([0.6325, 0.8110, 0.8083,  ..., 0.4927, 0.7217, 0.7562]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6752, 0.8314, 0.5534,  ..., 0.1964, 0.0025, 0.5959])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.838059663772583 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([69179, 69629, 89362,  ..., 28216, 37414, 39020]),
+       values=tensor([0.6325, 0.8110, 0.8083,  ..., 0.4927, 0.7217, 0.7562]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6752, 0.8314, 0.5534,  ..., 0.1964, 0.0025, 0.5959])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.838059663772583 seconds
+
+[18.44, 17.92, 18.39, 18.06, 17.93, 17.86, 18.18, 18.13, 18.19, 17.89]
+[82.78]
+14.244884729385376
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.838059663772583, 'TIME_S_1KI': 0.16524455180479025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1179.1915578985215, 'W': 82.78}
+[18.44, 17.92, 18.39, 18.06, 17.93, 17.86, 18.18, 18.13, 18.19, 17.89, 18.4, 17.89, 17.79, 17.88, 18.16, 18.32, 18.28, 17.67, 18.23, 18.52]
+325.505
+16.27525
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.838059663772583, 'TIME_S_1KI': 0.16524455180479025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1179.1915578985215, 'W': 82.78, 'J_1KI': 17.97876986489177, 'W_1KI': 1.2621211197170215, 'W_D': 66.50475, 'J_D': 947.3524977065921, 'W_D_1KI': 1.0139774044032446, 'J_D_1KI': 0.015459800640410512}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..ff48609
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 240931, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.228987216949463, "TIME_S_1KI": 0.04245608583764423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 972.0923814868927, "W": 74.28, "J_1KI": 4.034733519085932, "W_1KI": 0.30830403725547983, "W_D": 58.167500000000004, "J_D": 761.2302584832908, "W_D_1KI": 0.2414280437137604, "J_D_1KI": 0.0010020630127038877}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..36a267f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.062392234802246094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([7179, 9532, 8081,  ..., 4031, 8581, 2872]),
+       values=tensor([0.3998, 0.4929, 0.1773,  ..., 0.2243, 0.6349, 0.5923]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2436, 0.6971, 0.0487,  ..., 0.2986, 0.9140, 0.9941])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.062392234802246094 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '168290', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.3342225551605225}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([7117, 7845, 2903,  ...,  807, 7859, 5458]),
+       values=tensor([0.8544, 0.9061, 0.0037,  ..., 0.6594, 0.1915, 0.6916]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0592, 0.4192, 0.0774,  ..., 0.7897, 0.5835, 0.6060])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.3342225551605225 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '240931', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.228987216949463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1962,  399, 6914,  ..., 7707, 7379, 8204]),
+       values=tensor([0.6427, 0.2940, 0.2788,  ..., 0.7421, 0.9158, 0.7396]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3387, 0.7040, 0.3501,  ..., 0.4098, 0.3396, 0.7875])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.228987216949463 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1962,  399, 6914,  ..., 7707, 7379, 8204]),
+       values=tensor([0.6427, 0.2940, 0.2788,  ..., 0.7421, 0.9158, 0.7396]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3387, 0.7040, 0.3501,  ..., 0.4098, 0.3396, 0.7875])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.228987216949463 seconds
+
+[18.32, 17.9, 17.75, 17.66, 17.81, 17.98, 17.81, 17.63, 17.93, 18.05]
+[74.28]
+13.086865663528442
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240931, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.228987216949463, 'TIME_S_1KI': 0.04245608583764423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 972.0923814868927, 'W': 74.28}
+[18.32, 17.9, 17.75, 17.66, 17.81, 17.98, 17.81, 17.63, 17.93, 18.05, 18.35, 17.85, 17.96, 17.83, 18.33, 17.85, 17.85, 17.82, 18.0, 17.86]
+322.25
+16.1125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240931, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.228987216949463, 'TIME_S_1KI': 0.04245608583764423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 972.0923814868927, 'W': 74.28, 'J_1KI': 4.034733519085932, 'W_1KI': 0.30830403725547983, 'W_D': 58.167500000000004, 'J_D': 761.2302584832908, 'W_D_1KI': 0.2414280437137604, 'J_D_1KI': 0.0010020630127038877}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..4c2a534
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 201421, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7230703830719, "TIME_S_1KI": 0.053237102303493176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1117.283116903305, "W": 79.97, "J_1KI": 5.547004120242204, "W_1KI": 0.3970291081863361, "W_D": 63.60725, "J_D": 888.6745846898556, "W_D_1KI": 0.31579254397505724, "J_D_1KI": 0.001567823335079546}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..1cf380c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06886577606201172}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     19,  ...,  99984,  99991,
+                            100000]),
+       col_indices=tensor([1627, 2251, 2667,  ..., 7083, 9414, 9995]),
+       values=tensor([0.7763, 0.8562, 0.0227,  ..., 0.7081, 0.0734, 0.4206]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6749, 0.4550, 0.5239,  ..., 0.7938, 0.7493, 0.7052])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.06886577606201172 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '152470', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.948191404342651}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     22,  ...,  99977,  99992,
+                            100000]),
+       col_indices=tensor([ 579, 1179, 1463,  ..., 6326, 6539, 6627]),
+       values=tensor([0.4661, 0.6191, 0.1376,  ..., 0.4152, 0.1640, 0.4813]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0160, 0.8279, 0.2510,  ..., 0.4302, 0.2870, 0.5452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 7.948191404342651 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '201421', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7230703830719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     17,  ...,  99977,  99988,
+                            100000]),
+       col_indices=tensor([ 243, 1001, 2007,  ..., 7428, 8081, 8733]),
+       values=tensor([0.5597, 0.5588, 0.7631,  ..., 0.2707, 0.4657, 0.9680]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1756, 0.9887, 0.2623,  ..., 0.3846, 0.9664, 0.0716])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.7230703830719 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     17,  ...,  99977,  99988,
+                            100000]),
+       col_indices=tensor([ 243, 1001, 2007,  ..., 7428, 8081, 8733]),
+       values=tensor([0.5597, 0.5588, 0.7631,  ..., 0.2707, 0.4657, 0.9680]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1756, 0.9887, 0.2623,  ..., 0.3846, 0.9664, 0.0716])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.7230703830719 seconds
+
+[20.0, 17.95, 18.01, 18.57, 18.05, 17.91, 18.47, 18.3, 18.35, 18.45]
+[79.97]
+13.971278190612793
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 201421, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7230703830719, 'TIME_S_1KI': 0.053237102303493176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1117.283116903305, 'W': 79.97}
+[20.0, 17.95, 18.01, 18.57, 18.05, 17.91, 18.47, 18.3, 18.35, 18.45, 18.1, 18.11, 18.32, 17.8, 18.31, 17.97, 17.94, 17.86, 17.95, 18.22]
+327.255
+16.36275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 201421, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7230703830719, 'TIME_S_1KI': 0.053237102303493176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1117.283116903305, 'W': 79.97, 'J_1KI': 5.547004120242204, 'W_1KI': 0.3970291081863361, 'W_D': 63.60725, 'J_D': 888.6745846898556, 'W_D_1KI': 0.31579254397505724, 'J_D_1KI': 0.001567823335079546}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..7e50353
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.521214962005615, "TIME_S_1KI": 0.1790601273359477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1248.3679735660553, "W": 87.25, "J_1KI": 21.245923509412425, "W_1KI": 1.484904183260152, "W_D": 70.41275, "J_D": 1007.4615705525875, "W_D_1KI": 1.1983517138091835, "J_D_1KI": 0.020394698829251906}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..560bbb8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.19649839401245117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      90,     190,  ...,  999800,
+                             999902, 1000000]),
+       col_indices=tensor([  52,   87,  188,  ..., 9706, 9893, 9952]),
+       values=tensor([0.1675, 0.8959, 0.7675,  ..., 0.1378, 0.1178, 0.3486]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9304, 0.9814, 0.5110,  ..., 0.0040, 0.2898, 0.8662])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.19649839401245117 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53435', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.548681497573853}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      94,     197,  ...,  999809,
+                             999893, 1000000]),
+       col_indices=tensor([  61,  165,  222,  ..., 9905, 9907, 9919]),
+       values=tensor([0.6376, 0.5545, 0.9458,  ..., 0.6333, 0.2848, 0.3343]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2834, 0.7754, 0.6738,  ..., 0.4578, 0.3713, 0.7996])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.548681497573853 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58758', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.521214962005615}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     219,  ...,  999802,
+                             999904, 1000000]),
+       col_indices=tensor([  63,  137,  260,  ..., 9828, 9873, 9905]),
+       values=tensor([0.1449, 0.8321, 0.3255,  ..., 0.3929, 0.1108, 0.3040]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9048, 0.1055, 0.1608,  ..., 0.3713, 0.7919, 0.0232])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.521214962005615 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     219,  ...,  999802,
+                             999904, 1000000]),
+       col_indices=tensor([  63,  137,  260,  ..., 9828, 9873, 9905]),
+       values=tensor([0.1449, 0.8321, 0.3255,  ..., 0.3929, 0.1108, 0.3040]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9048, 0.1055, 0.1608,  ..., 0.3713, 0.7919, 0.0232])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.521214962005615 seconds
+
+[21.83, 18.04, 18.3, 17.99, 18.07, 21.44, 19.02, 18.2, 18.32, 17.87]
+[87.25]
+14.307942390441895
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.521214962005615, 'TIME_S_1KI': 0.1790601273359477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.3679735660553, 'W': 87.25}
+[21.83, 18.04, 18.3, 17.99, 18.07, 21.44, 19.02, 18.2, 18.32, 17.87, 18.33, 21.51, 17.89, 18.53, 18.27, 17.81, 18.21, 18.47, 18.07, 19.18]
+336.745
+16.83725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.521214962005615, 'TIME_S_1KI': 0.1790601273359477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.3679735660553, 'W': 87.25, 'J_1KI': 21.245923509412425, 'W_1KI': 1.484904183260152, 'W_D': 70.41275, 'J_D': 1007.4615705525875, 'W_D_1KI': 1.1983517138091835, 'J_D_1KI': 0.020394698829251906}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..c7df7b8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496035814285278, "TIME_S_1KI": 1.1925958202801135, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1351.109428257942, "W": 82.21, "J_1KI": 153.5177171069131, "W_1KI": 9.340983979093284, "W_D": 65.94874999999999, "J_D": 1083.858142644763, "W_D_1KI": 7.493324622202022, "J_D_1KI": 0.8514174096354984}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..7df5a22
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1929755210876465}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     485,     983,  ..., 4998996,
+                            4999523, 5000000]),
+       col_indices=tensor([  11,   47,  113,  ..., 9897, 9981, 9996]),
+       values=tensor([0.8953, 0.8081, 0.2668,  ..., 0.4279, 0.4927, 0.2076]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3301, 0.9128, 0.0218,  ..., 0.3705, 0.4449, 0.9102])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 1.1929755210876465 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8801', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496035814285278}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     509,    1022,  ..., 4998954,
+                            4999475, 5000000]),
+       col_indices=tensor([  16,   27,   72,  ..., 9970, 9971, 9996]),
+       values=tensor([0.8982, 0.6195, 0.1567,  ..., 0.8636, 0.4059, 0.3830]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3042, 0.6883, 0.8193,  ..., 0.9178, 0.9438, 0.4311])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.496035814285278 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     509,    1022,  ..., 4998954,
+                            4999475, 5000000]),
+       col_indices=tensor([  16,   27,   72,  ..., 9970, 9971, 9996]),
+       values=tensor([0.8982, 0.6195, 0.1567,  ..., 0.8636, 0.4059, 0.3830]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3042, 0.6883, 0.8193,  ..., 0.9178, 0.9438, 0.4311])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.496035814285278 seconds
+
+[18.39, 18.45, 17.88, 18.05, 18.11, 18.03, 17.97, 17.99, 18.04, 18.19]
+[82.21]
+16.434854984283447
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496035814285278, 'TIME_S_1KI': 1.1925958202801135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.109428257942, 'W': 82.21}
+[18.39, 18.45, 17.88, 18.05, 18.11, 18.03, 17.97, 17.99, 18.04, 18.19, 18.32, 17.88, 17.95, 17.82, 18.37, 18.15, 18.04, 17.93, 18.22, 17.79]
+325.225
+16.26125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496035814285278, 'TIME_S_1KI': 1.1925958202801135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1351.109428257942, 'W': 82.21, 'J_1KI': 153.5177171069131, 'W_1KI': 9.340983979093284, 'W_D': 65.94874999999999, 'J_D': 1083.858142644763, 'W_D_1KI': 7.493324622202022, 'J_D_1KI': 0.8514174096354984}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..e2fe041
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 282031, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.372447967529297, "TIME_S_1KI": 0.036777687444037345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1003.1455927085877, "W": 73.48, "J_1KI": 3.5568628721969846, "W_1KI": 0.26053873510358794, "W_D": 57.203, "J_D": 780.9327346177101, "W_D_1KI": 0.20282522134091643, "J_D_1KI": 0.0007191593170286829}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..e246539
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1414 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05941200256347656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (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([9369, 4292, 7681,  931, 2083, 7410, 1936, 8751,  948,
+                           4175, 1888, 1345, 5560, 8024, 9671, 1813,  200, 3639,
+                           4209, 3914, 3664, 7493, 1335, 4478, 2357, 7163, 3091,
+                           1065, 1561, 1003, 6097, 7558, 3015, 3713, 2757, 4724,
+                           4711, 6411, 2158,  193, 8251,  155,  227, 7018, 2516,
+                           6344, 9278, 6665, 5922, 3495, 9818, 2312, 5020,  771,
+                           4972, 1230, 8287, 7235, 5784, 9154, 6363, 9057, 5066,
+                           5544, 6958,  630, 2095, 1478, 1039, 1263, 2930, 2777,
+                           7763, 5296, 5286, 2070,  731, 3847, 9033, 1007, 9514,
+                           5297, 1206, 2620, 5020, 6860, 5221, 4179, 9153, 5412,
+                           6136, 2995, 6232, 9878, 9074, 9378, 3413, 1935, 7692,
+                           8374, 9520,  993, 5604, 4102, 1183, 6775, 1244, 8245,
+                           5932, 1440, 3804, 7398, 4378, 8195, 8257, 8791, 1040,
+                           7963, 4734, 2450, 6959, 5246, 9222,    1, 7047, 8234,
+                            866, 6402, 4633, 7505, 2769, 4851, 6107, 7344, 7189,
+                           7328, 3897,  965, 5441, 5357, 5066, 1754, 6254, 5498,
+                           7113,   89,  760, 8137, 2865, 5201, 4600, 5721, 1748,
+                           4799,  198,  561,  973, 1342, 1462, 7299, 4893, 7284,
+                           4504, 5041, 5263, 8101, 2983, 3117, 9593, 1776,  912,
+                           1800, 6114,  293, 8830, 5057, 9808, 6301,  342, 7503,
+                           1661, 4113, 4237, 5473, 6194, 2962, 5280, 3993, 7639,
+                           2123, 1005,  432, 3307, 4099, 1416, 6710, 7849,  719,
+                           3636, 4849, 2857, 6681, 3061, 4205, 4356, 4430, 9178,
+                             19, 6497, 5369, 4515, 7344, 3800, 3469, 5969, 5902,
+                           4203, 2153, 6321, 2664, 9623, 5060, 3065, 9561, 4385,
+                           5272, 8081, 7587, 6655, 6103, 4233, 3378, 5753,  279,
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+                      7.6458e-01, 7.8318e-01, 6.1479e-01, 6.7696e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.9856, 0.2722, 0.4021,  ..., 0.8237, 0.4550, 0.5626])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.05941200256347656 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '176731', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.579680919647217}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.8842, 0.7470, 0.1408, 0.9051, 0.3976, 0.8983]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.6331, 0.4592, 0.8230,  ..., 0.6920, 0.8755, 0.3375])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 6.579680919647217 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '282031', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.372447967529297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 641, 1820, 5085, 7616, 6287, 2843, 3768,  139,  557,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3038, 0.6445, 0.5741,  ..., 0.8215, 0.9151, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.372447967529297 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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)
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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.372447967529297 seconds
+
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..49601d0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8372, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.861924409866333, "TIME_S_1KI": 1.297410942411172, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1310.9924754476547, "W": 87.74, "J_1KI": 156.5925078174456, "W_1KI": 10.480172001911132, "W_D": 71.52799999999999, "J_D": 1068.7562090702056, "W_D_1KI": 8.543717152412803, "J_D_1KI": 1.0205108877702822}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..5b5d205
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2540500164031982}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,       6,  ..., 2499994,
+                            2499996, 2500000]),
+       col_indices=tensor([ 37595, 347043, 415637,  ..., 145391, 181131,
+                           323148]),
+       values=tensor([0.9304, 0.5156, 0.8153,  ..., 0.0582, 0.6116, 0.3872]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4551, 0.3395, 0.9990,  ..., 0.2154, 0.7020, 0.1344])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 1.2540500164031982 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8372', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.861924409866333}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      10,  ..., 2499992,
+                            2499994, 2500000]),
+       col_indices=tensor([140767, 212572, 418184,  ..., 257460, 329048,
+                           350732]),
+       values=tensor([0.1302, 0.7593, 0.7287,  ..., 0.1348, 0.8551, 0.2122]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3362, 0.7821, 0.5665,  ..., 0.5113, 0.4644, 0.7174])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.861924409866333 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      10,  ..., 2499992,
+                            2499994, 2500000]),
+       col_indices=tensor([140767, 212572, 418184,  ..., 257460, 329048,
+                           350732]),
+       values=tensor([0.1302, 0.7593, 0.7287,  ..., 0.1348, 0.8551, 0.2122]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3362, 0.7821, 0.5665,  ..., 0.5113, 0.4644, 0.7174])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.861924409866333 seconds
+
+[18.33, 17.76, 18.07, 18.0, 18.02, 17.78, 17.96, 17.97, 18.01, 17.77]
+[87.74]
+14.941787958145142
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8372, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.861924409866333, 'TIME_S_1KI': 1.297410942411172, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1310.9924754476547, 'W': 87.74}
+[18.33, 17.76, 18.07, 18.0, 18.02, 17.78, 17.96, 17.97, 18.01, 17.77, 18.44, 18.07, 17.99, 17.91, 18.26, 18.15, 17.93, 17.77, 18.22, 18.2]
+324.24
+16.212
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8372, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.861924409866333, 'TIME_S_1KI': 1.297410942411172, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1310.9924754476547, 'W': 87.74, 'J_1KI': 156.5925078174456, 'W_1KI': 10.480172001911132, 'W_D': 71.52799999999999, 'J_D': 1068.7562090702056, 'W_D_1KI': 8.543717152412803, 'J_D_1KI': 1.0205108877702822}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..bafcc4c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 78280, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.048285484313965, "TIME_S_1KI": 0.12836338125081712, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1146.701253862381, "W": 82.44, "J_1KI": 14.648713002840841, "W_1KI": 1.053142565150741, "W_D": 66.134, "J_D": 919.8925366683006, "W_D_1KI": 0.8448390393459376, "J_D_1KI": 0.010792527329406458}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..d22c5fa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1482532024383545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      8,  ..., 249991, 249997,
+                            250000]),
+       col_indices=tensor([11188, 48325,  9835,  ..., 16403, 16442, 24121]),
+       values=tensor([0.5273, 0.3289, 0.0892,  ..., 0.0153, 0.8132, 0.4919]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4620, 0.4376, 0.8938,  ..., 0.9801, 0.7388, 0.7080])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.1482532024383545 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70824', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.499845743179321}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     12,  ..., 249991, 249995,
+                            250000]),
+       col_indices=tensor([ 9700, 17110, 17880,  ..., 40636, 42079, 45237]),
+       values=tensor([0.5791, 0.9394, 0.7161,  ..., 0.4792, 0.4698, 0.8140]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2158, 0.6632, 0.3616,  ..., 0.9096, 0.8324, 0.6259])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.499845743179321 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78280', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.048285484313965}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([ 1806, 10529, 23120,  ..., 17166, 35800, 40447]),
+       values=tensor([0.3161, 0.7150, 0.6424,  ..., 0.5169, 0.8858, 0.3422]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5244, 0.0456, 0.6715,  ..., 0.9006, 0.5240, 0.6616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.048285484313965 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([ 1806, 10529, 23120,  ..., 17166, 35800, 40447]),
+       values=tensor([0.3161, 0.7150, 0.6424,  ..., 0.5169, 0.8858, 0.3422]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5244, 0.0456, 0.6715,  ..., 0.9006, 0.5240, 0.6616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.048285484313965 seconds
+
+[18.31, 18.07, 18.04, 17.95, 17.89, 18.22, 18.08, 17.87, 18.03, 18.16]
+[82.44]
+13.909525156021118
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78280, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.048285484313965, 'TIME_S_1KI': 0.12836338125081712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1146.701253862381, 'W': 82.44}
+[18.31, 18.07, 18.04, 17.95, 17.89, 18.22, 18.08, 17.87, 18.03, 18.16, 18.33, 19.12, 18.1, 18.07, 17.96, 17.95, 18.08, 18.08, 18.21, 18.0]
+326.12
+16.306
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78280, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.048285484313965, 'TIME_S_1KI': 0.12836338125081712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1146.701253862381, 'W': 82.44, 'J_1KI': 14.648713002840841, 'W_1KI': 1.053142565150741, 'W_D': 66.134, 'J_D': 919.8925366683006, 'W_D_1KI': 0.8448390393459376, 'J_D_1KI': 0.010792527329406458}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..9c50222
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17475, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.743166208267212, "TIME_S_1KI": 0.6147734597005557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1295.2754010248184, "W": 87.46, "J_1KI": 74.12162523747172, "W_1KI": 5.004864091559369, "W_D": 70.91274999999999, "J_D": 1050.2119905559418, "W_D_1KI": 4.057954220314734, "J_D_1KI": 0.2322148337805284}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..95a3055
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6008265018463135}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      42,      90,  ..., 2499902,
+                            2499947, 2500000]),
+       col_indices=tensor([ 1236,  2335,  2455,  ..., 44227, 44372, 44789]),
+       values=tensor([0.4453, 0.9405, 0.8001,  ..., 0.3243, 0.3638, 0.0708]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5116, 0.1335, 0.5143,  ..., 0.8718, 0.6117, 0.3765])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.6008265018463135 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17475', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.743166208267212}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      49,     107,  ..., 2499873,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1803,  2168,  2288,  ..., 48770, 49205, 49605]),
+       values=tensor([0.1814, 0.9281, 0.5481,  ..., 0.9692, 0.2397, 0.8106]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8810, 0.5797, 0.1795,  ..., 0.7146, 0.8135, 0.6945])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.743166208267212 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      49,     107,  ..., 2499873,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1803,  2168,  2288,  ..., 48770, 49205, 49605]),
+       values=tensor([0.1814, 0.9281, 0.5481,  ..., 0.9692, 0.2397, 0.8106]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8810, 0.5797, 0.1795,  ..., 0.7146, 0.8135, 0.6945])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.743166208267212 seconds
+
+[18.24, 17.93, 17.9, 17.93, 18.17, 17.82, 18.06, 21.0, 18.11, 19.61]
+[87.46]
+14.809917688369751
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.743166208267212, 'TIME_S_1KI': 0.6147734597005557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2754010248184, 'W': 87.46}
+[18.24, 17.93, 17.9, 17.93, 18.17, 17.82, 18.06, 21.0, 18.11, 19.61, 18.37, 18.2, 17.93, 17.83, 17.81, 18.07, 18.0, 17.85, 21.15, 18.15]
+330.94500000000005
+16.547250000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.743166208267212, 'TIME_S_1KI': 0.6147734597005557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1295.2754010248184, 'W': 87.46, 'J_1KI': 74.12162523747172, 'W_1KI': 5.004864091559369, 'W_D': 70.91274999999999, 'J_D': 1050.2119905559418, 'W_D_1KI': 4.057954220314734, 'J_D_1KI': 0.2322148337805284}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..598f4f1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 112560, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.602921962738037, "TIME_S_1KI": 0.0941979563143038, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1061.1344814062118, "W": 76.1, "J_1KI": 9.427278619458171, "W_1KI": 0.6760838663823738, "W_D": 59.91175, "J_D": 835.4063569827675, "W_D_1KI": 0.532265014214641, "J_D_1KI": 0.004728722585417919}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..fbbef6c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.10953974723815918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([20679, 38088, 21453,  ..., 14604, 22112, 37567]),
+       values=tensor([0.0203, 0.9911, 0.7304,  ..., 0.1348, 0.2520, 0.4128]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2000, 0.8382, 0.5478,  ..., 0.6017, 0.0874, 0.6263])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.10953974723815918 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '95855', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.94165301322937}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([31700, 16272, 20084,  ..., 46363,  9221, 39878]),
+       values=tensor([0.3577, 0.1970, 0.2573,  ..., 0.9498, 0.8667, 0.9638]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7704, 0.6386, 0.5878,  ..., 0.7750, 0.3511, 0.4334])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 8.94165301322937 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '112560', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.602921962738037}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([11228,  2410, 48293,  ..., 48555, 29403, 27641]),
+       values=tensor([0.9662, 0.4123, 0.9370,  ..., 0.4524, 0.0602, 0.8924]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4903, 0.0715, 0.0009,  ..., 0.3750, 0.8526, 0.7709])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.602921962738037 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([11228,  2410, 48293,  ..., 48555, 29403, 27641]),
+       values=tensor([0.9662, 0.4123, 0.9370,  ..., 0.4524, 0.0602, 0.8924]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4903, 0.0715, 0.0009,  ..., 0.3750, 0.8526, 0.7709])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.602921962738037 seconds
+
+[18.19, 18.01, 17.81, 17.69, 18.0, 18.19, 18.15, 17.85, 17.89, 17.99]
+[76.1]
+13.94394850730896
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.602921962738037, 'TIME_S_1KI': 0.0941979563143038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1061.1344814062118, 'W': 76.1}
+[18.19, 18.01, 17.81, 17.69, 18.0, 18.19, 18.15, 17.85, 17.89, 17.99, 18.3, 17.89, 18.04, 18.07, 17.99, 17.83, 18.01, 18.29, 17.73, 18.17]
+323.765
+16.18825
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 112560, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.602921962738037, 'TIME_S_1KI': 0.0941979563143038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1061.1344814062118, 'W': 76.1, 'J_1KI': 9.427278619458171, 'W_1KI': 0.6760838663823738, 'W_D': 59.91175, 'J_D': 835.4063569827675, 'W_D_1KI': 0.532265014214641, 'J_D_1KI': 0.004728722585417919}
diff --git a/pytorch/synthetic_densities b/pytorch/synthetic_densities
new file mode 100644
index 0000000..fcd06c2
--- /dev/null
+++ b/pytorch/synthetic_densities
@@ -0,0 +1,6 @@
+0.00001
+0.0001
+0.001
+0.01
+0.05
+0.1
diff --git a/pytorch/synthetic_sizes b/pytorch/synthetic_sizes
new file mode 100644
index 0000000..fc7c6bc
--- /dev/null
+++ b/pytorch/synthetic_sizes
@@ -0,0 +1,5 @@
+10000
+50000
+100000
+500000
+1000000