From 0def30ded2a73f11161da0ae100df6bdb9578472 Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Tue, 17 Dec 2024 19:30:42 -0500
Subject: [PATCH] More data

---
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
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 ...sr_10_10_10_synthetic_500000_1e-05.output} |   60 +-
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 ...r_10_10_10_synthetic_100000_0.0001.output} |   58 +-
 ...1_csr_10_10_10_synthetic_100000_0.001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.001.output} |   42 +-
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 ...sr_10_10_10_synthetic_100000_1e-05.output} |   52 +-
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 ..._1_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
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 ...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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 ...sr_10_10_10_synthetic_100000_0.0001.output |   45 +
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diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..980d684
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1755, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.253487825393677, "TIME_S_1KI": 5.842443205352523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 467.2838370990753, "W": 31.934101368331916, "J_1KI": 266.2585966376497, "W_1KI": 18.196069155744684, "W_D": 16.879101368331916, "J_D": 246.98773149132728, "W_D_1KI": 9.617721577397104, "J_D_1KI": 5.480183234984104}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..365e810
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 5.980836629867554}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999980,
+                             999989, 1000000]),
+       col_indices=tensor([ 6100, 13265, 27848,  ..., 84407, 91090, 94721]),
+       values=tensor([0.4400, 0.3445, 0.5606,  ..., 0.5861, 0.7102, 0.2795]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6757, 0.5029, 0.1898,  ..., 0.2612, 0.6123, 0.0844])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 5.980836629867554 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1755 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.253487825393677}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      18,  ...,  999983,
+                             999994, 1000000]),
+       col_indices=tensor([22305, 51740, 53616,  ..., 72974, 76091, 88145]),
+       values=tensor([0.7756, 0.0657, 0.7358,  ..., 0.9841, 0.0331, 0.5251]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4750, 0.6821, 0.2847,  ..., 0.3502, 0.4038, 0.5877])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.253487825393677 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      18,  ...,  999983,
+                             999994, 1000000]),
+       col_indices=tensor([22305, 51740, 53616,  ..., 72974, 76091, 88145]),
+       values=tensor([0.7756, 0.0657, 0.7358,  ..., 0.9841, 0.0331, 0.5251]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4750, 0.6821, 0.2847,  ..., 0.3502, 0.4038, 0.5877])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.253487825393677 seconds
+
+[16.48, 16.16, 16.52, 16.48, 16.48, 16.48, 16.72, 16.72, 16.84, 17.12]
+[17.08, 17.16, 17.96, 19.72, 22.2, 27.16, 34.52, 39.08, 43.72, 45.96, 46.32, 46.32, 46.2, 46.28]
+14.632753610610962
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1755, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.253487825393677, 'TIME_S_1KI': 5.842443205352523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 467.2838370990753, 'W': 31.934101368331916}
+[16.48, 16.16, 16.52, 16.48, 16.48, 16.48, 16.72, 16.72, 16.84, 17.12, 16.72, 16.8, 16.96, 17.0, 17.04, 16.88, 16.8, 16.88, 16.76, 16.84]
+301.1
+15.055000000000001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1755, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.253487825393677, 'TIME_S_1KI': 5.842443205352523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 467.2838370990753, 'W': 31.934101368331916, 'J_1KI': 266.2585966376497, 'W_1KI': 18.196069155744684, 'W_D': 16.879101368331916, 'J_D': 246.98773149132728, 'W_D_1KI': 9.617721577397104, 'J_D_1KI': 5.480183234984104}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..0a97dfa
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 57.53653693199158, "TIME_S_1KI": 57.53653693199158, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2684.526071929932, "W": 41.311972802980506, "J_1KI": 2684.526071929932, "W_1KI": 41.311972802980506, "W_D": 26.003972802980506, "J_D": 1689.784782156945, "W_D_1KI": 26.003972802980506, "J_D_1KI": 26.003972802980506}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..01b19d4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 57.53653693199158}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       92,      190,  ...,  9999802,
+                             9999900, 10000000]),
+       col_indices=tensor([  766,  3080,  3658,  ..., 98863, 99077, 99078]),
+       values=tensor([0.0329, 0.7493, 0.2063,  ..., 0.1215, 0.3807, 0.7288]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3516, 0.3347, 0.9443,  ..., 0.8917, 0.2195, 0.8723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 57.53653693199158 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       92,      190,  ...,  9999802,
+                             9999900, 10000000]),
+       col_indices=tensor([  766,  3080,  3658,  ..., 98863, 99077, 99078]),
+       values=tensor([0.0329, 0.7493, 0.2063,  ..., 0.1215, 0.3807, 0.7288]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3516, 0.3347, 0.9443,  ..., 0.8917, 0.2195, 0.8723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 57.53653693199158 seconds
+
+[17.2, 17.2, 16.8, 16.8, 17.0, 16.88, 16.88, 17.0, 17.0, 16.6]
+[16.88, 16.84, 17.36, 19.48, 20.16, 21.92, 24.16, 24.88, 27.6, 33.04, 37.56, 43.08, 47.0, 46.96, 47.68, 47.76, 47.76, 47.48, 47.4, 46.92, 47.28, 47.04, 47.56, 48.36, 48.0, 47.68, 47.44, 46.16, 45.68, 46.04, 46.32, 47.44, 47.76, 47.84, 47.64, 47.36, 47.08, 46.96, 46.96, 47.16, 46.68, 46.24, 46.2, 46.44, 46.56, 47.0, 48.08, 48.0, 48.12, 48.44, 48.48, 48.2, 47.64, 47.32, 47.2, 47.2, 47.56, 47.52, 47.68, 47.8, 47.8, 48.0]
+64.98179316520691
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 57.53653693199158, 'TIME_S_1KI': 57.53653693199158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2684.526071929932, 'W': 41.311972802980506}
+[17.2, 17.2, 16.8, 16.8, 17.0, 16.88, 16.88, 17.0, 17.0, 16.6, 17.12, 17.12, 17.04, 17.0, 17.0, 16.96, 16.88, 16.92, 17.32, 17.8]
+306.16
+15.308000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 57.53653693199158, 'TIME_S_1KI': 57.53653693199158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2684.526071929932, 'W': 41.311972802980506, 'J_1KI': 2684.526071929932, 'W_1KI': 41.311972802980506, 'W_D': 26.003972802980506, 'J_D': 1689.784782156945, 'W_D_1KI': 26.003972802980506, 'J_D_1KI': 26.003972802980506}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..9a23625
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11928, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.779903888702393, "TIME_S_1KI": 0.9037478109240772, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 428.71311614990236, "W": 29.263266081724595, "J_1KI": 35.94174347333186, "W_1KI": 2.4533254595677896, "W_D": 14.015266081724594, "J_D": 205.326650100708, "W_D_1KI": 1.1749887727803985, "J_D_1KI": 0.09850677169520444}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..0a26541
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8802759647369385}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([50190, 32056, 73796,  ..., 55938, 31334, 37461]),
+       values=tensor([0.0722, 0.7116, 0.8310,  ..., 0.7930, 0.8115, 0.4149]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5168, 0.3496, 0.0063,  ..., 0.9888, 0.0960, 0.5324])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.8802759647369385 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 11928 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.779903888702393}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99995,  99996,
+                            100000]),
+       col_indices=tensor([15079, 22431, 71484,  ..., 38240, 57604, 63673]),
+       values=tensor([0.6856, 0.2309, 0.0261,  ..., 0.6883, 0.7108, 0.1151]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6131, 0.6051, 0.4027,  ..., 0.3545, 0.9505, 0.4978])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.779903888702393 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99995,  99996,
+                            100000]),
+       col_indices=tensor([15079, 22431, 71484,  ..., 38240, 57604, 63673]),
+       values=tensor([0.6856, 0.2309, 0.0261,  ..., 0.6883, 0.7108, 0.1151]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6131, 0.6051, 0.4027,  ..., 0.3545, 0.9505, 0.4978])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.779903888702393 seconds
+
+[17.04, 17.24, 17.0, 17.0, 16.8, 16.64, 16.64, 16.68, 16.92, 16.84]
+[16.64, 16.52, 16.72, 17.8, 20.04, 25.4, 30.88, 34.96, 39.88, 41.96, 42.28, 42.44, 42.56, 42.56]
+14.650214195251465
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11928, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.779903888702393, 'TIME_S_1KI': 0.9037478109240772, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 428.71311614990236, 'W': 29.263266081724595}
+[17.04, 17.24, 17.0, 17.0, 16.8, 16.64, 16.64, 16.68, 16.92, 16.84, 16.84, 16.84, 17.0, 17.0, 16.92, 17.0, 17.16, 17.0, 17.16, 17.2]
+304.96000000000004
+15.248000000000001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11928, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.779903888702393, 'TIME_S_1KI': 0.9037478109240772, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 428.71311614990236, 'W': 29.263266081724595, 'J_1KI': 35.94174347333186, 'W_1KI': 2.4533254595677896, 'W_D': 14.015266081724594, 'J_D': 205.326650100708, 'W_D_1KI': 1.1749887727803985, 'J_D_1KI': 0.09850677169520444}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..6ac986d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3268, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551287174224854, "TIME_S_1KI": 3.2286680459684374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 514.9588033294677, "W": 35.14704993762719, "J_1KI": 157.57613320975145, "W_1KI": 10.754911241623986, "W_D": 16.333049937627187, "J_D": 239.30451817512508, "W_D_1KI": 4.997873297927536, "J_D_1KI": 1.5293369944698703}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..c2c09d9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.21274733543396}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 499991, 499996,
+                            500000]),
+       col_indices=tensor([ 6819, 16249, 65142,  ..., 35181, 90238, 95591]),
+       values=tensor([0.9907, 0.7784, 0.8470,  ..., 0.0401, 0.4552, 0.5172]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1211, 0.3699, 0.8120,  ..., 0.3387, 0.3308, 0.0427])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 3.21274733543396 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3268 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551287174224854}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 499988, 499994,
+                            500000]),
+       col_indices=tensor([ 4698, 29712, 45324,  ..., 47109, 54294, 79244]),
+       values=tensor([0.0467, 0.0395, 0.2018,  ..., 0.4601, 0.1623, 0.8954]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1366, 0.5632, 0.5706,  ..., 0.2593, 0.9938, 0.0917])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.551287174224854 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 499988, 499994,
+                            500000]),
+       col_indices=tensor([ 4698, 29712, 45324,  ..., 47109, 54294, 79244]),
+       values=tensor([0.0467, 0.0395, 0.2018,  ..., 0.4601, 0.1623, 0.8954]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1366, 0.5632, 0.5706,  ..., 0.2593, 0.9938, 0.0917])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.551287174224854 seconds
+
+[20.52, 20.48, 20.56, 20.6, 20.6, 20.88, 21.08, 21.04, 21.04, 20.92]
+[20.68, 20.64, 20.84, 22.28, 23.8, 29.96, 35.72, 42.6, 47.2, 50.88, 50.56, 50.96, 50.68, 50.8]
+14.651551246643066
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551287174224854, 'TIME_S_1KI': 3.2286680459684374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 514.9588033294677, 'W': 35.14704993762719}
+[20.52, 20.48, 20.56, 20.6, 20.6, 20.88, 21.08, 21.04, 21.04, 20.92, 20.72, 21.08, 21.16, 21.16, 21.24, 21.28, 21.16, 20.88, 20.68, 20.56]
+376.28
+18.814
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551287174224854, 'TIME_S_1KI': 3.2286680459684374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 514.9588033294677, 'W': 35.14704993762719, 'J_1KI': 157.57613320975145, 'W_1KI': 10.754911241623986, 'W_D': 16.333049937627187, 'J_D': 239.30451817512508, 'W_D_1KI': 4.997873297927536, 'J_D_1KI': 1.5293369944698703}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..47534f3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32824, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.612937211990356, "TIME_S_1KI": 0.3233285770165232, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 275.6484677696228, "W": 19.42651909848855, "J_1KI": 8.397771989081855, "W_1KI": 0.5918388709020398, "W_D": 4.498519098488551, "J_D": 63.83078154373167, "W_D_1KI": 0.13704969225227123, "J_D_1KI": 0.004175289186335341}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..0d66d25
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.3622722625732422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     4,  ...,  9997, 10000, 10000]),
+       col_indices=tensor([2430, 5032, 1477,  ...,  758, 3153, 4599]),
+       values=tensor([0.8038, 0.4543, 0.3152,  ..., 0.6785, 0.4391, 0.0535]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9594, 0.1900, 0.3074,  ..., 0.8950, 0.9459, 0.6732])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.3622722625732422 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 28983 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.27123761177063}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([1532, 2817,  884,  ..., 2356, 6175, 1948]),
+       values=tensor([0.3809, 0.2852, 0.7235,  ..., 0.6592, 0.2563, 0.7726]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.6771, 0.1497, 0.5070,  ..., 0.8092, 0.9643, 0.7887])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 9.27123761177063 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32824 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.612937211990356}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([3350, 4490, 6839,  ..., 6784, 8596, 8737]),
+       values=tensor([0.3991, 0.5600, 0.2439,  ..., 0.8859, 0.9485, 0.6345]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2861, 0.2741, 0.4038,  ..., 0.8389, 0.9796, 0.7969])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.612937211990356 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([3350, 4490, 6839,  ..., 6784, 8596, 8737]),
+       values=tensor([0.3991, 0.5600, 0.2439,  ..., 0.8859, 0.9485, 0.6345]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2861, 0.2741, 0.4038,  ..., 0.8389, 0.9796, 0.7969])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.612937211990356 seconds
+
+[16.64, 16.84, 16.96, 16.88, 16.84, 16.72, 17.08, 16.96, 16.92, 16.92]
+[17.08, 16.72, 16.76, 21.08, 22.52, 24.76, 25.6, 23.4, 22.04, 20.32, 20.04, 20.0, 20.0, 20.12]
+14.189287662506104
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32824, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.612937211990356, 'TIME_S_1KI': 0.3233285770165232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.6484677696228, 'W': 19.42651909848855}
+[16.64, 16.84, 16.96, 16.88, 16.84, 16.72, 17.08, 16.96, 16.92, 16.92, 16.36, 16.04, 15.84, 15.92, 16.12, 16.28, 16.36, 16.68, 16.72, 16.88]
+298.56
+14.928
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32824, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.612937211990356, 'TIME_S_1KI': 0.3233285770165232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.6484677696228, 'W': 19.42651909848855, 'J_1KI': 8.397771989081855, 'W_1KI': 0.5918388709020398, 'W_D': 4.498519098488551, 'J_D': 63.83078154373167, 'W_D_1KI': 0.13704969225227123, 'J_D_1KI': 0.004175289186335341}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..650d27e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4599, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.21649432182312, "TIME_S_1KI": 2.2214599525599303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 276.3690100479126, "W": 19.391688491473598, "J_1KI": 60.09328333287945, "W_1KI": 4.2165010853389, "W_D": 4.4646884914736, "J_D": 63.630433167457575, "W_D_1KI": 0.9707954971675582, "J_D_1KI": 0.21108838816428752}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..7a35653
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 2.282747268676758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     15,     26,  ...,  99983,  99992,
+                            100000]),
+       col_indices=tensor([ 746, 1254, 2691,  ..., 5665, 9904, 9986]),
+       values=tensor([0.7024, 0.2927, 0.8116,  ..., 0.2675, 0.5863, 0.1724]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2042, 0.3555, 0.3767,  ..., 0.6038, 0.4952, 0.0036])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.282747268676758 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4599 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.21649432182312}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99989,
+                            100000]),
+       col_indices=tensor([5193, 5456, 6247,  ..., 5100, 5946, 8330]),
+       values=tensor([0.7086, 0.0012, 0.4180,  ..., 0.5448, 0.8405, 0.8114]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0495, 0.0946, 0.7654,  ..., 0.8976, 0.3544, 0.9283])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.21649432182312 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99989,
+                            100000]),
+       col_indices=tensor([5193, 5456, 6247,  ..., 5100, 5946, 8330]),
+       values=tensor([0.7086, 0.0012, 0.4180,  ..., 0.5448, 0.8405, 0.8114]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0495, 0.0946, 0.7654,  ..., 0.8976, 0.3544, 0.9283])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.21649432182312 seconds
+
+[16.44, 16.28, 16.32, 16.56, 16.56, 16.6, 16.76, 16.76, 16.72, 16.68]
+[16.52, 16.48, 16.6, 20.0, 22.08, 24.8, 25.56, 23.6, 23.04, 20.28, 20.28, 20.04, 20.16, 20.2]
+14.251931190490723
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4599, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.21649432182312, 'TIME_S_1KI': 2.2214599525599303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 276.3690100479126, 'W': 19.391688491473598}
+[16.44, 16.28, 16.32, 16.56, 16.56, 16.6, 16.76, 16.76, 16.72, 16.68, 16.4, 16.48, 16.68, 16.36, 16.44, 16.64, 16.64, 16.8, 16.8, 16.76]
+298.53999999999996
+14.926999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4599, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.21649432182312, 'TIME_S_1KI': 2.2214599525599303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 276.3690100479126, 'W': 19.391688491473598, 'J_1KI': 60.09328333287945, 'W_1KI': 4.2165010853389, 'W_D': 4.4646884914736, 'J_D': 63.630433167457575, 'W_D_1KI': 0.9707954971675582, 'J_D_1KI': 0.21108838816428752}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..d799afd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 21.366477489471436, "TIME_S_1KI": 21.366477489471436, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 489.4337509441374, "W": 19.31282985940674, "J_1KI": 489.4337509441374, "W_1KI": 19.31282985940674, "W_D": 4.539829859406739, "J_D": 115.05025275492645, "W_D_1KI": 4.539829859406739, "J_D_1KI": 4.539829859406739}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..7e1dd33
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 21.366477489471436}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     201,  ...,  999801,
+                             999899, 1000000]),
+       col_indices=tensor([ 106,  113,  159,  ..., 9934, 9937, 9966]),
+       values=tensor([0.1214, 0.4144, 0.1866,  ..., 0.5194, 0.7412, 0.0565]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4749, 0.5757, 0.5717,  ..., 0.5026, 0.5396, 0.1085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.366477489471436 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     201,  ...,  999801,
+                             999899, 1000000]),
+       col_indices=tensor([ 106,  113,  159,  ..., 9934, 9937, 9966]),
+       values=tensor([0.1214, 0.4144, 0.1866,  ..., 0.5194, 0.7412, 0.0565]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4749, 0.5757, 0.5717,  ..., 0.5026, 0.5396, 0.1085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.366477489471436 seconds
+
+[16.76, 16.8, 16.72, 16.6, 16.36, 16.12, 16.0, 16.04, 16.28, 16.36]
+[16.48, 16.48, 16.36, 17.76, 18.4, 22.04, 22.96, 22.96, 22.68, 21.88, 20.28, 20.28, 20.36, 20.0, 20.0, 19.8, 19.72, 19.84, 19.96, 20.12, 20.32, 20.36, 20.56, 20.72, 20.6]
+25.34241509437561
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.366477489471436, 'TIME_S_1KI': 21.366477489471436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 489.4337509441374, 'W': 19.31282985940674}
+[16.76, 16.8, 16.72, 16.6, 16.36, 16.12, 16.0, 16.04, 16.28, 16.36, 16.6, 16.56, 16.28, 16.28, 16.28, 16.24, 16.56, 16.68, 16.52, 16.56]
+295.46000000000004
+14.773000000000001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.366477489471436, 'TIME_S_1KI': 21.366477489471436, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 489.4337509441374, 'W': 19.31282985940674, 'J_1KI': 489.4337509441374, 'W_1KI': 19.31282985940674, 'W_D': 4.539829859406739, 'J_D': 115.05025275492645, 'W_D_1KI': 4.539829859406739, 'J_D_1KI': 4.539829859406739}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..c5bc81a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 106.56549715995789, "TIME_S_1KI": 106.56549715995789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2685.1749729537964, "W": 24.108298502680796, "J_1KI": 2685.1749729537964, "W_1KI": 24.108298502680796, "W_D": 5.534298502680798, "J_D": 616.4084881644253, "W_D_1KI": 5.534298502680798, "J_D_1KI": 5.534298502680798}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..1729b52
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 106.56549715995789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     506,    1012,  ..., 4998963,
+                            4999486, 5000000]),
+       col_indices=tensor([  12,   18,   20,  ..., 9951, 9962, 9995]),
+       values=tensor([0.8717, 0.8420, 0.2460,  ..., 0.1141, 0.5771, 0.8192]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3519, 0.6034, 0.3549,  ..., 0.4924, 0.0253, 0.7056])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.56549715995789 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     506,    1012,  ..., 4998963,
+                            4999486, 5000000]),
+       col_indices=tensor([  12,   18,   20,  ..., 9951, 9962, 9995]),
+       values=tensor([0.8717, 0.8420, 0.2460,  ..., 0.1141, 0.5771, 0.8192]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3519, 0.6034, 0.3549,  ..., 0.4924, 0.0253, 0.7056])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.56549715995789 seconds
+
+[20.96, 20.76, 20.72, 20.52, 20.44, 20.56, 20.84, 20.84, 20.6, 20.6]
+[20.64, 20.64, 21.08, 23.16, 24.52, 27.56, 29.68, 29.72, 28.4, 27.2, 27.2, 24.36, 24.36, 24.36, 24.84, 24.88, 24.76, 24.96, 24.72, 24.4, 24.36, 24.16, 24.12, 24.12, 24.24, 24.64, 24.72, 24.76, 24.88, 24.76, 24.4, 24.4, 24.32, 24.32, 24.24, 24.24, 24.44, 24.36, 24.16, 24.16, 24.2, 24.24, 24.4, 24.68, 24.56, 24.6, 24.52, 24.48, 24.48, 24.24, 24.36, 24.36, 24.44, 24.52, 24.52, 24.32, 24.44, 24.28, 24.04, 23.96, 23.96, 24.04, 24.16, 24.28, 24.36, 24.44, 24.48, 24.56, 24.64, 24.56, 24.48, 24.24, 24.24, 24.24, 24.2, 24.28, 24.36, 24.2, 24.4, 24.12, 24.16, 24.24, 24.48, 24.48, 24.8, 24.8, 24.92, 24.92, 24.96, 24.84, 24.72, 24.88, 24.72, 24.68, 24.48, 24.28, 24.0, 24.04, 24.04, 24.16, 24.32, 24.28, 24.32, 24.32, 24.48, 24.4, 24.68, 24.8, 24.64, 24.48]
+111.37969660758972
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56549715995789, 'TIME_S_1KI': 106.56549715995789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2685.1749729537964, 'W': 24.108298502680796}
+[20.96, 20.76, 20.72, 20.52, 20.44, 20.56, 20.84, 20.84, 20.6, 20.6, 20.64, 20.6, 20.6, 20.72, 20.8, 20.6, 20.52, 20.28, 20.68, 20.6]
+371.47999999999996
+18.573999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56549715995789, 'TIME_S_1KI': 106.56549715995789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2685.1749729537964, 'W': 24.108298502680796, 'J_1KI': 2685.1749729537964, 'W_1KI': 24.108298502680796, 'W_D': 5.534298502680798, 'J_D': 616.4084881644253, 'W_D_1KI': 5.534298502680798, 'J_D_1KI': 5.534298502680798}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..f711a9d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 213.33555269241333, "TIME_S_1KI": 213.33555269241333, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5226.200544624329, "W": 24.133137838334523, "J_1KI": 5226.200544624329, "W_1KI": 24.133137838334523, "W_D": 5.8551378383345245, "J_D": 1267.97123376751, "W_D_1KI": 5.8551378383345245, "J_D_1KI": 5.8551378383345245}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..dc4c095
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 213.33555269241333}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1053,     2094,  ...,  9997974,
+                             9998964, 10000000]),
+       col_indices=tensor([   7,   12,   13,  ..., 9961, 9986, 9993]),
+       values=tensor([0.1704, 0.0678, 0.1172,  ..., 0.2208, 0.7370, 0.2820]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7446, 0.0915, 0.8679,  ..., 0.5982, 0.0596, 0.6566])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 213.33555269241333 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1053,     2094,  ...,  9997974,
+                             9998964, 10000000]),
+       col_indices=tensor([   7,   12,   13,  ..., 9961, 9986, 9993]),
+       values=tensor([0.1704, 0.0678, 0.1172,  ..., 0.2208, 0.7370, 0.2820]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7446, 0.0915, 0.8679,  ..., 0.5982, 0.0596, 0.6566])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 213.33555269241333 seconds
+
+[20.4, 20.4, 20.4, 20.36, 20.36, 20.16, 20.32, 20.2, 20.2, 20.28]
+[20.32, 20.48, 21.4, 22.68, 24.52, 24.52, 27.12, 28.4, 28.88, 28.48, 27.56, 25.92, 24.36, 24.48, 24.52, 24.56, 24.56, 24.24, 24.24, 24.2, 23.96, 23.92, 24.16, 24.36, 24.48, 24.52, 24.56, 24.56, 24.36, 24.28, 24.28, 24.48, 24.36, 24.36, 24.6, 24.36, 24.24, 24.2, 24.24, 24.12, 24.16, 24.12, 24.24, 24.24, 24.36, 24.48, 24.48, 24.56, 24.4, 24.4, 24.24, 24.56, 24.84, 24.76, 24.76, 24.76, 24.84, 24.52, 24.64, 24.64, 24.64, 24.64, 24.52, 24.52, 24.52, 24.4, 24.36, 24.36, 24.36, 24.32, 24.24, 24.44, 24.2, 24.08, 24.2, 24.0, 23.92, 23.88, 23.84, 24.08, 24.08, 24.4, 24.6, 24.72, 24.8, 24.68, 24.4, 24.36, 24.24, 24.12, 24.16, 24.2, 24.04, 24.04, 24.0, 24.12, 24.32, 24.36, 24.32, 24.4, 24.16, 24.08, 24.12, 24.32, 24.32, 24.32, 24.44, 24.56, 24.32, 24.36, 24.56, 24.68, 24.44, 24.48, 24.52, 24.44, 24.56, 24.56, 24.68, 24.6, 24.2, 24.6, 24.16, 24.24, 24.32, 24.56, 24.28, 24.48, 24.8, 24.68, 24.68, 24.84, 24.84, 24.84, 24.88, 24.56, 24.84, 24.68, 24.48, 24.76, 24.64, 24.64, 24.64, 24.52, 24.8, 24.68, 24.52, 24.68, 24.6, 24.44, 24.68, 24.76, 24.76, 24.64, 24.6, 24.6, 24.56, 24.44, 24.4, 24.52, 24.48, 24.44, 24.44, 24.32, 24.44, 24.28, 24.44, 24.44, 24.36, 24.16, 24.04, 24.0, 24.12, 24.0, 24.12, 24.4, 24.4, 24.32, 24.28, 24.28, 24.28, 24.2, 24.32, 24.32, 24.4, 24.6, 24.44, 24.56, 24.76, 24.84, 24.72, 24.72, 24.72, 24.56, 24.52, 24.56, 24.64, 24.6, 24.36, 24.44, 24.4, 24.4, 24.56, 24.72, 24.72, 24.6, 24.48, 24.36, 24.24, 24.24, 24.28, 24.44, 24.56, 24.56]
+216.55702543258667
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 213.33555269241333, 'TIME_S_1KI': 213.33555269241333, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5226.200544624329, 'W': 24.133137838334523}
+[20.4, 20.4, 20.4, 20.36, 20.36, 20.16, 20.32, 20.2, 20.2, 20.28, 20.2, 19.96, 20.08, 20.24, 20.24, 20.28, 20.48, 20.6, 20.64, 20.4]
+365.55999999999995
+18.278
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 213.33555269241333, 'TIME_S_1KI': 213.33555269241333, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5226.200544624329, 'W': 24.133137838334523, 'J_1KI': 5226.200544624329, 'W_1KI': 24.133137838334523, 'W_D': 5.8551378383345245, 'J_D': 1267.97123376751, 'W_D_1KI': 5.8551378383345245, 'J_D_1KI': 5.8551378383345245}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..1e2c2f4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.2.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": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4943735599518, "TIME_S_1KI": 424.4943735599518, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10473.538437499996, "W": 24.301530579701517, "J_1KI": 10473.538437499996, "W_1KI": 24.301530579701517, "W_D": 5.865530579701517, "J_D": 2527.942006835934, "W_D_1KI": 5.865530579701517, "J_D_1KI": 5.865530579701517}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..a51b684
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.2 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4943735599518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1949,     3966,  ..., 19996026,
+                            19998069, 20000000]),
+       col_indices=tensor([   7,   21,   26,  ..., 9986, 9990, 9999]),
+       values=tensor([0.4961, 0.8613, 0.9281,  ..., 0.1556, 0.7430, 0.8560]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.5082, 0.3131, 0.5448,  ..., 0.5922, 0.3726, 0.5476])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 424.4943735599518 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1949,     3966,  ..., 19996026,
+                            19998069, 20000000]),
+       col_indices=tensor([   7,   21,   26,  ..., 9986, 9990, 9999]),
+       values=tensor([0.4961, 0.8613, 0.9281,  ..., 0.1556, 0.7430, 0.8560]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.5082, 0.3131, 0.5448,  ..., 0.5922, 0.3726, 0.5476])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 424.4943735599518 seconds
+
+[20.56, 20.4, 20.48, 20.48, 20.56, 20.8, 20.96, 20.68, 20.68, 20.52]
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4943735599518, 'TIME_S_1KI': 424.4943735599518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10473.538437499996, 'W': 24.301530579701517}
+[20.56, 20.4, 20.48, 20.48, 20.56, 20.8, 20.96, 20.68, 20.68, 20.52, 20.2, 20.36, 20.48, 20.56, 20.28, 20.2, 20.2, 20.24, 20.52, 20.4]
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4943735599518, 'TIME_S_1KI': 424.4943735599518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10473.538437499996, 'W': 24.301530579701517, 'J_1KI': 10473.538437499996, 'W_1KI': 24.301530579701517, 'W_D': 5.865530579701517, 'J_D': 2527.942006835934, 'W_D_1KI': 5.865530579701517, 'J_D_1KI': 5.865530579701517}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..25c5266
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.3.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": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 637.8268127441406, "TIME_S_1KI": 637.8268127441406, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 15996.775521488189, "W": 24.370595996658984, "J_1KI": 15996.775521488189, "W_1KI": 24.370595996658984, "W_D": 5.917595996658985, "J_D": 3884.2896906743035, "W_D_1KI": 5.917595996658985, "J_D_1KI": 5.917595996658985}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..294dece
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 637.8268127441406}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2961,     5942,  ..., 29993896,
+                            29996981, 30000000]),
+       col_indices=tensor([   2,    5,   14,  ..., 9994, 9995, 9996]),
+       values=tensor([0.0155, 0.6045, 0.5805,  ..., 0.2136, 0.4050, 0.0107]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.2173, 0.9101, 0.0107,  ..., 0.2401, 0.6161, 0.6478])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 637.8268127441406 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2961,     5942,  ..., 29993896,
+                            29996981, 30000000]),
+       col_indices=tensor([   2,    5,   14,  ..., 9994, 9995, 9996]),
+       values=tensor([0.0155, 0.6045, 0.5805,  ..., 0.2136, 0.4050, 0.0107]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.2173, 0.9101, 0.0107,  ..., 0.2401, 0.6161, 0.6478])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 637.8268127441406 seconds
+
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 637.8268127441406, 'TIME_S_1KI': 637.8268127441406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15996.775521488189, 'W': 24.370595996658984}
+[20.6, 20.64, 20.76, 20.6, 20.44, 20.44, 20.48, 20.64, 20.64, 20.88, 20.28, 20.44, 20.44, 20.64, 20.68, 20.44, 20.36, 20.36, 20.12, 20.12]
+369.06
+18.453
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 637.8268127441406, 'TIME_S_1KI': 637.8268127441406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 15996.775521488189, 'W': 24.370595996658984, 'J_1KI': 15996.775521488189, 'W_1KI': 24.370595996658984, 'W_D': 5.917595996658985, 'J_D': 3884.2896906743035, 'W_D_1KI': 5.917595996658985, 'J_D_1KI': 5.917595996658985}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..7258aa7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 141920, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.197850227355957, "TIME_S_1KI": 0.0718563291104563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 278.53162281036373, "W": 19.575620643059622, "J_1KI": 1.9625959893627658, "W_1KI": 0.1379341928062262, "W_D": 4.584620643059623, "J_D": 65.2322524514198, "W_D_1KI": 0.032304260449969154, "J_D_1KI": 0.00022762303022808028}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..5a62a56
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08288788795471191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
+       col_indices=tensor([8276, 8512, 1857, 9453, 5594, 1786,  645, 6447, 5709,
+                           9757, 8196, 1491, 9162, 8503,  189,   10, 1296, 6876,
+                            119, 8563,  749, 1550, 2318, 8246, 5733, 8006, 6329,
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+                      0.8254, 0.8920, 0.2868, 0.2736, 0.3612, 0.1545]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4940, 0.1637, 0.9163,  ..., 0.2332, 0.8903, 0.4879])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.08288788795471191 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126677 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.372220277786255}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([4171, 3057, 7209, 2948, 4077, 8602, 6723, 7385, 3185,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.9110, 0.9462, 0.7927,  ..., 0.0987, 0.6084, 0.0709])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.372220277786255 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141920 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.197850227355957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0747, 0.3561, 0.1255,  ..., 0.0664, 0.4841, 0.3262])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.197850227355957 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.197850227355957 seconds
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diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..282c250
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52721, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.043588876724243, "TIME_S_1KI": 0.20947229522816796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.41147632598876, "W": 21.8334966893401, "J_1KI": 5.8878146530981725, "W_1KI": 0.4141328254270613, "W_D": 3.2964966893401026, "J_D": 46.86699609327319, "W_D_1KI": 0.06252720337892116, "J_D_1KI": 0.0011860018470613448}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..2fcd1ba
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.24570083618164062}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([7274, 1823, 9481,  ..., 3720, 7669, 6157]),
+       values=tensor([0.0699, 0.4403, 0.9366,  ..., 0.7220, 0.3462, 0.9666]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3652, 0.9468, 0.8818,  ..., 0.3143, 0.5478, 0.8274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.24570083618164062 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 42734 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.510959386825562}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([3889, 8009,  975,  ...,  383, 3476, 3024]),
+       values=tensor([0.2888, 0.9236, 0.0703,  ..., 0.2234, 0.4670, 0.5913]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.8206, 0.5304, 0.1258,  ..., 0.8056, 0.8493, 0.1547])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 8.510959386825562 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52721 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.043588876724243}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([3785, 9007, 8216,  ..., 3831, 7720, 5051]),
+       values=tensor([0.5982, 0.6190, 0.5749,  ..., 0.7670, 0.1287, 0.1052]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6986, 0.7258, 0.0612,  ..., 0.6419, 0.7078, 0.3008])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 11.043588876724243 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([3785, 9007, 8216,  ..., 3831, 7720, 5051]),
+       values=tensor([0.5982, 0.6190, 0.5749,  ..., 0.7670, 0.1287, 0.1052]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6986, 0.7258, 0.0612,  ..., 0.6419, 0.7078, 0.3008])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 11.043588876724243 seconds
+
+[20.64, 20.64, 20.64, 20.64, 20.48, 20.52, 20.4, 20.52, 20.6, 20.56]
+[20.56, 20.6, 20.72, 22.44, 23.64, 25.6, 26.32, 25.84, 25.0, 23.2, 23.28, 23.24, 23.44, 23.44]
+14.217213153839111
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52721, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.043588876724243, 'TIME_S_1KI': 0.20947229522816796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.41147632598876, 'W': 21.8334966893401}
+[20.64, 20.64, 20.64, 20.64, 20.48, 20.52, 20.4, 20.52, 20.6, 20.56, 20.36, 20.48, 20.48, 20.44, 20.56, 20.68, 20.88, 20.96, 20.72, 20.64]
+370.74
+18.537
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52721, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.043588876724243, 'TIME_S_1KI': 0.20947229522816796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.41147632598876, 'W': 21.8334966893401, 'J_1KI': 5.8878146530981725, 'W_1KI': 0.4141328254270613, 'W_D': 3.2964966893401026, 'J_D': 46.86699609327319, 'W_D_1KI': 0.06252720337892116, 'J_D_1KI': 0.0011860018470613448}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..8015441
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654, "TIME_S_1KI": 78.25872588157654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4354.838327121737, "W": 46.22946318790178, "J_1KI": 4354.838327121737, "W_1KI": 46.22946318790178, "W_D": 27.22746318790178, "J_D": 2564.8405165128734, "W_D_1KI": 27.22746318790178, "J_D_1KI": 27.22746318790178}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..f9d34b2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 78.25872588157654}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       62,      108,  ..., 24999902,
+                            24999957, 25000000]),
+       col_indices=tensor([  8122,  13146,  17492,  ..., 475877, 485043,
+                           496973]),
+       values=tensor([0.3209, 0.8894, 0.3965,  ..., 0.0269, 0.4047, 0.8747]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9161, 0.4114, 0.3584,  ..., 0.3416, 0.2120, 0.9282])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 78.25872588157654 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       62,      108,  ..., 24999902,
+                            24999957, 25000000]),
+       col_indices=tensor([  8122,  13146,  17492,  ..., 475877, 485043,
+                           496973]),
+       values=tensor([0.3209, 0.8894, 0.3965,  ..., 0.0269, 0.4047, 0.8747]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9161, 0.4114, 0.3584,  ..., 0.3416, 0.2120, 0.9282])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 78.25872588157654 seconds
+
+[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8]
+[20.96, 20.88, 21.6, 21.6, 22.56, 24.32, 26.6, 27.68, 30.32, 31.76, 31.8, 31.36, 31.8, 32.64, 38.12, 43.32, 49.08, 53.24, 53.16, 52.56, 52.56, 52.56, 52.6, 52.28, 52.68, 53.0, 53.04, 53.24, 53.08, 53.48, 53.36, 53.16, 53.16, 52.68, 52.84, 52.88, 53.12, 52.96, 52.88, 53.2, 53.16, 53.0, 53.0, 52.96, 53.12, 52.84, 52.76, 53.08, 52.96, 52.84, 52.96, 52.92, 53.12, 53.2, 53.04, 53.28, 53.16, 53.12, 52.68, 52.96, 52.88, 52.72, 53.04, 53.08, 52.76, 52.76, 52.92, 53.32, 53.28, 53.32, 53.44, 53.28, 53.32, 53.4, 53.48, 53.48, 53.4, 53.4, 53.44, 53.12, 53.32, 53.44, 53.56, 53.52, 53.4, 53.36, 53.12, 53.12, 53.12, 53.16]
+94.20049524307251
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178}
+[21.04, 21.2, 21.24, 21.2, 21.44, 21.2, 21.08, 21.04, 20.84, 20.8, 21.64, 21.52, 21.28, 21.2, 21.12, 20.8, 20.84, 20.76, 21.0, 21.08]
+380.03999999999996
+19.002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 78.25872588157654, 'TIME_S_1KI': 78.25872588157654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4354.838327121737, 'W': 46.22946318790178, 'J_1KI': 4354.838327121737, 'W_1KI': 46.22946318790178, 'W_D': 27.22746318790178, 'J_D': 2564.8405165128734, 'W_D_1KI': 27.22746318790178, 'J_D_1KI': 27.22746318790178}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..35c105f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1484, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.88543152809143, "TIME_S_1KI": 7.335196447500964, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 480.2575412559509, "W": 32.95171788766838, "J_1KI": 323.6236800916111, "W_1KI": 22.204661649372223, "W_D": 16.90171788766838, "J_D": 246.33548707246783, "W_D_1KI": 11.389297767970607, "J_D_1KI": 7.67472895415809}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..2875e7e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 7.073613166809082}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      13,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([  8141,  69274, 149925,  ..., 390687, 407872,
+                           439375]),
+       values=tensor([0.4271, 0.3560, 0.2859,  ..., 0.3294, 0.0849, 0.5690]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1896, 0.3447, 0.8973,  ..., 0.8957, 0.5716, 0.6993])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 7.073613166809082 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1484 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.88543152809143}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      15,  ..., 2499994,
+                            2500000, 2500000]),
+       col_indices=tensor([ 19808,  30523,  42041,  ..., 253465, 473291,
+                           475423]),
+       values=tensor([0.2655, 0.1335, 0.5252,  ..., 0.0072, 0.8874, 0.1974]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7673, 0.2797, 0.0430,  ..., 0.8352, 0.7956, 0.1250])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.88543152809143 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      15,  ..., 2499994,
+                            2500000, 2500000]),
+       col_indices=tensor([ 19808,  30523,  42041,  ..., 253465, 473291,
+                           475423]),
+       values=tensor([0.2655, 0.1335, 0.5252,  ..., 0.0072, 0.8874, 0.1974]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7673, 0.2797, 0.0430,  ..., 0.8352, 0.7956, 0.1250])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.88543152809143 seconds
+
+[20.16, 19.76, 19.4, 19.48, 19.12, 18.68, 18.6, 18.12, 18.12, 17.68]
+[17.44, 17.16, 17.36, 21.36, 23.28, 27.36, 35.28, 38.6, 44.16, 47.92, 48.84, 48.56, 48.96, 49.04]
+14.574582815170288
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1484, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.88543152809143, 'TIME_S_1KI': 7.335196447500964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 480.2575412559509, 'W': 32.95171788766838}
+[20.16, 19.76, 19.4, 19.48, 19.12, 18.68, 18.6, 18.12, 18.12, 17.68, 16.68, 16.56, 16.56, 16.56, 16.72, 16.64, 16.76, 17.08, 17.04, 17.08]
+321.0
+16.05
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1484, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.88543152809143, 'TIME_S_1KI': 7.335196447500964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 480.2575412559509, 'W': 32.95171788766838, 'J_1KI': 323.6236800916111, 'W_1KI': 22.204661649372223, 'W_D': 16.90171788766838, 'J_D': 246.33548707246783, 'W_D_1KI': 11.389297767970607, 'J_D_1KI': 7.67472895415809}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..e64af38
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 37.50764799118042, "TIME_S_1KI": 37.50764799118042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2045.1465811538696, "W": 43.475713756728496, "J_1KI": 2045.1465811538699, "W_1KI": 43.475713756728496, "W_D": 24.798713756728496, "J_D": 1166.5594483480452, "W_D_1KI": 24.798713756728496, "J_D_1KI": 24.798713756728496}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..c540d96
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 37.50764799118042}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       37,       66,  ..., 12499959,
+                            12499977, 12500000]),
+       col_indices=tensor([  2833,  12397,  17330,  ..., 457394, 482426,
+                           493028]),
+       values=tensor([0.7637, 0.4211, 0.1191,  ..., 0.6468, 0.8330, 0.4871]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3810, 0.7581, 0.5448,  ..., 0.8790, 0.4682, 0.1184])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 37.50764799118042 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       37,       66,  ..., 12499959,
+                            12499977, 12500000]),
+       col_indices=tensor([  2833,  12397,  17330,  ..., 457394, 482426,
+                           493028]),
+       values=tensor([0.7637, 0.4211, 0.1191,  ..., 0.6468, 0.8330, 0.4871]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3810, 0.7581, 0.5448,  ..., 0.8790, 0.4682, 0.1184])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 37.50764799118042 seconds
+
+[21.08, 20.96, 20.88, 20.72, 20.6, 20.6, 20.64, 20.2, 20.28, 20.6]
+[20.72, 20.68, 21.2, 23.0, 24.88, 26.4, 29.84, 29.72, 30.96, 36.72, 36.72, 41.28, 45.64, 50.76, 52.76, 52.48, 53.28, 52.92, 52.8, 52.76, 52.48, 52.52, 52.68, 52.72, 53.4, 53.44, 53.4, 53.36, 53.12, 53.4, 53.2, 53.0, 53.0, 53.24, 53.08, 52.92, 53.0, 52.72, 52.8, 52.64, 52.8, 52.96, 52.8, 52.8, 52.68]
+47.04112720489502
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 37.50764799118042, 'TIME_S_1KI': 37.50764799118042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.1465811538696, 'W': 43.475713756728496}
+[21.08, 20.96, 20.88, 20.72, 20.6, 20.6, 20.64, 20.2, 20.28, 20.6, 20.92, 21.12, 21.0, 20.84, 20.92, 20.72, 20.72, 20.56, 21.0, 20.96]
+373.54
+18.677
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 37.50764799118042, 'TIME_S_1KI': 37.50764799118042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.1465811538696, 'W': 43.475713756728496, 'J_1KI': 2045.1465811538699, 'W_1KI': 43.475713756728496, 'W_D': 24.798713756728496, 'J_D': 1166.5594483480452, 'W_D_1KI': 24.798713756728496, 'J_D_1KI': 24.798713756728496}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..0acd95a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3392, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.727018594741821, "TIME_S_1KI": 3.1624465196762443, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 423.8805270671845, "W": 29.00744018011372, "J_1KI": 124.96477802688223, "W_1KI": 8.55172175121277, "W_D": 13.914440180113719, "J_D": 203.32922177100187, "W_D_1KI": 4.102134487061827, "J_D_1KI": 1.2093556860441705}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..f123524
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 3.0953831672668457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249991,
+                            250000]),
+       col_indices=tensor([  782, 10679, 21591,  ..., 21721, 25862, 26402]),
+       values=tensor([0.1080, 0.2599, 0.9753,  ..., 0.8598, 0.0309, 0.7621]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0624, 0.3415, 0.4601,  ..., 0.0482, 0.7737, 0.1465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 3.0953831672668457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3392 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.727018594741821}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 249992, 249997,
+                            250000]),
+       col_indices=tensor([29888, 37512, 45145,  ..., 10362, 27481, 28096]),
+       values=tensor([0.5987, 0.4413, 0.1210,  ..., 0.9023, 0.1888, 0.1246]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0260, 0.0462, 0.3716,  ..., 0.4992, 0.3586, 0.2225])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.727018594741821 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 249992, 249997,
+                            250000]),
+       col_indices=tensor([29888, 37512, 45145,  ..., 10362, 27481, 28096]),
+       values=tensor([0.5987, 0.4413, 0.1210,  ..., 0.9023, 0.1888, 0.1246]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0260, 0.0462, 0.3716,  ..., 0.4992, 0.3586, 0.2225])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.727018594741821 seconds
+
+[16.52, 16.28, 16.24, 16.16, 16.44, 16.44, 16.6, 16.52, 16.56, 16.68]
+[16.44, 16.64, 17.44, 19.52, 22.44, 27.08, 32.32, 35.8, 38.92, 39.84, 40.12, 40.12, 40.24, 40.6]
+14.612820863723755
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3392, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.727018594741821, 'TIME_S_1KI': 3.1624465196762443, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 423.8805270671845, 'W': 29.00744018011372}
+[16.52, 16.28, 16.24, 16.16, 16.44, 16.44, 16.6, 16.52, 16.56, 16.68, 16.84, 16.68, 16.84, 17.24, 17.32, 17.48, 17.4, 17.24, 16.96, 16.88]
+301.86
+15.093
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3392, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.727018594741821, 'TIME_S_1KI': 3.1624465196762443, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 423.8805270671845, 'W': 29.00744018011372, 'J_1KI': 124.96477802688223, 'W_1KI': 8.55172175121277, 'W_D': 13.914440180113719, 'J_D': 203.32922177100187, 'W_D_1KI': 4.102134487061827, 'J_D_1KI': 1.2093556860441705}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..3f36d4c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 29.441463470458984, "TIME_S_1KI": 29.441463470458984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.5749865341186, "W": 32.87033016720936, "J_1KI": 1067.5749865341186, "W_1KI": 32.87033016720936, "W_D": 17.51733016720936, "J_D": 568.9344591989515, "W_D_1KI": 17.51733016720936, "J_D_1KI": 17.51733016720936}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..c095d19
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 29.441463470458984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      37,      79,  ..., 2499907,
+                            2499951, 2500000]),
+       col_indices=tensor([ 2466,  3763,  4276,  ..., 47502, 48879, 49149]),
+       values=tensor([0.0148, 0.9908, 0.2997,  ..., 0.9281, 0.7443, 0.4383]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6700, 0.5614, 0.5608,  ..., 0.8928, 0.8615, 0.5607])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 29.441463470458984 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      37,      79,  ..., 2499907,
+                            2499951, 2500000]),
+       col_indices=tensor([ 2466,  3763,  4276,  ..., 47502, 48879, 49149]),
+       values=tensor([0.0148, 0.9908, 0.2997,  ..., 0.9281, 0.7443, 0.4383]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6700, 0.5614, 0.5608,  ..., 0.8928, 0.8615, 0.5607])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 29.441463470458984 seconds
+
+[16.92, 17.36, 17.08, 16.84, 17.2, 17.28, 17.4, 17.44, 17.36, 17.36]
+[17.08, 17.24, 17.08, 21.52, 22.36, 25.24, 29.48, 32.12, 34.64, 38.4, 38.76, 38.64, 38.8, 39.0, 39.0, 39.44, 39.4, 39.28, 39.32, 39.32, 39.24, 39.12, 39.0, 39.08, 39.32, 39.36, 39.28, 39.28, 39.16, 38.92, 39.08]
+32.47837734222412
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 29.441463470458984, 'TIME_S_1KI': 29.441463470458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.5749865341186, 'W': 32.87033016720936}
+[16.92, 17.36, 17.08, 16.84, 17.2, 17.28, 17.4, 17.44, 17.36, 17.36, 16.68, 16.88, 16.76, 16.92, 16.84, 17.04, 17.0, 17.0, 16.68, 17.0]
+307.06000000000006
+15.353000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 29.441463470458984, 'TIME_S_1KI': 29.441463470458984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.5749865341186, 'W': 32.87033016720936, 'J_1KI': 1067.5749865341186, 'W_1KI': 32.87033016720936, 'W_D': 17.51733016720936, 'J_D': 568.9344591989515, 'W_D_1KI': 17.51733016720936, 'J_D_1KI': 17.51733016720936}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..cc8d9c0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.01.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": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317, "TIME_S_1KI": 324.79648518562317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 11877.425473241823, "W": 35.646067141867775, "J_1KI": 11877.425473241823, "W_1KI": 35.646067141867775, "W_D": 16.970067141867776, "J_D": 5654.50059192373, "W_D_1KI": 16.970067141867776, "J_D_1KI": 16.970067141867776}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..b2a5634
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 324.79648518562317}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      495,      976,  ..., 24999061,
+                            24999546, 25000000]),
+       col_indices=tensor([  151,   490,   496,  ..., 49361, 49363, 49505]),
+       values=tensor([0.8177, 0.6830, 0.3837,  ..., 0.7178, 0.0658, 0.9045]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7632, 0.2578, 0.8207,  ..., 0.6267, 0.5426, 0.4264])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 324.79648518562317 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      495,      976,  ..., 24999061,
+                            24999546, 25000000]),
+       col_indices=tensor([  151,   490,   496,  ..., 49361, 49363, 49505]),
+       values=tensor([0.8177, 0.6830, 0.3837,  ..., 0.7178, 0.0658, 0.9045]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7632, 0.2578, 0.8207,  ..., 0.6267, 0.5426, 0.4264])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 324.79648518562317 seconds
+
+[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88]
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+333.20437359809875
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775}
+[20.76, 20.6, 20.88, 20.96, 20.84, 20.88, 20.88, 20.68, 20.8, 20.88, 20.44, 20.44, 20.64, 20.68, 20.68, 20.68, 20.68, 20.84, 20.88, 20.88]
+373.52
+18.676
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 324.79648518562317, 'TIME_S_1KI': 324.79648518562317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 11877.425473241823, 'W': 35.646067141867775, 'J_1KI': 11877.425473241823, 'W_1KI': 35.646067141867775, 'W_D': 16.970067141867776, 'J_D': 5654.50059192373, 'W_D_1KI': 16.970067141867776, 'J_D_1KI': 16.970067141867776}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..719c8cd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 20098, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.650188207626343, "TIME_S_1KI": 0.5299128374776765, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 402.8303679275513, "W": 27.52967890413959, "J_1KI": 20.04330619601708, "W_1KI": 1.3697720621026763, "W_D": 12.350678904139592, "J_D": 180.72235947370535, "W_D_1KI": 0.6145227835674988, "J_D_1KI": 0.030576315233729664}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..b0c7cd1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.648245096206665}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24999, 25000]),
+       col_indices=tensor([  889, 16856, 49649,  ..., 20622, 24354, 47394]),
+       values=tensor([0.8512, 0.0995, 0.9072,  ..., 0.9114, 0.3857, 0.4483]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8531, 0.5584, 0.8209,  ..., 0.8853, 0.7506, 0.6837])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.648245096206665 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 16197 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.461615800857544}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([37259, 33129, 13575,  ..., 31298, 24333,  9136]),
+       values=tensor([0.0302, 0.8728, 0.1875,  ..., 0.5590, 0.6136, 0.6206]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6191, 0.3887, 0.4199,  ..., 0.2754, 0.8424, 0.8817])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 8.461615800857544 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 20098 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.650188207626343}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([   35,  8013, 35741,  ..., 26171, 43365,  6398]),
+       values=tensor([0.7135, 0.5997, 0.1893,  ..., 0.8752, 0.2236, 0.9882]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7523, 0.4685, 0.7648,  ..., 0.0829, 0.9708, 0.7467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.650188207626343 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([   35,  8013, 35741,  ..., 26171, 43365,  6398]),
+       values=tensor([0.7135, 0.5997, 0.1893,  ..., 0.8752, 0.2236, 0.9882]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7523, 0.4685, 0.7648,  ..., 0.0829, 0.9708, 0.7467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.650188207626343 seconds
+
+[16.64, 16.56, 16.56, 16.96, 17.08, 17.16, 16.88, 16.96, 16.48, 16.36]
+[16.44, 16.44, 16.68, 18.12, 19.04, 22.72, 28.64, 33.16, 36.92, 39.76, 39.32, 39.28, 39.68, 39.6]
+14.632585048675537
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 20098, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.650188207626343, 'TIME_S_1KI': 0.5299128374776765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 402.8303679275513, 'W': 27.52967890413959}
+[16.64, 16.56, 16.56, 16.96, 17.08, 17.16, 16.88, 16.96, 16.48, 16.36, 17.12, 16.92, 16.68, 16.88, 16.76, 16.8, 16.8, 17.12, 17.32, 17.2]
+303.58
+15.178999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 20098, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.650188207626343, 'TIME_S_1KI': 0.5299128374776765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 402.8303679275513, 'W': 27.52967890413959, 'J_1KI': 20.04330619601708, 'W_1KI': 1.3697720621026763, 'W_D': 12.350678904139592, 'J_D': 180.72235947370535, 'W_D_1KI': 0.6145227835674988, 'J_D_1KI': 0.030576315233729664}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..f5cb3fd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6265, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.399809837341309, "TIME_S_1KI": 1.6599856085141753, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 409.2376235961914, "W": 30.158092261325162, "J_1KI": 65.32124877832264, "W_1KI": 4.813741781536339, "W_D": 11.570092261325161, "J_D": 157.0032023506164, "W_D_1KI": 1.846782483850784, "J_D_1KI": 0.2947777308620565}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..80257e9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6757559776306152}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      4,  ..., 124996, 124997,
+                            125000]),
+       col_indices=tensor([11324, 36531, 41582,  ..., 26561, 37075, 42675]),
+       values=tensor([0.0907, 0.5500, 0.9495,  ..., 0.7742, 0.3202, 0.5187]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.4295, 0.8994, 0.1269,  ..., 0.0289, 0.7051, 0.4729])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 1.6757559776306152 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6265 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.399809837341309}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124998, 125000,
+                            125000]),
+       col_indices=tensor([ 5059, 34750, 35724,  ..., 34703,  6591, 31118]),
+       values=tensor([0.4217, 0.1867, 0.1593,  ..., 0.5339, 0.2274, 0.5888]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7798, 0.1756, 0.8709,  ..., 0.5047, 0.8577, 0.3016])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.399809837341309 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124998, 125000,
+                            125000]),
+       col_indices=tensor([ 5059, 34750, 35724,  ..., 34703,  6591, 31118]),
+       values=tensor([0.4217, 0.1867, 0.1593,  ..., 0.5339, 0.2274, 0.5888]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7798, 0.1756, 0.8709,  ..., 0.5047, 0.8577, 0.3016])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.399809837341309 seconds
+
+[20.92, 20.84, 20.96, 20.84, 20.68, 20.64, 20.84, 20.76, 20.84, 21.08]
+[21.08, 21.16, 21.84, 21.84, 23.04, 26.04, 31.36, 35.8, 40.08, 43.12, 43.68, 43.84, 43.84]
+13.569745063781738
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6265, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.399809837341309, 'TIME_S_1KI': 1.6599856085141753, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 409.2376235961914, 'W': 30.158092261325162}
+[20.92, 20.84, 20.96, 20.84, 20.68, 20.64, 20.84, 20.76, 20.84, 21.08, 20.04, 19.96, 20.16, 20.28, 20.56, 20.88, 20.76, 20.68, 20.68, 20.76]
+371.76000000000005
+18.588
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6265, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.399809837341309, 'TIME_S_1KI': 1.6599856085141753, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 409.2376235961914, 'W': 30.158092261325162, 'J_1KI': 65.32124877832264, 'W_1KI': 4.813741781536339, 'W_D': 11.570092261325161, 'J_D': 157.0032023506164, 'W_D_1KI': 1.846782483850784, 'J_D_1KI': 0.2947777308620565}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..c3c7f57
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 96690, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.663233041763306, "TIME_S_1KI": 0.11028268736956569, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 262.53495136260995, "W": 18.48739374467239, "J_1KI": 2.7152234084456506, "W_1KI": 0.19120274841940624, "W_D": 3.6973937446723912, "J_D": 52.505783147812, "W_D_1KI": 0.038239670541652615, "J_D_1KI": 0.0003954873362462779}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..61cae4f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.11520600318908691}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([4712, 1560, 1507,  ..., 2651,  244, 3781]),
+       values=tensor([0.1646, 0.3564, 0.3355,  ..., 0.5785, 0.6935, 0.4198]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6842, 0.2217, 0.0992,  ..., 0.1824, 0.3701, 0.4149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.11520600318908691 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 91141 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.897401094436646}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1451, 2006, 3586,  ..., 3975, 4446, 2086]),
+       values=tensor([0.6609, 0.8356, 0.1353,  ..., 0.7408, 0.3224, 0.8471]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2892, 0.1223, 0.3419,  ..., 0.7884, 0.7802, 0.0113])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 9.897401094436646 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 96690 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.663233041763306}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2205, 2444, 2425,  ..., 3761, 2544, 2990]),
+       values=tensor([0.2656, 0.9114, 0.3983,  ..., 0.8675, 0.7517, 0.7885]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1659, 0.6941, 0.7553,  ..., 0.7483, 0.8019, 0.7277])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.663233041763306 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2205, 2444, 2425,  ..., 3761, 2544, 2990]),
+       values=tensor([0.2656, 0.9114, 0.3983,  ..., 0.8675, 0.7517, 0.7885]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1659, 0.6941, 0.7553,  ..., 0.7483, 0.8019, 0.7277])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.663233041763306 seconds
+
+[16.32, 16.04, 16.32, 16.56, 16.56, 16.64, 16.96, 16.96, 16.76, 16.76]
+[16.76, 16.88, 17.08, 18.96, 20.48, 21.96, 22.64, 22.36, 21.0, 19.8, 19.8, 19.6, 19.72, 19.8]
+14.20075511932373
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 96690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.663233041763306, 'TIME_S_1KI': 0.11028268736956569, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 262.53495136260995, 'W': 18.48739374467239}
+[16.32, 16.04, 16.32, 16.56, 16.56, 16.64, 16.96, 16.96, 16.76, 16.76, 16.48, 16.4, 16.2, 16.28, 16.28, 16.12, 16.12, 16.28, 16.36, 16.36]
+295.79999999999995
+14.789999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 96690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.663233041763306, 'TIME_S_1KI': 0.11028268736956569, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 262.53495136260995, 'W': 18.48739374467239, 'J_1KI': 2.7152234084456506, 'W_1KI': 0.19120274841940624, 'W_D': 3.6973937446723912, 'J_D': 52.505783147812, 'W_D_1KI': 0.038239670541652615, 'J_D_1KI': 0.0003954873362462779}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..810fc32
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 17852, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.54783010482788, "TIME_S_1KI": 0.5908486502816425, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 261.2027643966674, "W": 18.408959018952206, "J_1KI": 14.6315686979984, "W_1KI": 1.0311986902841253, "W_D": 3.4579590189522076, "J_D": 49.0646132674217, "W_D_1KI": 0.19370149109075777, "J_D_1KI": 0.010850408418707023}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..5d5c1f0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6220724582672119}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ..., 24988, 24993, 25000]),
+       col_indices=tensor([2208, 3192, 3630,  ..., 2657, 2751, 4682]),
+       values=tensor([0.3516, 0.9043, 0.4344,  ..., 0.9354, 0.2858, 0.8708]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1847, 0.5253, 0.6086,  ..., 0.9552, 0.0514, 0.1920])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.6220724582672119 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 16879 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.927400827407837}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    17,  ..., 24988, 24992, 25000]),
+       col_indices=tensor([1765, 1880, 2380,  ..., 3402, 4335, 4928]),
+       values=tensor([0.8113, 0.6065, 0.0419,  ..., 0.8515, 0.2786, 0.9879]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6729, 0.2847, 0.7618,  ..., 0.5837, 0.8359, 0.7138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.927400827407837 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17852 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.54783010482788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,    10,  ..., 24988, 24991, 25000]),
+       col_indices=tensor([ 732, 2237, 2424,  ..., 1459, 1662, 4133]),
+       values=tensor([0.5131, 0.9715, 0.7721,  ..., 0.9714, 0.5730, 0.7149]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7581, 0.5458, 0.9932,  ..., 0.3205, 0.5744, 0.9847])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.54783010482788 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,    10,  ..., 24988, 24991, 25000]),
+       col_indices=tensor([ 732, 2237, 2424,  ..., 1459, 1662, 4133]),
+       values=tensor([0.5131, 0.9715, 0.7721,  ..., 0.9714, 0.5730, 0.7149]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7581, 0.5458, 0.9932,  ..., 0.3205, 0.5744, 0.9847])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.54783010482788 seconds
+
+[16.36, 16.36, 16.32, 16.6, 16.8, 17.04, 17.2, 17.08, 16.8, 16.52]
+[16.48, 16.4, 17.36, 19.52, 19.52, 21.32, 21.84, 22.56, 21.0, 20.28, 19.68, 19.84, 19.92, 19.92]
+14.188893795013428
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17852, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.54783010482788, 'TIME_S_1KI': 0.5908486502816425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 261.2027643966674, 'W': 18.408959018952206}
+[16.36, 16.36, 16.32, 16.6, 16.8, 17.04, 17.2, 17.08, 16.8, 16.52, 16.4, 16.36, 16.32, 16.32, 16.76, 16.96, 16.72, 16.44, 16.2, 16.2]
+299.02
+14.950999999999999
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17852, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.54783010482788, 'TIME_S_1KI': 0.5908486502816425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 261.2027643966674, 'W': 18.408959018952206, 'J_1KI': 14.6315686979984, 'W_1KI': 1.0311986902841253, 'W_D': 3.4579590189522076, 'J_D': 49.0646132674217, 'W_D_1KI': 0.19370149109075777, 'J_D_1KI': 0.010850408418707023}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..cca3281
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1933, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.477078676223755, "TIME_S_1KI": 5.420113127896407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 268.2967868804932, "W": 18.818848497168876, "J_1KI": 138.79813082281075, "W_1KI": 9.7355656995183, "W_D": 3.947848497168877, "J_D": 56.283734206199696, "W_D_1KI": 2.0423427300408057, "J_D_1KI": 1.0565663373206444}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..3224ea8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.431562900543213}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     36,     79,  ..., 249900, 249941,
+                            250000]),
+       col_indices=tensor([  80,  388,  404,  ..., 4737, 4807, 4857]),
+       values=tensor([0.4885, 0.5213, 0.1721,  ..., 0.5810, 0.1625, 0.7107]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1545, 0.4718, 0.9539,  ..., 0.2261, 0.6017, 0.7355])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 5.431562900543213 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1933 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.477078676223755}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     40,     89,  ..., 249917, 249964,
+                            250000]),
+       col_indices=tensor([ 165,  177,  195,  ..., 4656, 4719, 4927]),
+       values=tensor([0.2100, 0.9405, 0.2582,  ..., 0.7931, 0.5258, 0.8197]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9173, 0.2185, 0.4076,  ..., 0.3362, 0.1795, 0.2923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.477078676223755 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     40,     89,  ..., 249917, 249964,
+                            250000]),
+       col_indices=tensor([ 165,  177,  195,  ..., 4656, 4719, 4927]),
+       values=tensor([0.2100, 0.9405, 0.2582,  ..., 0.7931, 0.5258, 0.8197]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9173, 0.2185, 0.4076,  ..., 0.3362, 0.1795, 0.2923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.477078676223755 seconds
+
+[17.04, 17.04, 16.96, 17.04, 16.64, 16.4, 16.44, 16.32, 16.24, 16.36]
+[16.44, 16.52, 17.76, 19.52, 21.52, 21.52, 22.36, 22.76, 21.68, 21.36, 19.84, 20.04, 20.0, 20.08]
+14.25681209564209
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1933, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.477078676223755, 'TIME_S_1KI': 5.420113127896407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 268.2967868804932, 'W': 18.818848497168876}
+[17.04, 17.04, 16.96, 17.04, 16.64, 16.4, 16.44, 16.32, 16.24, 16.36, 16.4, 16.24, 16.32, 16.36, 16.36, 16.48, 16.68, 16.56, 16.4, 16.08]
+297.41999999999996
+14.870999999999999
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1933, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.477078676223755, 'TIME_S_1KI': 5.420113127896407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 268.2967868804932, 'W': 18.818848497168876, 'J_1KI': 138.79813082281075, 'W_1KI': 9.7355656995183, 'W_D': 3.947848497168877, 'J_D': 56.283734206199696, 'W_D_1KI': 2.0423427300408057, 'J_D_1KI': 1.0565663373206444}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..8f974e7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.830852508544922, "TIME_S_1KI": 26.830852508544922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 724.3529372215271, "W": 23.831275335163394, "J_1KI": 724.3529372215271, "W_1KI": 23.831275335163394, "W_D": 5.2862753351633955, "J_D": 160.67663237214092, "W_D_1KI": 5.2862753351633955, "J_D_1KI": 5.2862753351633955}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..e6b75ff
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.830852508544922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     229,     490,  ..., 1249494,
+                            1249740, 1250000]),
+       col_indices=tensor([  18,   28,   58,  ..., 4928, 4934, 4938]),
+       values=tensor([0.5574, 0.5312, 0.0435,  ..., 0.2785, 0.4540, 0.5116]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0667, 0.4882, 0.3630,  ..., 0.8923, 0.8020, 0.4280])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.830852508544922 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     229,     490,  ..., 1249494,
+                            1249740, 1250000]),
+       col_indices=tensor([  18,   28,   58,  ..., 4928, 4934, 4938]),
+       values=tensor([0.5574, 0.5312, 0.0435,  ..., 0.2785, 0.4540, 0.5116]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0667, 0.4882, 0.3630,  ..., 0.8923, 0.8020, 0.4280])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.830852508544922 seconds
+
+[20.52, 20.64, 20.8, 20.76, 20.84, 20.84, 20.8, 20.88, 20.8, 20.88]
+[20.64, 20.72, 20.92, 24.36, 26.68, 28.84, 29.6, 27.08, 27.08, 26.84, 23.8, 24.0, 24.32, 24.44, 24.48, 24.52, 24.36, 24.12, 24.24, 24.2, 24.2, 24.28, 24.28, 24.28, 24.2, 24.28, 24.16, 24.12, 24.08, 24.08]
+30.395055532455444
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.830852508544922, 'TIME_S_1KI': 26.830852508544922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.3529372215271, 'W': 23.831275335163394}
+[20.52, 20.64, 20.8, 20.76, 20.84, 20.84, 20.8, 20.88, 20.8, 20.88, 20.44, 20.56, 20.56, 20.6, 20.6, 20.48, 20.32, 20.2, 20.2, 20.2]
+370.9
+18.544999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.830852508544922, 'TIME_S_1KI': 26.830852508544922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.3529372215271, 'W': 23.831275335163394, 'J_1KI': 724.3529372215271, 'W_1KI': 23.831275335163394, 'W_D': 5.2862753351633955, 'J_D': 160.67663237214092, 'W_D_1KI': 5.2862753351633955, 'J_D_1KI': 5.2862753351633955}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..db59bda
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 52.60684037208557, "TIME_S_1KI": 52.60684037208557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1396.747187700272, "W": 24.19749919495284, "J_1KI": 1396.747187700272, "W_1KI": 24.19749919495284, "W_D": 5.574499194952839, "J_D": 321.77565171742464, "W_D_1KI": 5.574499194952839, "J_D_1KI": 5.574499194952839}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..919a009
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 52.60684037208557}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     495,    1009,  ..., 2499036,
+                            2499501, 2500000]),
+       col_indices=tensor([   3,    5,    7,  ..., 4975, 4989, 4996]),
+       values=tensor([0.3153, 0.9204, 0.9402,  ..., 0.8644, 0.1243, 0.2567]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0882, 0.6551, 0.2953,  ..., 0.5102, 0.2382, 0.8339])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 52.60684037208557 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     495,    1009,  ..., 2499036,
+                            2499501, 2500000]),
+       col_indices=tensor([   3,    5,    7,  ..., 4975, 4989, 4996]),
+       values=tensor([0.3153, 0.9204, 0.9402,  ..., 0.8644, 0.1243, 0.2567]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0882, 0.6551, 0.2953,  ..., 0.5102, 0.2382, 0.8339])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 52.60684037208557 seconds
+
+[20.32, 20.32, 20.52, 20.36, 20.68, 20.64, 20.64, 20.84, 20.84, 20.64]
+[21.04, 20.8, 23.84, 25.56, 28.32, 28.32, 30.0, 30.76, 27.04, 26.12, 23.96, 24.04, 23.96, 24.4, 24.48, 24.44, 24.4, 24.4, 24.4, 24.44, 24.52, 24.64, 24.44, 24.48, 24.36, 24.52, 24.36, 24.36, 24.28, 24.28, 24.2, 24.2, 24.04, 24.12, 24.04, 24.04, 24.08, 24.16, 24.12, 23.96, 24.0, 24.04, 24.04, 23.92, 23.92, 24.16, 24.32, 24.52, 24.68, 24.64, 24.24, 24.12, 24.2, 24.2, 24.2, 24.36, 24.36]
+57.72279095649719
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 52.60684037208557, 'TIME_S_1KI': 52.60684037208557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.747187700272, 'W': 24.19749919495284}
+[20.32, 20.32, 20.52, 20.36, 20.68, 20.64, 20.64, 20.84, 20.84, 20.64, 20.48, 20.4, 20.44, 20.76, 20.76, 20.96, 21.12, 21.08, 21.0, 20.76]
+372.46000000000004
+18.623
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 52.60684037208557, 'TIME_S_1KI': 52.60684037208557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.747187700272, 'W': 24.19749919495284, 'J_1KI': 1396.747187700272, 'W_1KI': 24.19749919495284, 'W_D': 5.574499194952839, 'J_D': 321.77565171742464, 'W_D_1KI': 5.574499194952839, 'J_D_1KI': 5.574499194952839}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..72274ad
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.2479407787323, "TIME_S_1KI": 105.2479407787323, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2877.626370677949, "W": 24.097306664358964, "J_1KI": 2877.626370677949, "W_1KI": 24.097306664358964, "W_D": 5.6223066643589625, "J_D": 671.3985984718811, "W_D_1KI": 5.6223066643589625, "J_D_1KI": 5.6223066643589625}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..f4956b7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.2 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.2479407787323}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     987,    1961,  ..., 4998062,
+                            4998993, 5000000]),
+       col_indices=tensor([   2,    8,   14,  ..., 4993, 4994, 4998]),
+       values=tensor([0.2058, 0.7964, 0.8021,  ..., 0.0689, 0.5253, 0.0161]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1259, 0.8957, 0.2222,  ..., 0.6970, 0.5570, 0.6933])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 105.2479407787323 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     987,    1961,  ..., 4998062,
+                            4998993, 5000000]),
+       col_indices=tensor([   2,    8,   14,  ..., 4993, 4994, 4998]),
+       values=tensor([0.2058, 0.7964, 0.8021,  ..., 0.0689, 0.5253, 0.0161]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1259, 0.8957, 0.2222,  ..., 0.6970, 0.5570, 0.6933])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 105.2479407787323 seconds
+
+[20.6, 20.48, 20.6, 20.6, 20.72, 20.72, 20.68, 20.68, 20.52, 20.76]
+[20.8, 20.6, 20.6, 24.4, 25.4, 28.96, 30.96, 31.56, 28.6, 27.8, 25.4, 24.24, 24.24, 24.24, 24.24, 24.24, 24.16, 24.04, 24.12, 24.2, 24.2, 24.28, 24.36, 24.2, 24.32, 24.4, 24.56, 24.56, 24.56, 24.44, 24.44, 24.24, 24.2, 24.16, 24.04, 24.32, 24.24, 24.32, 24.4, 24.4, 24.36, 24.4, 24.6, 24.8, 24.72, 24.88, 24.88, 24.64, 24.48, 24.48, 24.2, 24.12, 24.12, 24.28, 24.48, 24.56, 24.56, 24.6, 24.28, 24.16, 24.16, 24.04, 24.08, 24.24, 24.24, 24.64, 24.72, 24.6, 24.48, 24.12, 24.16, 24.08, 24.16, 24.2, 23.84, 23.92, 23.92, 23.92, 23.76, 24.2, 24.16, 24.28, 24.64, 24.44, 24.36, 24.52, 24.36, 24.4, 24.48, 24.48, 24.56, 24.56, 24.56, 24.36, 24.36, 24.2, 24.32, 24.08, 24.16, 24.24, 24.4, 24.4, 24.44, 24.68, 24.56, 24.4, 24.28, 24.4, 24.32, 24.4, 24.6, 24.48, 24.44, 24.6, 24.6, 24.48, 24.28, 24.24]
+119.4169294834137
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.2479407787323, 'TIME_S_1KI': 105.2479407787323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2877.626370677949, 'W': 24.097306664358964}
+[20.6, 20.48, 20.6, 20.6, 20.72, 20.72, 20.68, 20.68, 20.52, 20.76, 20.2, 20.32, 20.2, 20.12, 20.32, 20.24, 20.6, 20.72, 20.8, 20.8]
+369.5
+18.475
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.2479407787323, 'TIME_S_1KI': 105.2479407787323, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2877.626370677949, 'W': 24.097306664358964, 'J_1KI': 2877.626370677949, 'W_1KI': 24.097306664358964, 'W_D': 5.6223066643589625, 'J_D': 671.3985984718811, 'W_D_1KI': 5.6223066643589625, 'J_D_1KI': 5.6223066643589625}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..46e1ba0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 171.51510739326477, "TIME_S_1KI": 171.51510739326477, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4017.952876434326, "W": 24.359705854077962, "J_1KI": 4017.9528764343263, "W_1KI": 24.359705854077962, "W_D": 5.79370585407796, "J_D": 955.6288257770532, "W_D_1KI": 5.79370585407796, "J_D_1KI": 5.79370585407796}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..2f85bb6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 171.51510739326477}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1461,    2933,  ..., 7497082,
+                            7498527, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4992, 4997, 4999]),
+       values=tensor([0.2348, 0.4295, 0.7390,  ..., 0.0266, 0.0621, 0.6356]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3584, 0.9157, 0.1902,  ..., 0.2272, 0.0135, 0.3908])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 171.51510739326477 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1461,    2933,  ..., 7497082,
+                            7498527, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4992, 4997, 4999]),
+       values=tensor([0.2348, 0.4295, 0.7390,  ..., 0.0266, 0.0621, 0.6356]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3584, 0.9157, 0.1902,  ..., 0.2272, 0.0135, 0.3908])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 171.51510739326477 seconds
+
+[21.16, 20.96, 20.88, 20.76, 20.72, 20.72, 20.72, 20.6, 20.56, 20.56]
+[20.6, 20.48, 20.88, 21.96, 23.44, 26.4, 27.84, 28.8, 28.48, 27.04, 25.6, 24.28, 24.28, 24.48, 24.64, 24.64, 24.6, 24.6, 24.4, 24.24, 24.36, 24.48, 24.44, 24.52, 24.6, 24.6, 24.96, 25.04, 25.04, 25.12, 24.68, 24.56, 24.64, 24.6, 24.68, 24.92, 24.84, 24.84, 24.56, 24.48, 24.8, 24.56, 24.76, 24.76, 24.68, 24.64, 24.72, 24.64, 24.6, 24.6, 24.68, 24.68, 24.56, 24.48, 24.6, 24.48, 24.48, 24.56, 24.64, 24.52, 24.48, 24.6, 24.6, 24.52, 24.24, 24.32, 24.44, 24.32, 24.44, 24.44, 24.64, 24.84, 24.6, 24.76, 24.76, 24.8, 24.92, 25.08, 25.04, 24.92, 24.52, 24.6, 24.56, 24.6, 24.48, 24.6, 24.56, 24.56, 24.4, 24.36, 24.48, 24.48, 24.64, 24.64, 24.52, 24.56, 24.52, 24.44, 24.52, 24.52, 24.52, 24.48, 24.32, 24.52, 24.84, 25.04, 25.04, 25.0, 24.92, 24.68, 24.36, 24.36, 24.36, 24.24, 24.36, 24.36, 24.8, 24.72, 24.84, 24.68, 24.44, 24.56, 24.64, 24.72, 24.72, 25.16, 25.56, 25.68, 25.56, 25.36, 24.88, 24.8, 24.72, 24.52, 24.48, 24.6, 24.6, 24.6, 24.44, 24.44, 24.36, 24.44, 24.56, 24.56, 24.76, 24.64, 24.48, 24.72, 24.72, 24.72, 24.56, 24.92, 24.8, 24.56, 24.72, 24.8, 24.88, 24.88, 24.96, 24.84, 24.6, 24.6, 24.16]
+164.9425859451294
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 171.51510739326477, 'TIME_S_1KI': 171.51510739326477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4017.952876434326, 'W': 24.359705854077962}
+[21.16, 20.96, 20.88, 20.76, 20.72, 20.72, 20.72, 20.6, 20.56, 20.56, 20.0, 20.24, 20.64, 20.6, 20.8, 20.76, 20.44, 20.48, 20.4, 20.36]
+371.32000000000005
+18.566000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 171.51510739326477, 'TIME_S_1KI': 171.51510739326477, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4017.952876434326, 'W': 24.359705854077962, 'J_1KI': 4017.9528764343263, 'W_1KI': 24.359705854077962, 'W_D': 5.79370585407796, 'J_D': 955.6288257770532, 'W_D_1KI': 5.79370585407796, 'J_D_1KI': 5.79370585407796}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..aba0d5e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 293134, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.89356017112732, "TIME_S_1KI": 0.03716239048055606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 278.4485914421082, "W": 19.57952781791354, "J_1KI": 0.9499020633638819, "W_1KI": 0.06679377969772711, "W_D": 4.613527817913537, "J_D": 65.610893910408, "W_D_1KI": 0.01573863085794735, "J_D_1KI": 5.3690908792386244e-05}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..ebedd90
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,437 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04628562927246094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 683, 1119, 1321, 2450, 3482, 3631, 1761, 3022,  756,
+                           3517,   37, 3468, 4655, 1287,  913, 1692, 3561, 1823,
+                           1971, 4332,  175,  242, 3518, 2634, 2163, 1929, 1347,
+                           4194, 4673, 3242, 1554, 3336, 2363, 4819, 1624, 2276,
+                           4446, 1440, 2278, 2820, 2808, 2194, 1293, 3294, 2532,
+                           2630, 3533, 2517, 2068, 4492, 3196,   31, 2012, 3028,
+                           3263, 1298, 1827, 4518, 2739,  383, 2502, 2163, 2983,
+                            275, 3460,  724, 4585, 3927, 4513, 2645,  242, 1435,
+                           3115, 1351, 1335, 3004, 3671, 2087, 2361, 3470, 3033,
+                           1776, 2762, 2985,  544, 2787, 1009, 4955,  757, 4621,
+                           3559, 4933, 3451, 2535, 2363, 1115,  250,  284, 3453,
+                           4194, 4788, 4427,  434, 2792,  219, 1976,  286, 1619,
+                           3123, 2185,  100, 1443, 2614, 3193, 4750, 1625,   61,
+                           2975, 2813, 3271,  969, 1209, 2770, 2904, 1769,  343,
+                            239, 3167,  403, 2400, 1507, 4176, 1210,  627,  332,
+                           3526, 2019, 4707, 4667, 3689, 1411,  474, 2037, 1559,
+                           3233, 2371, 3442, 4237, 1757, 4685, 2495,  737,  562,
+                           4385, 4537, 1150, 2708, 4099, 4510, 4059,   58, 3153,
+                           2292, 1450, 3200, 4511, 1556,  237, 2082, 3442, 4661,
+                           3624,  407, 1680,  104, 2285, 3192, 1818, 2013, 2874,
+                           4274, 1703,  393, 4638, 3642, 1595, 4200, 2976,  747,
+                           1685,  436, 4175, 3319, 2858, 4687, 1967, 1550, 4498,
+                              5, 3295, 2892, 3076, 2947, 1470, 2928, 4594,  372,
+                           1505, 3795, 2014, 3988,  420, 2057, 4772, 3022, 3131,
+                            376, 1473, 4703,  771,  759,  172, 3505, 2361,  168,
+                           3559,  881, 3500,  894, 4238,  842,  291, 2606, 4128,
+                           2513, 4919, 1689, 1039, 4346, 4963,  184, 2438, 3794,
+                            631, 3050, 4745, 3174, 1910, 3181, 4415]),
+       values=tensor([0.5133, 0.9500, 0.6089, 0.1299, 0.0389, 0.7021, 0.0545,
+                      0.1504, 0.2775, 0.3654, 0.5414, 0.7066, 0.5062, 0.9276,
+                      0.5403, 0.1473, 0.0619, 0.8013, 0.5229, 0.9618, 0.3595,
+                      0.9768, 0.4894, 0.9436, 0.2586, 0.3228, 0.7550, 0.4654,
+                      0.5557, 0.6099, 0.1466, 0.3234, 0.9559, 0.4861, 0.6590,
+                      0.2645, 0.3128, 0.2881, 0.8916, 0.9625, 0.3287, 0.6208,
+                      0.1989, 0.4749, 0.6654, 0.5023, 0.5464, 0.6484, 0.8692,
+                      0.5946, 0.3095, 0.4520, 0.2934, 0.1142, 0.3825, 0.0692,
+                      0.4451, 0.9095, 0.2024, 0.8392, 0.4692, 0.1054, 0.2753,
+                      0.1688, 0.2684, 0.5848, 0.9464, 0.6200, 0.5357, 0.5307,
+                      0.7002, 0.6351, 0.9452, 0.4196, 0.3107, 0.9700, 0.4879,
+                      0.0926, 0.0442, 0.1064, 0.9432, 0.8436, 0.3680, 0.1497,
+                      0.1266, 0.6045, 0.6916, 0.0824, 0.1706, 0.8211, 0.8262,
+                      0.7835, 0.0310, 0.3323, 0.1890, 0.5250, 0.8324, 0.5975,
+                      0.0174, 0.0556, 0.9553, 0.6279, 0.3153, 0.4085, 0.9318,
+                      0.3588, 0.1032, 0.7200, 0.2145, 0.8631, 0.4178, 0.0372,
+                      0.7636, 0.4317, 0.2105, 0.2684, 0.0231, 0.6996, 0.0880,
+                      0.2381, 0.6281, 0.3203, 0.4143, 0.7477, 0.1347, 0.5900,
+                      0.7586, 0.5291, 0.6348, 0.4495, 0.3601, 0.9398, 0.3999,
+                      0.2033, 0.1346, 0.0706, 0.9911, 0.9515, 0.0420, 0.6637,
+                      0.2691, 0.3435, 0.7224, 0.4624, 0.4390, 0.3084, 0.3677,
+                      0.2556, 0.8927, 0.7015, 0.4402, 0.6275, 0.9141, 0.3633,
+                      0.0870, 0.2460, 0.1945, 0.8036, 0.3884, 0.5353, 0.6776,
+                      0.4646, 0.1680, 0.4783, 0.9893, 0.5596, 0.0460, 0.9167,
+                      0.8564, 0.2217, 0.2454, 0.6476, 0.0091, 0.6634, 0.6906,
+                      0.5109, 0.0619, 0.8391, 0.3721, 0.4015, 0.1086, 0.8568,
+                      0.0263, 0.0960, 0.2106, 0.8204, 0.3496, 0.0650, 0.0530,
+                      0.2300, 0.7920, 0.0833, 0.8839, 0.6947, 0.7490, 0.6930,
+                      0.4034, 0.9770, 0.5568, 0.5813, 0.4457, 0.4409, 0.3165,
+                      0.4290, 0.8018, 0.4890, 0.7248, 0.5066, 0.4197, 0.9251,
+                      0.4526, 0.8257, 0.6029, 0.9210, 0.8099, 0.1966, 0.6605,
+                      0.5583, 0.0851, 0.2553, 0.8703, 0.6237, 0.8267, 0.9769,
+                      0.6623, 0.5390, 0.0172, 0.1684, 0.4788, 0.5289, 0.0477,
+                      0.8018, 0.0914, 0.3275, 0.7127, 0.1031, 0.8096, 0.1163,
+                      0.3143, 0.8185, 0.2797, 0.8908, 0.1307, 0.5822, 0.2044,
+                      0.6227, 0.4853, 0.6034, 0.6732, 0.0321]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7002, 0.3467, 0.9676,  ..., 0.8135, 0.6463, 0.9360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04628562927246094 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 226852 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.58342981338501}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([2568, 3647,  442,  965,  263, 2383,  651, 4423, 3036,
+                           1922, 4223, 1539, 1097, 1063, 4112, 2393, 2048,  391,
+                           3893,  324, 2099, 2458, 2688,  682,   96, 2079, 4917,
+                           1561, 2320, 1455, 2135, 3126, 2991, 4240, 1021, 4993,
+                           3258, 3975, 1172, 1489, 4782,  364, 2199,   94, 1257,
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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 8.58342981338501 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 277505 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.940149784088135}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3014, 4957, 1583, 2867, 2783,  475, 2139, 2382, 3400,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5019, 0.1367, 0.6742,  ..., 0.0249, 0.2703, 0.5698])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 9.940149784088135 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 293134 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.89356017112732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3878, 1793,  196, 4523, 2590, 2367,  223, 4753,  811,
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+                      0.8841, 0.4123, 0.1102, 0.1371, 0.7232, 0.6598, 0.7427,
+                      0.8150, 0.1180, 0.3866, 0.1447, 0.4442, 0.5099, 0.1417,
+                      0.2917, 0.8599, 0.3553, 0.2307, 0.1388, 0.1482, 0.8529,
+                      0.3988, 0.9926, 0.3184, 0.2404, 0.4847, 0.5288, 0.0738,
+                      0.0517, 0.1797, 0.1796, 0.7215, 0.5955, 0.6432, 0.0017,
+                      0.6486, 0.6664, 0.4487, 0.7630, 0.7774, 0.9276, 0.9518,
+                      0.4507, 0.3399, 0.7495, 0.4581, 0.6140, 0.0659, 0.8137,
+                      0.4343, 0.4836, 0.5681, 0.7947, 0.1417, 0.8229, 0.0824,
+                      0.2070, 0.8783, 0.3511, 0.9580, 0.1053, 0.3375, 0.2396,
+                      0.0513, 0.5334, 0.3977, 0.6765, 0.1035, 0.8974, 0.8093,
+                      0.7238, 0.8002, 0.6243, 0.9654, 0.2803]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4617, 0.6014, 0.4133,  ..., 0.3579, 0.3877, 0.5185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.89356017112732 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3878, 1793,  196, 4523, 2590, 2367,  223, 4753,  811,
+                           1344, 3831, 3891, 1471,  342,   75, 2706, 3424, 2402,
+                           4777, 1498,  772, 4383, 2094, 2326, 2946, 1126, 2886,
+                           1923,  932, 4969, 3541,  896,  863, 2668, 4869, 4410,
+                           1937, 3764,   94, 3879, 1282, 4972, 2002, 1481, 4954,
+                           2173, 3425, 2770, 4498,  897,  827,  829, 4189, 4991,
+                           1934, 1179, 1128, 2114, 2395, 2017, 3561, 4691, 2694,
+                           1416, 4554,  620, 4382, 4973, 3582,  230, 4891, 3107,
+                           1979,  140,  933, 4466,  308, 3556, 3173, 4882, 4797,
+                           1238, 3925, 3354,  291, 1290,  414, 2445, 3042, 2325,
+                           2660, 1731,  777, 3782, 2858, 3541, 2112, 2412, 4004,
+                            942, 1750, 1450,   45,  960, 1184, 3190, 4107, 4960,
+                           4877, 4158, 1493, 3953, 2210, 1987, 3321, 3595, 1029,
+                           1281, 3168, 3718, 1747, 2899, 1694, 1765, 2658, 2297,
+                           2811, 4794, 2166, 3385, 3144,  590,  756, 1857, 2472,
+                           2864, 4092, 2483, 1698, 3039, 4797, 2893, 4815, 1160,
+                             92, 3486,  728,  546, 1233, 1206, 3098,  284, 3217,
+                           1908, 3538,  556, 1018, 3457, 4789, 3509,  986, 1353,
+                           3714,  674, 3739, 3917,  378, 4295, 1240,  678,  634,
+                            156,  182, 2959,  787, 2431,  894, 4155, 1278, 4710,
+                            115, 3800, 3528, 4651, 4055, 3457, 4797, 3790, 2898,
+                           2898, 1677, 2106, 3532, 1869, 3926, 1788, 4954, 1802,
+                           3662, 3116, 1672, 1899, 2743, 4402,  201, 2790, 4915,
+                           3309, 2448, 4340,   71, 1083, 3547, 1833, 4517, 4811,
+                           4522, 4837, 4905, 1773, 2748, 2712, 2488, 4842, 2297,
+                            377, 2695, 2905,  534, 1022, 2504, 1436, 3486, 3980,
+                           4913,  361, 4684, 2741,  722, 1718, 3274,  513, 1785,
+                           4555,  575,  662, 3842, 1584, 2198,  215]),
+       values=tensor([0.8076, 0.3370, 0.6780, 0.9299, 0.5410, 0.0897, 0.3343,
+                      0.8017, 0.5673, 0.5602, 0.9800, 0.8553, 0.3447, 0.6119,
+                      0.3490, 0.5758, 0.1388, 0.7568, 0.7228, 0.2456, 0.6799,
+                      0.7488, 0.1368, 0.7230, 0.3714, 0.8061, 0.2178, 0.6691,
+                      0.7090, 0.6240, 0.5615, 0.6385, 0.8034, 0.6963, 0.9896,
+                      0.1078, 0.2316, 0.3754, 0.7350, 0.4907, 0.3665, 0.2209,
+                      0.4611, 0.7569, 0.4815, 0.7270, 0.4688, 0.5127, 0.0439,
+                      0.4951, 0.3454, 0.1899, 0.8750, 0.1915, 0.8080, 0.6042,
+                      0.7305, 0.2510, 0.4960, 0.3143, 0.3207, 0.3323, 0.5478,
+                      0.3218, 0.1649, 0.9155, 0.2697, 0.4415, 0.6177, 0.1457,
+                      0.9256, 0.6524, 0.8106, 0.1943, 0.2636, 0.7375, 0.5837,
+                      0.4529, 0.4107, 0.4337, 0.4074, 0.1673, 0.9988, 0.7338,
+                      0.1243, 0.9778, 0.4221, 0.2348, 0.5442, 0.1259, 0.4222,
+                      0.6127, 0.0857, 0.6974, 0.0596, 0.3553, 0.4614, 0.5799,
+                      0.4404, 0.3360, 0.3314, 0.9445, 0.7231, 0.9851, 0.8853,
+                      0.4987, 0.3871, 0.5069, 0.6349, 0.9384, 0.3450, 0.4613,
+                      0.2127, 0.4994, 0.0034, 0.9538, 0.3203, 0.8248, 0.5140,
+                      0.0568, 0.3913, 0.0456, 0.0790, 0.1457, 0.8710, 0.7025,
+                      0.5191, 0.7160, 0.2410, 0.7547, 0.7169, 0.9282, 0.0473,
+                      0.4454, 0.5093, 0.4795, 0.3417, 0.5014, 0.0605, 0.9341,
+                      0.8068, 0.8325, 0.0916, 0.8219, 0.9882, 0.3617, 0.8114,
+                      0.3412, 0.2133, 0.4138, 0.2870, 0.1987, 0.5576, 0.8136,
+                      0.4067, 0.6195, 0.5018, 0.5513, 0.5252, 0.0402, 0.7889,
+                      0.3122, 0.6215, 0.9385, 0.7669, 0.1080, 0.2818, 0.0494,
+                      0.0251, 0.3317, 0.4666, 0.5981, 0.5539, 0.1688, 0.7416,
+                      0.8841, 0.4123, 0.1102, 0.1371, 0.7232, 0.6598, 0.7427,
+                      0.8150, 0.1180, 0.3866, 0.1447, 0.4442, 0.5099, 0.1417,
+                      0.2917, 0.8599, 0.3553, 0.2307, 0.1388, 0.1482, 0.8529,
+                      0.3988, 0.9926, 0.3184, 0.2404, 0.4847, 0.5288, 0.0738,
+                      0.0517, 0.1797, 0.1796, 0.7215, 0.5955, 0.6432, 0.0017,
+                      0.6486, 0.6664, 0.4487, 0.7630, 0.7774, 0.9276, 0.9518,
+                      0.4507, 0.3399, 0.7495, 0.4581, 0.6140, 0.0659, 0.8137,
+                      0.4343, 0.4836, 0.5681, 0.7947, 0.1417, 0.8229, 0.0824,
+                      0.2070, 0.8783, 0.3511, 0.9580, 0.1053, 0.3375, 0.2396,
+                      0.0513, 0.5334, 0.3977, 0.6765, 0.1035, 0.8974, 0.8093,
+                      0.7238, 0.8002, 0.6243, 0.9654, 0.2803]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4617, 0.6014, 0.4133,  ..., 0.3579, 0.3877, 0.5185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.89356017112732 seconds
+
+[16.36, 16.36, 16.4, 16.6, 16.6, 16.96, 16.96, 16.88, 16.88, 16.48]
+[16.32, 16.28, 19.04, 20.36, 23.52, 24.24, 24.96, 24.96, 22.16, 21.36, 19.68, 19.76, 19.8, 19.64]
+14.221415042877197
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 293134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.89356017112732, 'TIME_S_1KI': 0.03716239048055606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.4485914421082, 'W': 19.57952781791354}
+[16.36, 16.36, 16.4, 16.6, 16.6, 16.96, 16.96, 16.88, 16.88, 16.48, 16.6, 16.64, 16.68, 16.76, 16.68, 16.56, 16.4, 16.4, 16.52, 16.64]
+299.32000000000005
+14.966000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 293134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.89356017112732, 'TIME_S_1KI': 0.03716239048055606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 278.4485914421082, 'W': 19.57952781791354, 'J_1KI': 0.9499020633638819, 'W_1KI': 0.06679377969772711, 'W_D': 4.613527817913537, 'J_D': 65.610893910408, 'W_D_1KI': 0.01573863085794735, 'J_D_1KI': 5.3690908792386244e-05}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..c47b89b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 151147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.619585275650024, "TIME_S_1KI": 0.07025998051995755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.34527337074286, "W": 23.06683265939357, "J_1KI": 2.1723571977660345, "W_1KI": 0.15261191197571614, "W_D": 4.6928326593935665, "J_D": 66.80021679544454, "W_D_1KI": 0.031048136313612352, "J_D_1KI": 0.00020541682146263144}
diff --git a/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..b0a08a7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.07715368270874023}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4186,  604, 2911,  ..., 3524, 2664,  807]),
+       values=tensor([0.1303, 0.5472, 0.9541,  ..., 0.4453, 0.4813, 0.2933]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2363, 0.5745, 0.8536,  ..., 0.3028, 0.7626, 0.7945])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.07715368270874023 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 136092 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.454103946685791}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2642, 3295, 3317,  ...,  552, 1688, 3754]),
+       values=tensor([0.5853, 0.8410, 0.7758,  ..., 0.7543, 0.4171, 0.3907]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4145, 0.1634, 0.4401,  ..., 0.9903, 0.7928, 0.8495])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 9.454103946685791 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 151147 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.619585275650024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2120, 4070, 3230,  ..., 2901, 3405,  168]),
+       values=tensor([0.1519, 0.7232, 0.6282,  ..., 0.2528, 0.5199, 0.9452]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2553, 0.4766, 0.2217,  ..., 0.4056, 0.3500, 0.9553])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.619585275650024 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2120, 4070, 3230,  ..., 2901, 3405,  168]),
+       values=tensor([0.1519, 0.7232, 0.6282,  ..., 0.2528, 0.5199, 0.9452]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2553, 0.4766, 0.2217,  ..., 0.4056, 0.3500, 0.9553])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.619585275650024 seconds
+
+[20.44, 20.36, 20.28, 20.16, 20.04, 20.16, 20.28, 20.32, 20.56, 20.56]
+[20.72, 20.76, 20.8, 24.32, 26.28, 28.48, 29.36, 29.52, 26.16, 23.92, 23.8, 23.64, 23.64, 23.56]
+14.234519243240356
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 151147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.619585275650024, 'TIME_S_1KI': 0.07025998051995755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.34527337074286, 'W': 23.06683265939357}
+[20.44, 20.36, 20.28, 20.16, 20.04, 20.16, 20.28, 20.32, 20.56, 20.56, 20.24, 20.2, 20.36, 20.68, 20.96, 20.8, 20.68, 20.4, 20.44, 20.36]
+367.48
+18.374000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 151147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.619585275650024, 'TIME_S_1KI': 0.07025998051995755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.34527337074286, 'W': 23.06683265939357, 'J_1KI': 2.1723571977660345, 'W_1KI': 0.15261191197571614, 'W_D': 4.6928326593935665, 'J_D': 66.80021679544454, 'W_D_1KI': 0.031048136313612352, 'J_D_1KI': 0.00020541682146263144}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..f73c7ea
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 63031, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.264819622039795, "TIME_S_1KI": 0.16285351052719765, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1907.2002365589144, "W": 143.8, "J_1KI": 30.25813070646054, "W_1KI": 2.2814170804842067, "W_D": 106.65425000000002, "J_D": 1414.5411045202616, "W_D_1KI": 1.6920919864828419, "J_D_1KI": 0.026845393322061237}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..b2fc454
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.20868682861328125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      23,  ...,  999975,
+                             999990, 1000000]),
+       col_indices=tensor([ 1102,  1885,  5689,  ..., 70464, 82505, 82637]),
+       values=tensor([0.9145, 0.6563, 0.0210,  ..., 0.3467, 0.9517, 0.4307]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3954, 0.8531, 0.4592,  ..., 0.1653, 0.9288, 0.8508])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.20868682861328125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50314', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.38151502609253}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      15,  ...,  999976,
+                             999987, 1000000]),
+       col_indices=tensor([ 9326, 16949, 19479,  ..., 70135, 76689, 93251]),
+       values=tensor([0.2491, 0.4486, 0.5526,  ..., 0.3620, 0.8491, 0.1510]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1294, 0.2549, 0.0676,  ..., 0.6377, 0.6452, 0.0657])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 8.38151502609253 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63031', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.264819622039795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
+                             999986, 1000000]),
+       col_indices=tensor([ 1906, 11602, 20474,  ..., 94634, 95193, 99629]),
+       values=tensor([0.7595, 0.5479, 0.3671,  ..., 0.3196, 0.4186, 0.5082]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5397, 0.2720, 0.7091,  ..., 0.7919, 0.2241, 0.5973])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.264819622039795 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
+                             999986, 1000000]),
+       col_indices=tensor([ 1906, 11602, 20474,  ..., 94634, 95193, 99629]),
+       values=tensor([0.7595, 0.5479, 0.3671,  ..., 0.3196, 0.4186, 0.5082]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5397, 0.2720, 0.7091,  ..., 0.7919, 0.2241, 0.5973])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.264819622039795 seconds
+
+[40.75, 39.66, 39.67, 39.19, 39.32, 45.15, 39.35, 39.19, 39.95, 39.54]
+[143.8]
+13.262866735458374
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 63031, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.264819622039795, 'TIME_S_1KI': 0.16285351052719765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1907.2002365589144, 'W': 143.8}
+[40.75, 39.66, 39.67, 39.19, 39.32, 45.15, 39.35, 39.19, 39.95, 39.54, 40.42, 44.44, 57.71, 39.25, 40.02, 40.75, 39.74, 39.58, 39.68, 39.82]
+742.915
+37.14575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 63031, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.264819622039795, 'TIME_S_1KI': 0.16285351052719765, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1907.2002365589144, 'W': 143.8, 'J_1KI': 30.25813070646054, 'W_1KI': 2.2814170804842067, 'W_D': 106.65425000000002, 'J_D': 1414.5411045202616, 'W_D_1KI': 1.6920919864828419, 'J_D_1KI': 0.026845393322061237}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..47e1eaa
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4290, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.725477457046509, "TIME_S_1KI": 2.500111295348837, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2014.465433692932, "W": 126.69, "J_1KI": 469.57236216618463, "W_1KI": 29.53146853146853, "W_D": 91.17699999999999, "J_D": 1449.7822625923156, "W_D_1KI": 21.25337995337995, "J_D_1KI": 4.954167821300688}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..2b06cc0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.4475483894348145}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      108,      211,  ...,  9999795,
+                             9999912, 10000000]),
+       col_indices=tensor([  147,  1138,  2699,  ..., 95915, 96101, 99505]),
+       values=tensor([0.5370, 0.7637, 0.8320,  ..., 0.1671, 0.6910, 0.1145]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0022, 0.6683, 0.3307,  ..., 0.4747, 0.3475, 0.4636])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 2.4475483894348145 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4290', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.725477457046509}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      113,      209,  ...,  9999816,
+                             9999914, 10000000]),
+       col_indices=tensor([  524,  3053,  3097,  ..., 98248, 99944, 99996]),
+       values=tensor([0.2951, 0.6504, 0.4617,  ..., 0.6241, 0.9747, 0.0943]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1529, 0.0141, 0.4287,  ..., 0.1937, 0.2308, 0.9820])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.725477457046509 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      113,      209,  ...,  9999816,
+                             9999914, 10000000]),
+       col_indices=tensor([  524,  3053,  3097,  ..., 98248, 99944, 99996]),
+       values=tensor([0.2951, 0.6504, 0.4617,  ..., 0.6241, 0.9747, 0.0943]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1529, 0.0141, 0.4287,  ..., 0.1937, 0.2308, 0.9820])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.725477457046509 seconds
+
+[39.92, 39.19, 39.59, 39.28, 39.81, 39.63, 39.63, 39.45, 39.41, 39.18]
+[126.69]
+15.900745391845703
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4290, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.725477457046509, 'TIME_S_1KI': 2.500111295348837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2014.465433692932, 'W': 126.69}
+[39.92, 39.19, 39.59, 39.28, 39.81, 39.63, 39.63, 39.45, 39.41, 39.18, 40.85, 39.15, 39.3, 39.24, 39.74, 39.48, 39.55, 39.09, 39.1, 39.29]
+710.26
+35.513
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4290, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.725477457046509, 'TIME_S_1KI': 2.500111295348837, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2014.465433692932, 'W': 126.69, 'J_1KI': 469.57236216618463, 'W_1KI': 29.53146853146853, 'W_D': 91.17699999999999, 'J_D': 1449.7822625923156, 'W_D_1KI': 21.25337995337995, 'J_D_1KI': 4.954167821300688}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..7f413fc
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102924, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.60866904258728, "TIME_S_1KI": 0.103072840567674, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1542.0244372987747, "W": 115.47, "J_1KI": 14.982165843717448, "W_1KI": 1.121895767750962, "W_D": 79.97325000000001, "J_D": 1067.989138565898, "W_D_1KI": 0.7770126501107615, "J_D_1KI": 0.00754938255519375}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..fb29049
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.12978029251098633}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      4,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([21616, 77637, 85619,  ..., 53732, 81470,  6094]),
+       values=tensor([0.4857, 0.1991, 0.9153,  ..., 0.9203, 0.8308, 0.8562]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0197, 0.8164, 0.2872,  ..., 0.9903, 0.3891, 0.9778])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.12978029251098633 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '80905', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.253613233566284}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([18950, 61338, 17160,  ..., 57514, 79997, 96494]),
+       values=tensor([0.7220, 0.1840, 0.6067,  ..., 0.9597, 0.4652, 0.5228]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0221, 0.6414, 0.1516,  ..., 0.3018, 0.8902, 0.3461])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 8.253613233566284 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102924', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.60866904258728}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 4611, 80501,  8771,  ..., 95435, 27789, 45343]),
+       values=tensor([0.8274, 0.0201, 0.6109,  ..., 0.4116, 0.6491, 0.0785]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0461, 0.3256, 0.3375,  ..., 0.6234, 0.9526, 0.7301])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.60866904258728 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 4611, 80501,  8771,  ..., 95435, 27789, 45343]),
+       values=tensor([0.8274, 0.0201, 0.6109,  ..., 0.4116, 0.6491, 0.0785]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0461, 0.3256, 0.3375,  ..., 0.6234, 0.9526, 0.7301])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.60866904258728 seconds
+
+[39.93, 39.22, 39.43, 40.14, 39.51, 39.36, 39.3, 39.3, 39.26, 39.1]
+[115.47]
+13.354329586029053
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102924, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.60866904258728, 'TIME_S_1KI': 0.103072840567674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1542.0244372987747, 'W': 115.47}
+[39.93, 39.22, 39.43, 40.14, 39.51, 39.36, 39.3, 39.3, 39.26, 39.1, 41.76, 39.08, 39.78, 39.45, 39.66, 39.16, 39.27, 39.06, 39.08, 38.96]
+709.935
+35.49675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102924, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.60866904258728, 'TIME_S_1KI': 0.103072840567674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1542.0244372987747, 'W': 115.47, 'J_1KI': 14.982165843717448, 'W_1KI': 1.121895767750962, 'W_D': 79.97325000000001, 'J_D': 1067.989138565898, 'W_D_1KI': 0.7770126501107615, 'J_D_1KI': 0.00754938255519375}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..4188099
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 85448, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.79839825630188, "TIME_S_1KI": 0.12637391461826936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1847.2158340501787, "W": 132.18, "J_1KI": 21.618011352520583, "W_1KI": 1.5469057204381613, "W_D": 96.35400000000001, "J_D": 1346.5473935093883, "W_D_1KI": 1.127633180413819, "J_D_1KI": 0.013196718242835633}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..be89160
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.157515287399292}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      8,  ..., 499988, 499996,
+                            500000]),
+       col_indices=tensor([50162, 75153, 30191,  ..., 32389, 47580, 60210]),
+       values=tensor([0.9007, 0.9447, 0.0410,  ..., 0.6472, 0.2952, 0.4267]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.3259, 0.8902, 0.7186,  ..., 0.8330, 0.5312, 0.8917])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.157515287399292 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66660', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.191283702850342}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499990, 499997,
+                            500000]),
+       col_indices=tensor([ 3937, 41482, 51345,  ..., 57028, 62776, 96568]),
+       values=tensor([0.3669, 0.7790, 0.6636,  ..., 0.0088, 0.3191, 0.1015]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1888, 0.6317, 0.9833,  ..., 0.5078, 0.6417, 0.5906])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 8.191283702850342 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '85448', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.79839825630188}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     13,  ..., 499988, 499995,
+                            500000]),
+       col_indices=tensor([ 3698, 26087, 35796,  ..., 95832, 96289, 98226]),
+       values=tensor([0.4478, 0.6896, 0.5878,  ..., 0.3885, 0.0788, 0.0500]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6913, 0.6407, 0.8664,  ..., 0.8625, 0.1823, 0.9429])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.79839825630188 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     13,  ..., 499988, 499995,
+                            500000]),
+       col_indices=tensor([ 3698, 26087, 35796,  ..., 95832, 96289, 98226]),
+       values=tensor([0.4478, 0.6896, 0.5878,  ..., 0.3885, 0.0788, 0.0500]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6913, 0.6407, 0.8664,  ..., 0.8625, 0.1823, 0.9429])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.79839825630188 seconds
+
+[40.27, 39.58, 39.76, 40.26, 40.31, 39.92, 40.36, 39.5, 39.65, 39.54]
+[132.18]
+13.975002527236938
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 85448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.79839825630188, 'TIME_S_1KI': 0.12637391461826936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1847.2158340501787, 'W': 132.18}
+[40.27, 39.58, 39.76, 40.26, 40.31, 39.92, 40.36, 39.5, 39.65, 39.54, 40.41, 39.45, 40.31, 39.36, 39.58, 39.39, 39.62, 39.75, 39.86, 39.5]
+716.52
+35.826
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 85448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.79839825630188, 'TIME_S_1KI': 0.12637391461826936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1847.2158340501787, 'W': 132.18, 'J_1KI': 21.618011352520583, 'W_1KI': 1.5469057204381613, 'W_D': 96.35400000000001, 'J_D': 1346.5473935093883, 'W_D_1KI': 1.127633180413819, 'J_D_1KI': 0.013196718242835633}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..cb11125
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 278690, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.3841392993927, "TIME_S_1KI": 0.0372605378714439, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1292.3170569992064, "W": 98.52, "J_1KI": 4.63711312569237, "W_1KI": 0.3535110696472783, "W_D": 63.16824999999999, "J_D": 828.5973095390796, "W_D_1KI": 0.2266613441458251, "J_D_1KI": 0.0008133099291177477}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..26aaa7d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.05305743217468262}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([2207,  830, 7633,  ..., 2513, 8541, 2972]),
+       values=tensor([0.9417, 0.1071, 0.2127,  ..., 0.2034, 0.4535, 0.3737]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2095, 0.5712, 0.5435,  ..., 0.2564, 0.5818, 0.1577])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.05305743217468262 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '197898', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.456049680709839}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([7930, 9951, 4041,  ..., 9045, 6420, 8503]),
+       values=tensor([0.2418, 0.2435, 0.4116,  ..., 0.5201, 0.9725, 0.0713]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5895, 0.0291, 0.5304,  ..., 0.4324, 0.9976, 0.6205])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.456049680709839 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '278690', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.3841392993927}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([9574, 4944, 2003,  ..., 2641, 7523, 8416]),
+       values=tensor([0.4157, 0.2537, 0.8916,  ..., 0.3966, 0.1591, 0.0732]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4368, 0.0363, 0.2687,  ..., 0.3029, 0.2331, 0.6830])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.3841392993927 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([9574, 4944, 2003,  ..., 2641, 7523, 8416]),
+       values=tensor([0.4157, 0.2537, 0.8916,  ..., 0.3966, 0.1591, 0.0732]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4368, 0.0363, 0.2687,  ..., 0.3029, 0.2331, 0.6830])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.3841392993927 seconds
+
+[40.74, 38.88, 38.93, 39.04, 38.99, 38.97, 39.42, 39.32, 39.26, 38.71]
+[98.52]
+13.11730670928955
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 278690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.3841392993927, 'TIME_S_1KI': 0.0372605378714439, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1292.3170569992064, 'W': 98.52}
+[40.74, 38.88, 38.93, 39.04, 38.99, 38.97, 39.42, 39.32, 39.26, 38.71, 39.37, 39.1, 39.73, 39.04, 39.15, 38.73, 39.2, 38.61, 38.78, 44.95]
+707.0350000000001
+35.35175
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 278690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.3841392993927, 'TIME_S_1KI': 0.0372605378714439, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1292.3170569992064, 'W': 98.52, 'J_1KI': 4.63711312569237, 'W_1KI': 0.3535110696472783, 'W_D': 63.16824999999999, 'J_D': 828.5973095390796, 'W_D_1KI': 0.2266613441458251, 'J_D_1KI': 0.0008133099291177477}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..9de9eeb
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 181643, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.058825254440308, "TIME_S_1KI": 0.05537689453730839, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1334.3994569778442, "W": 108.0, "J_1KI": 7.346275149484671, "W_1KI": 0.5945728709611711, "W_D": 72.86524999999999, "J_D": 900.2902780792116, "W_D_1KI": 0.4011453785722543, "J_D_1KI": 0.002208427401949177}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..62ff1c2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,86 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07387351989746094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     20,  ...,  99983,  99988,
+                            100000]),
+       col_indices=tensor([2080, 2520, 2867,  ..., 8307, 8901, 9286]),
+       values=tensor([0.8261, 0.1055, 0.9939,  ..., 0.1447, 0.1951, 0.2617]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7373, 0.8108, 0.8070,  ..., 0.3032, 0.8916, 0.0356])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.07387351989746094 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '142134', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.216149806976318}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     16,  ...,  99977,  99988,
+                            100000]),
+       col_indices=tensor([ 929, 1145, 1167,  ..., 7253, 9439, 9881]),
+       values=tensor([3.5267e-01, 8.9746e-01, 4.0379e-01,  ...,
+                      8.5718e-04, 5.6681e-01, 4.6851e-01]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4055, 0.0658, 0.7904,  ..., 0.2959, 0.0826, 0.7426])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.216149806976318 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '181643', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.058825254440308}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     23,  ...,  99984,  99996,
+                            100000]),
+       col_indices=tensor([2026, 2065, 2399,  ..., 4623, 7297, 9355]),
+       values=tensor([0.4157, 0.6883, 0.2119,  ..., 0.3441, 0.2622, 0.5721]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4888, 0.3451, 0.6891,  ..., 0.9797, 0.8702, 0.1612])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.058825254440308 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     23,  ...,  99984,  99996,
+                            100000]),
+       col_indices=tensor([2026, 2065, 2399,  ..., 4623, 7297, 9355]),
+       values=tensor([0.4157, 0.6883, 0.2119,  ..., 0.3441, 0.2622, 0.5721]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4888, 0.3451, 0.6891,  ..., 0.9797, 0.8702, 0.1612])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.058825254440308 seconds
+
+[40.01, 39.91, 39.26, 39.32, 39.03, 38.69, 39.03, 38.68, 38.9, 38.67]
+[108.0]
+12.355550527572632
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 181643, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.058825254440308, 'TIME_S_1KI': 0.05537689453730839, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.3994569778442, 'W': 108.0}
+[40.01, 39.91, 39.26, 39.32, 39.03, 38.69, 39.03, 38.68, 38.9, 38.67, 40.03, 39.23, 39.15, 39.23, 38.85, 38.69, 38.72, 38.72, 38.62, 38.62]
+702.6950000000002
+35.13475000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 181643, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.058825254440308, 'TIME_S_1KI': 0.05537689453730839, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.3994569778442, 'W': 108.0, 'J_1KI': 7.346275149484671, 'W_1KI': 0.5945728709611711, 'W_D': 72.86524999999999, 'J_D': 900.2902780792116, 'W_D_1KI': 0.4011453785722543, 'J_D_1KI': 0.002208427401949177}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..37875a8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 104114, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.469164609909058, "TIME_S_1KI": 0.10055482077250953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1774.261237783432, "W": 135.86, "J_1KI": 17.041524077294426, "W_1KI": 1.304915765410992, "W_D": 100.35275000000001, "J_D": 1310.5549420725108, "W_D_1KI": 0.9638737345601938, "J_D_1KI": 0.009257868630157269}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..0c99b95
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.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', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.14159297943115234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     124,     236,  ...,  999773,
+                             999882, 1000000]),
+       col_indices=tensor([  35,   69,  144,  ..., 9773, 9862, 9873]),
+       values=tensor([0.1838, 0.7773, 0.5109,  ..., 0.8192, 0.8376, 0.6812]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0358, 0.2032, 0.7087,  ..., 0.4931, 0.1706, 0.1726])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.14159297943115234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '74156', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.9134438037872314}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     199,  ...,  999798,
+                             999892, 1000000]),
+       col_indices=tensor([  57,  323,  325,  ..., 9719, 9779, 9889]),
+       values=tensor([0.3339, 0.1610, 0.8675,  ..., 0.7107, 0.3615, 0.1870]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9536, 0.3002, 0.1616,  ..., 0.3121, 0.8413, 0.9505])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 7.9134438037872314 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '98394', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.923112392425537}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      97,     191,  ...,  999779,
+                             999891, 1000000]),
+       col_indices=tensor([  18,   52,  269,  ..., 9883, 9995, 9999]),
+       values=tensor([0.5511, 0.2767, 0.8168,  ..., 0.6887, 0.5827, 0.0686]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2767, 0.4380, 0.7945,  ..., 0.2102, 0.5547, 0.8740])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.923112392425537 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '104114', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.469164609909058}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     221,  ...,  999805,
+                             999915, 1000000]),
+       col_indices=tensor([ 402,  501,  665,  ..., 9291, 9326, 9607]),
+       values=tensor([0.0486, 0.5637, 0.4384,  ..., 0.7973, 0.3634, 0.8351]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7936, 0.9785, 0.9590,  ..., 0.6005, 0.0137, 0.6516])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.469164609909058 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     221,  ...,  999805,
+                             999915, 1000000]),
+       col_indices=tensor([ 402,  501,  665,  ..., 9291, 9326, 9607]),
+       values=tensor([0.0486, 0.5637, 0.4384,  ..., 0.7973, 0.3634, 0.8351]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7936, 0.9785, 0.9590,  ..., 0.6005, 0.0137, 0.6516])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.469164609909058 seconds
+
+[42.04, 39.8, 39.79, 39.52, 39.3, 39.73, 39.12, 39.49, 39.14, 38.98]
+[135.86]
+13.059482097625732
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 104114, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.469164609909058, 'TIME_S_1KI': 0.10055482077250953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1774.261237783432, 'W': 135.86}
+[42.04, 39.8, 39.79, 39.52, 39.3, 39.73, 39.12, 39.49, 39.14, 38.98, 39.91, 39.55, 39.06, 39.42, 39.43, 39.34, 38.96, 39.36, 38.94, 39.46]
+710.145
+35.50725
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 104114, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.469164609909058, 'TIME_S_1KI': 0.10055482077250953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1774.261237783432, 'W': 135.86, 'J_1KI': 17.041524077294426, 'W_1KI': 1.304915765410992, 'W_D': 100.35275000000001, 'J_D': 1310.5549420725108, 'W_D_1KI': 0.9638737345601938, 'J_D_1KI': 0.009257868630157269}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..5302269
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27894, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.822905540466309, "TIME_S_1KI": 0.3880012024258374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2101.6757108688353, "W": 151.95, "J_1KI": 75.34508176915593, "W_1KI": 5.447408044740804, "W_D": 116.03349999999999, "J_D": 1604.90153732872, "W_D_1KI": 4.159801390980138, "J_D_1KI": 0.14912889477952743}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..3e6dd10
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.4579291343688965}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     485,    1001,  ..., 4998993,
+                            4999541, 5000000]),
+       col_indices=tensor([   5,   20,   61,  ..., 9897, 9942, 9998]),
+       values=tensor([0.7241, 0.0945, 0.6836,  ..., 0.9220, 0.2796, 0.2745]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6528, 0.9454, 0.7224,  ..., 0.5670, 0.2826, 0.8750])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.4579291343688965 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22929', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.630967855453491}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     483,     987,  ..., 4998961,
+                            4999465, 5000000]),
+       col_indices=tensor([  47,   67,   96,  ..., 9993, 9994, 9997]),
+       values=tensor([0.9705, 0.3882, 0.2458,  ..., 0.5796, 0.8899, 0.6056]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2409, 0.7584, 0.7571,  ..., 0.5444, 0.5564, 0.6333])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 8.630967855453491 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27894', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.822905540466309}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     505,     994,  ..., 4999000,
+                            4999548, 5000000]),
+       col_indices=tensor([  26,   89,   94,  ..., 9963, 9967, 9969]),
+       values=tensor([0.5738, 0.3729, 0.9664,  ..., 0.7914, 0.9130, 0.5932]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2560, 0.0745, 0.3692,  ..., 0.2024, 0.4618, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.822905540466309 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     505,     994,  ..., 4999000,
+                            4999548, 5000000]),
+       col_indices=tensor([  26,   89,   94,  ..., 9963, 9967, 9969]),
+       values=tensor([0.5738, 0.3729, 0.9664,  ..., 0.7914, 0.9130, 0.5932]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2560, 0.0745, 0.3692,  ..., 0.2024, 0.4618, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.822905540466309 seconds
+
+[41.38, 40.1, 39.88, 40.17, 39.72, 39.62, 39.69, 39.64, 39.9, 39.73]
+[151.95]
+13.831363677978516
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27894, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.822905540466309, 'TIME_S_1KI': 0.3880012024258374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.6757108688353, 'W': 151.95}
+[41.38, 40.1, 39.88, 40.17, 39.72, 39.62, 39.69, 39.64, 39.9, 39.73, 40.26, 39.63, 39.55, 39.93, 41.22, 40.33, 39.48, 39.56, 39.48, 39.49]
+718.3299999999999
+35.9165
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27894, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.822905540466309, 'TIME_S_1KI': 0.3880012024258374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.6757108688353, 'W': 151.95, 'J_1KI': 75.34508176915593, 'W_1KI': 5.447408044740804, 'W_D': 116.03349999999999, 'J_D': 1604.90153732872, 'W_D_1KI': 4.159801390980138, 'J_D_1KI': 0.14912889477952743}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..a6d8fa4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4679, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.427535057067871, "TIME_S_1KI": 2.4423028546843066, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1873.982270450592, "W": 124.72, "J_1KI": 400.5091409383612, "W_1KI": 26.655268219705064, "W_D": 88.71, "J_D": 1332.9134638524054, "W_D_1KI": 18.959179311818765, "J_D_1KI": 4.051972496648593}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..a87f9d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.382111072540283}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1024,     2032,  ...,  9997994,
+                             9998974, 10000000]),
+       col_indices=tensor([  25,   59,   80,  ..., 9969, 9975, 9986]),
+       values=tensor([0.6759, 0.5147, 0.7066,  ..., 0.5276, 0.4088, 0.2550]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6863, 0.6243, 0.0191,  ..., 0.9166, 0.1487, 0.8503])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 2.382111072540283 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4407', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.887728929519653}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      980,     2002,  ...,  9998027,
+                             9998983, 10000000]),
+       col_indices=tensor([   0,    5,   25,  ..., 9979, 9984, 9986]),
+       values=tensor([0.6732, 0.9055, 0.4649,  ..., 0.1468, 0.7629, 0.6148]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4732, 0.0327, 0.4956,  ..., 0.7189, 0.9869, 0.4026])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 9.887728929519653 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4679', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.427535057067871}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1053,     2097,  ...,  9998095,
+                             9999109, 10000000]),
+       col_indices=tensor([  10,   15,   24,  ..., 9977, 9991, 9995]),
+       values=tensor([0.4155, 0.1468, 0.0149,  ..., 0.0226, 0.9383, 0.4708]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3975, 0.8045, 0.4645,  ..., 0.1781, 0.4097, 0.1046])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.427535057067871 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1053,     2097,  ...,  9998095,
+                             9999109, 10000000]),
+       col_indices=tensor([  10,   15,   24,  ..., 9977, 9991, 9995]),
+       values=tensor([0.4155, 0.1468, 0.0149,  ..., 0.0226, 0.9383, 0.4708]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3975, 0.8045, 0.4645,  ..., 0.1781, 0.4097, 0.1046])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.427535057067871 seconds
+
+[40.62, 41.67, 40.6, 39.62, 39.77, 39.59, 39.62, 40.85, 40.24, 40.01]
+[124.72]
+15.02551531791687
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.427535057067871, 'TIME_S_1KI': 2.4423028546843066, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.982270450592, 'W': 124.72}
+[40.62, 41.67, 40.6, 39.62, 39.77, 39.59, 39.62, 40.85, 40.24, 40.01, 40.22, 39.64, 40.11, 39.47, 39.58, 39.84, 39.97, 39.93, 39.51, 39.53]
+720.2
+36.010000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.427535057067871, 'TIME_S_1KI': 2.4423028546843066, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.982270450592, 'W': 124.72, 'J_1KI': 400.5091409383612, 'W_1KI': 26.655268219705064, 'W_D': 88.71, 'J_D': 1332.9134638524054, 'W_D_1KI': 18.959179311818765, 'J_D_1KI': 4.051972496648593}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..4f270a6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2251, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.887785911560059, "TIME_S_1KI": 4.83686624236342, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2027.23151884079, "W": 120.43, "J_1KI": 900.591523252239, "W_1KI": 53.500666370501996, "W_D": 84.27900000000001, "J_D": 1418.6917311000825, "W_D_1KI": 37.44069302532208, "J_D_1KI": 16.632915604319006}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..c0dab55
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.959461212158203}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1995,     4040,  ..., 19996024,
+                            19998015, 20000000]),
+       col_indices=tensor([   0,   11,   12,  ..., 9985, 9992, 9995]),
+       values=tensor([0.1207, 0.1695, 0.9340,  ..., 0.6555, 0.7804, 0.2569]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.9596, 0.9534, 0.3471,  ..., 0.1162, 0.8421, 0.0589])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 4.959461212158203 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2117', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.870691061019897}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2060,     4088,  ..., 19995982,
+                            19997996, 20000000]),
+       col_indices=tensor([   3,    8,   18,  ..., 9972, 9995, 9999]),
+       values=tensor([0.9088, 0.2769, 0.7723,  ..., 0.9463, 0.8275, 0.8743]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1663, 0.5238, 0.4734,  ..., 0.4751, 0.9551, 0.4862])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 9.870691061019897 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2251', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.887785911560059}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1987,     4056,  ..., 19996028,
+                            19998022, 20000000]),
+       col_indices=tensor([   5,   19,   20,  ..., 9991, 9993, 9995]),
+       values=tensor([0.9673, 0.5215, 0.1097,  ..., 0.0767, 0.9506, 0.8361]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1308, 0.6150, 0.9184,  ..., 0.0574, 0.4962, 0.6674])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.887785911560059 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1987,     4056,  ..., 19996028,
+                            19998022, 20000000]),
+       col_indices=tensor([   5,   19,   20,  ..., 9991, 9993, 9995]),
+       values=tensor([0.9673, 0.5215, 0.1097,  ..., 0.0767, 0.9506, 0.8361]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1308, 0.6150, 0.9184,  ..., 0.0574, 0.4962, 0.6674])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.887785911560059 seconds
+
+[40.74, 39.87, 40.58, 40.31, 40.56, 40.27, 40.45, 40.39, 39.9, 40.0]
+[120.43]
+16.833276748657227
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2251, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.887785911560059, 'TIME_S_1KI': 4.83686624236342, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2027.23151884079, 'W': 120.43}
+[40.74, 39.87, 40.58, 40.31, 40.56, 40.27, 40.45, 40.39, 39.9, 40.0, 40.84, 39.74, 40.34, 40.47, 40.2, 40.2, 39.72, 39.73, 39.66, 39.68]
+723.02
+36.150999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2251, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.887785911560059, 'TIME_S_1KI': 4.83686624236342, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2027.23151884079, 'W': 120.43, 'J_1KI': 900.591523252239, 'W_1KI': 53.500666370501996, 'W_D': 84.27900000000001, 'J_D': 1418.6917311000825, 'W_D_1KI': 37.44069302532208, 'J_D_1KI': 16.632915604319006}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..0f6b467
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1475, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.550647735595703, "TIME_S_1KI": 7.152981515658104, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2246.3171599555017, "W": 116.09, "J_1KI": 1522.9268881054247, "W_1KI": 78.7050847457627, "W_D": 80.02975, "J_D": 1548.5588830385805, "W_D_1KI": 54.25745762711865, "J_D_1KI": 36.78471703533467}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..d7eb761
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.117977619171143}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2996,     5947,  ..., 29993941,
+                            29997016, 30000000]),
+       col_indices=tensor([   2,    4,    5,  ..., 9994, 9995, 9997]),
+       values=tensor([0.7643, 0.7440, 0.4862,  ..., 0.6436, 0.3641, 0.9418]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.8566, 0.8595, 0.2293,  ..., 0.0057, 0.7338, 0.0583])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 7.117977619171143 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1475', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.550647735595703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2975,     5983,  ..., 29994011,
+                            29997029, 30000000]),
+       col_indices=tensor([   7,    9,   13,  ..., 9994, 9995, 9998]),
+       values=tensor([0.9780, 0.9139, 0.8008,  ..., 0.7340, 0.6785, 0.0713]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.9158, 0.5873, 0.3730,  ..., 0.5358, 0.1305, 0.5051])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.550647735595703 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2975,     5983,  ..., 29994011,
+                            29997029, 30000000]),
+       col_indices=tensor([   7,    9,   13,  ..., 9994, 9995, 9998]),
+       values=tensor([0.9780, 0.9139, 0.8008,  ..., 0.7340, 0.6785, 0.0713]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.9158, 0.5873, 0.3730,  ..., 0.5358, 0.1305, 0.5051])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.550647735595703 seconds
+
+[41.39, 39.87, 39.86, 40.02, 39.88, 39.87, 40.22, 40.42, 40.2, 40.31]
+[116.09]
+19.349790334701538
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.550647735595703, 'TIME_S_1KI': 7.152981515658104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2246.3171599555017, 'W': 116.09}
+[41.39, 39.87, 39.86, 40.02, 39.88, 39.87, 40.22, 40.42, 40.2, 40.31, 41.51, 39.76, 39.79, 39.7, 39.79, 40.38, 40.29, 39.67, 39.96, 39.84]
+721.2049999999999
+36.060249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1475, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.550647735595703, 'TIME_S_1KI': 7.152981515658104, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2246.3171599555017, 'W': 116.09, 'J_1KI': 1522.9268881054247, 'W_1KI': 78.7050847457627, 'W_D': 80.02975, 'J_D': 1548.5588830385805, 'W_D_1KI': 54.25745762711865, 'J_D_1KI': 36.78471703533467}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..6beae0d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 355068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.454679489135742, "TIME_S_1KI": 0.029444161369472165, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1283.4049695920944, "W": 97.06, "J_1KI": 3.614532905224054, "W_1KI": 0.27335608953777873, "W_D": 61.48125, "J_D": 812.9542735084892, "W_D_1KI": 0.17315345229646154, "J_D_1KI": 0.0004876627921875853}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..f912c9b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1414 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1263408660888672}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([6511, 7342, 6569, 9549, 4200, 6366, 6222, 7592,  169,
+                           4880, 5467, 9729, 3186, 7579, 7366, 6461, 1569,  540,
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+                           5891]),
+       values=tensor([0.0306, 0.2996, 0.3526, 0.5136, 0.1960, 0.0494, 0.4248,
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+                      0.5556, 0.3834, 0.2160, 0.8095, 0.1449, 0.1013]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7754, 0.7775, 0.1646,  ..., 0.9136, 0.8074, 0.4658])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.1263408660888672 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '83108', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.457648515701294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([4728, 8270, 5747, 4190, 8816, 3485, 4267, 3845, 4253,
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+                      7.3674e-01, 5.8085e-01, 3.4811e-01, 4.7061e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8610, 0.2031, 0.4276,  ..., 0.7862, 0.1485, 0.1233])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 2.457648515701294 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '355068', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.454679489135742}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      1.2136e-01, 2.2091e-01, 6.2259e-01, 8.8417e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1885, 0.9110, 0.8668,  ..., 0.5988, 0.5354, 0.4490])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.454679489135742 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 992, 6197, 9017, 9363, 9552,  813, 7391, 7914, 9771,
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+                      1.2136e-01, 2.2091e-01, 6.2259e-01, 8.8417e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1885, 0.9110, 0.8668,  ..., 0.5988, 0.5354, 0.4490])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.454679489135742 seconds
+
+[39.64, 44.74, 39.71, 38.92, 39.06, 38.91, 39.09, 38.87, 39.14, 39.48]
+[97.06]
+13.222800016403198
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 355068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.454679489135742, 'TIME_S_1KI': 0.029444161369472165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.4049695920944, 'W': 97.06}
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diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..b4ec291
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 307566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.194913387298584, "TIME_S_1KI": 0.033147075383165185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1254.457565395832, "W": 97.91, "J_1KI": 4.078661378032136, "W_1KI": 0.3183381778219959, "W_D": 62.19175, "J_D": 796.8227075141073, "W_D_1KI": 0.2022061931422849, "J_D_1KI": 0.0006574400068352317}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..037a253
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04550504684448242}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 5000, 5000]),
+       col_indices=tensor([9281,  526, 5110,  ..., 4172,  680, 4833]),
+       values=tensor([0.9710, 0.4177, 0.1273,  ..., 0.7621, 0.2431, 0.8030]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6244, 0.3231, 0.3638,  ..., 0.2586, 0.1943, 0.4038])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.04550504684448242 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '230743', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.877320289611816}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4996, 4999, 5000]),
+       col_indices=tensor([5149,  830, 3827,  ..., 6947, 7825, 8143]),
+       values=tensor([0.7974, 0.8672, 0.6352,  ..., 0.0945, 0.9729, 0.8206]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.8724, 0.1762, 0.3345,  ..., 0.8958, 0.7321, 0.5036])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.877320289611816 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '307566', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.194913387298584}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6013, 2562, 3841,  ..., 8262, 3270, 1424]),
+       values=tensor([0.2685, 0.3885, 0.6243,  ..., 0.1198, 0.9466, 0.9233]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.2690, 0.9597, 0.8597,  ..., 0.6996, 0.9887, 0.3737])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.194913387298584 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6013, 2562, 3841,  ..., 8262, 3270, 1424]),
+       values=tensor([0.2685, 0.3885, 0.6243,  ..., 0.1198, 0.9466, 0.9233]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.2690, 0.9597, 0.8597,  ..., 0.6996, 0.9887, 0.3737])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.194913387298584 seconds
+
+[40.9, 39.57, 39.65, 39.48, 39.52, 39.37, 39.42, 39.88, 40.21, 39.84]
+[97.91]
+12.81235384941101
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 307566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.194913387298584, 'TIME_S_1KI': 0.033147075383165185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1254.457565395832, 'W': 97.91}
+[40.9, 39.57, 39.65, 39.48, 39.52, 39.37, 39.42, 39.88, 40.21, 39.84, 41.61, 39.93, 39.26, 40.39, 39.17, 39.17, 39.39, 39.38, 39.35, 40.1]
+714.365
+35.71825
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 307566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.194913387298584, 'TIME_S_1KI': 0.033147075383165185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1254.457565395832, 'W': 97.91, 'J_1KI': 4.078661378032136, 'W_1KI': 0.3183381778219959, 'W_D': 62.19175, 'J_D': 796.8227075141073, 'W_D_1KI': 0.2022061931422849, 'J_D_1KI': 0.0006574400068352317}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..f4ead28
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1316, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738, "TIME_S_1KI": 7.964192915107703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2349.371359462738, "W": 119.72, "J_1KI": 1785.2365953364272, "W_1KI": 90.9726443768997, "W_D": 83.6515, "J_D": 1641.5673093559742, "W_D_1KI": 63.564969604863215, "J_D_1KI": 48.3016486359143}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..a121e20
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.476217031478882}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       58,      110,  ..., 24999904,
+                            24999948, 25000000]),
+       col_indices=tensor([  6107,  36475,  44542,  ..., 455197, 482838,
+                           484709]),
+       values=tensor([0.8741, 0.1087, 0.7265,  ..., 0.9387, 0.2139, 0.8984]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2008, 0.1464, 0.5363,  ..., 0.5258, 0.1478, 0.0153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 8.476217031478882 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1238', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.872223138809204}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       42,      103,  ..., 24999895,
+                            24999951, 25000000]),
+       col_indices=tensor([  1782,  32597,  35292,  ..., 490788, 494408,
+                           495086]),
+       values=tensor([0.7532, 0.4055, 0.7849,  ..., 0.0826, 0.2837, 0.9366]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.6389, 0.8135, 0.6286,  ..., 0.0387, 0.4513, 0.2151])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 9.872223138809204 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1316', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480877876281738}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       49,       93,  ..., 24999907,
+                            24999956, 25000000]),
+       col_indices=tensor([  1315,   9605,  36829,  ..., 467295, 483577,
+                           490282]),
+       values=tensor([0.4504, 0.6377, 0.8962,  ..., 0.8735, 0.2973, 0.1824]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1673, 0.3950, 0.6065,  ..., 0.2020, 0.8580, 0.9739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.480877876281738 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       49,       93,  ..., 24999907,
+                            24999956, 25000000]),
+       col_indices=tensor([  1315,   9605,  36829,  ..., 467295, 483577,
+                           490282]),
+       values=tensor([0.4504, 0.6377, 0.8962,  ..., 0.8735, 0.2973, 0.1824]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1673, 0.3950, 0.6065,  ..., 0.2020, 0.8580, 0.9739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.480877876281738 seconds
+
+[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68]
+[119.72]
+19.623883724212646
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72}
+[40.78, 40.23, 40.39, 40.14, 40.33, 40.17, 39.83, 39.85, 39.79, 39.68, 41.19, 39.75, 40.31, 41.27, 39.67, 39.76, 39.72, 39.58, 39.67, 40.17]
+721.3699999999999
+36.06849999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1316, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480877876281738, 'TIME_S_1KI': 7.964192915107703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2349.371359462738, 'W': 119.72, 'J_1KI': 1785.2365953364272, 'W_1KI': 90.9726443768997, 'W_D': 83.6515, 'J_D': 1641.5673093559742, 'W_D_1KI': 63.564969604863215, 'J_D_1KI': 48.3016486359143}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..98c0109
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21497, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.492619514465332, "TIME_S_1KI": 0.488096921173435, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2075.744820713997, "W": 155.25, "J_1KI": 96.55974418356035, "W_1KI": 7.221937944829511, "W_D": 119.6075, "J_D": 1599.1925838553905, "W_D_1KI": 5.5639158952411965, "J_D_1KI": 0.25882290064851826}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..4dbd4ff
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.5443899631500244}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      13,  ..., 2499984,
+                            2499993, 2500000]),
+       col_indices=tensor([ 29642,  73796, 205405,  ..., 362365, 387524,
+                           440531]),
+       values=tensor([0.6565, 0.4150, 0.8341,  ..., 0.7997, 0.8212, 0.8706]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3188, 0.4041, 0.2486,  ..., 0.5189, 0.6175, 0.2446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.5443899631500244 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19287', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.420541286468506}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,      12,  ..., 2499994,
+                            2499998, 2500000]),
+       col_indices=tensor([131466, 192610, 285983,  ..., 398857,   7127,
+                           216070]),
+       values=tensor([0.3766, 0.1095, 0.0818,  ..., 0.7673, 0.9998, 0.7256]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6672, 0.9862, 0.6354,  ..., 0.4943, 0.9100, 0.2548])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.420541286468506 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21497', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.492619514465332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      12,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([124091, 157764, 160136,  ..., 120950, 171105,
+                           490445]),
+       values=tensor([0.1739, 0.8424, 0.0842,  ..., 0.2028, 0.9911, 0.7243]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9645, 0.6044, 0.9036,  ..., 0.9779, 0.7664, 0.8298])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.492619514465332 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      12,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([124091, 157764, 160136,  ..., 120950, 171105,
+                           490445]),
+       values=tensor([0.1739, 0.8424, 0.0842,  ..., 0.2028, 0.9911, 0.7243]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9645, 0.6044, 0.9036,  ..., 0.9779, 0.7664, 0.8298])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.492619514465332 seconds
+
+[41.35, 39.34, 39.96, 39.31, 39.6, 39.93, 39.84, 39.39, 39.3, 39.31]
+[155.25]
+13.370337009429932
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.492619514465332, 'TIME_S_1KI': 0.488096921173435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2075.744820713997, 'W': 155.25}
+[41.35, 39.34, 39.96, 39.31, 39.6, 39.93, 39.84, 39.39, 39.3, 39.31, 40.05, 40.08, 39.33, 39.83, 39.32, 39.88, 39.28, 39.25, 39.29, 39.13]
+712.85
+35.6425
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.492619514465332, 'TIME_S_1KI': 0.488096921173435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2075.744820713997, 'W': 155.25, 'J_1KI': 96.55974418356035, 'W_1KI': 7.221937944829511, 'W_D': 119.6075, 'J_D': 1599.1925838553905, 'W_D_1KI': 5.5639158952411965, 'J_D_1KI': 0.25882290064851826}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..340d1c5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2443, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.091211557388306, "TIME_S_1KI": 4.5399965441622205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2093.1614422798157, "W": 125.74, "J_1KI": 856.7996079737272, "W_1KI": 51.469504707327054, "W_D": 89.3905, "J_D": 1488.0606641173363, "W_D_1KI": 36.59046254604994, "J_D_1KI": 14.977676031948402}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..6a66c91
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-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', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.620434761047363}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       28,       55,  ..., 12499942,
+                            12499972, 12500000]),
+       col_indices=tensor([ 19855,  24177,  33309,  ..., 430292, 468270,
+                           470726]),
+       values=tensor([0.1735, 0.2720, 0.9086,  ..., 0.2697, 0.0473, 0.0416]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2844, 0.4487, 0.9137,  ..., 0.5004, 0.3000, 0.1233])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 4.620434761047363 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2272', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.761992931365967}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       25,       50,  ..., 12499939,
+                            12499968, 12500000]),
+       col_indices=tensor([ 35309, 102593, 109410,  ..., 438712, 452154,
+                           489935]),
+       values=tensor([0.4991, 0.7582, 0.4985,  ..., 0.8355, 0.6986, 0.3665]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.1755, 0.5499, 0.0031,  ..., 0.2944, 0.6143, 0.3232])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 9.761992931365967 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2443', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.091211557388306}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       29,       54,  ..., 12499940,
+                            12499966, 12500000]),
+       col_indices=tensor([ 15104,  53699,  66016,  ..., 451008, 478949,
+                           498027]),
+       values=tensor([0.3834, 0.0166, 0.8269,  ..., 0.7083, 0.6634, 0.5753]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3028, 0.2567, 0.4384,  ..., 0.4923, 0.2329, 0.9462])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 11.091211557388306 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       29,       54,  ..., 12499940,
+                            12499966, 12500000]),
+       col_indices=tensor([ 15104,  53699,  66016,  ..., 451008, 478949,
+                           498027]),
+       values=tensor([0.3834, 0.0166, 0.8269,  ..., 0.7083, 0.6634, 0.5753]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3028, 0.2567, 0.4384,  ..., 0.4923, 0.2329, 0.9462])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 11.091211557388306 seconds
+
+[41.47, 41.03, 39.87, 40.83, 39.9, 40.09, 40.23, 40.2, 39.74, 39.65]
+[125.74]
+16.646742820739746
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.091211557388306, 'TIME_S_1KI': 4.5399965441622205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2093.1614422798157, 'W': 125.74}
+[41.47, 41.03, 39.87, 40.83, 39.9, 40.09, 40.23, 40.2, 39.74, 39.65, 40.36, 39.88, 39.75, 39.86, 39.72, 40.13, 40.1, 39.56, 45.29, 40.14]
+726.99
+36.3495
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.091211557388306, 'TIME_S_1KI': 4.5399965441622205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2093.1614422798157, 'W': 125.74, 'J_1KI': 856.7996079737272, 'W_1KI': 51.469504707327054, 'W_D': 89.3905, 'J_D': 1488.0606641173363, 'W_D_1KI': 36.59046254604994, 'J_D_1KI': 14.977676031948402}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..c71ff3b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91834, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.82576298713684, "TIME_S_1KI": 0.11788404062914433, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1647.9860616016388, "W": 116.97, "J_1KI": 17.945271485524305, "W_1KI": 1.273711261624235, "W_D": 80.83175, "J_D": 1138.8355760867596, "W_D_1KI": 0.8801941546703835, "J_D_1KI": 0.009584621759592129}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..504044c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,125 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.14647722244262695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      8,  ..., 249988, 249995,
+                            250000]),
+       col_indices=tensor([  544,  6056, 19594,  ..., 16208, 31107, 37035]),
+       values=tensor([0.8576, 0.5005, 0.2810,  ..., 0.0063, 0.7171, 0.8258]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4318, 0.7107, 0.2576,  ..., 0.8496, 0.3705, 0.3608])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.14647722244262695 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '71683', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.233005046844482}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     10,  ..., 249988, 249994,
+                            250000]),
+       col_indices=tensor([ 4979, 12449, 23825,  ..., 32585, 40358, 48594]),
+       values=tensor([0.7825, 0.8569, 0.5029,  ..., 0.3250, 0.4106, 0.3303]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8033, 0.4755, 0.5204,  ..., 0.8611, 0.9528, 0.0172])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.233005046844482 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '81519', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.805182695388794}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 249983, 249992,
+                            250000]),
+       col_indices=tensor([ 7422, 17911, 31055,  ..., 30707, 32021, 38558]),
+       values=tensor([0.7718, 0.8036, 0.8293,  ..., 0.2159, 0.0251, 0.0647]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3183, 0.3041, 0.1046,  ..., 0.2603, 0.8118, 0.2097])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.805182695388794 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '87295', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.980920553207397}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 249989, 249993,
+                            250000]),
+       col_indices=tensor([19530, 21432, 40127,  ..., 33319, 45642, 48654]),
+       values=tensor([0.8438, 0.0330, 0.2387,  ..., 0.6115, 0.5796, 0.5067]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1992, 0.5617, 0.3460,  ..., 0.4818, 0.9372, 0.6597])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.980920553207397 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91834', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.82576298713684}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 249987, 249995,
+                            250000]),
+       col_indices=tensor([ 2714,  5631, 18387,  ..., 39061, 48792, 49070]),
+       values=tensor([0.1970, 0.9435, 0.9859,  ..., 0.7944, 0.6863, 0.0587]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5128, 0.8861, 0.8900,  ..., 0.3721, 0.4809, 0.7353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.82576298713684 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 249987, 249995,
+                            250000]),
+       col_indices=tensor([ 2714,  5631, 18387,  ..., 39061, 48792, 49070]),
+       values=tensor([0.1970, 0.9435, 0.9859,  ..., 0.7944, 0.6863, 0.0587]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5128, 0.8861, 0.8900,  ..., 0.3721, 0.4809, 0.7353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.82576298713684 seconds
+
+[40.37, 39.3, 39.3, 39.19, 39.7, 39.15, 40.14, 44.33, 39.43, 39.81]
+[116.97]
+14.088963508605957
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91834, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.82576298713684, 'TIME_S_1KI': 0.11788404062914433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1647.9860616016388, 'W': 116.97}
+[40.37, 39.3, 39.3, 39.19, 39.7, 39.15, 40.14, 44.33, 39.43, 39.81, 40.26, 39.09, 41.97, 44.11, 39.87, 39.01, 40.55, 38.99, 38.97, 38.89]
+722.7650000000001
+36.138250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91834, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.82576298713684, 'TIME_S_1KI': 0.11788404062914433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1647.9860616016388, 'W': 116.97, 'J_1KI': 17.945271485524305, 'W_1KI': 1.273711261624235, 'W_D': 80.83175, 'J_D': 1138.8355760867596, 'W_D_1KI': 0.8801941546703835, 'J_D_1KI': 0.009584621759592129}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..3676d30
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.762548208236694, "TIME_S_1KI": 0.2300918911434889, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2033.0924378275872, "W": 149.27, "J_1KI": 43.46536478519695, "W_1KI": 3.1912346338856232, "W_D": 113.87475, "J_D": 1551.0008245763183, "W_D_1KI": 2.434521646178514, "J_D_1KI": 0.052047496444222636}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..3112aac
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.29659008979797363}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,      83,  ..., 2499888,
+                            2499946, 2500000]),
+       col_indices=tensor([ 2168,  2264,  3614,  ..., 46868, 47216, 48811]),
+       values=tensor([0.2788, 0.0512, 0.3475,  ..., 0.9281, 0.1898, 0.0144]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5080, 0.1629, 0.0847,  ..., 0.6599, 0.4582, 0.2341])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.29659008979797363 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35402', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.946921348571777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      54,     117,  ..., 2499905,
+                            2499953, 2500000]),
+       col_indices=tensor([ 1300,  1442,  2491,  ..., 47415, 49147, 49910]),
+       values=tensor([0.1149, 0.9707, 0.0968,  ..., 0.7933, 0.6392, 0.9343]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2903, 0.7408, 0.0968,  ..., 0.3344, 0.5691, 0.3821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 7.946921348571777 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46775', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.762548208236694}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      65,     117,  ..., 2499903,
+                            2499954, 2500000]),
+       col_indices=tensor([ 2232,  2981,  3015,  ..., 49447, 49836, 49877]),
+       values=tensor([0.3281, 0.9452, 0.1004,  ..., 0.4282, 0.2346, 0.1167]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5238, 0.6081, 0.9163,  ..., 0.2866, 0.2457, 0.9117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.762548208236694 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      65,     117,  ..., 2499903,
+                            2499954, 2500000]),
+       col_indices=tensor([ 2232,  2981,  3015,  ..., 49447, 49836, 49877]),
+       values=tensor([0.3281, 0.9452, 0.1004,  ..., 0.4282, 0.2346, 0.1167]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5238, 0.6081, 0.9163,  ..., 0.2866, 0.2457, 0.9117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.762548208236694 seconds
+
+[41.04, 39.15, 39.26, 39.11, 39.17, 39.29, 39.32, 39.24, 39.56, 39.01]
+[149.27]
+13.620234727859497
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.762548208236694, 'TIME_S_1KI': 0.2300918911434889, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2033.0924378275872, 'W': 149.27}
+[41.04, 39.15, 39.26, 39.11, 39.17, 39.29, 39.32, 39.24, 39.56, 39.01, 39.97, 39.18, 39.31, 39.08, 39.46, 39.32, 39.13, 39.45, 39.15, 39.43]
+707.905
+35.39525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.762548208236694, 'TIME_S_1KI': 0.2300918911434889, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2033.0924378275872, 'W': 149.27, 'J_1KI': 43.46536478519695, 'W_1KI': 3.1912346338856232, 'W_D': 113.87475, 'J_D': 1551.0008245763183, 'W_D_1KI': 2.434521646178514, 'J_D_1KI': 0.052047496444222636}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..032b229
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1728, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495, "TIME_S_1KI": 6.277456741642069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2262.675802116394, "W": 116.47, "J_1KI": 1309.418866965506, "W_1KI": 67.40162037037037, "W_D": 80.83224999999999, "J_D": 1570.3372207918164, "W_D_1KI": 46.7779224537037, "J_D_1KI": 27.070556975522976}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..44057a0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.073575019836426}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      511,      999,  ..., 24998996,
+                            24999515, 25000000]),
+       col_indices=tensor([   50,   140,   163,  ..., 49849, 49891, 49909]),
+       values=tensor([0.6896, 0.4241, 0.6835,  ..., 0.3809, 0.0330, 0.1679]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8743, 0.1159, 0.4633,  ..., 0.1043, 0.2471, 0.3798])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 6.073575019836426 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1728', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.847445249557495}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      553,     1047,  ..., 24998955,
+                            24999507, 25000000]),
+       col_indices=tensor([   47,   133,   262,  ..., 49731, 49773, 49776]),
+       values=tensor([0.8665, 0.6889, 0.7366,  ..., 0.8541, 0.3572, 0.3739]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0392, 0.1826, 0.6698,  ..., 0.4937, 0.5808, 0.8286])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.847445249557495 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      553,     1047,  ..., 24998955,
+                            24999507, 25000000]),
+       col_indices=tensor([   47,   133,   262,  ..., 49731, 49773, 49776]),
+       values=tensor([0.8665, 0.6889, 0.7366,  ..., 0.8541, 0.3572, 0.3739]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0392, 0.1826, 0.6698,  ..., 0.4937, 0.5808, 0.8286])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.847445249557495 seconds
+
+[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51]
+[116.47]
+19.427112579345703
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47}
+[40.17, 39.47, 39.62, 39.58, 39.57, 39.35, 40.27, 39.27, 39.48, 39.51, 40.76, 39.62, 39.66, 39.43, 39.52, 39.33, 39.97, 39.4, 39.29, 39.41]
+712.7550000000001
+35.637750000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1728, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.847445249557495, 'TIME_S_1KI': 6.277456741642069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2262.675802116394, 'W': 116.47, 'J_1KI': 1309.418866965506, 'W_1KI': 67.40162037037037, 'W_D': 80.83224999999999, 'J_D': 1570.3372207918164, 'W_D_1KI': 46.7779224537037, 'J_D_1KI': 27.070556975522976}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..efee96e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 128043, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.502545356750488, "TIME_S_1KI": 0.08202358080293722, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1345.7720102190972, "W": 103.43, "J_1KI": 10.510313021556017, "W_1KI": 0.8077755129136307, "W_D": 68.14325000000001, "J_D": 886.6409990850092, "W_D_1KI": 0.532190357926634, "J_D_1KI": 0.004156340900530557}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..1baca85
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.1170039176940918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([20669, 48572, 15521,  ...,  4942, 37440, 49163]),
+       values=tensor([0.4805, 0.0794, 0.3246,  ..., 0.3038, 0.8605, 0.6038]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4235, 0.9189, 0.0697,  ..., 0.8234, 0.9093, 0.0251])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.1170039176940918 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89740', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.3589677810668945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([42797, 39277, 20964,  ..., 31232, 43143, 42518]),
+       values=tensor([0.7162, 0.4091, 0.9127,  ..., 0.7828, 0.7816, 0.8353]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2017, 0.4349, 0.5577,  ..., 0.2868, 0.8229, 0.7966])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 7.3589677810668945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '128043', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.502545356750488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([18076, 25567, 40242,  ..., 19386, 42443, 43843]),
+       values=tensor([0.1613, 0.4932, 0.9378,  ..., 0.7394, 0.5576, 0.1832]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1994, 0.9899, 0.9038,  ..., 0.7869, 0.4416, 0.9952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.502545356750488 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([18076, 25567, 40242,  ..., 19386, 42443, 43843]),
+       values=tensor([0.1613, 0.4932, 0.9378,  ..., 0.7394, 0.5576, 0.1832]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1994, 0.9899, 0.9038,  ..., 0.7869, 0.4416, 0.9952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.502545356750488 seconds
+
+[40.3, 39.43, 39.29, 39.36, 38.95, 39.04, 39.78, 38.84, 39.12, 39.2]
+[103.43]
+13.011428117752075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128043, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.502545356750488, 'TIME_S_1KI': 0.08202358080293722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1345.7720102190972, 'W': 103.43}
+[40.3, 39.43, 39.29, 39.36, 38.95, 39.04, 39.78, 38.84, 39.12, 39.2, 40.2, 39.28, 39.13, 39.22, 38.89, 38.97, 38.85, 39.3, 39.03, 38.81]
+705.7349999999999
+35.28675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128043, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.502545356750488, 'TIME_S_1KI': 0.08202358080293722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1345.7720102190972, 'W': 103.43, 'J_1KI': 10.510313021556017, 'W_1KI': 0.8077755129136307, 'W_D': 68.14325000000001, 'J_D': 886.6409990850092, 'W_D_1KI': 0.532190357926634, 'J_D_1KI': 0.004156340900530557}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..e2a64dc
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 110048, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.860910654067993, "TIME_S_1KI": 0.09869248558872486, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1513.3099088716508, "W": 110.42, "J_1KI": 13.751362213503661, "W_1KI": 1.0033803431230008, "W_D": 74.49324999999999, "J_D": 1020.9325608499645, "W_D_1KI": 0.6769159821168944, "J_D_1KI": 0.006151097540317811}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..99bfaff
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1521902084350586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 124998, 124999,
+                            125000]),
+       col_indices=tensor([28568, 23377, 33207,  ..., 35070, 20237, 35086]),
+       values=tensor([0.0970, 0.0746, 0.1789,  ..., 0.4665, 0.3762, 0.5874]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5202, 0.5834, 0.5039,  ..., 0.2581, 0.4110, 0.2043])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.1521902084350586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68992', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.582725286483765}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      4,  ..., 124994, 124996,
+                            125000]),
+       col_indices=tensor([13407, 30849, 37582,  ...,  4235,  7510, 16049]),
+       values=tensor([0.4132, 0.1824, 0.9780,  ..., 0.4864, 0.4697, 0.1823]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.9509, 0.2372, 0.8108,  ..., 0.6237, 0.0261, 0.7128])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 6.582725286483765 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '110048', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.860910654067993}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124995, 124996,
+                            125000]),
+       col_indices=tensor([ 8770, 24657, 47529,  ..., 37234, 42798, 47480]),
+       values=tensor([0.5915, 0.6574, 0.6205,  ..., 0.3170, 0.3438, 0.3659]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7258, 0.6385, 0.7203,  ..., 0.8359, 0.3176, 0.3735])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.860910654067993 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124995, 124996,
+                            125000]),
+       col_indices=tensor([ 8770, 24657, 47529,  ..., 37234, 42798, 47480]),
+       values=tensor([0.5915, 0.6574, 0.6205,  ..., 0.3170, 0.3438, 0.3659]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7258, 0.6385, 0.7203,  ..., 0.8359, 0.3176, 0.3735])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.860910654067993 seconds
+
+[40.58, 39.41, 39.64, 39.39, 39.67, 39.83, 39.65, 39.43, 39.45, 39.8]
+[110.42]
+13.705034494400024
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110048, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.860910654067993, 'TIME_S_1KI': 0.09869248558872486, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1513.3099088716508, 'W': 110.42}
+[40.58, 39.41, 39.64, 39.39, 39.67, 39.83, 39.65, 39.43, 39.45, 39.8, 40.29, 39.44, 40.08, 39.34, 39.59, 39.49, 39.42, 44.77, 39.96, 39.28]
+718.5350000000001
+35.926750000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110048, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.860910654067993, 'TIME_S_1KI': 0.09869248558872486, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1513.3099088716508, 'W': 110.42, 'J_1KI': 13.751362213503661, 'W_1KI': 1.0033803431230008, 'W_D': 74.49324999999999, 'J_D': 1020.9325608499645, 'W_D_1KI': 0.6769159821168944, 'J_D_1KI': 0.006151097540317811}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..4891577
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 435807, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.511547565460205, "TIME_S_1KI": 0.024119730902578906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1232.545183532238, "W": 96.09, "J_1KI": 2.828190422669296, "W_1KI": 0.2204875093791518, "W_D": 60.76, "J_D": 779.3677318286896, "W_D_1KI": 0.1394195136838096, "J_D_1KI": 0.0003199111388385446}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..0251f49
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.040769100189208984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([4125, 1116, 4300,  ...,  690, 2880, 3382]),
+       values=tensor([0.0653, 0.6541, 0.1575,  ..., 0.5764, 0.0907, 0.6553]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7803, 0.2089, 0.7573,  ..., 0.7596, 0.3125, 0.6078])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.040769100189208984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '257547', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.205128908157349}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1273, 2247, 4850,  ..., 2520, 1394, 3793]),
+       values=tensor([0.8733, 0.7089, 0.0515,  ..., 0.3445, 0.4099, 0.0495]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.0086, 0.7636, 0.4685,  ..., 0.9955, 0.7657, 0.7966])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.205128908157349 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '435807', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.511547565460205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([4068, 4690, 1058,  ..., 2571, 4364, 3391]),
+       values=tensor([0.9209, 0.6933, 0.9201,  ..., 0.0738, 0.0357, 0.7845]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3918, 0.7384, 0.9927,  ..., 0.9998, 0.6009, 0.1634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.511547565460205 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([4068, 4690, 1058,  ..., 2571, 4364, 3391]),
+       values=tensor([0.9209, 0.6933, 0.9201,  ..., 0.0738, 0.0357, 0.7845]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3918, 0.7384, 0.9927,  ..., 0.9998, 0.6009, 0.1634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.511547565460205 seconds
+
+[39.78, 39.82, 44.51, 38.6, 40.39, 38.56, 38.63, 38.62, 38.67, 38.55]
+[96.09]
+12.826987028121948
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 435807, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.511547565460205, 'TIME_S_1KI': 0.024119730902578906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1232.545183532238, 'W': 96.09}
+[39.78, 39.82, 44.51, 38.6, 40.39, 38.56, 38.63, 38.62, 38.67, 38.55, 39.31, 38.89, 38.76, 38.73, 39.09, 38.53, 38.74, 39.13, 38.76, 38.7]
+706.6000000000001
+35.330000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 435807, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.511547565460205, 'TIME_S_1KI': 0.024119730902578906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1232.545183532238, 'W': 96.09, 'J_1KI': 2.828190422669296, 'W_1KI': 0.2204875093791518, 'W_D': 60.76, 'J_D': 779.3677318286896, 'W_D_1KI': 0.1394195136838096, 'J_D_1KI': 0.0003199111388385446}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..fe8abca
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 245735, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.373013734817505, "TIME_S_1KI": 0.042212194985726516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1260.703432407379, "W": 98.11, "J_1KI": 5.130337283689255, "W_1KI": 0.39925122591409445, "W_D": 62.99425, "J_D": 809.4696483225822, "W_D_1KI": 0.2563503367448674, "J_D_1KI": 0.0010431983101506395}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..79cf966
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05801510810852051}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,    14,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([2155, 3530, 3567,  ..., 2695, 4305, 4878]),
+       values=tensor([0.7077, 0.9384, 0.0254,  ..., 0.2116, 0.4863, 0.3277]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5025, 0.8306, 0.5455,  ..., 0.1180, 0.7485, 0.4884])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05801510810852051 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '180987', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.7333667278289795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([ 162,  480,  815,  ..., 2232, 2732, 2847]),
+       values=tensor([0.8302, 0.2791, 0.7518,  ..., 0.7674, 0.4968, 0.3066]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5831, 0.9483, 0.7910,  ..., 0.0226, 0.1378, 0.9053])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.7333667278289795 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '245735', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.373013734817505}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,    13,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([1389, 1769, 1783,  ..., 2323, 3077, 3881]),
+       values=tensor([0.3893, 0.4927, 0.3928,  ..., 0.2440, 0.9871, 0.0384]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3455, 0.7497, 0.7321,  ..., 0.5403, 0.0178, 0.6295])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.373013734817505 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,    13,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([1389, 1769, 1783,  ..., 2323, 3077, 3881]),
+       values=tensor([0.3893, 0.4927, 0.3928,  ..., 0.2440, 0.9871, 0.0384]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3455, 0.7497, 0.7321,  ..., 0.5403, 0.0178, 0.6295])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.373013734817505 seconds
+
+[40.07, 38.96, 38.85, 38.93, 38.85, 39.21, 39.52, 38.55, 38.74, 38.71]
+[98.11]
+12.849897384643555
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 245735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.373013734817505, 'TIME_S_1KI': 0.042212194985726516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1260.703432407379, 'W': 98.11}
+[40.07, 38.96, 38.85, 38.93, 38.85, 39.21, 39.52, 38.55, 38.74, 38.71, 40.05, 38.72, 38.65, 39.11, 38.9, 39.03, 39.06, 38.96, 38.91, 39.9]
+702.3149999999999
+35.11575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 245735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.373013734817505, 'TIME_S_1KI': 0.042212194985726516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1260.703432407379, 'W': 98.11, 'J_1KI': 5.130337283689255, 'W_1KI': 0.39925122591409445, 'W_D': 62.99425, 'J_D': 809.4696483225822, 'W_D_1KI': 0.2563503367448674, 'J_D_1KI': 0.0010431983101506395}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..65befab
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 145666, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.058181524276733, "TIME_S_1KI": 0.06904961709854553, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1397.6345458984374, "W": 116.8, "J_1KI": 9.594789078428992, "W_1KI": 0.80183433333791, "W_D": 81.52975, "J_D": 975.5889993019105, "W_D_1KI": 0.5597033624867849, "J_D_1KI": 0.0038423747647823438}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..930f25a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.10852384567260742}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     51,     93,  ..., 249898, 249945,
+                            250000]),
+       col_indices=tensor([ 121,  263,  268,  ..., 4347, 4657, 4780]),
+       values=tensor([0.9155, 0.4457, 0.5767,  ..., 0.8561, 0.2482, 0.9078]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9909, 0.5337, 0.2877,  ..., 0.9413, 0.4687, 0.7116])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.10852384567260742 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '96752', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.974123954772949}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     54,    108,  ..., 249899, 249958,
+                            250000]),
+       col_indices=tensor([  30,   44,  230,  ..., 4553, 4620, 4987]),
+       values=tensor([0.7207, 0.9659, 0.8009,  ..., 0.1897, 0.2795, 0.9074]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9275, 0.8053, 0.7107,  ..., 0.1305, 0.9789, 0.9894])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 6.974123954772949 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '145666', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.058181524276733}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     49,    108,  ..., 249901, 249948,
+                            250000]),
+       col_indices=tensor([ 207,  226,  430,  ..., 4797, 4906, 4947]),
+       values=tensor([0.9242, 0.6665, 0.8223,  ..., 0.0998, 0.8618, 0.4766]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6439, 0.4458, 0.8465,  ..., 0.5021, 0.5940, 0.7614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.058181524276733 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     49,    108,  ..., 249901, 249948,
+                            250000]),
+       col_indices=tensor([ 207,  226,  430,  ..., 4797, 4906, 4947]),
+       values=tensor([0.9242, 0.6665, 0.8223,  ..., 0.0998, 0.8618, 0.4766]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6439, 0.4458, 0.8465,  ..., 0.5021, 0.5940, 0.7614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.058181524276733 seconds
+
+[39.46, 38.8, 39.74, 39.98, 38.95, 39.2, 39.78, 38.76, 39.24, 38.86]
+[116.8]
+11.966049194335938
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 145666, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.058181524276733, 'TIME_S_1KI': 0.06904961709854553, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.6345458984374, 'W': 116.8}
+[39.46, 38.8, 39.74, 39.98, 38.95, 39.2, 39.78, 38.76, 39.24, 38.86, 39.39, 39.72, 39.13, 38.67, 39.47, 39.2, 39.27, 38.58, 38.78, 38.56]
+705.405
+35.27025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 145666, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.058181524276733, 'TIME_S_1KI': 0.06904961709854553, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.6345458984374, 'W': 116.8, 'J_1KI': 9.594789078428992, 'W_1KI': 0.80183433333791, 'W_D': 81.52975, 'J_D': 975.5889993019105, 'W_D_1KI': 0.5597033624867849, 'J_D_1KI': 0.0038423747647823438}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..11bcfb3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 92460, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.897745609283447, "TIME_S_1KI": 0.1178644344503942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1737.196626586914, "W": 132.16, "J_1KI": 18.788628883700127, "W_1KI": 1.4293748648064026, "W_D": 96.69825, "J_D": 1271.0644196190835, "W_D_1KI": 1.0458387410772227, "J_D_1KI": 0.011311256122401284}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..f6c9fdd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.15929031372070312}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     260,     503,  ..., 1249486,
+                            1249755, 1250000]),
+       col_indices=tensor([   4,   17,   88,  ..., 4971, 4985, 4987]),
+       values=tensor([0.6362, 0.8148, 0.9153,  ..., 0.4566, 0.5649, 0.0413]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3069, 0.3781, 0.2833,  ..., 0.0090, 0.7599, 0.7166])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.15929031372070312 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65917', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 7.485628128051758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     243,     508,  ..., 1249492,
+                            1249760, 1250000]),
+       col_indices=tensor([  22,   30,   57,  ..., 4895, 4917, 4934]),
+       values=tensor([0.3367, 0.7320, 0.6215,  ..., 0.3721, 0.4144, 0.7665]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8371, 0.0930, 0.7011,  ..., 0.7680, 0.1649, 0.0938])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 7.485628128051758 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '92460', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.897745609283447}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     258,     501,  ..., 1249501,
+                            1249766, 1250000]),
+       col_indices=tensor([   3,   26,   36,  ..., 4973, 4974, 4975]),
+       values=tensor([0.1574, 0.4230, 0.3584,  ..., 0.2058, 0.0488, 0.1761]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.1704, 0.2409, 0.1947,  ..., 0.5321, 0.6051, 0.5827])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.897745609283447 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     258,     501,  ..., 1249501,
+                            1249766, 1250000]),
+       col_indices=tensor([   3,   26,   36,  ..., 4973, 4974, 4975]),
+       values=tensor([0.1574, 0.4230, 0.3584,  ..., 0.2058, 0.0488, 0.1761]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.1704, 0.2409, 0.1947,  ..., 0.5321, 0.6051, 0.5827])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.897745609283447 seconds
+
+[39.99, 38.92, 38.9, 38.99, 39.15, 39.51, 39.42, 41.37, 39.31, 39.41]
+[132.16]
+13.144647598266602
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92460, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.897745609283447, 'TIME_S_1KI': 0.1178644344503942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1737.196626586914, 'W': 132.16}
+[39.99, 38.92, 38.9, 38.99, 39.15, 39.51, 39.42, 41.37, 39.31, 39.41, 40.8, 39.06, 39.17, 39.04, 39.15, 39.09, 39.05, 39.4, 39.89, 39.43]
+709.2349999999999
+35.461749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92460, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.897745609283447, 'TIME_S_1KI': 0.1178644344503942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1737.196626586914, 'W': 132.16, 'J_1KI': 18.788628883700127, 'W_1KI': 1.4293748648064026, 'W_D': 96.69825, 'J_D': 1271.0644196190835, 'W_D_1KI': 1.0458387410772227, 'J_D_1KI': 0.011311256122401284}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..0b5b4c3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 53552, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.574474811553955, "TIME_S_1KI": 0.19746180929851276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1844.3806961250305, "W": 137.23, "J_1KI": 34.44093023836702, "W_1KI": 2.562556020316701, "W_D": 101.71924999999999, "J_D": 1367.113758830547, "W_D_1KI": 1.8994481998804897, "J_D_1KI": 0.03546922990514808}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..ab97b45
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.24988102912902832}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     499,    1048,  ..., 2499022,
+                            2499519, 2500000]),
+       col_indices=tensor([   0,   10,   32,  ..., 4963, 4977, 4991]),
+       values=tensor([0.6726, 0.1161, 0.9278,  ..., 0.2840, 0.7697, 0.2554]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1233, 0.5107, 0.9675,  ..., 0.9055, 0.5032, 0.4140])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.24988102912902832 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42019', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.238577604293823}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     520,    1002,  ..., 2498984,
+                            2499504, 2500000]),
+       col_indices=tensor([   4,   20,   21,  ..., 4945, 4966, 4991]),
+       values=tensor([0.6805, 0.1607, 0.7488,  ..., 0.5436, 0.2045, 0.6809]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8501, 0.5629, 0.1238,  ..., 0.7287, 0.6927, 0.0708])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 8.238577604293823 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53552', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.574474811553955}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     495,    1006,  ..., 2499053,
+                            2499503, 2500000]),
+       col_indices=tensor([   5,   23,   39,  ..., 4985, 4986, 4988]),
+       values=tensor([0.9333, 0.4318, 0.5588,  ..., 0.9224, 0.9203, 0.9677]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4521, 0.6662, 0.5969,  ..., 0.4716, 0.4029, 0.2909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.574474811553955 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     495,    1006,  ..., 2499053,
+                            2499503, 2500000]),
+       col_indices=tensor([   5,   23,   39,  ..., 4985, 4986, 4988]),
+       values=tensor([0.9333, 0.4318, 0.5588,  ..., 0.9224, 0.9203, 0.9677]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4521, 0.6662, 0.5969,  ..., 0.4716, 0.4029, 0.2909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.574474811553955 seconds
+
+[40.03, 39.26, 39.99, 39.12, 39.6, 39.27, 39.21, 40.1, 39.2, 39.55]
+[137.23]
+13.440069198608398
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.574474811553955, 'TIME_S_1KI': 0.19746180929851276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1844.3806961250305, 'W': 137.23}
+[40.03, 39.26, 39.99, 39.12, 39.6, 39.27, 39.21, 40.1, 39.2, 39.55, 39.94, 39.28, 39.24, 39.36, 39.26, 39.73, 39.25, 39.64, 39.4, 39.09]
+710.2149999999999
+35.510749999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 53552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.574474811553955, 'TIME_S_1KI': 0.19746180929851276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1844.3806961250305, 'W': 137.23, 'J_1KI': 34.44093023836702, 'W_1KI': 2.562556020316701, 'W_D': 101.71924999999999, 'J_D': 1367.113758830547, 'W_D_1KI': 1.8994481998804897, 'J_D_1KI': 0.03546922990514808}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..3628bb4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28750, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.407469034194946, "TIME_S_1KI": 0.3619989229285199, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1928.9614020395277, "W": 139.14, "J_1KI": 67.09430963615748, "W_1KI": 4.839652173913042, "W_D": 103.18299999999998, "J_D": 1430.47307996726, "W_D_1KI": 3.588973913043478, "J_D_1KI": 0.12483387523629487}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..de85415
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43890881538391113}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1013,    1983,  ..., 4997973,
+                            4999012, 5000000]),
+       col_indices=tensor([   4,   12,   14,  ..., 4994, 4995, 4999]),
+       values=tensor([0.7248, 0.5151, 0.3464,  ..., 0.0289, 0.6444, 0.6982]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8578, 0.9750, 0.0786,  ..., 0.8483, 0.5183, 0.1076])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.43890881538391113 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23922', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 8.73651671409607}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1001,    1993,  ..., 4997975,
+                            4998974, 5000000]),
+       col_indices=tensor([   0,    6,    7,  ..., 4986, 4993, 4997]),
+       values=tensor([0.9981, 0.9465, 0.8571,  ..., 0.5801, 0.6800, 0.5830]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7841, 0.9192, 0.6592,  ..., 0.6410, 0.6781, 0.3236])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 8.73651671409607 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28750', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.407469034194946}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     962,    2002,  ..., 4997962,
+                            4998964, 5000000]),
+       col_indices=tensor([   3,    4,    7,  ..., 4990, 4997, 4999]),
+       values=tensor([0.7874, 0.2567, 0.8616,  ..., 0.3490, 0.8581, 0.1320]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5757, 0.3666, 0.5675,  ..., 0.4574, 0.0199, 0.8861])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.407469034194946 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     962,    2002,  ..., 4997962,
+                            4998964, 5000000]),
+       col_indices=tensor([   3,    4,    7,  ..., 4990, 4997, 4999]),
+       values=tensor([0.7874, 0.2567, 0.8616,  ..., 0.3490, 0.8581, 0.1320]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5757, 0.3666, 0.5675,  ..., 0.4574, 0.0199, 0.8861])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.407469034194946 seconds
+
+[40.02, 39.78, 39.89, 39.91, 39.49, 39.87, 40.34, 39.56, 39.63, 39.54]
+[139.14]
+13.863456964492798
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.407469034194946, 'TIME_S_1KI': 0.3619989229285199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1928.9614020395277, 'W': 139.14}
+[40.02, 39.78, 39.89, 39.91, 39.49, 39.87, 40.34, 39.56, 39.63, 39.54, 42.22, 40.02, 39.42, 39.79, 40.33, 39.7, 39.95, 40.8, 39.86, 39.82]
+719.1400000000001
+35.95700000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.407469034194946, 'TIME_S_1KI': 0.3619989229285199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1928.9614020395277, 'W': 139.14, 'J_1KI': 67.09430963615748, 'W_1KI': 4.839652173913042, 'W_D': 103.18299999999998, 'J_D': 1430.47307996726, 'W_D_1KI': 3.588973913043478, 'J_D_1KI': 0.12483387523629487}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..b67d5e2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.31110143661499, "TIME_S_1KI": 0.5428895612391402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1973.4541555023193, "W": 138.56, "J_1KI": 103.90428871175271, "W_1KI": 7.295319328173537, "W_D": 102.64975000000001, "J_D": 1461.9989585650565, "W_D_1KI": 5.404609593007951, "J_D_1KI": 0.2845579736222793}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..95ac928
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.6186671257019043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1447,    2956,  ..., 7496936,
+                            7498490, 7500000]),
+       col_indices=tensor([   4,    5,    6,  ..., 4990, 4991, 4998]),
+       values=tensor([0.3748, 0.3720, 0.6580,  ..., 0.1069, 0.0058, 0.6452]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.1517, 0.2007, 0.5208,  ..., 0.9824, 0.7905, 0.4002])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.6186671257019043 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '16971', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.381728649139404}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1500,    3040,  ..., 7497021,
+                            7498445, 7500000]),
+       col_indices=tensor([   1,    5,    9,  ..., 4989, 4995, 4997]),
+       values=tensor([0.5527, 0.4050, 0.9810,  ..., 0.2200, 0.7595, 0.4370]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.0260, 0.1554, 0.7193,  ..., 0.7760, 0.7155, 0.7186])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 9.381728649139404 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18993', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.31110143661499}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1505,    3034,  ..., 7497030,
+                            7498523, 7500000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4987, 4992, 4993]),
+       values=tensor([0.4621, 0.5996, 0.9420,  ..., 0.5197, 0.1170, 0.3387]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7133, 0.0520, 0.6752,  ..., 0.8724, 0.9726, 0.0718])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.31110143661499 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1505,    3034,  ..., 7497030,
+                            7498523, 7500000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4987, 4992, 4993]),
+       values=tensor([0.4621, 0.5996, 0.9420,  ..., 0.5197, 0.1170, 0.3387]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7133, 0.0520, 0.6752,  ..., 0.8724, 0.9726, 0.0718])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.31110143661499 seconds
+
+[41.45, 39.54, 41.3, 40.23, 39.52, 39.85, 39.55, 39.54, 39.68, 39.47]
+[138.56]
+14.24259638786316
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.31110143661499, 'TIME_S_1KI': 0.5428895612391402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1973.4541555023193, 'W': 138.56}
+[41.45, 39.54, 41.3, 40.23, 39.52, 39.85, 39.55, 39.54, 39.68, 39.47, 40.91, 39.98, 39.59, 39.45, 39.99, 39.93, 40.26, 39.5, 39.6, 39.56]
+718.2049999999999
+35.91025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.31110143661499, 'TIME_S_1KI': 0.5428895612391402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1973.4541555023193, 'W': 138.56, 'J_1KI': 103.90428871175271, 'W_1KI': 7.295319328173537, 'W_D': 102.64975000000001, 'J_D': 1461.9989585650565, 'W_D_1KI': 5.404609593007951, 'J_D_1KI': 0.2845579736222793}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..f4429df
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 491380, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.363765478134155, "TIME_S_1KI": 0.021091142248634776, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1327.781383190155, "W": 95.12, "J_1KI": 2.7021477943549903, "W_1KI": 0.1935772721722496, "W_D": 60.282000000000004, "J_D": 841.4772638926506, "W_D_1KI": 0.12267898571370427, "J_D_1KI": 0.00024966214683891136}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..5c32150
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,464 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.0375218391418457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4395, 1896, 1016, 4725, 4287, 4964, 4709, 2475, 4723,
+                           2193, 4334, 4011, 1534,  947, 2980, 1276, 2745, 2145,
+                           4595, 3295, 1907, 4436,  575, 2869, 2437, 1774,  103,
+                           4181, 1510, 1361, 4237, 4144, 4620, 1378, 4923, 2023,
+                            170, 2835, 1311, 2663, 3014,  105, 2833, 4415, 2179,
+                           2930,  693, 1558, 1071, 3383, 2339, 3436,  478, 4648,
+                           3106, 1411, 4257,  307, 1671, 1884, 1213, 4984,  642,
+                           1762, 3957, 2642, 3601, 1788,  779,  952, 1165, 4886,
+                           1883, 1290, 3845,  617, 3725, 3513, 4081, 2223, 1340,
+                           3232, 3261, 4162, 1911, 3991, 2182, 2166, 4202, 2629,
+                           1539, 1110,  990, 1798, 3362,   92, 4378, 3447, 4318,
+                           1039, 2930, 1879, 4375, 2295, 3990,  746, 3339, 2924,
+                           1503, 2112, 2677, 3879, 2287, 1293, 3194, 3630, 2849,
+                           3363, 1715,  457, 1006,  888, 2409, 2177, 4389, 4129,
+                           1812, 2617, 4717, 2316, 4949, 4158, 4435, 1917, 1201,
+                           1815,  715,  270,  923, 1913, 3452, 2985, 4782, 1099,
+                           4541, 1002, 2896, 4712, 4267, 2282,  628, 3973, 2938,
+                            376, 3252,   94, 2656, 4853, 4987, 1689, 1656,  463,
+                           3165,  992, 2823, 3447, 1273, 2259, 3674, 3345, 2191,
+                           1553, 3931,  925, 4111, 4050, 2652, 4860, 4434, 4407,
+                           4679, 4167, 4708, 2520, 3526, 2887, 3132, 3816, 2503,
+                           1957, 3455, 1933, 2402, 1540, 2844, 1178, 2305, 1831,
+                           1888, 1548, 3851, 4681,  615, 1793,  720, 2902,  503,
+                           2399, 4452, 2482, 1672,  109, 4558,  522, 4488, 4193,
+                           4882, 4297, 3385, 3297, 4242, 2939,  945,  273, 1189,
+                           1168, 4866,  495, 4965, 2390, 1391, 2738, 4804, 1124,
+                           3476, 3768,  384, 2163, 1378, 1422, 3827,   12, 4549,
+                           4524, 1374, 4468, 1024, 3152,  985, 3013]),
+       values=tensor([0.3589, 0.1660, 0.5969, 0.5688, 0.7752, 0.6324, 0.0921,
+                      0.8083, 0.2140, 0.4448, 0.7196, 0.7942, 0.0476, 0.7765,
+                      0.8012, 0.3506, 0.5836, 0.4105, 0.9051, 0.1137, 0.9336,
+                      0.6799, 0.2082, 0.3357, 0.2380, 0.0294, 0.3136, 0.6271,
+                      0.0480, 0.8189, 0.3762, 0.4307, 0.7550, 0.2975, 0.8129,
+                      0.6595, 0.2962, 0.6547, 0.2906, 0.5665, 0.2166, 0.0083,
+                      0.8507, 0.4177, 0.3111, 0.7802, 0.8212, 0.9638, 0.3557,
+                      0.0980, 0.2482, 0.5366, 0.7901, 0.2480, 0.2830, 0.7633,
+                      0.5347, 0.7196, 0.5079, 0.6330, 0.0116, 0.5729, 0.0163,
+                      0.3271, 0.1166, 0.7494, 0.8340, 0.1356, 0.0263, 0.4976,
+                      0.5250, 0.2124, 0.2063, 0.9876, 0.3997, 0.7903, 0.7881,
+                      0.3414, 0.4348, 0.0748, 0.0069, 0.4733, 0.7388, 0.7424,
+                      0.3306, 0.5022, 0.7748, 0.6669, 0.3713, 0.6478, 0.6388,
+                      0.2317, 0.6064, 0.6536, 0.7202, 0.1361, 0.2493, 0.4139,
+                      0.3712, 0.5295, 0.2695, 0.1631, 0.6452, 0.1880, 0.6974,
+                      0.2683, 0.9017, 0.1561, 0.7046, 0.4239, 0.3874, 0.9700,
+                      0.0969, 0.1337, 0.7109, 0.6092, 0.5278, 0.2182, 0.9419,
+                      0.1230, 0.0570, 0.8053, 0.7324, 0.3831, 0.6385, 0.6323,
+                      0.1642, 0.2573, 0.2933, 0.0240, 0.6775, 0.2145, 0.7747,
+                      0.7540, 0.1746, 0.4005, 0.6380, 0.0383, 0.0075, 0.4765,
+                      0.6191, 0.5223, 0.3245, 0.6164, 0.9290, 0.7803, 0.7819,
+                      0.8932, 0.7100, 0.6960, 0.3784, 0.1869, 0.3217, 0.0764,
+                      0.2134, 0.0336, 0.4501, 0.8327, 0.9741, 0.2640, 0.8758,
+                      0.2835, 0.3411, 0.2947, 0.0888, 0.7701, 0.5229, 0.9266,
+                      0.0848, 0.6607, 0.1111, 0.4010, 0.5304, 0.7457, 0.6466,
+                      0.5183, 0.6236, 0.8001, 0.5880, 0.2006, 0.1409, 0.4395,
+                      0.5142, 0.7264, 0.5640, 0.9227, 0.8507, 0.0543, 0.7639,
+                      0.4626, 0.9840, 0.9821, 0.7239, 0.8139, 0.7906, 0.7453,
+                      0.9443, 0.9108, 0.4282, 0.6493, 0.3251, 0.2113, 0.5069,
+                      0.2668, 0.6773, 0.2164, 0.4803, 0.1428, 0.5884, 0.9624,
+                      0.2800, 0.1414, 0.8042, 0.8031, 0.1028, 0.1173, 0.0795,
+                      0.0760, 0.4125, 0.2705, 0.8781, 0.8291, 0.9000, 0.5426,
+                      0.0626, 0.4498, 0.1347, 0.0120, 0.0110, 0.1303, 0.5281,
+                      0.8963, 0.0447, 0.5862, 0.0936, 0.4003, 0.0188, 0.9347,
+                      0.9400, 0.0108, 0.8998, 0.5855, 0.1393, 0.5266, 0.4851,
+                      0.4774, 0.1186, 0.4945, 0.1561, 0.6695]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1727, 0.0592, 0.5429,  ..., 0.7822, 0.3152, 0.8983])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.0375218391418457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '279837', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.313635349273682}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1000,  776, 2038, 1918, 4703, 1006, 4783,  345, 4138,
+                           3890, 4809, 2179, 2654,   39, 3277, 4397,  222, 2644,
+                           2751, 2925, 4735, 3220, 3118, 2167,  634, 4745, 1720,
+                           1787, 1820, 1926,  473, 4992, 3200, 1675, 2855, 1802,
+                           1163, 3602, 1443, 3413, 1710, 3667,  710, 2344,  517,
+                            391,  713,  190, 1392, 2043, 4585,  625, 4376,  675,
+                           2895, 3693, 1220, 3427, 1249, 2791, 1410, 4832,  399,
+                           4671, 1556,  854, 3021, 1498, 4986, 3565,  408,  836,
+                            665, 2782,  351, 4429,   75, 2826, 2951, 2393, 4532,
+                           3245, 2288, 2902, 2022,  286,   18,  633, 1739, 1345,
+                            793, 3242, 1720,  741,   64, 3142, 2934, 4827, 3950,
+                           3991, 4310, 1432, 1925, 3941, 4723, 3084, 4330,  746,
+                            137,  294,  384, 3827,  745, 1424, 1461, 4954, 1830,
+                             44, 2741,  256, 4697, 1693, 4846,  724, 3317, 1459,
+                           3982, 3106, 1079, 1976, 1040, 1694, 3341, 1005, 3602,
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+                           3475, 1094, 2503, 3587, 3228, 2141, 4874, 4780, 4139,
+                           1118, 4510, 2959, 1803, 1379, 4711, 1070, 1400,  798,
+                           3550, 1131, 4486, 1422,  239,  651, 4998, 1567, 1515,
+                           4080, 2218, 4949,  747, 3327, 4701,  733, 1212,  419,
+                             63, 3878, 2875, 4861, 4483,  644,  592, 3560, 3073,
+                           4937,  789,  208, 3509, 3077, 4039, 4563, 1839,  362,
+                           2824, 2672, 4373, 1492, 1152, 3845,  328, 2405, 4091,
+                           2931, 2541, 2530, 3217, 4233, 1852, 2606, 3892,  380,
+                           2119, 1221, 1290,  592, 3077, 4909,  282, 3215,  863,
+                           1452, 3214, 1592,  795,  193, 4254, 3986, 1847, 2461,
+                           4353, 1361, 3013, 1482, 4277, 3046,  277]),
+       values=tensor([0.1993, 0.0335, 0.2936, 0.2778, 0.6825, 0.6252, 0.3746,
+                      0.6011, 0.3211, 0.0488, 0.6153, 0.4477, 0.3116, 0.5339,
+                      0.8158, 0.9445, 0.2638, 0.2848, 0.0424, 0.7741, 0.0547,
+                      0.0033, 0.5605, 0.2034, 0.9731, 0.4334, 0.6773, 0.7018,
+                      0.1534, 0.3665, 0.8519, 0.3002, 0.6885, 0.4688, 0.2572,
+                      0.6610, 0.5022, 0.8309, 0.6908, 0.4905, 0.2911, 0.9203,
+                      0.1018, 0.0930, 0.0540, 0.4357, 0.3509, 0.7870, 0.0358,
+                      0.8075, 0.3342, 0.2290, 0.0496, 0.3593, 0.2995, 0.8746,
+                      0.4914, 0.4993, 0.3891, 0.1546, 0.8356, 0.6696, 0.4824,
+                      0.2231, 0.1034, 0.1057, 0.9353, 0.7565, 0.0205, 0.6134,
+                      0.2384, 0.3674, 0.3962, 0.9296, 0.6846, 0.4976, 0.1741,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7126, 0.1651, 0.2523,  ..., 0.5242, 0.8574, 0.9519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 6.313635349273682 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '465387', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.944559097290039}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3699,  186,  998, 2786, 4646, 2125, 3758, 4753, 3164,
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+                      0.0383, 0.7353, 0.3583, 0.0622, 0.0459]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5720, 0.9223, 0.3340,  ..., 0.6697, 0.2837, 0.3607])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 9.944559097290039 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '491380', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.363765478134155}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1815,  573,  313, 4753, 4998, 3650,  708, 3756, 4632,
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+                      8.3701e-01, 7.5848e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.7970, 0.8043, 0.6125,  ..., 0.7108, 0.2175, 0.0136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.363765478134155 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1815,  573,  313, 4753, 4998, 3650,  708, 3756, 4632,
+                           3171, 1063, 2555, 1629, 3246, 3070, 2176, 3197, 2451,
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+                      4.4607e-01, 1.5447e-02, 1.0137e-01, 3.8368e-01,
+                      8.2513e-01, 8.9986e-01, 2.3061e-01, 9.8290e-01,
+                      4.3469e-01, 7.3495e-01, 1.5216e-01, 3.9507e-01,
+                      7.1334e-01, 7.7117e-01, 9.9550e-01, 9.2278e-01,
+                      3.0890e-01, 6.6914e-01, 1.2145e-01, 9.1632e-01,
+                      5.0784e-01, 6.2243e-01, 6.5077e-01, 6.2687e-01,
+                      2.0114e-01, 7.5097e-01, 2.0777e-01, 4.2757e-01,
+                      2.2520e-01, 5.5414e-01, 9.1256e-01, 1.3031e-01,
+                      1.5351e-01, 4.1244e-01, 2.4735e-01, 9.5465e-01,
+                      3.7976e-01, 3.1882e-01, 2.8598e-02, 8.3393e-01,
+                      7.4047e-01, 7.3298e-01, 9.7843e-01, 4.0729e-01,
+                      9.2998e-02, 4.3465e-01, 3.2636e-01, 9.5106e-02,
+                      4.8367e-02, 3.1339e-01, 4.7275e-01, 6.9317e-01,
+                      6.7922e-01, 7.2355e-01, 6.1366e-01, 7.6219e-01,
+                      2.1995e-01, 3.9216e-01, 8.5252e-01, 7.1761e-01,
+                      4.5198e-01, 9.8165e-01, 7.6941e-01, 8.2823e-01,
+                      7.6982e-01, 4.3963e-01, 2.2626e-01, 2.9003e-01,
+                      7.3718e-01, 8.0941e-01, 4.5213e-01, 1.9323e-01,
+                      3.6014e-01, 6.7950e-02, 2.6777e-01, 7.5770e-01,
+                      8.8988e-01, 1.1815e-01, 1.1244e-01, 9.2625e-01,
+                      7.6156e-01, 9.7142e-01, 2.3564e-01, 3.8882e-01,
+                      5.9567e-01, 4.8258e-01, 5.5462e-01, 2.7503e-01,
+                      2.0411e-01, 3.1168e-01, 7.6951e-01, 7.2732e-01,
+                      4.6023e-02, 4.7740e-01, 9.9557e-01, 7.3789e-02,
+                      6.2383e-02, 3.5543e-01, 1.8242e-01, 3.6846e-01,
+                      5.3628e-02, 5.3874e-01, 3.0038e-01, 9.6174e-01,
+                      9.6554e-01, 4.7430e-01, 2.2738e-01, 8.6557e-01,
+                      5.4122e-02, 8.5019e-01, 5.0852e-01, 5.3410e-01,
+                      1.7285e-01, 5.4149e-01, 8.0869e-01, 6.5103e-01,
+                      2.7217e-01, 7.0732e-01, 5.5532e-01, 9.9150e-01,
+                      7.5543e-01, 2.6834e-01, 2.8447e-01, 3.5912e-01,
+                      4.5601e-01, 7.0765e-01, 6.6949e-01, 5.9725e-01,
+                      4.8923e-01, 9.9235e-01, 7.6412e-02, 4.1164e-02,
+                      4.3938e-01, 9.1861e-01, 8.8739e-01, 9.4972e-01,
+                      4.9408e-01, 4.9347e-01, 3.4149e-01, 5.0322e-01,
+                      3.1901e-02, 5.2875e-01, 3.3499e-01, 9.5821e-01,
+                      5.2956e-01, 4.7216e-01, 2.0353e-01, 3.0726e-02,
+                      5.1848e-01, 2.6131e-01, 8.5289e-02, 4.9542e-01,
+                      1.5835e-01, 6.7945e-01, 7.8119e-01, 3.4856e-01,
+                      7.3888e-01, 4.3503e-01, 4.8394e-01, 1.0914e-01,
+                      5.9027e-01, 7.1288e-01, 9.8329e-01, 5.5542e-02,
+                      1.2536e-01, 1.9606e-01, 5.4455e-01, 4.3811e-01,
+                      5.8744e-01, 3.2588e-01, 6.3981e-02, 1.1337e-01,
+                      5.4324e-01, 8.4644e-01, 5.6165e-02, 5.0125e-01,
+                      1.5973e-01, 1.8614e-01, 7.8747e-01, 9.1964e-01,
+                      9.1086e-01, 5.6162e-01, 9.8390e-01, 1.9761e-01,
+                      4.5863e-01, 7.9353e-01, 3.8658e-02, 1.4135e-01,
+                      8.1843e-01, 3.0910e-01, 1.5630e-01, 6.8785e-01,
+                      4.2323e-01, 9.6230e-02, 7.4216e-01, 2.9855e-02,
+                      3.1890e-01, 2.8569e-01, 1.1579e-01, 7.3771e-01,
+                      8.3701e-01, 7.5848e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.7970, 0.8043, 0.6125,  ..., 0.7108, 0.2175, 0.0136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.363765478134155 seconds
+
+[39.35, 38.55, 38.58, 38.65, 38.55, 38.56, 38.58, 38.95, 38.97, 38.84]
+[95.12]
+13.95901370048523
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 491380, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.363765478134155, 'TIME_S_1KI': 0.021091142248634776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1327.781383190155, 'W': 95.12}
+[39.35, 38.55, 38.58, 38.65, 38.55, 38.56, 38.58, 38.95, 38.97, 38.84, 39.11, 38.56, 38.47, 38.42, 38.44, 38.49, 38.99, 38.76, 39.03, 39.12]
+696.76
+34.838
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 491380, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.363765478134155, 'TIME_S_1KI': 0.021091142248634776, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1327.781383190155, 'W': 95.12, 'J_1KI': 2.7021477943549903, 'W_1KI': 0.1935772721722496, 'W_D': 60.282000000000004, 'J_D': 841.4772638926506, 'W_D_1KI': 0.12267898571370427, 'J_D_1KI': 0.00024966214683891136}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..b474ab7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 471062, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.751366138458252, "TIME_S_1KI": 0.022823675309106343, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1247.6856990122794, "W": 95.07, "J_1KI": 2.6486655663421788, "W_1KI": 0.20182056714402774, "W_D": 59.887249999999995, "J_D": 785.9520919130443, "W_D_1KI": 0.12713241569050357, "J_D_1KI": 0.0002698846769438069}
diff --git a/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..b89ee01
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.06770849227905273}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([ 631, 2604, 2210,  ..., 3865,  405, 4638]),
+       values=tensor([0.5383, 0.4516, 0.3480,  ..., 0.7821, 0.4439, 0.3322]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7026, 0.4971, 0.6548,  ..., 0.0098, 0.8140, 0.3691])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.06770849227905273 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '155076', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.4566495418548584}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2690, 1150, 3956,  ...,  712, 3582,  778]),
+       values=tensor([0.8228, 0.9297, 0.1263,  ..., 0.8170, 0.9056, 0.4551]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8564, 0.8640, 0.2790,  ..., 0.6413, 0.1958, 0.4583])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 3.4566495418548584 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '471062', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.751366138458252}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([1001, 2591,  823,  ...,  591, 3447, 2958]),
+       values=tensor([0.9383, 0.1843, 0.8614,  ..., 0.3843, 0.5733, 0.2218]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8666, 0.6997, 0.8565,  ..., 0.1580, 0.8946, 0.5984])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.751366138458252 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([1001, 2591,  823,  ...,  591, 3447, 2958]),
+       values=tensor([0.9383, 0.1843, 0.8614,  ..., 0.3843, 0.5733, 0.2218]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8666, 0.6997, 0.8565,  ..., 0.1580, 0.8946, 0.5984])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.751366138458252 seconds
+
+[39.99, 39.18, 38.82, 38.83, 39.11, 38.71, 39.17, 38.68, 38.75, 38.85]
+[95.07]
+13.123863458633423
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 471062, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.751366138458252, 'TIME_S_1KI': 0.022823675309106343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1247.6856990122794, 'W': 95.07}
+[39.99, 39.18, 38.82, 38.83, 39.11, 38.71, 39.17, 38.68, 38.75, 38.85, 39.95, 39.47, 39.27, 39.32, 39.37, 38.76, 39.37, 39.0, 39.09, 38.72]
+703.655
+35.18275
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 471062, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.751366138458252, 'TIME_S_1KI': 0.022823675309106343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1247.6856990122794, 'W': 95.07, 'J_1KI': 2.6486655663421788, 'W_1KI': 0.20182056714402774, 'W_D': 59.887249999999995, 'J_D': 785.9520919130443, 'W_D_1KI': 0.12713241569050357, 'J_D_1KI': 0.0002698846769438069}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..174f326
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 32214, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.624319791793823, "TIME_S_1KI": 0.3298044263920601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1308.5074545145035, "W": 90.38, "J_1KI": 40.619216940290045, "W_1KI": 2.8056124666294155, "W_D": 74.09325, "J_D": 1072.7104442819953, "W_D_1KI": 2.30003259452412, "J_D_1KI": 0.07139854083703111}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
similarity index 51%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output
rename to pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
index a35639b..48c062d 100644
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,15 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.7099568843841553}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3259446620941162}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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,
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999982,
                              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]),
+       col_indices=tensor([10285, 14477, 16251,  ..., 79839, 98536, 99886]),
+       values=tensor([0.0755, 0.8469, 0.4749,  ..., 0.2250, 0.2555, 0.2499]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([2.2552e-01, 8.2165e-04, 8.9899e-01,  ..., 7.1003e-01, 6.8443e-02,
-        6.7507e-01])
+tensor([0.5289, 0.3805, 0.4649,  ..., 0.7570, 0.9550, 0.1372])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -17,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 1.7099568843841553 seconds
+Time: 0.3259446620941162 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '32214', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.624319791793823}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,       9,      15,  ...,  999974,
+                             999991, 1000000]),
+       col_indices=tensor([   27,  9769, 50112,  ..., 53126, 61224, 82066]),
+       values=tensor([0.2467, 0.4042, 0.1080,  ..., 0.3359, 0.4921, 0.7955]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1330, 0.4019, 0.6390,  ..., 0.8808, 0.7758, 0.9416])
+tensor([0.8754, 0.9877, 0.9510,  ..., 0.4555, 0.1143, 0.3690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -37,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 21.43535876274109 seconds
+Time: 10.624319791793823 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,       9,      15,  ...,  999974,
+                             999991, 1000000]),
+       col_indices=tensor([   27,  9769, 50112,  ..., 53126, 61224, 82066]),
+       values=tensor([0.2467, 0.4042, 0.1080,  ..., 0.3359, 0.4921, 0.7955]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1330, 0.4019, 0.6390,  ..., 0.8808, 0.7758, 0.9416])
+tensor([0.8754, 0.9877, 0.9510,  ..., 0.4555, 0.1143, 0.3690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -54,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 21.43535876274109 seconds
+Time: 10.624319791793823 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}
+[18.22, 17.55, 18.1, 17.52, 17.64, 17.6, 18.51, 17.5, 17.7, 17.75]
+[90.38]
+14.477843046188354
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 32214, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.624319791793823, 'TIME_S_1KI': 0.3298044263920601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1308.5074545145035, 'W': 90.38}
+[18.22, 17.55, 18.1, 17.52, 17.64, 17.6, 18.51, 17.5, 17.7, 17.75, 18.28, 17.75, 17.59, 17.7, 17.88, 17.82, 18.16, 18.33, 22.4, 17.72]
+325.735
+16.28675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 32214, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.624319791793823, 'TIME_S_1KI': 0.3298044263920601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1308.5074545145035, 'W': 90.38, 'J_1KI': 40.619216940290045, 'W_1KI': 2.8056124666294155, 'W_D': 74.09325, 'J_D': 1072.7104442819953, 'W_D_1KI': 2.30003259452412, 'J_D_1KI': 0.07139854083703111}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..be7a2cb
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2697, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.332640647888184, "TIME_S_1KI": 3.8311607889833827, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1417.5436856460572, "W": 83.44, "J_1KI": 525.6001800689867, "W_1KI": 30.938079347423063, "W_D": 67.3325, "J_D": 1143.8969344890118, "W_D_1KI": 24.965702632554688, "J_D_1KI": 9.25684191047634}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
similarity index 50%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output
rename to pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
index 56baef1..d59582d 100644
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.892810106277466}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      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]),
+tensor(crow_indices=tensor([       0,       92,      187,  ...,  9999795,
+                             9999888, 10000000]),
+       col_indices=tensor([ 1843,  1850,  4412,  ..., 98725, 98752, 98846]),
+       values=tensor([0.9343, 0.4740, 0.0577,  ..., 0.9099, 0.1721, 0.4592]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0307, 0.5740, 0.3084,  ..., 0.9686, 0.7857, 0.7900])
+tensor([0.5874, 0.0844, 0.8298,  ..., 0.9009, 0.0712, 0.0168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 16.660033226013184 seconds
+Time: 3.892810106277466 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2697', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.332640647888184}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,       99,      214,  ...,  9999796,
+                             9999890, 10000000]),
+       col_indices=tensor([  133,   206,   762,  ..., 95508, 95519, 98505]),
+       values=tensor([0.7799, 0.5247, 0.9444,  ..., 0.2262, 0.0403, 0.9029]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9534, 0.2929, 0.7145,  ..., 0.1886, 0.7155, 0.7573])
+tensor([0.3536, 0.8501, 0.0907,  ..., 0.0431, 0.6064, 0.5575])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 21.127803564071655 seconds
+Time: 10.332640647888184 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       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]),
+tensor(crow_indices=tensor([       0,       99,      214,  ...,  9999796,
+                             9999890, 10000000]),
+       col_indices=tensor([  133,   206,   762,  ..., 95508, 95519, 98505]),
+       values=tensor([0.7799, 0.5247, 0.9444,  ..., 0.2262, 0.0403, 0.9029]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9534, 0.2929, 0.7145,  ..., 0.1886, 0.7155, 0.7573])
+tensor([0.3536, 0.8501, 0.0907,  ..., 0.0431, 0.6064, 0.5575])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 21.127803564071655 seconds
+Time: 10.332640647888184 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}
+[18.26, 17.98, 17.82, 17.72, 17.76, 17.93, 17.75, 17.95, 17.83, 18.05]
+[83.44]
+16.988778591156006
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2697, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.332640647888184, 'TIME_S_1KI': 3.8311607889833827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1417.5436856460572, 'W': 83.44}
+[18.26, 17.98, 17.82, 17.72, 17.76, 17.93, 17.75, 17.95, 17.83, 18.05, 18.55, 18.9, 17.56, 17.66, 17.87, 17.73, 17.82, 17.88, 17.78, 17.56]
+322.15
+16.107499999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2697, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.332640647888184, 'TIME_S_1KI': 3.8311607889833827, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1417.5436856460572, 'W': 83.44, 'J_1KI': 525.6001800689867, 'W_1KI': 30.938079347423063, 'W_D': 67.3325, 'J_D': 1143.8969344890118, 'W_D_1KI': 24.965702632554688, 'J_D_1KI': 9.25684191047634}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..60a31fb
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 63032, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.23182988166809, "TIME_S_1KI": 0.1623275460348409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.4584797358514, "W": 89.17, "J_1KI": 19.473576591824017, "W_1KI": 1.4146782586622668, "W_D": 73.15375, "J_D": 1006.9887940111756, "W_D_1KI": 1.1605811333925626, "J_D_1KI": 0.018412570335584508}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..14be47f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.17937397956848145}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      4,  ...,  99996, 100000,
+                            100000]),
+       col_indices=tensor([ 6463, 19403, 32975,  ..., 50312, 73566, 75866]),
+       values=tensor([0.6504, 0.4570, 0.8704,  ..., 0.7277, 0.1675, 0.6048]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7096, 0.4020, 0.6001,  ..., 0.3911, 0.2531, 0.2591])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.17937397956848145 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58536', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.751020431518555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([64186, 21974, 57698,  ..., 75952, 18460, 38945]),
+       values=tensor([0.5668, 0.1226, 0.0967,  ..., 0.2541, 0.6343, 0.4356]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9872, 0.9595, 0.0420,  ..., 0.0153, 0.9518, 0.5571])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 9.751020431518555 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '63032', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.23182988166809}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998, 100000,
+                            100000]),
+       col_indices=tensor([35835, 88904, 80345,  ..., 79801,  8127, 81515]),
+       values=tensor([0.8153, 0.8474, 0.9328,  ..., 0.8046, 0.4857, 0.5161]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1493, 0.1613, 0.9905,  ..., 0.3209, 0.7704, 0.3686])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.23182988166809 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998, 100000,
+                            100000]),
+       col_indices=tensor([35835, 88904, 80345,  ..., 79801,  8127, 81515]),
+       values=tensor([0.8153, 0.8474, 0.9328,  ..., 0.8046, 0.4857, 0.5161]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1493, 0.1613, 0.9905,  ..., 0.3209, 0.7704, 0.3686])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.23182988166809 seconds
+
+[18.1, 17.83, 17.83, 17.64, 17.89, 17.76, 17.87, 17.83, 17.97, 17.52]
+[89.17]
+13.765374898910522
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 63032, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.23182988166809, 'TIME_S_1KI': 0.1623275460348409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.4584797358514, 'W': 89.17}
+[18.1, 17.83, 17.83, 17.64, 17.89, 17.76, 17.87, 17.83, 17.97, 17.52, 18.59, 17.72, 17.71, 17.51, 17.74, 17.62, 17.64, 18.03, 17.89, 17.48]
+320.32500000000005
+16.016250000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 63032, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.23182988166809, 'TIME_S_1KI': 0.1623275460348409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.4584797358514, 'W': 89.17, 'J_1KI': 19.473576591824017, 'W_1KI': 1.4146782586622668, 'W_D': 73.15375, 'J_D': 1006.9887940111756, 'W_D_1KI': 1.1605811333925626, 'J_D_1KI': 0.018412570335584508}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..b14656f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 43635, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050816535949707, "TIME_S_1KI": 0.23033841035750444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.4814233660697, "W": 89.55, "J_1KI": 28.06190955347931, "W_1KI": 2.052251632863527, "W_D": 73.275, "J_D": 1001.9416671931745, "W_D_1KI": 1.679271227225851, "J_D_1KI": 0.03848450159793402}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..fe4dfe5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-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', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.24062752723693848}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499990, 499993,
+                            500000]),
+       col_indices=tensor([25845, 82264, 90566,  ..., 92820, 97145, 99590]),
+       values=tensor([0.6382, 0.4794, 0.9065,  ..., 0.0565, 0.2096, 0.7456]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.3202, 0.7109, 0.9868,  ..., 0.4243, 0.8639, 0.9226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.24062752723693848 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43635', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050816535949707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 499985, 499991,
+                            500000]),
+       col_indices=tensor([ 2943, 35530, 40183,  ..., 77324, 82017, 92181]),
+       values=tensor([0.7176, 0.7408, 0.8709,  ..., 0.2236, 0.1509, 0.6788]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0661, 0.4747, 0.8915,  ..., 0.6458, 0.7762, 0.4694])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.050816535949707 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 499985, 499991,
+                            500000]),
+       col_indices=tensor([ 2943, 35530, 40183,  ..., 77324, 82017, 92181]),
+       values=tensor([0.7176, 0.7408, 0.8709,  ..., 0.2236, 0.1509, 0.6788]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0661, 0.4747, 0.8915,  ..., 0.6458, 0.7762, 0.4694])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.050816535949707 seconds
+
+[18.28, 18.77, 17.89, 17.54, 18.06, 17.6, 17.83, 17.74, 18.0, 17.55]
+[89.55]
+13.673717737197876
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.050816535949707, 'TIME_S_1KI': 0.23033841035750444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.4814233660697, 'W': 89.55}
+[18.28, 18.77, 17.89, 17.54, 18.06, 17.6, 17.83, 17.74, 18.0, 17.55, 18.31, 17.61, 17.7, 17.7, 17.86, 17.61, 17.69, 21.85, 18.07, 17.82]
+325.5
+16.275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.050816535949707, 'TIME_S_1KI': 0.23033841035750444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.4814233660697, 'W': 89.55, 'J_1KI': 28.06190955347931, 'W_1KI': 2.052251632863527, 'W_D': 73.275, 'J_D': 1001.9416671931745, 'W_D_1KI': 1.679271227225851, 'J_D_1KI': 0.03848450159793402}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..05a227c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253876, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.530851364135742, "TIME_S_1KI": 0.041480294963429955, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1171.4377240467072, "W": 80.68, "J_1KI": 4.614212150997759, "W_1KI": 0.317792938284832, "W_D": 64.302, "J_D": 933.636446847439, "W_D_1KI": 0.2532811293702437, "J_D_1KI": 0.0009976568457445514}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..04a16aa
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.05977439880371094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([6615, 8991, 2810,  ..., 6295, 8510, 7610]),
+       values=tensor([0.3885, 0.8426, 0.7862,  ..., 0.5955, 0.1672, 0.2063]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1595, 0.0624, 0.6993,  ..., 0.5987, 0.7271, 0.9533])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.05977439880371094 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '175660', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.265056371688843}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([6157, 6465, 6955,  ..., 9189, 5553, 9168]),
+       values=tensor([0.9492, 0.4977, 0.7776,  ..., 0.2833, 0.2034, 0.6430]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2429, 0.7570, 0.9101,  ..., 0.6676, 0.5300, 0.9328])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.265056371688843 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253876', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.530851364135742}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([ 868, 4014, 6169,  ..., 4688, 7367, 6538]),
+       values=tensor([0.9131, 0.0133, 0.5134,  ..., 0.5757, 0.9187, 0.1463]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.7710, 0.0750, 0.1717,  ..., 0.8123, 0.4992, 0.1144])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.530851364135742 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([ 868, 4014, 6169,  ..., 4688, 7367, 6538]),
+       values=tensor([0.9131, 0.0133, 0.5134,  ..., 0.5757, 0.9187, 0.1463]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.7710, 0.0750, 0.1717,  ..., 0.8123, 0.4992, 0.1144])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.530851364135742 seconds
+
+[18.32, 17.72, 17.73, 18.28, 18.02, 18.18, 17.93, 18.12, 17.89, 21.22]
+[80.68]
+14.51955533027649
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253876, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.530851364135742, 'TIME_S_1KI': 0.041480294963429955, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1171.4377240467072, 'W': 80.68}
+[18.32, 17.72, 17.73, 18.28, 18.02, 18.18, 17.93, 18.12, 17.89, 21.22, 18.16, 17.68, 17.59, 17.54, 18.03, 22.12, 17.56, 17.65, 17.87, 17.6]
+327.56000000000006
+16.378000000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253876, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.530851364135742, 'TIME_S_1KI': 0.041480294963429955, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1171.4377240467072, 'W': 80.68, 'J_1KI': 4.614212150997759, 'W_1KI': 0.317792938284832, 'W_D': 64.302, 'J_D': 933.636446847439, 'W_D_1KI': 0.2532811293702437, 'J_D_1KI': 0.0009976568457445514}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..fdd9d92
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 195071, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.382015228271484, "TIME_S_1KI": 0.05322172556798029, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.0013425803186, "W": 86.31, "J_1KI": 6.131107866265712, "W_1KI": 0.4424542858753992, "W_D": 70.10050000000001, "J_D": 971.3856113492252, "W_D_1KI": 0.35935890009278676, "J_D_1KI": 0.0018421954062509895}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..5d9701a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.06870174407958984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     25,  ...,  99981,  99995,
+                            100000]),
+       col_indices=tensor([   3,  150,  370,  ..., 2691, 9535, 9749]),
+       values=tensor([0.2561, 0.9230, 0.8831,  ..., 0.2203, 0.7623, 0.4185]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1427, 0.1860, 0.4972,  ..., 0.5058, 0.8744, 0.6551])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.06870174407958984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '152834', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.226486682891846}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99982,  99990,
+                            100000]),
+       col_indices=tensor([ 560, 3215, 3961,  ..., 6911, 7414, 7504]),
+       values=tensor([0.0904, 0.0706, 0.8224,  ..., 0.0963, 0.3127, 0.0052]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8141, 0.4563, 0.6350,  ..., 0.0924, 0.8861, 0.1694])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.226486682891846 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '195071', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.382015228271484}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ...,  99988,  99994,
+                            100000]),
+       col_indices=tensor([1742, 3653, 4110,  ..., 7414, 9186, 9217]),
+       values=tensor([0.4393, 0.0633, 0.6988,  ..., 0.9636, 0.3600, 0.6461]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5362, 0.5145, 0.3988,  ..., 0.1543, 0.7121, 0.2032])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.382015228271484 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ...,  99988,  99994,
+                            100000]),
+       col_indices=tensor([1742, 3653, 4110,  ..., 7414, 9186, 9217]),
+       values=tensor([0.4393, 0.0633, 0.6988,  ..., 0.9636, 0.3600, 0.6461]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5362, 0.5145, 0.3988,  ..., 0.1543, 0.7121, 0.2032])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.382015228271484 seconds
+
+[18.32, 18.17, 18.0, 18.26, 17.83, 17.86, 19.18, 17.87, 17.68, 18.06]
+[86.31]
+13.85704255104065
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195071, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.382015228271484, 'TIME_S_1KI': 0.05322172556798029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0013425803186, 'W': 86.31}
+[18.32, 18.17, 18.0, 18.26, 17.83, 17.86, 19.18, 17.87, 17.68, 18.06, 18.29, 17.98, 17.81, 17.75, 18.0, 18.2, 17.75, 17.66, 17.87, 17.97]
+324.18999999999994
+16.2095
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195071, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.382015228271484, 'TIME_S_1KI': 0.05322172556798029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0013425803186, 'W': 86.31, 'J_1KI': 6.131107866265712, 'W_1KI': 0.4424542858753992, 'W_D': 70.10050000000001, 'J_D': 971.3856113492252, 'W_D_1KI': 0.35935890009278676, 'J_D_1KI': 0.0018421954062509895}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..5933eb4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 53507, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.015070676803589, "TIME_S_1KI": 0.18717309280661576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1202.573525466919, "W": 88.64, "J_1KI": 22.47506915855718, "W_1KI": 1.6566056777617881, "W_D": 72.22725, "J_D": 979.9027376723885, "W_D_1KI": 1.3498654381669688, "J_D_1KI": 0.02522782884794455}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..d725fe1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.19623374938964844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     102,     197,  ...,  999814,
+                             999918, 1000000]),
+       col_indices=tensor([  21,  221,  266,  ..., 9711, 9962, 9983]),
+       values=tensor([0.8240, 0.1342, 0.9347,  ..., 0.9531, 0.8710, 0.7315]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2953, 0.0740, 0.7231,  ..., 0.2507, 0.0704, 0.5422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.19623374938964844 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53507', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.015070676803589}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     117,     202,  ...,  999813,
+                             999911, 1000000]),
+       col_indices=tensor([ 101,  231,  245,  ..., 9677, 9872, 9873]),
+       values=tensor([0.6066, 0.1771, 0.9671,  ..., 0.7083, 0.4630, 0.7862]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1666, 0.0462, 0.0015,  ..., 0.3047, 0.2438, 0.6174])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.015070676803589 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     117,     202,  ...,  999813,
+                             999911, 1000000]),
+       col_indices=tensor([ 101,  231,  245,  ..., 9677, 9872, 9873]),
+       values=tensor([0.6066, 0.1771, 0.9671,  ..., 0.7083, 0.4630, 0.7862]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1666, 0.0462, 0.0015,  ..., 0.3047, 0.2438, 0.6174])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.015070676803589 seconds
+
+[18.69, 17.91, 17.85, 17.85, 22.65, 17.8, 18.01, 17.72, 17.94, 17.78]
+[88.64]
+13.56693959236145
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 53507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.015070676803589, 'TIME_S_1KI': 0.18717309280661576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1202.573525466919, 'W': 88.64}
+[18.69, 17.91, 17.85, 17.85, 22.65, 17.8, 18.01, 17.72, 17.94, 17.78, 20.07, 19.06, 18.03, 17.54, 17.79, 18.03, 17.73, 17.66, 17.6, 17.63]
+328.255
+16.41275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 53507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.015070676803589, 'TIME_S_1KI': 0.18717309280661576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1202.573525466919, 'W': 88.64, 'J_1KI': 22.47506915855718, 'W_1KI': 1.6566056777617881, 'W_D': 72.22725, 'J_D': 979.9027376723885, 'W_D_1KI': 1.3498654381669688, 'J_D_1KI': 0.02522782884794455}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..91f897d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8596, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.36475419998169, "TIME_S_1KI": 1.2057647975781398, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.4265056419372, "W": 84.46, "J_1KI": 159.77507045625143, "W_1KI": 9.825500232666355, "W_D": 68.352, "J_D": 1111.4900368652345, "W_D_1KI": 7.95160539785947, "J_D_1KI": 0.9250355279036144}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..a70ca9f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 1.2214851379394531}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     508,    1039,  ..., 4998996,
+                            4999496, 5000000]),
+       col_indices=tensor([  26,   30,   52,  ..., 9932, 9973, 9998]),
+       values=tensor([0.9442, 0.4994, 0.3456,  ..., 0.3930, 0.0474, 0.1408]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1466, 0.3658, 0.5068,  ..., 0.5229, 0.0306, 0.8484])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 1.2214851379394531 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8596', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.36475419998169}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     466,     997,  ..., 4998966,
+                            4999476, 5000000]),
+       col_indices=tensor([   0,    1,    5,  ..., 9962, 9965, 9989]),
+       values=tensor([0.4765, 0.8415, 0.0752,  ..., 0.8745, 0.5765, 0.8508]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2258, 0.4418, 0.4951,  ..., 0.4808, 0.0990, 0.6508])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.36475419998169 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     466,     997,  ..., 4998966,
+                            4999476, 5000000]),
+       col_indices=tensor([   0,    1,    5,  ..., 9962, 9965, 9989]),
+       values=tensor([0.4765, 0.8415, 0.0752,  ..., 0.8745, 0.5765, 0.8508]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2258, 0.4418, 0.4951,  ..., 0.4808, 0.0990, 0.6508])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.36475419998169 seconds
+
+[18.03, 17.93, 17.79, 17.69, 17.86, 17.92, 18.02, 17.88, 17.82, 18.11]
+[84.46]
+16.261265754699707
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8596, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.36475419998169, 'TIME_S_1KI': 1.2057647975781398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.4265056419372, 'W': 84.46}
+[18.03, 17.93, 17.79, 17.69, 17.86, 17.92, 18.02, 17.88, 17.82, 18.11, 18.58, 18.42, 17.91, 17.71, 17.91, 17.76, 17.61, 17.59, 17.81, 18.34]
+322.15999999999997
+16.107999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8596, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.36475419998169, 'TIME_S_1KI': 1.2057647975781398, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.4265056419372, 'W': 84.46, 'J_1KI': 159.77507045625143, 'W_1KI': 9.825500232666355, 'W_D': 68.352, 'J_D': 1111.4900368652345, 'W_D_1KI': 7.95160539785947, 'J_D_1KI': 0.9250355279036144}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..06b2e69
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.339906692504883, "TIME_S_1KI": 3.855297051642387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1411.3430524110793, "W": 81.1, "J_1KI": 526.227834605175, "W_1KI": 30.2386278896346, "W_D": 64.94024999999999, "J_D": 1130.1229427785277, "W_D_1KI": 24.21336689038031, "J_D_1KI": 9.02810100312465}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..fee56d8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.9144444465637207}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1047,     2059,  ...,  9998021,
+                             9999010, 10000000]),
+       col_indices=tensor([   5,    8,   10,  ..., 9982, 9985, 9996]),
+       values=tensor([0.6280, 0.3451, 0.1654,  ..., 0.9055, 0.0155, 0.3514]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4839, 0.5284, 0.4128,  ..., 0.3450, 0.9191, 0.1662])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 3.9144444465637207 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2682', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.339906692504883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1046,     1985,  ...,  9998014,
+                             9998990, 10000000]),
+       col_indices=tensor([  17,   31,   38,  ..., 9973, 9978, 9985]),
+       values=tensor([0.0174, 0.0178, 0.9247,  ..., 0.4423, 0.5021, 0.4230]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8246, 0.4652, 0.0479,  ..., 0.0955, 0.8235, 0.1184])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.339906692504883 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1046,     1985,  ...,  9998014,
+                             9998990, 10000000]),
+       col_indices=tensor([  17,   31,   38,  ..., 9973, 9978, 9985]),
+       values=tensor([0.0174, 0.0178, 0.9247,  ..., 0.4423, 0.5021, 0.4230]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8246, 0.4652, 0.0479,  ..., 0.0955, 0.8235, 0.1184])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.339906692504883 seconds
+
+[18.32, 17.68, 17.65, 18.12, 17.79, 17.54, 17.66, 17.93, 19.1, 17.92]
+[81.1]
+17.402503728866577
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.339906692504883, 'TIME_S_1KI': 3.855297051642387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1411.3430524110793, 'W': 81.1}
+[18.32, 17.68, 17.65, 18.12, 17.79, 17.54, 17.66, 17.93, 19.1, 17.92, 18.28, 17.68, 17.82, 17.91, 18.12, 17.75, 18.95, 17.54, 17.87, 17.65]
+323.195
+16.15975
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.339906692504883, 'TIME_S_1KI': 3.855297051642387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1411.3430524110793, 'W': 81.1, 'J_1KI': 526.227834605175, 'W_1KI': 30.2386278896346, 'W_D': 64.94024999999999, 'J_D': 1130.1229427785277, 'W_D_1KI': 24.21336689038031, 'J_D_1KI': 9.02810100312465}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..bd7f556
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1445, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.100359916687012, "TIME_S_1KI": 6.98986845445468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2160.905471148491, "W": 62.46, "J_1KI": 1495.4363122134887, "W_1KI": 43.22491349480969, "W_D": 46.34675, "J_D": 1603.4413327721954, "W_D_1KI": 32.07387543252595, "J_D_1KI": 22.196453586523152}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..345e6fc
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.263561964035034}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1926,     3961,  ..., 19995971,
+                            19997972, 20000000]),
+       col_indices=tensor([   0,    6,   15,  ..., 9987, 9993, 9997]),
+       values=tensor([0.0314, 0.6999, 0.8853,  ..., 0.3076, 0.0508, 0.1862]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4423, 0.2673, 0.1161,  ..., 0.1117, 0.7670, 0.7166])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 7.263561964035034 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1445', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.100359916687012}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2031,     4064,  ..., 19995881,
+                            19997886, 20000000]),
+       col_indices=tensor([   6,    7,   12,  ..., 9992, 9994, 9996]),
+       values=tensor([0.1267, 0.2771, 0.5074,  ..., 0.4122, 0.4435, 0.1814]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.2940, 0.6612, 0.2260,  ..., 0.5354, 0.3344, 0.1796])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.100359916687012 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2031,     4064,  ..., 19995881,
+                            19997886, 20000000]),
+       col_indices=tensor([   6,    7,   12,  ..., 9992, 9994, 9996]),
+       values=tensor([0.1267, 0.2771, 0.5074,  ..., 0.4122, 0.4435, 0.1814]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.2940, 0.6612, 0.2260,  ..., 0.5354, 0.3344, 0.1796])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.100359916687012 seconds
+
+[18.37, 18.02, 17.98, 17.77, 18.08, 17.81, 18.57, 18.23, 17.73, 17.95]
+[62.46]
+34.59662938117981
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1445, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.100359916687012, 'TIME_S_1KI': 6.98986845445468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2160.905471148491, 'W': 62.46}
+[18.37, 18.02, 17.98, 17.77, 18.08, 17.81, 18.57, 18.23, 17.73, 17.95, 18.39, 17.61, 17.65, 17.9, 17.98, 17.61, 17.59, 17.93, 17.63, 17.64]
+322.265
+16.11325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1445, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.100359916687012, 'TIME_S_1KI': 6.98986845445468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2160.905471148491, 'W': 62.46, 'J_1KI': 1495.4363122134887, 'W_1KI': 43.22491349480969, 'W_D': 46.34675, 'J_D': 1603.4413327721954, 'W_D_1KI': 32.07387543252595, 'J_D_1KI': 22.196453586523152}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..a3c7052
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 11.531069993972778, "TIME_S_1KI": 11.531069993972778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4119.280698208809, "W": 53.23, "J_1KI": 4119.280698208809, "W_1KI": 53.23, "W_D": 37.1785, "J_D": 2877.1121066758633, "W_D_1KI": 37.1785, "J_D_1KI": 37.1785}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..b999f8b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 11.531069993972778}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3017,     6060,  ..., 29994067,
+                            29997064, 30000000]),
+       col_indices=tensor([   8,    9,   10,  ..., 9986, 9991, 9993]),
+       values=tensor([0.4919, 0.2111, 0.3595,  ..., 0.1115, 0.4648, 0.4893]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.2808, 0.4380, 0.4720,  ..., 0.7949, 0.9847, 0.6708])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 11.531069993972778 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3017,     6060,  ..., 29994067,
+                            29997064, 30000000]),
+       col_indices=tensor([   8,    9,   10,  ..., 9986, 9991, 9993]),
+       values=tensor([0.4919, 0.2111, 0.3595,  ..., 0.1115, 0.4648, 0.4893]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.2808, 0.4380, 0.4720,  ..., 0.7949, 0.9847, 0.6708])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 11.531069993972778 seconds
+
+[18.81, 17.57, 18.39, 17.6, 17.66, 17.56, 18.03, 17.8, 17.96, 17.55]
+[53.23]
+77.38644933700562
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 11.531069993972778, 'TIME_S_1KI': 11.531069993972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4119.280698208809, 'W': 53.23}
+[18.81, 17.57, 18.39, 17.6, 17.66, 17.56, 18.03, 17.8, 17.96, 17.55, 18.93, 17.87, 17.97, 17.66, 17.7, 17.69, 17.69, 17.51, 17.83, 17.79]
+321.03
+16.051499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 11.531069993972778, 'TIME_S_1KI': 11.531069993972778, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4119.280698208809, 'W': 53.23, 'J_1KI': 4119.280698208809, 'W_1KI': 53.23, 'W_D': 37.1785, 'J_D': 2877.1121066758633, 'W_D_1KI': 37.1785, 'J_D_1KI': 37.1785}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..a7f50f8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 286739, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.441989183425903, "TIME_S_1KI": 0.036416354885194915, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1103.374533557892, "W": 79.9, "J_1KI": 3.8480099796605693, "W_1KI": 0.27865061955297327, "W_D": 63.605500000000006, "J_D": 878.3565568737985, "W_D_1KI": 0.22182367937392544, "J_D_1KI": 0.0007736083315277149}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..03249c3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.05496048927307129}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([9138,   88, 9925, 6110, 2605,  340, 5956, 3778, 8300,
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+                      4.6489e-01, 3.7186e-01, 4.7115e-01, 3.1902e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0435, 0.9300, 0.8297,  ..., 0.3222, 0.7823, 0.3267])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.05496048927307129 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '191046', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.995845317840576}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([7403,  728, 1285, 8550, 1815,  156, 5673, 7884,  804,
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+                      2.1746e-01, 6.6617e-01, 3.8200e-01, 6.7181e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7926, 0.8036, 0.9264,  ..., 0.8315, 0.2335, 0.7753])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 6.995845317840576 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '286739', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.441989183425903}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request 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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+Matrix Type: synthetic
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+Time: 10.441989183425903 seconds
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+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
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+                      9.1105e-02, 9.8166e-01, 3.3675e-02, 3.1123e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1121, 0.3867, 0.9452,  ..., 0.7986, 0.0788, 0.8344])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.441989183425903 seconds
+
+[18.13, 22.04, 18.79, 17.71, 17.77, 17.9, 17.83, 17.54, 17.97, 17.85]
+[79.9]
+13.809443473815918
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286739, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.441989183425903, 'TIME_S_1KI': 0.036416354885194915, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.374533557892, 'W': 79.9}
+[18.13, 22.04, 18.79, 17.71, 17.77, 17.9, 17.83, 17.54, 17.97, 17.85, 18.13, 17.63, 18.09, 17.81, 17.93, 17.7, 17.59, 17.82, 17.87, 17.69]
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diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..91b8311
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 269593, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.567216634750366, "TIME_S_1KI": 0.03919692512324269, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1116.8250253772735, "W": 79.97999999999999, "J_1KI": 4.142633619482974, "W_1KI": 0.29666942390937445, "W_D": 63.59324999999999, "J_D": 888.003663979411, "W_D_1KI": 0.23588613205832493, "J_D_1KI": 0.0008749712791442097}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..271dd36
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.056284427642822266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([8618, 2157,  180,  ..., 6301, 3776, 9276]),
+       values=tensor([0.6370, 0.4514, 0.1911,  ..., 0.9917, 0.3039, 0.3760]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3110, 0.1501, 0.3783,  ..., 0.1317, 0.1435, 0.1761])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.056284427642822266 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '186552', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.265737533569336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([3104,  929, 8564,  ..., 6717, 8359,  928]),
+       values=tensor([0.4370, 0.1053, 0.0742,  ..., 0.7004, 0.4944, 0.4492]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3493, 0.4826, 0.4715,  ..., 0.3349, 0.2581, 0.7669])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.265737533569336 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '269593', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.567216634750366}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2457,  434, 2201,  ..., 5356,   91, 9583]),
+       values=tensor([0.6200, 0.7893, 0.7607,  ..., 0.6419, 0.6044, 0.8766]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5913, 0.4546, 0.3506,  ..., 0.4687, 0.7353, 0.8006])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.567216634750366 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2457,  434, 2201,  ..., 5356,   91, 9583]),
+       values=tensor([0.6200, 0.7893, 0.7607,  ..., 0.6419, 0.6044, 0.8766]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5913, 0.4546, 0.3506,  ..., 0.4687, 0.7353, 0.8006])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.567216634750366 seconds
+
+[17.89, 17.35, 17.61, 17.72, 17.83, 17.6, 17.61, 17.87, 22.45, 17.59]
+[79.98]
+13.963803768157959
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 269593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.567216634750366, 'TIME_S_1KI': 0.03919692512324269, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1116.8250253772735, 'W': 79.97999999999999}
+[17.89, 17.35, 17.61, 17.72, 17.83, 17.6, 17.61, 17.87, 22.45, 17.59, 18.03, 17.7, 17.74, 17.72, 21.48, 17.84, 17.81, 17.57, 18.13, 17.9]
+327.735
+16.38675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 269593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.567216634750366, 'TIME_S_1KI': 0.03919692512324269, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1116.8250253772735, 'W': 79.97999999999999, 'J_1KI': 4.142633619482974, 'W_1KI': 0.29666942390937445, 'W_D': 63.59324999999999, 'J_D': 888.003663979411, 'W_D_1KI': 0.23588613205832493, 'J_D_1KI': 0.0008749712791442097}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..250b22c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186, "TIME_S_1KI": 15.963828563690184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3837.0465870952603, "W": 57.08, "J_1KI": 3837.0465870952603, "W_1KI": 57.08, "W_D": 41.032999999999994, "J_D": 2758.3309847280975, "W_D_1KI": 41.032999999999994, "J_D_1KI": 41.032999999999994}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..8f6ef0a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.963828563690186}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       49,      110,  ..., 24999910,
+                            24999948, 25000000]),
+       col_indices=tensor([  6869,   7642,  11671,  ..., 455502, 470939,
+                           478512]),
+       values=tensor([0.8757, 0.6946, 0.8023,  ..., 0.8472, 0.7183, 0.5606]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4126, 0.9616, 0.6093,  ..., 0.3863, 0.8636, 0.0433])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 15.963828563690186 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       49,      110,  ..., 24999910,
+                            24999948, 25000000]),
+       col_indices=tensor([  6869,   7642,  11671,  ..., 455502, 470939,
+                           478512]),
+       values=tensor([0.8757, 0.6946, 0.8023,  ..., 0.8472, 0.7183, 0.5606]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4126, 0.9616, 0.6093,  ..., 0.3863, 0.8636, 0.0433])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 15.963828563690186 seconds
+
+[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59]
+[57.08]
+67.22225975990295
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08}
+[18.28, 17.95, 18.0, 17.55, 17.82, 17.7, 17.82, 17.54, 17.77, 17.59, 18.37, 17.79, 17.89, 17.88, 18.0, 17.73, 17.87, 17.96, 17.67, 17.76]
+320.94000000000005
+16.047000000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.963828563690186, 'TIME_S_1KI': 15.963828563690184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3837.0465870952603, 'W': 57.08, 'J_1KI': 3837.0465870952603, 'W_1KI': 57.08, 'W_D': 41.032999999999994, 'J_D': 2758.3309847280975, 'W_D_1KI': 41.032999999999994, 'J_D_1KI': 41.032999999999994}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..d6eca5a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 7939, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.723366260528564, "TIME_S_1KI": 1.350720022739459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1313.216650083065, "W": 88.63, "J_1KI": 165.41335811601778, "W_1KI": 11.163874543393375, "W_D": 72.31174999999999, "J_D": 1071.4317284964918, "W_D_1KI": 9.108420455976821, "J_D_1KI": 1.1473007250254217}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..2715834
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 1.3224358558654785}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499992,
+                            2499995, 2500000]),
+       col_indices=tensor([ 81446, 111347, 262323,  ...,  95785, 329641,
+                           405148]),
+       values=tensor([0.7472, 0.0566, 0.1215,  ..., 0.1323, 0.4741, 0.2377]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0977, 0.7761, 0.5514,  ..., 0.9913, 0.3768, 0.8332])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 1.3224358558654785 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7939', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.723366260528564}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       1,       9,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([185711,  60363, 105088,  ..., 318731, 319175,
+                           323232]),
+       values=tensor([0.5920, 0.0659, 0.0171,  ..., 0.3410, 0.9352, 0.3450]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5701, 0.8906, 0.4066,  ..., 0.2438, 0.9359, 0.5479])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.723366260528564 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       1,       9,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([185711,  60363, 105088,  ..., 318731, 319175,
+                           323232]),
+       values=tensor([0.5920, 0.0659, 0.0171,  ..., 0.3410, 0.9352, 0.3450]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5701, 0.8906, 0.4066,  ..., 0.2438, 0.9359, 0.5479])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.723366260528564 seconds
+
+[18.35, 18.0, 17.81, 18.15, 20.9, 17.73, 17.87, 17.99, 18.01, 17.97]
+[88.63]
+14.81684136390686
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7939, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.723366260528564, 'TIME_S_1KI': 1.350720022739459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1313.216650083065, 'W': 88.63}
+[18.35, 18.0, 17.81, 18.15, 20.9, 17.73, 17.87, 17.99, 18.01, 17.97, 18.83, 17.84, 18.13, 18.11, 17.94, 17.77, 17.81, 17.81, 18.09, 17.66]
+326.365
+16.31825
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7939, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.723366260528564, 'TIME_S_1KI': 1.350720022739459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1313.216650083065, 'W': 88.63, 'J_1KI': 165.41335811601778, 'W_1KI': 11.163874543393375, 'W_D': 72.31174999999999, 'J_D': 1071.4317284964918, 'W_D_1KI': 9.108420455976821, 'J_D_1KI': 1.1473007250254217}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..3709697
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.266479253768921, "TIME_S_1KI": 7.748286229259563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1511.6237173461914, "W": 78.72, "J_1KI": 1140.8480885631634, "W_1KI": 59.41132075471698, "W_D": 51.030249999999995, "J_D": 979.9102667950391, "W_D_1KI": 38.5133962264151, "J_D_1KI": 29.06671413314347}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..4090da6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.920783042907715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       58,  ..., 12499955,
+                            12499976, 12500000]),
+       col_indices=tensor([  1130,  30184,  52843,  ..., 432238, 460389,
+                           464098]),
+       values=tensor([0.9711, 0.9391, 0.1931,  ..., 0.2077, 0.5139, 0.7168]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.7084, 0.4119, 0.9069,  ..., 0.7058, 0.3504, 0.1364])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 7.920783042907715 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1325', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.266479253768921}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       28,       47,  ..., 12499960,
+                            12499984, 12500000]),
+       col_indices=tensor([105223, 111339, 112839,  ..., 478264, 484121,
+                           494514]),
+       values=tensor([0.5455, 0.8774, 0.0623,  ..., 0.6447, 0.0740, 0.9564]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8058, 0.5348, 0.2222,  ..., 0.5938, 0.1996, 0.3404])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.266479253768921 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       28,       47,  ..., 12499960,
+                            12499984, 12500000]),
+       col_indices=tensor([105223, 111339, 112839,  ..., 478264, 484121,
+                           494514]),
+       values=tensor([0.5455, 0.8774, 0.0623,  ..., 0.6447, 0.0740, 0.9564]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8058, 0.5348, 0.2222,  ..., 0.5938, 0.1996, 0.3404])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.266479253768921 seconds
+
+[18.04, 18.12, 17.82, 17.66, 17.96, 17.84, 17.85, 17.56, 17.9, 17.9]
+[78.72]
+19.202537059783936
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.266479253768921, 'TIME_S_1KI': 7.748286229259563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1511.6237173461914, 'W': 78.72}
+[18.04, 18.12, 17.82, 17.66, 17.96, 17.84, 17.85, 17.56, 17.9, 17.9, 42.26, 43.36, 45.35, 45.57, 48.14, 42.56, 42.0, 41.9, 42.22, 41.77]
+553.7950000000001
+27.689750000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.266479253768921, 'TIME_S_1KI': 7.748286229259563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1511.6237173461914, 'W': 78.72, 'J_1KI': 1140.8480885631634, 'W_1KI': 59.41132075471698, 'W_D': 51.030249999999995, 'J_D': 979.9102667950391, 'W_D_1KI': 38.5133962264151, 'J_D_1KI': 29.06671413314347}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..c8d8a61
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 78314, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.500975370407104, "TIME_S_1KI": 0.1340880988125636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1246.381588702202, "W": 89.06, "J_1KI": 15.91518232630439, "W_1KI": 1.1372168450085551, "W_D": 72.72325000000001, "J_D": 1017.7511775273682, "W_D_1KI": 0.9286111040171617, "J_D_1KI": 0.0118575363794106}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..e0ea23e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.14957594871520996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     15,  ..., 249987, 249991,
+                            250000]),
+       col_indices=tensor([ 3249, 11393, 14942,  ..., 33826, 38027, 48849]),
+       values=tensor([0.4435, 0.3887, 0.6766,  ..., 0.7020, 0.9117, 0.7998]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4072, 0.0290, 0.9610,  ..., 0.4695, 0.4913, 0.1254])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.14957594871520996 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70198', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.41176462173462}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 249984, 249993,
+                            250000]),
+       col_indices=tensor([  257,   837, 13772,  ..., 26625, 34572, 42693]),
+       values=tensor([0.6771, 0.0630, 0.4952,  ..., 0.2009, 0.3453, 0.0186]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1005, 0.4396, 0.3760,  ..., 0.8175, 0.2613, 0.1136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.41176462173462 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78314', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.500975370407104}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     13,  ..., 249994, 249998,
+                            250000]),
+       col_indices=tensor([  417,  3050, 28352,  ..., 48782,  1625, 48386]),
+       values=tensor([0.9216, 0.4652, 0.6011,  ..., 0.6170, 0.6564, 0.4691]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8482, 0.9835, 0.6846,  ..., 0.7970, 0.3559, 0.9710])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.500975370407104 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     13,  ..., 249994, 249998,
+                            250000]),
+       col_indices=tensor([  417,  3050, 28352,  ..., 48782,  1625, 48386]),
+       values=tensor([0.9216, 0.4652, 0.6011,  ..., 0.6170, 0.6564, 0.4691]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8482, 0.9835, 0.6846,  ..., 0.7970, 0.3559, 0.9710])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.500975370407104 seconds
+
+[18.34, 17.82, 17.88, 21.83, 18.66, 17.91, 17.8, 17.7, 17.85, 17.95]
+[89.06]
+13.994852781295776
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78314, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.500975370407104, 'TIME_S_1KI': 0.1340880988125636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.381588702202, 'W': 89.06}
+[18.34, 17.82, 17.88, 21.83, 18.66, 17.91, 17.8, 17.7, 17.85, 17.95, 18.61, 18.02, 17.66, 18.02, 17.94, 17.65, 17.89, 18.0, 17.71, 17.89]
+326.735
+16.336750000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78314, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.500975370407104, 'TIME_S_1KI': 0.1340880988125636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.381588702202, 'W': 89.06, 'J_1KI': 15.91518232630439, 'W_1KI': 1.1372168450085551, 'W_D': 72.72325000000001, 'J_D': 1017.7511775273682, 'W_D_1KI': 0.9286111040171617, 'J_D_1KI': 0.0118575363794106}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..2dcd593
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 16503, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.453594446182251, "TIME_S_1KI": 0.6334360083731595, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1283.8258463859559, "W": 88.2, "J_1KI": 77.7934827840972, "W_1KI": 5.344482821305218, "W_D": 72.15925, "J_D": 1050.3391179798841, "W_D_1KI": 4.372492880082409, "J_D_1KI": 0.2649513955088414}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..eb8fa27
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.6362464427947998}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      51,     104,  ..., 2499889,
+                            2499946, 2500000]),
+       col_indices=tensor([  554,  2346,  3623,  ..., 48601, 49342, 49458]),
+       values=tensor([0.6346, 0.6039, 0.4681,  ..., 0.0926, 0.5934, 0.5905]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3504, 0.0589, 0.7648,  ..., 0.3104, 0.5013, 0.0863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.6362464427947998 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16503', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.453594446182251}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      44,      95,  ..., 2499888,
+                            2499946, 2500000]),
+       col_indices=tensor([   29,    59,  1099,  ..., 49158, 49549, 49729]),
+       values=tensor([0.6925, 0.1264, 0.7717,  ..., 0.9011, 0.2629, 0.2267]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0922, 0.7073, 0.7429,  ..., 0.1285, 0.2485, 0.0697])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.453594446182251 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      44,      95,  ..., 2499888,
+                            2499946, 2500000]),
+       col_indices=tensor([   29,    59,  1099,  ..., 49158, 49549, 49729]),
+       values=tensor([0.6925, 0.1264, 0.7717,  ..., 0.9011, 0.2629, 0.2267]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0922, 0.7073, 0.7429,  ..., 0.1285, 0.2485, 0.0697])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.453594446182251 seconds
+
+[18.19, 17.67, 17.57, 17.63, 17.67, 17.54, 18.18, 18.16, 17.63, 17.67]
+[88.2]
+14.555848598480225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16503, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.453594446182251, 'TIME_S_1KI': 0.6334360083731595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.8258463859559, 'W': 88.2}
+[18.19, 17.67, 17.57, 17.63, 17.67, 17.54, 18.18, 18.16, 17.63, 17.67, 18.06, 18.43, 18.51, 17.47, 17.4, 17.92, 17.64, 17.73, 17.72, 17.97]
+320.81500000000005
+16.040750000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16503, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.453594446182251, 'TIME_S_1KI': 0.6334360083731595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.8258463859559, 'W': 88.2, 'J_1KI': 77.7934827840972, 'W_1KI': 5.344482821305218, 'W_D': 72.15925, 'J_D': 1050.3391179798841, 'W_D_1KI': 4.372492880082409, 'J_D_1KI': 0.2649513955088414}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..aff89af
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389, "TIME_S_1KI": 10.122996807098389, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3257.7272432255745, "W": 53.79, "J_1KI": 3257.7272432255745, "W_1KI": 53.79, "W_D": 37.451750000000004, "J_D": 2268.2206038571003, "W_D_1KI": 37.451750000000004, "J_D_1KI": 37.451750000000004}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..bbe283a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.122996807098389}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      509,     1001,  ..., 24999037,
+                            24999516, 25000000]),
+       col_indices=tensor([   11,   136,   275,  ..., 49665, 49739, 49958]),
+       values=tensor([0.3161, 0.2173, 0.0956,  ..., 0.2198, 0.0588, 0.5951]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4562, 0.2414, 0.7128,  ..., 0.4854, 0.8312, 0.1880])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.122996807098389 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      509,     1001,  ..., 24999037,
+                            24999516, 25000000]),
+       col_indices=tensor([   11,   136,   275,  ..., 49665, 49739, 49958]),
+       values=tensor([0.3161, 0.2173, 0.0956,  ..., 0.2198, 0.0588, 0.5951]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4562, 0.2414, 0.7128,  ..., 0.4854, 0.8312, 0.1880])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.122996807098389 seconds
+
+[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81]
+[53.79]
+60.56380820274353
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79}
+[18.4, 17.81, 22.08, 19.0, 18.1, 17.63, 17.8, 17.68, 17.76, 17.81, 18.18, 18.11, 17.85, 17.68, 17.63, 17.92, 17.78, 17.67, 18.05, 18.04]
+326.765
+16.33825
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.122996807098389, 'TIME_S_1KI': 10.122996807098389, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3257.7272432255745, 'W': 53.79, 'J_1KI': 3257.7272432255745, 'W_1KI': 53.79, 'W_D': 37.451750000000004, 'J_D': 2268.2206038571003, 'W_D_1KI': 37.451750000000004, 'J_D_1KI': 37.451750000000004}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..774716c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.608571290969849, "TIME_S_1KI": 0.09542656553899297, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1151.6899712467193, "W": 82.52, "J_1KI": 10.35971909010272, "W_1KI": 0.7422865881083026, "W_D": 66.40424999999999, "J_D": 926.7705861992239, "W_D_1KI": 0.5973216695151569, "J_D_1KI": 0.005373047310561814}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..05a9200
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/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": 0.11198759078979492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([42990, 31865, 45603,  ...,    32, 31145, 42502]),
+       values=tensor([0.2680, 0.8494, 0.1049,  ..., 0.3912, 0.0276, 0.1741]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9789, 0.0522, 0.6759,  ..., 0.0240, 0.3185, 0.8367])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.11198759078979492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '93760', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.855576515197754}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([  213, 39463,  2534,  ..., 21769, 20293, 48702]),
+       values=tensor([0.6944, 0.6922, 0.1012,  ..., 0.1071, 0.8204, 0.4025]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8784, 0.5968, 0.0083,  ..., 0.0039, 0.6938, 0.6481])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 8.855576515197754 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '111170', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.608571290969849}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([37263, 14810, 49193,  ..., 22299, 19031, 40338]),
+       values=tensor([0.7995, 0.8033, 0.3510,  ..., 0.6585, 0.0621, 0.7519]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9318, 0.0252, 0.9296,  ..., 0.2820, 0.1820, 0.1630])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.608571290969849 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([37263, 14810, 49193,  ..., 22299, 19031, 40338]),
+       values=tensor([0.7995, 0.8033, 0.3510,  ..., 0.6585, 0.0621, 0.7519]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9318, 0.0252, 0.9296,  ..., 0.2820, 0.1820, 0.1630])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.608571290969849 seconds
+
+[18.42, 17.72, 18.1, 17.98, 17.9, 18.19, 17.99, 17.99, 17.89, 17.81]
+[82.52]
+13.9564950466156
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.608571290969849, 'TIME_S_1KI': 0.09542656553899297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1151.6899712467193, 'W': 82.52}
+[18.42, 17.72, 18.1, 17.98, 17.9, 18.19, 17.99, 17.99, 17.89, 17.81, 18.18, 17.91, 17.74, 18.05, 17.74, 17.64, 17.73, 17.76, 17.96, 17.64]
+322.315
+16.11575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.608571290969849, 'TIME_S_1KI': 0.09542656553899297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1151.6899712467193, 'W': 82.52, 'J_1KI': 10.35971909010272, 'W_1KI': 0.7422865881083026, 'W_D': 66.40424999999999, 'J_D': 926.7705861992239, 'W_D_1KI': 0.5973216695151569, 'J_D_1KI': 0.005373047310561814}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..cda8c88
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87647, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.566825151443481, "TIME_S_1KI": 0.12056117324544459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.4293744707109, "W": 86.61000000000001, "J_1KI": 13.935780739451559, "W_1KI": 0.9881684484352005, "W_D": 70.62725000000002, "J_D": 996.0304559298756, "W_D_1KI": 0.8058148025602704, "J_D_1KI": 0.009193866333819417}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..97cfaa9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.14960765838623047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([ 5328,  5758, 21245,  ..., 40217,  2052, 16010]),
+       values=tensor([0.8699, 0.1385, 0.5605,  ..., 0.3914, 0.4912, 0.7839]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.1699, 0.5973, 0.2081,  ..., 0.0281, 0.7559, 0.8178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.14960765838623047 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70183', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.40775990486145}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 124997, 124999,
+                            125000]),
+       col_indices=tensor([ 5449, 14906, 26173,  ..., 30325, 48181,  9186]),
+       values=tensor([0.5897, 0.0944, 0.5204,  ..., 0.1014, 0.9614, 0.5057]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5323, 0.2435, 0.2265,  ..., 0.3258, 0.3469, 0.7874])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 8.40775990486145 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87647', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.566825151443481}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124995, 124996,
+                            125000]),
+       col_indices=tensor([14301, 36836,  2496,  ..., 26599, 44389, 45216]),
+       values=tensor([0.0827, 0.6231, 0.3315,  ..., 0.4386, 0.5843, 0.2734]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3713, 0.0426, 0.5176,  ..., 0.5970, 0.6758, 0.3745])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.566825151443481 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124995, 124996,
+                            125000]),
+       col_indices=tensor([14301, 36836,  2496,  ..., 26599, 44389, 45216]),
+       values=tensor([0.0827, 0.6231, 0.3315,  ..., 0.4386, 0.5843, 0.2734]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3713, 0.0426, 0.5176,  ..., 0.5970, 0.6758, 0.3745])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.566825151443481 seconds
+
+[18.15, 17.58, 18.01, 17.67, 17.78, 17.59, 17.99, 17.46, 17.88, 17.7]
+[86.61]
+14.102636814117432
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.566825151443481, 'TIME_S_1KI': 0.12056117324544459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4293744707109, 'W': 86.61000000000001}
+[18.15, 17.58, 18.01, 17.67, 17.78, 17.59, 17.99, 17.46, 17.88, 17.7, 18.22, 17.6, 17.52, 17.68, 18.1, 17.84, 17.59, 17.63, 17.89, 17.62]
+319.655
+15.98275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.566825151443481, 'TIME_S_1KI': 0.12056117324544459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4293744707109, 'W': 86.61000000000001, 'J_1KI': 13.935780739451559, 'W_1KI': 0.9881684484352005, 'W_D': 70.62725000000002, 'J_D': 996.0304559298756, 'W_D_1KI': 0.8058148025602704, 'J_D_1KI': 0.009193866333819417}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..0ba39e0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 323751, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.660384178161621, "TIME_S_1KI": 0.03292772587007182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1091.8019851422312, "W": 79.43, "J_1KI": 3.3723509275407064, "W_1KI": 0.24534287152781, "W_D": 62.86500000000001, "J_D": 864.1084199416639, "W_D_1KI": 0.19417700640306904, "J_D_1KI": 0.0005997726845726161}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..9a09fc6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04866957664489746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([2014, 2333, 4073,  ..., 1117, 3505, 2207]),
+       values=tensor([0.1339, 0.9980, 0.7024,  ..., 0.3782, 0.0544, 0.2308]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2751, 0.2895, 0.5101,  ..., 0.3933, 0.2935, 0.0678])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.04866957664489746 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '215740', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.996948957443237}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([3059, 3492, 4969,  ..., 3863, 1265, 1575]),
+       values=tensor([0.7839, 0.7068, 0.1359,  ..., 0.6765, 0.7179, 0.7182]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2637, 0.1133, 0.2354,  ..., 0.5397, 0.9545, 0.7707])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.996948957443237 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '323751', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.660384178161621}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4531, 3967, 1182,  ..., 4005, 3234, 3449]),
+       values=tensor([0.1835, 0.1001, 0.2805,  ..., 0.8615, 0.2040, 0.1828]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2085, 0.7612, 0.9816,  ..., 0.7337, 0.6921, 0.5494])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.660384178161621 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4531, 3967, 1182,  ..., 4005, 3234, 3449]),
+       values=tensor([0.1835, 0.1001, 0.2805,  ..., 0.8615, 0.2040, 0.1828]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2085, 0.7612, 0.9816,  ..., 0.7337, 0.6921, 0.5494])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.660384178161621 seconds
+
+[18.46, 17.59, 17.71, 17.79, 18.06, 17.81, 22.16, 18.13, 17.88, 17.67]
+[79.43]
+13.745461225509644
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 323751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.660384178161621, 'TIME_S_1KI': 0.03292772587007182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1091.8019851422312, 'W': 79.43}
+[18.46, 17.59, 17.71, 17.79, 18.06, 17.81, 22.16, 18.13, 17.88, 17.67, 18.24, 17.78, 22.87, 17.85, 17.88, 17.75, 18.09, 17.86, 17.93, 17.95]
+331.3
+16.565
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 323751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.660384178161621, 'TIME_S_1KI': 0.03292772587007182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1091.8019851422312, 'W': 79.43, 'J_1KI': 3.3723509275407064, 'W_1KI': 0.24534287152781, 'W_D': 62.86500000000001, 'J_D': 864.1084199416639, 'W_D_1KI': 0.19417700640306904, 'J_D_1KI': 0.0005997726845726161}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..9b486c5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 244536, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.47565221786499, "TIME_S_1KI": 0.04283889577757463, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1122.3107054543495, "W": 80.61, "J_1KI": 4.589552071900863, "W_1KI": 0.32964471488860536, "W_D": 64.2855, "J_D": 895.0292129448652, "W_D_1KI": 0.2628876729806654, "J_D_1KI": 0.0010750469173482246}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..a34514c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05913829803466797}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,    10,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([  91, 1225, 4183,  ..., 1260, 1498, 1816]),
+       values=tensor([0.4538, 0.5289, 0.0869,  ..., 0.3885, 0.0043, 0.2412]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3917, 0.0968, 0.9015,  ..., 0.9180, 0.2586, 0.0822])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05913829803466797 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '177549', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.6236653327941895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,    10,  ..., 24989, 24994, 25000]),
+       col_indices=tensor([ 412, 1102, 1155,  ...,  695, 1250, 1499]),
+       values=tensor([0.5017, 0.7691, 0.1146,  ..., 0.5300, 0.6967, 0.6559]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7930, 0.9227, 0.2342,  ..., 0.4335, 0.3949, 0.6803])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.6236653327941895 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '244536', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.47565221786499}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,     8,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([ 194,  369, 2258,  ..., 1755, 2835, 2987]),
+       values=tensor([0.8194, 0.2005, 0.5023,  ..., 0.6221, 0.3751, 0.8448]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5474, 0.2694, 0.2646,  ..., 0.5254, 0.5763, 0.9998])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.47565221786499 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,     8,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([ 194,  369, 2258,  ..., 1755, 2835, 2987]),
+       values=tensor([0.8194, 0.2005, 0.5023,  ..., 0.6221, 0.3751, 0.8448]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5474, 0.2694, 0.2646,  ..., 0.5254, 0.5763, 0.9998])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.47565221786499 seconds
+
+[18.33, 18.0, 17.95, 18.79, 18.11, 18.49, 17.87, 17.96, 17.71, 17.91]
+[80.61]
+13.922723054885864
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 244536, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.47565221786499, 'TIME_S_1KI': 0.04283889577757463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1122.3107054543495, 'W': 80.61}
+[18.33, 18.0, 17.95, 18.79, 18.11, 18.49, 17.87, 17.96, 17.71, 17.91, 18.2, 17.81, 17.83, 18.46, 18.26, 17.74, 18.1, 18.0, 17.87, 20.64]
+326.49
+16.3245
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 244536, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.47565221786499, 'TIME_S_1KI': 0.04283889577757463, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1122.3107054543495, 'W': 80.61, 'J_1KI': 4.589552071900863, 'W_1KI': 0.32964471488860536, 'W_D': 64.2855, 'J_D': 895.0292129448652, 'W_D_1KI': 0.2628876729806654, 'J_D_1KI': 0.0010750469173482246}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..6d5d9d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 162920, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.414216756820679, "TIME_S_1KI": 0.06392227324343652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1267.1163623809814, "W": 89.2, "J_1KI": 7.7775372107843195, "W_1KI": 0.5475079793763811, "W_D": 72.96625, "J_D": 1036.5104178988934, "W_D_1KI": 0.44786551681807024, "J_D_1KI": 0.0027489904052177155}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..548b49c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.08090949058532715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     56,     99,  ..., 249898, 249957,
+                            250000]),
+       col_indices=tensor([  27,  423,  607,  ..., 4371, 4379, 4963]),
+       values=tensor([0.2630, 0.0898, 0.5767,  ..., 0.9425, 0.5823, 0.3558]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9446, 0.5109, 0.8342,  ..., 0.1182, 0.7217, 0.5335])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.08090949058532715 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '129774', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.36373782157898}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     58,    109,  ..., 249903, 249955,
+                            250000]),
+       col_indices=tensor([ 168,  371,  372,  ..., 4708, 4876, 4879]),
+       values=tensor([0.3469, 0.2972, 0.5901,  ..., 0.0640, 0.2331, 0.9267]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6978, 0.0250, 0.3323,  ..., 0.6356, 0.0847, 0.1678])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.36373782157898 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '162920', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.414216756820679}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     50,    103,  ..., 249909, 249962,
+                            250000]),
+       col_indices=tensor([  86,  107,  119,  ..., 4571, 4629, 4973]),
+       values=tensor([0.3206, 0.5923, 0.4852,  ..., 0.3807, 0.1641, 0.9581]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7369, 0.6267, 0.7979,  ..., 0.0231, 0.0899, 0.6643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.414216756820679 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     50,    103,  ..., 249909, 249962,
+                            250000]),
+       col_indices=tensor([  86,  107,  119,  ..., 4571, 4629, 4973]),
+       values=tensor([0.3206, 0.5923, 0.4852,  ..., 0.3807, 0.1641, 0.9581]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7369, 0.6267, 0.7979,  ..., 0.0231, 0.0899, 0.6643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.414216756820679 seconds
+
+[18.27, 17.91, 18.14, 17.62, 17.8, 17.9, 19.35, 17.84, 17.83, 17.75]
+[89.2]
+14.205340385437012
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 162920, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.414216756820679, 'TIME_S_1KI': 0.06392227324343652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.1163623809814, 'W': 89.2}
+[18.27, 17.91, 18.14, 17.62, 17.8, 17.9, 19.35, 17.84, 17.83, 17.75, 18.16, 17.99, 17.94, 17.95, 17.67, 17.72, 18.79, 18.01, 18.17, 17.91]
+324.675
+16.23375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 162920, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.414216756820679, 'TIME_S_1KI': 0.06392227324343652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1267.1163623809814, 'W': 89.2, 'J_1KI': 7.7775372107843195, 'W_1KI': 0.5475079793763811, 'W_D': 72.96625, 'J_D': 1036.5104178988934, 'W_D_1KI': 0.44786551681807024, 'J_D_1KI': 0.0027489904052177155}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..373bd0d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 44182, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.514305114746094, "TIME_S_1KI": 0.2379771199752409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1269.0750387310982, "W": 89.25, "J_1KI": 28.723802424767964, "W_1KI": 2.020053415418044, "W_D": 73.10575, "J_D": 1039.51465000242, "W_D_1KI": 1.6546500837445115, "J_D_1KI": 0.03745077370296753}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..f73f661
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.25171899795532227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     257,     510,  ..., 1249508,
+                            1249742, 1250000]),
+       col_indices=tensor([  10,   14,   18,  ..., 4971, 4976, 4993]),
+       values=tensor([0.7215, 0.2673, 0.1887,  ..., 0.2021, 0.2524, 0.6553]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6580, 0.5682, 0.5133,  ..., 0.8598, 0.8673, 0.3117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.25171899795532227 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '41713', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.913089752197266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     230,     476,  ..., 1249535,
+                            1249754, 1250000]),
+       col_indices=tensor([  26,   32,   49,  ..., 4901, 4965, 4968]),
+       values=tensor([0.6798, 0.0159, 0.6379,  ..., 0.7230, 0.8415, 0.2703]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9905, 0.3660, 0.2565,  ..., 0.2843, 0.2598, 0.4388])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.913089752197266 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44182', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.514305114746094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     234,     486,  ..., 1249518,
+                            1249775, 1250000]),
+       col_indices=tensor([   2,   39,   64,  ..., 4915, 4964, 4987]),
+       values=tensor([0.0513, 0.5642, 0.6511,  ..., 0.0332, 0.5293, 0.6294]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8655, 0.6659, 0.2976,  ..., 0.0599, 0.2467, 0.0329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.514305114746094 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     234,     486,  ..., 1249518,
+                            1249775, 1250000]),
+       col_indices=tensor([   2,   39,   64,  ..., 4915, 4964, 4987]),
+       values=tensor([0.0513, 0.5642, 0.6511,  ..., 0.0332, 0.5293, 0.6294]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8655, 0.6659, 0.2976,  ..., 0.0599, 0.2467, 0.0329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.514305114746094 seconds
+
+[18.16, 17.71, 17.62, 17.44, 17.74, 17.67, 17.83, 17.6, 17.55, 17.84]
+[89.25]
+14.219328165054321
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44182, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.514305114746094, 'TIME_S_1KI': 0.2379771199752409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.0750387310982, 'W': 89.25}
+[18.16, 17.71, 17.62, 17.44, 17.74, 17.67, 17.83, 17.6, 17.55, 17.84, 18.13, 17.61, 17.91, 17.95, 20.68, 17.51, 17.88, 18.51, 17.79, 17.64]
+322.885
+16.14425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44182, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.514305114746094, 'TIME_S_1KI': 0.2379771199752409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.0750387310982, 'W': 89.25, 'J_1KI': 28.723802424767964, 'W_1KI': 2.020053415418044, 'W_D': 73.10575, 'J_D': 1039.51465000242, 'W_D_1KI': 1.6546500837445115, 'J_D_1KI': 0.03745077370296753}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..6e5c014
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19098, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.554213762283325, "TIME_S_1KI": 0.552634504256117, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1299.880751209259, "W": 88.77, "J_1KI": 68.06371092309452, "W_1KI": 4.64813069431354, "W_D": 72.8915, "J_D": 1067.3680046949385, "W_D_1KI": 3.8167085558697242, "J_D_1KI": 0.19984859963712034}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..a64ec1d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5497848987579346}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     487,    1016,  ..., 2499032,
+                            2499521, 2500000]),
+       col_indices=tensor([   8,   18,   46,  ..., 4955, 4960, 4970]),
+       values=tensor([0.9231, 0.4693, 0.9835,  ..., 0.3329, 0.5539, 0.9296]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7808, 0.5215, 0.8582,  ..., 0.9627, 0.6165, 0.7692])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.5497848987579346 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19098', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.554213762283325}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     480,     948,  ..., 2499011,
+                            2499537, 2500000]),
+       col_indices=tensor([   1,    8,   11,  ..., 4950, 4969, 4990]),
+       values=tensor([0.1158, 0.8497, 0.7920,  ..., 0.6315, 0.2224, 0.2676]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4700, 0.1718, 0.6444,  ..., 0.1980, 0.7458, 0.7705])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.554213762283325 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     480,     948,  ..., 2499011,
+                            2499537, 2500000]),
+       col_indices=tensor([   1,    8,   11,  ..., 4950, 4969, 4990]),
+       values=tensor([0.1158, 0.8497, 0.7920,  ..., 0.6315, 0.2224, 0.2676]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4700, 0.1718, 0.6444,  ..., 0.1980, 0.7458, 0.7705])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.554213762283325 seconds
+
+[18.14, 17.83, 17.44, 17.66, 17.78, 17.6, 17.85, 17.79, 17.38, 17.41]
+[88.77]
+14.643243789672852
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19098, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.554213762283325, 'TIME_S_1KI': 0.552634504256117, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.880751209259, 'W': 88.77}
+[18.14, 17.83, 17.44, 17.66, 17.78, 17.6, 17.85, 17.79, 17.38, 17.41, 18.1, 17.72, 17.57, 17.35, 17.6, 17.54, 17.53, 17.37, 18.02, 17.43]
+317.57000000000005
+15.878500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19098, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.554213762283325, 'TIME_S_1KI': 0.552634504256117, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1299.880751209259, 'W': 88.77, 'J_1KI': 68.06371092309452, 'W_1KI': 4.64813069431354, 'W_D': 72.8915, 'J_D': 1067.3680046949385, 'W_D_1KI': 3.8167085558697242, 'J_D_1KI': 0.19984859963712034}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..ae3e053
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.657127857208252, "TIME_S_1KI": 1.175245683415114, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1354.9685973644257, "W": 87.3, "J_1KI": 149.42309190167904, "W_1KI": 9.62726069695633, "W_D": 71.26675, "J_D": 1106.11922435534, "W_D_1KI": 7.859147551830612, "J_D_1KI": 0.8666902902327539}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..5903d4b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,67 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.157815933227539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     951,    1952,  ..., 4997990,
+                            4999010, 5000000]),
+       col_indices=tensor([   1,    2,    7,  ..., 4975, 4984, 4994]),
+       values=tensor([0.2978, 0.3383, 0.4211,  ..., 0.6173, 0.3619, 0.1875]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3712, 0.9173, 0.1615,  ..., 0.0466, 0.6664, 0.8295])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 1.157815933227539 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9068', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.657127857208252}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1038,    2022,  ..., 4998028,
+                            4999030, 5000000]),
+       col_indices=tensor([   5,    8,   18,  ..., 4988, 4996, 4999]),
+       values=tensor([0.3556, 0.0118, 0.2581,  ..., 0.9541, 0.9641, 0.1138]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([2.9156e-01, 7.5281e-01, 6.6249e-01,  ..., 5.6286e-01, 2.5839e-04,
+        4.1914e-01])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.657127857208252 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1038,    2022,  ..., 4998028,
+                            4999030, 5000000]),
+       col_indices=tensor([   5,    8,   18,  ..., 4988, 4996, 4999]),
+       values=tensor([0.3556, 0.0118, 0.2581,  ..., 0.9541, 0.9641, 0.1138]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([2.9156e-01, 7.5281e-01, 6.6249e-01,  ..., 5.6286e-01, 2.5839e-04,
+        4.1914e-01])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.657127857208252 seconds
+
+[18.19, 17.62, 17.78, 17.94, 17.96, 17.76, 17.78, 17.85, 17.63, 17.71]
+[87.3]
+15.52083158493042
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.657127857208252, 'TIME_S_1KI': 1.175245683415114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1354.9685973644257, 'W': 87.3}
+[18.19, 17.62, 17.78, 17.94, 17.96, 17.76, 17.78, 17.85, 17.63, 17.71, 18.05, 17.78, 17.91, 17.54, 18.13, 17.46, 18.2, 17.86, 17.68, 17.62]
+320.6650000000001
+16.033250000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.657127857208252, 'TIME_S_1KI': 1.175245683415114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1354.9685973644257, 'W': 87.3, 'J_1KI': 149.42309190167904, 'W_1KI': 9.62726069695633, 'W_D': 71.26675, 'J_D': 1106.11922435534, 'W_D_1KI': 7.859147551830612, 'J_D_1KI': 0.8666902902327539}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..fca72d3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5664, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.680659770965576, "TIME_S_1KI": 1.8857097053258434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1382.186832485199, "W": 86.06, "J_1KI": 244.0301611026128, "W_1KI": 15.194209039548022, "W_D": 70.05975000000001, "J_D": 1125.2110613200666, "W_D_1KI": 12.369306144067798, "J_D_1KI": 2.1838464237407833}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..e9cfac6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.8537497520446777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1463,    2978,  ..., 7496989,
+                            7498439, 7500000]),
+       col_indices=tensor([   1,    2,   10,  ..., 4991, 4992, 4997]),
+       values=tensor([0.5985, 0.8707, 0.1470,  ..., 0.1515, 0.9789, 0.5190]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7595, 0.9851, 0.8459,  ..., 0.8698, 0.1337, 0.7899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 1.8537497520446777 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5664', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.680659770965576}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1531,    3042,  ..., 7497046,
+                            7498567, 7500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4993, 4996, 4997]),
+       values=tensor([0.2133, 0.5634, 0.4796,  ..., 0.0244, 0.1739, 0.8367]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6801, 0.4978, 0.3141,  ..., 0.6717, 0.6784, 0.5569])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.680659770965576 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1531,    3042,  ..., 7497046,
+                            7498567, 7500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4993, 4996, 4997]),
+       values=tensor([0.2133, 0.5634, 0.4796,  ..., 0.0244, 0.1739, 0.8367]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6801, 0.4978, 0.3141,  ..., 0.6717, 0.6784, 0.5569])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.680659770965576 seconds
+
+[18.09, 17.53, 17.63, 17.45, 17.68, 17.68, 17.63, 17.31, 17.79, 17.45]
+[86.06]
+16.060734748840332
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.680659770965576, 'TIME_S_1KI': 1.8857097053258434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1382.186832485199, 'W': 86.06}
+[18.09, 17.53, 17.63, 17.45, 17.68, 17.68, 17.63, 17.31, 17.79, 17.45, 18.18, 17.48, 18.72, 17.88, 18.28, 17.87, 17.95, 17.58, 17.89, 17.59]
+320.00500000000005
+16.00025
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.680659770965576, 'TIME_S_1KI': 1.8857097053258434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1382.186832485199, 'W': 86.06, 'J_1KI': 244.0301611026128, 'W_1KI': 15.194209039548022, 'W_D': 70.05975000000001, 'J_D': 1125.2110613200666, 'W_D_1KI': 12.369306144067798, 'J_D_1KI': 2.1838464237407833}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..5c599da
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 353197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.226954936981201, "TIME_S_1KI": 0.0289553844935863, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1121.6130926513672, "W": 79.36, "J_1KI": 3.1756019803434548, "W_1KI": 0.22469047019085664, "W_D": 63.1995, "J_D": 893.2130374120474, "W_D_1KI": 0.1789355515477198, "J_D_1KI": 0.0005066168499384757}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..bd4d211
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04602789878845215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 578,  489, 2035, 2602, 4011, 1806, 4187, 4466, 1701,
+                           3439, 1855, 2207, 4634, 4913, 4351, 2699, 4950, 1482,
+                           1630, 4011,  847,  262,  526,  120, 2856, 4636, 1597,
+                           3213,  516, 2941, 2174,  769, 2517,  499, 1934, 2295,
+                           4580, 2315, 3877, 4302, 1055, 1440, 1706, 4670,  429,
+                           2051, 2186, 1313, 1143, 3111, 4634, 2695, 4779, 4363,
+                           1595, 4655, 1338, 4420, 4067, 1526,  282, 2445,  369,
+                           1772, 4270, 1668, 3875,  412, 4249, 1643,  914,  856,
+                           3381, 2292, 4030, 3967, 3653, 1168, 3730,  661, 3997,
+                           1557, 2874, 4414,  241, 2576, 1853, 4254,  862,  608,
+                           4068, 2069,  967,  859, 1460, 3895, 2418, 1568, 1236,
+                           2469, 4377, 1881,  774, 3150, 4962, 4438, 3334, 3370,
+                           3850,  469, 1930, 1600, 4349, 1435, 2755, 2777, 2752,
+                           1750, 2319, 2825, 2053, 4982, 3998, 1031,  158, 2744,
+                           1843, 2266, 4999, 4122,  250, 2042, 4015, 3394, 4312,
+                            559, 3260,  296, 3827, 4372, 2993, 1918, 3134,  621,
+                            557, 2314, 3437, 2519, 4868, 4567,  645, 1366, 3758,
+                           4230,  810,  851, 1555, 4001, 1607,  876, 3143, 3677,
+                            620, 2976, 4865, 2725, 1890,  514, 1960, 1749, 2271,
+                           3746, 3959, 4437, 4381, 2386, 2843, 3407, 4429, 2460,
+                           1759, 3731, 4851, 3169,  994, 1771, 4332, 2376, 2120,
+                             69,  919, 3516,   57, 1846, 3363, 4747, 3055, 4318,
+                           1028, 4163, 4665, 4823,  505,  247, 4342, 3354, 2982,
+                           3367, 3474, 1671, 2141, 1806,  254, 2129,  187, 1832,
+                           3940, 4918,  419, 4670,  303,  921,   39, 4798, 1396,
+                           2176, 2156, 2536,  266, 4518, 4967, 4630, 2593, 1182,
+                           2488, 2445,  979, 1019, 4241, 1675, 1170, 2324, 2271,
+                           3633, 2309, 4715, 1380, 4338, 2573, 2764]),
+       values=tensor([0.6984, 0.1478, 0.0323, 0.8260, 0.6827, 0.1000, 0.4915,
+                      0.6587, 0.0376, 0.3470, 0.7142, 0.7494, 0.0897, 0.2827,
+                      0.6630, 0.3710, 0.5106, 0.3028, 0.3002, 0.0863, 0.1240,
+                      0.1798, 0.6305, 0.3002, 0.5649, 0.4551, 0.6642, 0.1708,
+                      0.5500, 0.6807, 0.3124, 0.4343, 0.1155, 0.5562, 0.7660,
+                      0.5677, 0.3794, 0.3402, 0.7695, 0.1890, 0.5328, 0.3628,
+                      0.6604, 0.2382, 0.4320, 0.8974, 0.3878, 0.2382, 0.2066,
+                      0.8734, 0.7091, 0.8197, 0.8175, 0.2812, 0.4902, 0.1894,
+                      0.3966, 0.5276, 0.7667, 0.0175, 0.7037, 0.7601, 0.1810,
+                      0.4741, 0.3863, 0.8670, 0.4845, 0.6586, 0.0648, 0.8124,
+                      0.7536, 0.0293, 0.5547, 0.4571, 0.0817, 0.7764, 0.3555,
+                      0.5853, 0.3952, 0.4216, 0.4013, 0.1391, 0.8172, 0.9389,
+                      0.3613, 0.8906, 0.6121, 0.5615, 0.7545, 0.1340, 0.0792,
+                      0.8924, 0.1038, 0.5565, 0.0169, 0.8812, 0.4265, 0.0727,
+                      0.1083, 0.5669, 0.5957, 0.1631, 0.9558, 0.7748, 0.9411,
+                      0.7256, 0.5800, 0.4846, 0.9970, 0.8586, 0.7723, 0.4078,
+                      0.6823, 0.7466, 0.9258, 0.1331, 0.3558, 0.7864, 0.4232,
+                      0.6710, 0.9708, 0.0475, 0.1393, 0.7271, 0.7770, 0.3222,
+                      0.4988, 0.2948, 0.5044, 0.9371, 0.0161, 0.2536, 0.5990,
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+                      0.7479, 0.4857, 0.1928, 0.9739, 0.1023]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.6347, 0.6451, 0.4713,  ..., 0.2060, 0.2664, 0.4890])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04602789878845215 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '228122', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.781703472137451}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([4412, 3431, 2377, 4102, 3105,  469, 2716, 4410, 2733,
+                           2349,  109, 3862,  716, 2870, 2405, 4409, 3017,  152,
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+                           4639,  116, 1322, 3984, 4640, 1253, 1197, 4103, 4814,
+                           4947, 1925, 1050,  735,   66, 1794,  677]),
+       values=tensor([0.8584, 0.2940, 0.8361, 0.6545, 0.0599, 0.3888, 0.5851,
+                      0.6940, 0.8362, 0.8362, 0.9462, 0.2506, 0.0683, 0.7589,
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+                      0.4486, 0.2404, 0.7355, 0.7961, 0.7130]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8182, 0.2605, 0.1489,  ..., 0.1484, 0.3699, 0.6778])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 6.781703472137451 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '353197', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.226954936981201}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1961, 1566,  179,  628, 4168, 3230,   47, 1058,  848,
+                           1307,  863, 3163,  497, 3237, 3835, 4225, 1765, 2385,
+                           2578, 3624, 2513, 1168, 2630, 3631, 1864,  568, 4361,
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+                           2173, 3550, 3891, 1058, 4695, 4487, 1810, 3555, 3979,
+                           4408, 2688,  366, 1825, 2362, 2165,  528]),
+       values=tensor([0.4900, 0.1519, 0.0910, 0.3336, 0.1203, 0.0899, 0.6181,
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+                      0.7706, 0.7962, 0.5827, 0.3612, 0.1219, 0.5026, 0.1788,
+                      0.6829, 0.9316, 0.0223, 0.3259, 0.0955]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8734, 0.8080, 0.1055,  ..., 0.8475, 0.7666, 0.2333])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.226954936981201 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1961, 1566,  179,  628, 4168, 3230,   47, 1058,  848,
+                           1307,  863, 3163,  497, 3237, 3835, 4225, 1765, 2385,
+                           2578, 3624, 2513, 1168, 2630, 3631, 1864,  568, 4361,
+                           4779, 4022,  399, 4958, 2227, 3685,  929, 3248, 4399,
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+                           3708,  478, 2565, 4710, 4857,  937, 3612, 4449, 1275,
+                           3883,  720, 2924, 2672,  816, 3571, 2100, 2481, 4778,
+                           4274, 2449, 1483, 3559, 3509, 2069, 4491,  301, 3501,
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+                            677,  812, 4696, 2299, 2787, 3449, 4225,  795,  357,
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+                           2173, 3550, 3891, 1058, 4695, 4487, 1810, 3555, 3979,
+                           4408, 2688,  366, 1825, 2362, 2165,  528]),
+       values=tensor([0.4900, 0.1519, 0.0910, 0.3336, 0.1203, 0.0899, 0.6181,
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+                      0.0135, 0.0908, 0.7106, 0.9979, 0.7791, 0.6211, 0.9269,
+                      0.0715, 0.4671, 0.4465, 0.5092, 0.0890, 0.6377, 0.1978,
+                      0.5935, 0.9471, 0.6538, 0.5919, 0.8443, 0.4530, 0.0807,
+                      0.9258, 0.4523, 0.4554, 0.2932, 0.8921, 0.0589, 0.3042,
+                      0.4416, 0.9399, 0.0639, 0.1672, 0.2592, 0.9334, 0.7784,
+                      0.2523, 0.4009, 0.3271, 0.4901, 0.0985, 0.6126, 0.3137,
+                      0.5938, 0.4894, 0.3721, 0.8337, 0.3234, 0.9788, 0.2330,
+                      0.2625, 0.8031, 0.0536, 0.2237, 0.3051, 0.9123, 0.3222,
+                      0.8402, 0.3156, 0.2969, 0.2334, 0.9665, 0.7377, 0.6395,
+                      0.4451, 0.7617, 0.6622, 0.5325, 0.4459, 0.0092, 0.7370,
+                      0.4452, 0.8857, 0.5499, 0.2713, 0.3315, 0.9736, 0.3753,
+                      0.9983, 0.8451, 0.4842, 0.0958, 0.3583, 0.1831, 0.1567,
+                      0.8604, 0.6328, 0.2541, 0.3850, 0.8555, 0.4146, 0.1263,
+                      0.1834, 0.2208, 0.6295, 0.4250, 0.5900, 0.7980, 0.5475,
+                      0.9764, 0.2051, 0.6760, 0.3076, 0.0382, 0.6317, 0.2634,
+                      0.3634, 0.2930, 0.9653, 0.5672, 0.1508, 0.6672, 0.4422,
+                      0.7693, 0.8897, 0.4264, 0.4859, 0.4197, 0.0661, 0.6678,
+                      0.0402, 0.8927, 0.4292, 0.2572, 0.1798, 0.3259, 0.6416,
+                      0.0733, 0.9193, 0.7059, 0.2676, 0.4781, 0.7963, 0.9337,
+                      0.7706, 0.7962, 0.5827, 0.3612, 0.1219, 0.5026, 0.1788,
+                      0.6829, 0.9316, 0.0223, 0.3259, 0.0955]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8734, 0.8080, 0.1055,  ..., 0.8475, 0.7666, 0.2333])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.226954936981201 seconds
+
+[18.35, 17.76, 18.03, 17.72, 17.87, 18.0, 18.15, 17.7, 17.8, 17.85]
+[79.36]
+14.133229494094849
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 353197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.226954936981201, 'TIME_S_1KI': 0.0289553844935863, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1121.6130926513672, 'W': 79.36}
+[18.35, 17.76, 18.03, 17.72, 17.87, 18.0, 18.15, 17.7, 17.8, 17.85, 18.33, 17.94, 18.05, 17.86, 17.95, 18.18, 18.05, 17.92, 18.13, 17.67]
+323.21
+16.1605
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 353197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.226954936981201, 'TIME_S_1KI': 0.0289553844935863, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1121.6130926513672, 'W': 79.36, 'J_1KI': 3.1756019803434548, 'W_1KI': 0.22469047019085664, 'W_D': 63.1995, 'J_D': 893.2130374120474, 'W_D_1KI': 0.1789355515477198, 'J_D_1KI': 0.0005066168499384757}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..4385053
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 355197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.57451844215393, "TIME_S_1KI": 0.029770855165313703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1141.788979816437, "W": 79.9, "J_1KI": 3.214523151424243, "W_1KI": 0.2249455935720178, "W_D": 63.777750000000005, "J_D": 911.3983993427754, "W_D_1KI": 0.17955599287156143, "J_D_1KI": 0.000505511006206588}
diff --git a/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..5fcc662
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04642629623413086}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([1959, 1786,   16,  ..., 4660,  524, 2490]),
+       values=tensor([0.5665, 0.4844, 0.2984,  ..., 0.5218, 0.3017, 0.5058]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6773, 0.1820, 0.1692,  ..., 0.1637, 0.2279, 0.2140])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.04642629623413086 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '226164', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.685633659362793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4154, 3836, 1179,  ..., 2527, 1259,  153]),
+       values=tensor([0.7262, 0.5127, 0.2351,  ..., 0.4025, 0.3877, 0.1384]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5042, 0.5821, 0.3979,  ..., 0.3479, 0.7780, 0.3728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 6.685633659362793 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355197', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.57451844215393}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2741, 2960, 2565,  ...,  976,  304, 1232]),
+       values=tensor([0.5180, 0.1270, 0.6648,  ..., 0.3095, 0.3853, 0.9447]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1571, 0.5465, 0.3582,  ..., 0.1118, 0.4116, 0.5757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.57451844215393 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2741, 2960, 2565,  ...,  976,  304, 1232]),
+       values=tensor([0.5180, 0.1270, 0.6648,  ..., 0.3095, 0.3853, 0.9447]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1571, 0.5465, 0.3582,  ..., 0.1118, 0.4116, 0.5757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.57451844215393 seconds
+
+[17.86, 17.95, 18.35, 17.84, 17.76, 17.65, 18.09, 17.61, 17.89, 17.57]
+[79.9]
+14.2902250289917
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.57451844215393, 'TIME_S_1KI': 0.029770855165313703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1141.788979816437, 'W': 79.9}
+[17.86, 17.95, 18.35, 17.84, 17.76, 17.65, 18.09, 17.61, 17.89, 17.57, 18.28, 17.96, 17.74, 18.66, 18.09, 17.53, 17.73, 17.6, 18.05, 18.18]
+322.44500000000005
+16.12225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.57451844215393, 'TIME_S_1KI': 0.029770855165313703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1141.788979816437, 'W': 79.9, 'J_1KI': 3.214523151424243, 'W_1KI': 0.2249455935720178, 'W_D': 63.777750000000005, 'J_D': 911.3983993427754, 'W_D_1KI': 0.17955599287156143, 'J_D_1KI': 0.000505511006206588}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
index 59c1962..1bdafea 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 23.50609064102173, "TIME_S_1KI": 23.50609064102173, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 609.4492170715331, "W": 22.389903061636694, "J_1KI": 609.4492170715331, "W_1KI": 22.389903061636694, "W_D": 3.917903061636693, "J_D": 106.64463114929183, "W_D_1KI": 3.9179030616366926, "J_D_1KI": 3.9179030616366926}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 426, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.062527418136597, "TIME_S_1KI": 23.62095638060234, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.93658995628357, "W": 22.910177826692795, "J_1KI": 729.8980984889287, "W_1KI": 53.77976015655585, "W_D": 4.4831778266927955, "J_D": 60.845622244596484, "W_D_1KI": 10.52389161195492, "J_D_1KI": 24.70397092008197}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
index 81f61e7..6496241 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 23.50609064102173}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.4617955684661865}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      16,  ...,  999976,
-                             999990, 1000000]),
-       col_indices=tensor([35450, 44241, 45004,  ..., 57756, 61659, 92730]),
-       values=tensor([0.6041, 0.1643, 0.4254,  ..., 0.8911, 0.3600, 0.5834]),
+tensor(crow_indices=tensor([      0,      14,      23,  ...,  999981,
+                             999993, 1000000]),
+       col_indices=tensor([ 4955,  8657, 25975,  ..., 77712, 83219, 89598]),
+       values=tensor([0.6839, 0.0631, 0.2295,  ..., 0.4308, 0.9509, 0.3745]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3461, 0.8147, 0.1835,  ..., 0.7972, 0.9198, 0.6224])
+tensor([0.8318, 0.0587, 0.7825,  ..., 0.4905, 0.7506, 0.0148])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 23.50609064102173 seconds
+Time: 2.4617955684661865 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 426 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.062527418136597}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      16,  ...,  999976,
-                             999990, 1000000]),
-       col_indices=tensor([35450, 44241, 45004,  ..., 57756, 61659, 92730]),
-       values=tensor([0.6041, 0.1643, 0.4254,  ..., 0.8911, 0.3600, 0.5834]),
+tensor(crow_indices=tensor([      0,       9,      16,  ...,  999978,
+                             999987, 1000000]),
+       col_indices=tensor([ 4266, 12843, 25231,  ..., 84479, 87700, 95752]),
+       values=tensor([0.9986, 0.4680, 0.6719,  ..., 0.1198, 0.1607, 0.3222]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3461, 0.8147, 0.1835,  ..., 0.7972, 0.9198, 0.6224])
+tensor([0.1538, 0.6601, 0.2448,  ..., 0.8405, 0.0282, 0.7640])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 23.50609064102173 seconds
+Time: 10.062527418136597 seconds
 
-[20.48, 20.4, 20.48, 20.48, 20.28, 20.4, 20.28, 20.28, 20.4, 20.44]
-[20.28, 20.36, 20.36, 21.44, 23.4, 25.2, 26.04, 26.28, 25.2, 24.36, 24.32, 24.36, 24.52, 24.6, 24.68, 24.76, 24.68, 24.48, 24.52, 24.52, 24.48, 24.76, 24.72, 24.72, 24.64, 24.88]
-27.219823837280273
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 23.50609064102173, 'TIME_S_1KI': 23.50609064102173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 609.4492170715331, 'W': 22.389903061636694}
-[20.48, 20.4, 20.48, 20.48, 20.28, 20.4, 20.28, 20.28, 20.4, 20.44, 20.6, 20.64, 20.72, 21.0, 20.92, 20.76, 20.68, 20.44, 20.28, 20.48]
-369.44000000000005
-18.472
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 23.50609064102173, 'TIME_S_1KI': 23.50609064102173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 609.4492170715331, 'W': 22.389903061636694, 'J_1KI': 609.4492170715331, 'W_1KI': 22.389903061636694, 'W_D': 3.917903061636693, 'J_D': 106.64463114929183, 'W_D_1KI': 3.9179030616366926, 'J_D_1KI': 3.9179030616366926}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      16,  ...,  999978,
+                             999987, 1000000]),
+       col_indices=tensor([ 4266, 12843, 25231,  ..., 84479, 87700, 95752]),
+       values=tensor([0.9986, 0.4680, 0.6719,  ..., 0.1198, 0.1607, 0.3222]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1538, 0.6601, 0.2448,  ..., 0.8405, 0.0282, 0.7640])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.062527418136597 seconds
+
+[20.6, 20.44, 20.32, 20.4, 20.44, 20.84, 21.0, 20.88, 20.52, 20.4]
+[20.4, 20.24, 20.28, 24.24, 25.56, 27.32, 28.24, 28.56, 25.84, 24.76, 24.56, 24.68, 24.68]
+13.571985006332397
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 426, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.062527418136597, 'TIME_S_1KI': 23.62095638060234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.93658995628357, 'W': 22.910177826692795}
+[20.6, 20.44, 20.32, 20.4, 20.44, 20.84, 21.0, 20.88, 20.52, 20.4, 20.64, 20.56, 20.4, 20.2, 20.36, 20.36, 20.12, 20.2, 20.4, 20.56]
+368.53999999999996
+18.427
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 426, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.062527418136597, 'TIME_S_1KI': 23.62095638060234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.93658995628357, 'W': 22.910177826692795, 'J_1KI': 729.8980984889287, 'W_1KI': 53.77976015655585, 'W_D': 4.4831778266927955, 'J_D': 60.845622244596484, 'W_D_1KI': 10.52389161195492, 'J_D_1KI': 24.70397092008197}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
index 05631c7..9db277a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 227.44817399978638, "TIME_S_1KI": 227.44817399978638, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5651.356887254718, "W": 23.406634424755232, "J_1KI": 5651.356887254718, "W_1KI": 23.406634424755232, "W_D": 5.256634424755234, "J_D": 1269.1750817751913, "W_D_1KI": 5.256634424755234, "J_D_1KI": 5.256634424755234}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 22.583206176757812, "TIME_S_1KI": 225.83206176757812, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 712.6889356327057, "W": 23.468990304831248, "J_1KI": 7126.889356327057, "W_1KI": 234.68990304831246, "W_D": 4.866990304831251, "J_D": 147.7971610636712, "W_D_1KI": 48.66990304831251, "J_D_1KI": 486.6990304831251}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output
index 4847746..1c6b2fe 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 227.44817399978638}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 22.583206176757812}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      105,      212,  ...,  9999786,
-                             9999896, 10000000]),
-       col_indices=tensor([  310,  1031,  2044,  ..., 96924, 97369, 99264]),
-       values=tensor([0.4389, 0.5701, 0.8338,  ..., 0.1266, 0.7107, 0.7989]),
+tensor(crow_indices=tensor([       0,      115,      205,  ...,  9999778,
+                             9999875, 10000000]),
+       col_indices=tensor([ 1402,  2097,  3965,  ..., 98532, 99293, 99429]),
+       values=tensor([0.3375, 0.2900, 0.6603,  ..., 0.1611, 0.9536, 0.6072]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1044, 0.8564, 0.8953,  ..., 0.3136, 0.0570, 0.9535])
+tensor([0.8425, 0.9618, 0.5102,  ..., 0.7524, 0.4133, 0.9192])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 227.44817399978638 seconds
+Time: 22.583206176757812 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      105,      212,  ...,  9999786,
-                             9999896, 10000000]),
-       col_indices=tensor([  310,  1031,  2044,  ..., 96924, 97369, 99264]),
-       values=tensor([0.4389, 0.5701, 0.8338,  ..., 0.1266, 0.7107, 0.7989]),
+tensor(crow_indices=tensor([       0,      115,      205,  ...,  9999778,
+                             9999875, 10000000]),
+       col_indices=tensor([ 1402,  2097,  3965,  ..., 98532, 99293, 99429]),
+       values=tensor([0.3375, 0.2900, 0.6603,  ..., 0.1611, 0.9536, 0.6072]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1044, 0.8564, 0.8953,  ..., 0.3136, 0.0570, 0.9535])
+tensor([0.8425, 0.9618, 0.5102,  ..., 0.7524, 0.4133, 0.9192])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 227.44817399978638 seconds
+Time: 22.583206176757812 seconds
 
-[20.56, 20.48, 20.48, 20.36, 20.4, 20.32, 20.32, 19.96, 20.0, 20.04]
-[20.12, 20.12, 20.4, 21.88, 23.4, 25.08, 27.48, 28.24, 27.96, 27.08, 26.24, 25.68, 24.72, 24.48, 24.4, 24.28, 24.36, 24.4, 24.48, 24.68, 24.72, 24.72, 24.8, 24.88, 24.76, 24.68, 24.64, 24.48, 24.44, 24.28, 24.44, 24.64, 24.76, 24.8, 24.84, 24.72, 24.44, 24.36, 24.32, 24.48, 24.48, 24.6, 24.76, 24.76, 24.68, 24.64, 24.72, 24.64, 24.4, 24.4, 24.68, 24.52, 24.6, 24.56, 24.48, 24.28, 24.32, 24.28, 24.32, 24.52, 24.52, 24.64, 24.76, 24.76, 24.68, 24.68, 24.6, 24.56, 24.72, 24.52, 24.76, 24.76, 24.68, 24.56, 24.48, 24.24, 24.4, 24.6, 24.76, 24.76, 24.8, 24.64, 24.64, 24.56, 24.76, 24.72, 24.8, 24.8, 24.8, 24.8, 24.6, 24.56, 24.44, 24.68, 24.72, 24.72, 24.68, 24.72, 24.68, 24.88, 24.92, 24.84, 24.8, 24.8, 25.0, 25.08, 25.0, 24.92, 24.8, 24.8, 24.6, 24.72, 24.84, 25.0, 25.0, 24.88, 24.96, 24.92, 24.96, 24.92, 24.92, 24.64, 24.56, 24.44, 24.44, 24.36, 24.6, 24.44, 24.52, 24.88, 25.12, 25.12, 25.2, 25.32, 24.96, 24.96, 24.8, 24.56, 24.64, 24.52, 24.44, 24.48, 24.4, 24.28, 24.56, 24.52, 24.48, 24.6, 24.52, 24.6, 24.64, 24.88, 24.8, 24.8, 24.76, 24.76, 24.76, 24.4, 24.28, 24.28, 24.28, 24.32, 24.8, 25.04, 24.92, 24.8, 24.8, 24.56, 24.52, 24.52, 24.48, 24.64, 24.52, 24.64, 24.68, 24.68, 24.72, 24.56, 24.56, 24.96, 24.96, 24.88, 24.72, 24.4, 24.32, 24.36, 24.4, 24.64, 24.92, 24.8, 24.76, 24.72, 24.64, 24.52, 24.8, 24.72, 24.76, 24.76, 24.72, 25.04, 24.96, 24.64, 24.32, 24.16, 24.12, 24.36, 24.44, 24.32, 24.16, 24.04, 24.32, 24.68, 24.56, 24.68, 24.72, 24.28, 24.4, 24.36, 24.36, 24.36, 24.44, 24.44, 24.16, 24.32, 24.44, 24.36, 24.6, 24.68, 24.8, 24.76]
-241.44252371788025
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 227.44817399978638, 'TIME_S_1KI': 227.44817399978638, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5651.356887254718, 'W': 23.406634424755232}
-[20.56, 20.48, 20.48, 20.36, 20.4, 20.32, 20.32, 19.96, 20.0, 20.04, 20.08, 20.32, 20.08, 20.0, 19.84, 19.84, 19.92, 20.08, 20.08, 20.36]
-363.0
-18.15
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 227.44817399978638, 'TIME_S_1KI': 227.44817399978638, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5651.356887254718, 'W': 23.406634424755232, 'J_1KI': 5651.356887254718, 'W_1KI': 23.406634424755232, 'W_D': 5.256634424755234, 'J_D': 1269.1750817751913, 'W_D_1KI': 5.256634424755234, 'J_D_1KI': 5.256634424755234}
+[20.72, 21.0, 21.0, 21.04, 20.76, 20.44, 20.16, 20.32, 20.24, 20.52]
+[20.4, 20.36, 21.96, 22.92, 24.92, 26.56, 28.48, 28.48, 28.88, 28.96, 27.92, 26.48, 25.56, 24.72, 24.64, 24.76, 24.96, 24.76, 24.6, 24.56, 24.6, 24.36, 24.76, 24.88, 24.92, 24.68, 24.6, 24.36, 24.04]
+30.367260217666626
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 22.583206176757812, 'TIME_S_1KI': 225.83206176757812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.6889356327057, 'W': 23.468990304831248}
+[20.72, 21.0, 21.0, 21.04, 20.76, 20.44, 20.16, 20.32, 20.24, 20.52, 20.24, 20.24, 20.2, 20.4, 20.64, 21.08, 21.24, 21.12, 21.0, 20.84]
+372.03999999999996
+18.601999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 22.583206176757812, 'TIME_S_1KI': 225.83206176757812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.6889356327057, 'W': 23.468990304831248, 'J_1KI': 7126.889356327057, 'W_1KI': 234.68990304831246, 'W_D': 4.866990304831251, 'J_D': 147.7971610636712, 'W_D_1KI': 48.66990304831251, 'J_D_1KI': 486.6990304831251}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
index 4c6f614..21e4694 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3195, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.793879747390747, "TIME_S_1KI": 3.3783661181191698, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 338.5230771541595, "W": 23.150052849773353, "J_1KI": 105.95401475873538, "W_1KI": 7.245712942026088, "W_D": 4.780052849773355, "J_D": 69.89868274450302, "W_D_1KI": 1.4961041783328186, "J_D_1KI": 0.46826421857052225}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3104, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.44502592086792, "TIME_S_1KI": 3.3650212373930155, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.514190454483, "W": 23.934119588627873, "J_1KI": 112.27905620311952, "W_1KI": 7.710734403552794, "W_D": 5.569119588627871, "J_D": 81.09415505290025, "W_D_1KI": 1.794175125202278, "J_D_1KI": 0.578020336727538}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
index a9ff479..9aacb6b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.2854795455932617}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3382716178894043}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      4,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([96494, 10713, 51050,  ..., 77096, 58241, 39394]),
-       values=tensor([0.9472, 0.0468, 0.6571,  ..., 0.2815, 0.5696, 0.0055]),
+       col_indices=tensor([91034, 37166, 45389,  ..., 40200, 40353,   102]),
+       values=tensor([0.2917, 0.4189, 0.5553,  ..., 0.7170, 0.1120, 0.1885]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9254, 0.6847, 0.8457,  ..., 0.6275, 0.7476, 0.1010])
+tensor([0.2165, 0.9661, 0.1946,  ..., 0.3640, 0.8184, 0.1773])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 3.2854795455932617 seconds
+Time: 0.3382716178894043 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3195 -ss 100000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.793879747390747}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3104 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.44502592086792}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([41532, 61839,  3968,  ..., 19432, 54156, 77664]),
-       values=tensor([0.2018, 0.3494, 0.0819,  ..., 0.4942, 0.5843, 0.6732]),
+       col_indices=tensor([  252, 18132, 64781,  ..., 90653, 85542, 48452]),
+       values=tensor([0.2676, 0.4026, 0.9927,  ..., 0.1189, 0.3190, 0.1177]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4618, 0.8259, 0.6206,  ..., 0.7480, 0.2960, 0.5870])
+tensor([0.6783, 0.3478, 0.4100,  ..., 0.2741, 0.0736, 0.8098])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.793879747390747 seconds
+Time: 10.44502592086792 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([41532, 61839,  3968,  ..., 19432, 54156, 77664]),
-       values=tensor([0.2018, 0.3494, 0.0819,  ..., 0.4942, 0.5843, 0.6732]),
+       col_indices=tensor([  252, 18132, 64781,  ..., 90653, 85542, 48452]),
+       values=tensor([0.2676, 0.4026, 0.9927,  ..., 0.1189, 0.3190, 0.1177]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4618, 0.8259, 0.6206,  ..., 0.7480, 0.2960, 0.5870])
+tensor([0.6783, 0.3478, 0.4100,  ..., 0.2741, 0.0736, 0.8098])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.793879747390747 seconds
+Time: 10.44502592086792 seconds
 
-[20.88, 20.84, 20.52, 20.4, 20.32, 20.2, 20.2, 20.44, 20.48, 20.8]
-[20.92, 20.8, 21.0, 22.96, 24.6, 25.56, 26.6, 27.0, 26.4, 25.96, 26.12, 26.12, 25.88, 25.88]
-14.62299370765686
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.793879747390747, 'TIME_S_1KI': 3.3783661181191698, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.5230771541595, 'W': 23.150052849773353}
-[20.88, 20.84, 20.52, 20.4, 20.32, 20.2, 20.2, 20.44, 20.48, 20.8, 20.4, 20.4, 20.2, 20.36, 20.52, 20.36, 20.4, 20.36, 20.2, 20.32]
-367.4
-18.369999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.793879747390747, 'TIME_S_1KI': 3.3783661181191698, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.5230771541595, 'W': 23.150052849773353, 'J_1KI': 105.95401475873538, 'W_1KI': 7.245712942026088, 'W_D': 4.780052849773355, 'J_D': 69.89868274450302, 'W_D_1KI': 1.4961041783328186, 'J_D_1KI': 0.46826421857052225}
+[20.36, 20.36, 20.36, 20.36, 20.36, 20.52, 20.6, 20.76, 20.96, 20.92]
+[20.92, 20.6, 23.92, 25.4, 27.24, 28.24, 28.24, 29.08, 26.32, 25.76, 25.4, 25.2, 25.44, 25.64]
+14.56139588356018
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.44502592086792, 'TIME_S_1KI': 3.3650212373930155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.514190454483, 'W': 23.934119588627873}
+[20.36, 20.36, 20.36, 20.36, 20.36, 20.52, 20.6, 20.76, 20.96, 20.92, 20.24, 20.2, 20.48, 20.2, 20.2, 20.2, 20.12, 20.28, 20.28, 20.6]
+367.30000000000007
+18.365000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.44502592086792, 'TIME_S_1KI': 3.3650212373930155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.514190454483, 'W': 23.934119588627873, 'J_1KI': 112.27905620311952, 'W_1KI': 7.710734403552794, 'W_D': 5.569119588627871, 'J_D': 81.09415505290025, 'W_D_1KI': 1.794175125202278, 'J_D_1KI': 0.578020336727538}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..9e7c05a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 868, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.049697399139404, "TIME_S_1KI": 12.730066128040788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 347.2841278934479, "W": 23.818433138573972, "J_1KI": 400.0969215362303, "W_1KI": 27.44059117347232, "W_D": 5.198433138573975, "J_D": 75.79563728809362, "W_D_1KI": 5.988978270246515, "J_D_1KI": 6.899744550975249}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..5c6adba
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.209580421447754}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     11,  ..., 499991, 499997,
+                            500000]),
+       col_indices=tensor([ 8709, 33303, 39829,  ..., 65447, 85964, 93697]),
+       values=tensor([0.2765, 0.3303, 0.4846,  ..., 0.1571, 0.7749, 0.0327]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.2847, 0.7446, 0.1507,  ..., 0.7274, 0.5755, 0.0187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 1.209580421447754 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 868 -ss 100000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.049697399139404}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499985, 499993,
+                            500000]),
+       col_indices=tensor([  930,  5720, 18229,  ..., 18263, 29630, 53753]),
+       values=tensor([0.0983, 0.1468, 0.4729,  ..., 0.5988, 0.3077, 0.5585]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4408, 0.1732, 0.5273,  ..., 0.8772, 0.6136, 0.9894])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 11.049697399139404 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499985, 499993,
+                            500000]),
+       col_indices=tensor([  930,  5720, 18229,  ..., 18263, 29630, 53753]),
+       values=tensor([0.0983, 0.1468, 0.4729,  ..., 0.5988, 0.3077, 0.5585]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4408, 0.1732, 0.5273,  ..., 0.8772, 0.6136, 0.9894])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 11.049697399139404 seconds
+
+[20.76, 20.76, 20.64, 20.6, 20.48, 20.24, 20.32, 20.36, 20.4, 20.64]
+[20.8, 20.68, 23.8, 24.88, 26.88, 27.84, 27.84, 28.76, 26.08, 26.28, 25.56, 25.36, 25.48, 25.4]
+14.580477476119995
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 868, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.049697399139404, 'TIME_S_1KI': 12.730066128040788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 347.2841278934479, 'W': 23.818433138573972}
+[20.76, 20.76, 20.64, 20.6, 20.48, 20.24, 20.32, 20.36, 20.4, 20.64, 20.68, 20.8, 20.6, 20.6, 20.92, 21.0, 21.0, 21.08, 21.04, 21.04]
+372.4
+18.619999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 868, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.049697399139404, 'TIME_S_1KI': 12.730066128040788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 347.2841278934479, 'W': 23.818433138573972, 'J_1KI': 400.0969215362303, 'W_1KI': 27.44059117347232, 'W_D': 5.198433138573975, 'J_D': 75.79563728809362, 'W_D_1KI': 5.988978270246515, 'J_D_1KI': 6.899744550975249}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
index 8de09b8..669cf9a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32341, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.187894582748413, "TIME_S_1KI": 0.3150148289399961, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.80105960845947, "W": 22.074332714462717, "J_1KI": 9.981171256561623, "W_1KI": 0.6825494794367124, "W_D": 3.644332714462717, "J_D": 53.29241327524185, "W_D_1KI": 0.11268460203650836, "J_D_1KI": 0.0034842646187968327}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32669, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.073116302490234, "TIME_S_1KI": 0.33894873741131454, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.2707403564453, "W": 23.410284009847928, "J_1KI": 10.476927373242074, "W_1KI": 0.7165901622286549, "W_D": 4.943284009847925, "J_D": 72.2734280853271, "W_D_1KI": 0.15131421255159097, "J_D_1KI": 0.004631736892821665}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
index a571cff..4a81d8b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3246574401855469}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03962588310241699}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     4,  ...,  9996,  9998, 10000]),
-       col_indices=tensor([ 702,  590, 2393,  ..., 5106, 4251, 5881]),
-       values=tensor([0.8131, 0.4443, 0.5032,  ..., 0.0454, 0.7892, 0.7021]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9995,  9999, 10000]),
+       col_indices=tensor([5736, 4740, 5169,  ..., 5050, 7314, 6933]),
+       values=tensor([0.2904, 0.9920, 0.0901,  ..., 0.6475, 0.2992, 0.6153]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5617, 0.3540, 0.6665,  ..., 0.2887, 0.4752, 0.2274])
+tensor([0.6798, 0.4263, 0.2506,  ..., 0.2181, 0.0906, 0.7562])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.3246574401855469 seconds
+Time: 0.03962588310241699 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32341 -ss 10000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.187894582748413}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26497 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.51607346534729}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9996,  9998, 10000]),
-       col_indices=tensor([5513, 4819, 4488,  ..., 7223, 1569, 1749]),
-       values=tensor([0.7502, 0.9864, 0.0219,  ..., 0.7577, 0.3030, 0.6500]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([5374, 4189, 5897,  ..., 9913, 4567, 8496]),
+       values=tensor([0.8167, 0.6460, 0.7856,  ..., 0.9381, 0.0308, 0.1187]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4735, 0.0629, 0.6403,  ..., 0.2218, 0.6036, 0.6062])
+tensor([0.9083, 0.0911, 0.6427,  ..., 0.4641, 0.3576, 0.6926])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,15 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.187894582748413 seconds
+Time: 8.51607346534729 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32669 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.073116302490234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9996,  9998, 10000]),
-       col_indices=tensor([5513, 4819, 4488,  ..., 7223, 1569, 1749]),
-       values=tensor([0.7502, 0.9864, 0.0219,  ..., 0.7577, 0.3030, 0.6500]),
+tensor(crow_indices=tensor([    0,     1,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([2638,  262,  675,  ..., 9893, 8606, 4272]),
+       values=tensor([0.0918, 0.5777, 0.8540,  ..., 0.4523, 0.7955, 0.7135]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4735, 0.0629, 0.6403,  ..., 0.2218, 0.6036, 0.6062])
+tensor([0.1165, 0.4058, 0.2834,  ..., 0.7342, 0.6568, 0.2677])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -50,13 +53,29 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.187894582748413 seconds
+Time: 11.073116302490234 seconds
 
-[20.16, 20.36, 20.44, 20.64, 20.64, 20.64, 20.68, 20.0, 19.96, 20.04]
-[20.04, 20.36, 20.64, 22.2, 24.32, 25.36, 25.96, 26.0, 25.44, 24.12, 24.0, 23.8, 23.8, 23.84]
-14.623366594314575
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32341, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.187894582748413, 'TIME_S_1KI': 0.3150148289399961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.80105960845947, 'W': 22.074332714462717}
-[20.16, 20.36, 20.44, 20.64, 20.64, 20.64, 20.68, 20.0, 19.96, 20.04, 19.96, 20.28, 20.36, 20.44, 20.48, 20.48, 20.72, 20.76, 20.96, 21.36]
-368.6
-18.43
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32341, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.187894582748413, 'TIME_S_1KI': 0.3150148289399961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.80105960845947, 'W': 22.074332714462717, 'J_1KI': 9.981171256561623, 'W_1KI': 0.6825494794367124, 'W_D': 3.644332714462717, 'J_D': 53.29241327524185, 'W_D_1KI': 0.11268460203650836, 'J_D_1KI': 0.0034842646187968327}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([2638,  262,  675,  ..., 9893, 8606, 4272]),
+       values=tensor([0.0918, 0.5777, 0.8540,  ..., 0.4523, 0.7955, 0.7135]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1165, 0.4058, 0.2834,  ..., 0.7342, 0.6568, 0.2677])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 11.073116302490234 seconds
+
+[20.76, 20.6, 20.6, 20.6, 20.44, 20.48, 20.48, 20.48, 20.48, 20.64]
+[20.44, 20.4, 23.56, 25.72, 27.84, 28.36, 29.08, 29.08, 26.36, 24.68, 23.72, 23.68, 23.44, 23.32]
+14.620529174804688
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32669, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.073116302490234, 'TIME_S_1KI': 0.33894873741131454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.2707403564453, 'W': 23.410284009847928}
+[20.76, 20.6, 20.6, 20.6, 20.44, 20.48, 20.48, 20.48, 20.48, 20.64, 20.6, 20.88, 20.92, 20.76, 20.64, 20.48, 20.16, 20.16, 20.08, 20.2]
+369.34000000000003
+18.467000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32669, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.073116302490234, 'TIME_S_1KI': 0.33894873741131454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.2707403564453, 'W': 23.410284009847928, 'J_1KI': 10.476927373242074, 'W_1KI': 0.7165901622286549, 'W_D': 4.943284009847925, 'J_D': 72.2734280853271, 'W_D_1KI': 0.15131421255159097, 'J_D_1KI': 0.004631736892821665}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
index f0f5b59..3604623 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4681, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.577015399932861, "TIME_S_1KI": 2.2595632129743346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.1403603744507, "W": 22.94669291080223, "J_1KI": 71.38226028080554, "W_1KI": 4.902092055287809, "W_D": 4.40869291080223, "J_D": 64.19758366584783, "W_D_1KI": 0.9418271546255567, "J_D_1KI": 0.20120212660234066}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4348, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135539293289185, "TIME_S_1KI": 2.3310807942247433, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.55518106460573, "W": 22.140804095253714, "J_1KI": 68.89493584742543, "W_1KI": 5.092181254658168, "W_D": 3.514804095253716, "J_D": 47.55372806835178, "W_D_1KI": 0.8083726070040745, "J_D_1KI": 0.1859182628804219}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
index 7485b00..4918d44 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.242969274520874}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2414851188659668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     21,  ...,  99980,  99990,
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99982,  99991,
                             100000]),
-       col_indices=tensor([ 158,  243, 1021,  ..., 9060, 9386, 9562]),
-       values=tensor([0.4026, 0.0672, 0.1618,  ..., 0.9478, 0.4676, 0.6061]),
+       col_indices=tensor([ 302, 1349, 1385,  ..., 9083, 9115, 9373]),
+       values=tensor([0.3908, 0.9700, 0.7778,  ..., 0.9299, 0.7856, 0.5693]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1276, 0.9367, 0.3121,  ..., 0.3681, 0.2222, 0.5819])
+tensor([0.9198, 0.1049, 0.3911,  ..., 0.9152, 0.2471, 0.8814])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 2.242969274520874 seconds
+Time: 0.2414851188659668 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4681 -ss 10000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.577015399932861}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4348 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135539293289185}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     13,     23,  ...,  99978,  99989,
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99987,  99990,
                             100000]),
-       col_indices=tensor([1463, 2229, 2458,  ..., 6913, 8671, 9837]),
-       values=tensor([0.1583, 0.2191, 0.0082,  ..., 0.3537, 0.5043, 0.1355]),
+       col_indices=tensor([  62,  627, 2703,  ..., 9273, 9381, 9947]),
+       values=tensor([0.4329, 0.2872, 0.8964,  ..., 0.9783, 0.1219, 0.9101]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7982, 0.2389, 0.8535,  ..., 0.4532, 0.2540, 0.6422])
+tensor([0.8278, 0.7584, 0.9132,  ..., 0.6086, 0.4680, 0.0616])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.577015399932861 seconds
+Time: 10.135539293289185 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     13,     23,  ...,  99978,  99989,
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99987,  99990,
                             100000]),
-       col_indices=tensor([1463, 2229, 2458,  ..., 6913, 8671, 9837]),
-       values=tensor([0.1583, 0.2191, 0.0082,  ..., 0.3537, 0.5043, 0.1355]),
+       col_indices=tensor([  62,  627, 2703,  ..., 9273, 9381, 9947]),
+       values=tensor([0.4329, 0.2872, 0.8964,  ..., 0.9783, 0.1219, 0.9101]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7982, 0.2389, 0.8535,  ..., 0.4532, 0.2540, 0.6422])
+tensor([0.8278, 0.7584, 0.9132,  ..., 0.6086, 0.4680, 0.0616])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.577015399932861 seconds
+Time: 10.135539293289185 seconds
 
-[20.84, 20.96, 20.84, 20.76, 20.88, 20.8, 20.72, 20.8, 20.8, 20.72]
-[20.72, 21.0, 21.04, 25.64, 26.56, 27.96, 28.44, 25.92, 25.0, 24.0, 23.88, 24.12, 24.36, 24.36]
-14.561591148376465
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.577015399932861, 'TIME_S_1KI': 2.2595632129743346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.1403603744507, 'W': 22.94669291080223}
-[20.84, 20.96, 20.84, 20.76, 20.88, 20.8, 20.72, 20.8, 20.8, 20.72, 20.52, 20.24, 20.24, 20.2, 20.44, 20.16, 20.56, 20.64, 20.52, 20.32]
-370.76
-18.538
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.577015399932861, 'TIME_S_1KI': 2.2595632129743346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.1403603744507, 'W': 22.94669291080223, 'J_1KI': 71.38226028080554, 'W_1KI': 4.902092055287809, 'W_D': 4.40869291080223, 'J_D': 64.19758366584783, 'W_D_1KI': 0.9418271546255567, 'J_D_1KI': 0.20120212660234066}
+[20.76, 20.76, 21.0, 21.0, 20.84, 20.76, 20.68, 20.44, 20.6, 20.72]
+[20.88, 20.96, 21.56, 22.88, 24.32, 25.12, 25.84, 25.2, 25.2, 24.92, 24.16, 24.04, 24.2]
+13.529552936553955
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4348, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135539293289185, 'TIME_S_1KI': 2.3310807942247433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.55518106460573, 'W': 22.140804095253714}
+[20.76, 20.76, 21.0, 21.0, 20.84, 20.76, 20.68, 20.44, 20.6, 20.72, 20.52, 20.44, 20.36, 20.68, 20.84, 20.96, 20.92, 20.68, 20.36, 20.4]
+372.52
+18.625999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4348, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135539293289185, 'TIME_S_1KI': 2.3310807942247433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.55518106460573, 'W': 22.140804095253714, 'J_1KI': 68.89493584742543, 'W_1KI': 5.092181254658168, 'W_D': 3.514804095253716, 'J_D': 47.55372806835178, 'W_D_1KI': 0.8083726070040745, 'J_D_1KI': 0.1859182628804219}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
index 7587c79..1a09527 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.648348808288574, "TIME_S_1KI": 21.648348808288574, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 597.1462204551697, "W": 23.932854010192194, "J_1KI": 597.1462204551697, "W_1KI": 23.932854010192194, "W_D": 5.369854010192196, "J_D": 133.98268443942072, "W_D_1KI": 5.369854010192196, "J_D_1KI": 5.369854010192196}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 493, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.442772150039673, "TIME_S_1KI": 21.18209361062814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 317.52540328025816, "W": 21.825092476629965, "J_1KI": 644.0677551323695, "W_1KI": 44.26996445563888, "W_D": 3.2900924766299617, "J_D": 47.86636948227874, "W_D_1KI": 6.673615571257529, "J_D_1KI": 13.536745580644075}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
index ed3ac8e..449d7aa 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.648348808288574}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1293396949768066}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      93,     181,  ...,  999807,
-                             999904, 1000000]),
-       col_indices=tensor([  20,   39,  173,  ..., 9424, 9617, 9690]),
-       values=tensor([0.7771, 0.0078, 0.5851,  ..., 0.0250, 0.0076, 0.8688]),
+tensor(crow_indices=tensor([      0,      94,     210,  ...,  999806,
+                             999898, 1000000]),
+       col_indices=tensor([ 197,  225,  349,  ..., 9664, 9718, 9909]),
+       values=tensor([0.2825, 0.4405, 0.0615,  ..., 0.4764, 0.3721, 0.7741]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6163, 0.0977, 0.8617,  ..., 0.7477, 0.6432, 0.7227])
+tensor([0.2003, 0.0291, 0.9415,  ..., 0.2751, 0.8368, 0.8186])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.648348808288574 seconds
+Time: 2.1293396949768066 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 493 -ss 10000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.442772150039673}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      93,     181,  ...,  999807,
-                             999904, 1000000]),
-       col_indices=tensor([  20,   39,  173,  ..., 9424, 9617, 9690]),
-       values=tensor([0.7771, 0.0078, 0.5851,  ..., 0.0250, 0.0076, 0.8688]),
+tensor(crow_indices=tensor([      0,     103,     211,  ...,  999799,
+                             999895, 1000000]),
+       col_indices=tensor([  29,  259,  296,  ..., 9649, 9833, 9895]),
+       values=tensor([0.6562, 0.6337, 0.8410,  ..., 0.1779, 0.9179, 0.3279]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6163, 0.0977, 0.8617,  ..., 0.7477, 0.6432, 0.7227])
+tensor([0.2837, 0.1453, 0.4499,  ..., 0.4322, 0.7993, 0.4344])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.648348808288574 seconds
+Time: 10.442772150039673 seconds
 
-[20.48, 20.52, 20.6, 20.6, 20.6, 20.72, 20.48, 20.56, 20.64, 20.72]
-[20.72, 21.08, 23.92, 25.96, 27.92, 28.72, 29.32, 26.52, 26.52, 25.36, 24.24, 24.4, 24.24, 24.44, 24.16, 24.12, 24.16, 24.12, 24.16, 24.2, 24.32, 24.36, 24.68, 24.72]
-24.95089888572693
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.648348808288574, 'TIME_S_1KI': 21.648348808288574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.1462204551697, 'W': 23.932854010192194}
-[20.48, 20.52, 20.6, 20.6, 20.6, 20.72, 20.48, 20.56, 20.64, 20.72, 20.68, 20.72, 20.52, 20.52, 20.64, 20.76, 20.64, 20.8, 20.68, 20.64]
-371.26
-18.563
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.648348808288574, 'TIME_S_1KI': 21.648348808288574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.1462204551697, 'W': 23.932854010192194, 'J_1KI': 597.1462204551697, 'W_1KI': 23.932854010192194, 'W_D': 5.369854010192196, 'J_D': 133.98268443942072, 'W_D_1KI': 5.369854010192196, 'J_D_1KI': 5.369854010192196}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     103,     211,  ...,  999799,
+                             999895, 1000000]),
+       col_indices=tensor([  29,  259,  296,  ..., 9649, 9833, 9895]),
+       values=tensor([0.6562, 0.6337, 0.8410,  ..., 0.1779, 0.9179, 0.3279]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2837, 0.1453, 0.4499,  ..., 0.4322, 0.7993, 0.4344])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.442772150039673 seconds
+
+[20.48, 20.72, 20.88, 20.72, 20.92, 20.68, 20.44, 20.48, 20.4, 20.4]
+[20.48, 20.48, 20.76, 21.64, 23.08, 24.24, 25.0, 25.48, 24.92, 24.24, 24.2, 24.12, 24.0, 23.96]
+14.54863953590393
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 493, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.442772150039673, 'TIME_S_1KI': 21.18209361062814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.52540328025816, 'W': 21.825092476629965}
+[20.48, 20.72, 20.88, 20.72, 20.92, 20.68, 20.44, 20.48, 20.4, 20.4, 20.6, 20.6, 20.36, 20.32, 20.48, 20.48, 20.64, 20.64, 20.8, 20.8]
+370.70000000000005
+18.535000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 493, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.442772150039673, 'TIME_S_1KI': 21.18209361062814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.52540328025816, 'W': 21.825092476629965, 'J_1KI': 644.0677551323695, 'W_1KI': 44.26996445563888, 'W_D': 3.2900924766299617, 'J_D': 47.86636948227874, 'W_D_1KI': 6.673615571257529, 'J_D_1KI': 13.536745580644075}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
index e29f054..ad31b62 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.87029075622559, "TIME_S_1KI": 106.87029075622559, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2633.659623832703, "W": 23.232350154828893, "J_1KI": 2633.659623832703, "W_1KI": 23.232350154828893, "W_D": 4.519350154828892, "J_D": 512.3213944957257, "W_D_1KI": 4.519350154828892, "J_D_1KI": 4.519350154828892}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.699479103088379, "TIME_S_1KI": 106.99479103088379, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 372.40043621063234, "W": 23.7347384855202, "J_1KI": 3724.0043621063232, "W_1KI": 237.34738485520202, "W_D": 5.215738485520202, "J_D": 81.83546190547948, "W_D_1KI": 52.15738485520202, "J_D_1KI": 521.5738485520202}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
index 5edda98..f0aea1a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.87029075622559}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.699479103088379}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     497,     970,  ..., 4998958,
-                            4999495, 5000000]),
-       col_indices=tensor([   3,   19,   30,  ..., 9933, 9939, 9986]),
-       values=tensor([0.6521, 0.8632, 0.3100,  ..., 0.6388, 0.4505, 0.0265]),
+tensor(crow_indices=tensor([      0,     534,    1091,  ..., 4998975,
+                            4999490, 5000000]),
+       col_indices=tensor([   4,   42,   44,  ..., 9941, 9942, 9945]),
+       values=tensor([0.3387, 0.3479, 0.7697,  ..., 0.0992, 0.1573, 0.7910]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1776, 0.4739, 0.9893,  ..., 0.4929, 0.9525, 0.7109])
+tensor([0.0536, 0.4974, 0.9494,  ..., 0.5617, 0.8582, 0.7161])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 106.87029075622559 seconds
+Time: 10.699479103088379 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     497,     970,  ..., 4998958,
-                            4999495, 5000000]),
-       col_indices=tensor([   3,   19,   30,  ..., 9933, 9939, 9986]),
-       values=tensor([0.6521, 0.8632, 0.3100,  ..., 0.6388, 0.4505, 0.0265]),
+tensor(crow_indices=tensor([      0,     534,    1091,  ..., 4998975,
+                            4999490, 5000000]),
+       col_indices=tensor([   4,   42,   44,  ..., 9941, 9942, 9945]),
+       values=tensor([0.3387, 0.3479, 0.7697,  ..., 0.0992, 0.1573, 0.7910]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1776, 0.4739, 0.9893,  ..., 0.4929, 0.9525, 0.7109])
+tensor([0.0536, 0.4974, 0.9494,  ..., 0.5617, 0.8582, 0.7161])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 106.87029075622559 seconds
+Time: 10.699479103088379 seconds
 
-[20.76, 20.72, 20.52, 20.48, 20.68, 20.6, 20.6, 20.56, 20.68, 20.6]
-[20.64, 20.72, 20.72, 24.72, 25.96, 28.32, 29.48, 30.12, 26.68, 25.6, 25.08, 24.6, 24.6, 24.6, 24.8, 24.8, 24.88, 24.92, 24.84, 24.8, 24.72, 24.52, 24.52, 24.52, 24.6, 24.56, 24.4, 24.48, 24.32, 24.16, 24.28, 24.36, 24.48, 24.64, 24.68, 24.64, 24.4, 24.68, 24.72, 24.72, 24.56, 24.64, 24.48, 24.32, 24.12, 24.12, 24.2, 24.52, 24.4, 24.56, 24.68, 24.48, 24.28, 24.24, 24.2, 24.04, 23.92, 24.04, 24.28, 24.12, 24.28, 24.36, 24.28, 24.44, 24.52, 24.6, 24.72, 24.72, 24.88, 24.84, 24.72, 24.44, 24.16, 24.2, 24.0, 24.2, 24.44, 24.32, 24.2, 24.2, 24.16, 24.12, 24.24, 24.2, 24.12, 24.16, 24.2, 24.16, 24.4, 24.4, 24.36, 24.2, 24.28, 24.52, 24.12, 24.36, 24.64, 24.6, 24.6, 24.52, 24.48, 24.2, 24.4, 24.4, 24.4, 24.52, 24.4, 24.16]
-113.36173939704895
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.87029075622559, 'TIME_S_1KI': 106.87029075622559, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2633.659623832703, 'W': 23.232350154828893}
-[20.76, 20.72, 20.52, 20.48, 20.68, 20.6, 20.6, 20.56, 20.68, 20.6, 20.52, 20.76, 20.76, 21.2, 21.2, 21.28, 21.12, 21.0, 20.88, 20.56]
-374.26
-18.713
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.87029075622559, 'TIME_S_1KI': 106.87029075622559, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2633.659623832703, 'W': 23.232350154828893, 'J_1KI': 2633.659623832703, 'W_1KI': 23.232350154828893, 'W_D': 4.519350154828892, 'J_D': 512.3213944957257, 'W_D_1KI': 4.519350154828892, 'J_D_1KI': 4.519350154828892}
+[20.52, 20.4, 20.48, 20.48, 20.48, 20.64, 20.76, 20.8, 20.8, 20.56]
+[20.56, 20.2, 20.28, 23.96, 25.84, 29.28, 30.68, 31.08, 27.28, 26.24, 25.12, 24.52, 24.52, 24.32, 24.2]
+15.69010066986084
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.699479103088379, 'TIME_S_1KI': 106.99479103088379, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 372.40043621063234, 'W': 23.7347384855202}
+[20.52, 20.4, 20.48, 20.48, 20.48, 20.64, 20.76, 20.8, 20.8, 20.56, 20.6, 20.6, 20.6, 20.68, 20.68, 20.68, 20.56, 20.4, 20.28, 20.44]
+370.38
+18.519
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.699479103088379, 'TIME_S_1KI': 106.99479103088379, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 372.40043621063234, 'W': 23.7347384855202, 'J_1KI': 3724.0043621063232, 'W_1KI': 237.34738485520202, 'W_D': 5.215738485520202, 'J_D': 81.83546190547948, 'W_D_1KI': 52.15738485520202, 'J_D_1KI': 521.5738485520202}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
index 8cd094e..f0b391a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.98000812530518, "TIME_S_1KI": 210.98000812530518, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5224.394376831054, "W": 23.586192508859664, "J_1KI": 5224.394376831054, "W_1KI": 23.586192508859664, "W_D": 5.122192508859662, "J_D": 1134.5770933685287, "W_D_1KI": 5.122192508859662, "J_D_1KI": 5.122192508859662}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31538224220276, "TIME_S_1KI": 213.1538224220276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 656.8145585250853, "W": 23.224732449579832, "J_1KI": 6568.145585250853, "W_1KI": 232.24732449579832, "W_D": 4.7157324495798285, "J_D": 133.36479693436596, "W_D_1KI": 47.157324495798285, "J_D_1KI": 471.57324495798287}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output
index d1e3dfe..f5c642e 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.1 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.98000812530518}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31538224220276}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      993,     1975,  ...,  9997956,
-                             9998997, 10000000]),
-       col_indices=tensor([  19,   22,   26,  ..., 9979, 9989, 9990]),
-       values=tensor([0.9746, 0.4059, 0.0503,  ..., 0.3598, 0.3506, 0.0768]),
+tensor(crow_indices=tensor([       0,      941,     1920,  ...,  9998069,
+                             9999051, 10000000]),
+       col_indices=tensor([   4,   12,   19,  ..., 9982, 9986, 9989]),
+       values=tensor([0.3288, 0.1903, 0.7853,  ..., 0.1848, 0.4723, 0.3439]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8784, 0.5931, 0.4456,  ..., 0.6081, 0.2914, 0.4121])
+tensor([0.8250, 0.2999, 0.1337,  ..., 0.5908, 0.0422, 0.7676])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 210.98000812530518 seconds
+Time: 21.31538224220276 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      993,     1975,  ...,  9997956,
-                             9998997, 10000000]),
-       col_indices=tensor([  19,   22,   26,  ..., 9979, 9989, 9990]),
-       values=tensor([0.9746, 0.4059, 0.0503,  ..., 0.3598, 0.3506, 0.0768]),
+tensor(crow_indices=tensor([       0,      941,     1920,  ...,  9998069,
+                             9999051, 10000000]),
+       col_indices=tensor([   4,   12,   19,  ..., 9982, 9986, 9989]),
+       values=tensor([0.3288, 0.1903, 0.7853,  ..., 0.1848, 0.4723, 0.3439]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8784, 0.5931, 0.4456,  ..., 0.6081, 0.2914, 0.4121])
+tensor([0.8250, 0.2999, 0.1337,  ..., 0.5908, 0.0422, 0.7676])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 210.98000812530518 seconds
+Time: 21.31538224220276 seconds
 
-[20.72, 20.64, 20.6, 20.6, 20.48, 20.44, 20.4, 20.36, 20.28, 20.28]
-[20.2, 20.36, 21.16, 23.28, 25.44, 27.76, 29.48, 29.48, 28.08, 28.08, 26.32, 25.08, 24.24, 24.16, 24.44, 24.76, 24.64, 24.68, 24.72, 24.64, 24.56, 24.64, 24.68, 24.64, 24.76, 24.76, 24.64, 24.56, 24.52, 24.48, 24.6, 24.6, 24.64, 24.96, 24.88, 25.04, 24.84, 24.68, 24.6, 24.48, 24.64, 24.52, 24.56, 24.4, 24.6, 24.6, 24.68, 24.84, 25.0, 24.68, 24.68, 24.68, 24.68, 24.68, 24.92, 24.68, 24.96, 25.12, 24.88, 24.8, 24.92, 24.72, 24.6, 24.64, 24.64, 24.96, 25.12, 25.0, 24.92, 24.88, 24.6, 24.48, 24.32, 24.48, 24.52, 24.52, 24.6, 24.76, 24.84, 24.76, 25.0, 24.72, 24.6, 24.92, 24.88, 24.88, 24.84, 24.92, 25.12, 25.2, 25.2, 25.12, 24.96, 24.52, 24.52, 24.32, 24.4, 24.4, 24.48, 24.36, 24.32, 24.28, 24.2, 24.16, 24.0, 24.08, 24.32, 24.36, 24.88, 25.12, 25.12, 25.08, 24.76, 25.0, 25.2, 24.84, 25.24, 25.16, 24.96, 24.96, 25.08, 25.24, 25.04, 25.12, 25.24, 25.16, 25.12, 25.24, 25.44, 25.64, 25.68, 25.44, 26.16, 26.24, 26.0, 26.24, 26.4, 25.56, 25.68, 25.56, 25.4, 25.4, 25.32, 25.24, 25.4, 25.6, 25.36, 25.16, 24.84, 24.52, 24.4, 24.24, 24.44, 24.48, 24.4, 24.56, 24.36, 24.24, 24.24, 24.44, 24.52, 24.68, 24.72, 24.72, 24.88, 24.76, 24.64, 24.36, 24.68, 24.84, 24.6, 24.84, 24.56, 24.28, 24.36, 24.52, 24.32, 24.4, 24.36, 24.4, 24.44, 24.44, 24.72, 24.64, 24.76, 24.76, 24.64, 24.52, 24.76, 24.68, 24.56, 24.72, 24.36, 24.44, 24.48, 24.88, 24.88, 25.0, 25.0, 24.68, 24.4, 24.44, 24.52, 24.36, 24.6, 24.52, 24.56, 24.56, 24.56, 24.64, 24.32]
-221.50223588943481
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.98000812530518, 'TIME_S_1KI': 210.98000812530518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5224.394376831054, 'W': 23.586192508859664}
-[20.72, 20.64, 20.6, 20.6, 20.48, 20.44, 20.4, 20.36, 20.28, 20.28, 20.24, 20.44, 20.68, 20.92, 21.04, 20.8, 20.44, 20.28, 20.2, 20.12]
-369.28000000000003
-18.464000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.98000812530518, 'TIME_S_1KI': 210.98000812530518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5224.394376831054, 'W': 23.586192508859664, 'J_1KI': 5224.394376831054, 'W_1KI': 23.586192508859664, 'W_D': 5.122192508859662, 'J_D': 1134.5770933685287, 'W_D_1KI': 5.122192508859662, 'J_D_1KI': 5.122192508859662}
+[20.48, 20.28, 20.4, 20.48, 20.4, 20.6, 20.96, 20.96, 21.04, 20.88]
+[20.92, 20.84, 20.88, 24.28, 26.48, 27.96, 29.4, 28.08, 28.12, 26.52, 25.88, 24.92, 24.44, 24.32, 24.56, 24.84, 24.84, 24.96, 24.88, 24.72, 24.72, 24.48, 24.56, 24.28, 24.16, 24.12, 24.48]
+28.280823469161987
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31538224220276, 'TIME_S_1KI': 213.1538224220276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 656.8145585250853, 'W': 23.224732449579832}
+[20.48, 20.28, 20.4, 20.48, 20.4, 20.6, 20.96, 20.96, 21.04, 20.88, 20.44, 20.52, 20.68, 20.36, 20.52, 20.4, 20.4, 20.4, 20.52, 20.72]
+370.18000000000006
+18.509000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31538224220276, 'TIME_S_1KI': 213.1538224220276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 656.8145585250853, 'W': 23.224732449579832, 'J_1KI': 6568.145585250853, 'W_1KI': 232.24732449579832, 'W_D': 4.7157324495798285, 'J_D': 133.36479693436596, 'W_D_1KI': 47.157324495798285, 'J_D_1KI': 471.57324495798287}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..6cc4360
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.396695137023926, "TIME_S_1KI": 423.96695137023926, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1244.9685798645019, "W": 23.740989470007165, "J_1KI": 12449.68579864502, "W_1KI": 237.40989470007165, "W_D": 5.275989470007168, "J_D": 276.6709081840517, "W_D_1KI": 52.75989470007168, "J_D_1KI": 527.5989470007169}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..8c655a7
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.396695137023926}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1981,     3970,  ..., 19995969,
+                            19997989, 20000000]),
+       col_indices=tensor([   3,    4,    9,  ..., 9978, 9982, 9987]),
+       values=tensor([0.8747, 0.4611, 0.0013,  ..., 0.7048, 0.8145, 0.2728]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1891, 0.5511, 0.0831,  ..., 0.7428, 0.4718, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.396695137023926 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1981,     3970,  ..., 19995969,
+                            19997989, 20000000]),
+       col_indices=tensor([   3,    4,    9,  ..., 9978, 9982, 9987]),
+       values=tensor([0.8747, 0.4611, 0.0013,  ..., 0.7048, 0.8145, 0.2728]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1891, 0.5511, 0.0831,  ..., 0.7428, 0.4718, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.396695137023926 seconds
+
+[20.28, 20.48, 20.28, 20.48, 20.68, 20.72, 20.92, 20.84, 20.6, 20.6]
+[20.68, 20.48, 20.48, 24.96, 25.96, 28.64, 31.0, 30.08, 29.88, 29.2, 29.04, 27.32, 26.6, 25.64, 24.48, 24.28, 24.48, 24.6, 24.64, 24.64, 24.8, 24.8, 24.96, 24.76, 25.0, 24.92, 24.88, 24.72, 24.76, 24.72, 24.68, 24.72, 24.56, 24.6, 24.6, 24.56, 24.72, 24.8, 24.76, 24.64, 24.64, 24.64, 24.68, 24.56, 24.52, 24.68, 24.44, 24.28, 24.24, 24.04]
+52.43962478637695
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.396695137023926, 'TIME_S_1KI': 423.96695137023926, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.9685798645019, 'W': 23.740989470007165}
+[20.28, 20.48, 20.28, 20.48, 20.68, 20.72, 20.92, 20.84, 20.6, 20.6, 20.92, 20.6, 20.68, 20.68, 20.32, 20.36, 20.28, 20.24, 20.16, 20.16]
+369.29999999999995
+18.464999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.396695137023926, 'TIME_S_1KI': 423.96695137023926, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.9685798645019, 'W': 23.740989470007165, 'J_1KI': 12449.68579864502, 'W_1KI': 237.40989470007165, 'W_D': 5.275989470007168, 'J_D': 276.6709081840517, 'W_D_1KI': 52.75989470007168, 'J_D_1KI': 527.5989470007169}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..33ea6fd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.99070644378662, "TIME_S_1KI": 629.9070644378662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1817.8575790786747, "W": 23.739082320260188, "J_1KI": 18178.57579078675, "W_1KI": 237.3908232026019, "W_D": 5.089082320260189, "J_D": 389.7044856929784, "W_D_1KI": 50.890823202601894, "J_D_1KI": 508.908232026019}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..58e2c22
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.99070644378662}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2976,     6021,  ..., 29993904,
+                            29996986, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9993, 9995, 9997]),
+       values=tensor([0.2230, 0.6279, 0.9702,  ..., 0.2815, 0.5420, 0.7025]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3830, 0.2972, 0.7622,  ..., 0.1887, 0.7379, 0.3841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 62.99070644378662 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2976,     6021,  ..., 29993904,
+                            29996986, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9993, 9995, 9997]),
+       values=tensor([0.2230, 0.6279, 0.9702,  ..., 0.2815, 0.5420, 0.7025]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3830, 0.2972, 0.7622,  ..., 0.1887, 0.7379, 0.3841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 62.99070644378662 seconds
+
+[20.4, 20.36, 20.44, 20.44, 20.72, 20.96, 20.88, 21.12, 20.72, 20.36]
+[20.44, 20.2, 23.44, 25.08, 27.68, 28.88, 31.04, 29.08, 29.08, 29.48, 28.4, 29.16, 28.52, 28.08, 27.8, 26.96, 25.8, 24.96, 24.8, 24.68, 24.76, 24.68, 24.72, 24.68, 24.52, 24.44, 24.4, 24.76, 24.6, 24.88, 24.88, 24.72, 24.44, 24.48, 24.24, 24.32, 24.24, 24.16, 24.36, 24.56, 24.28, 24.24, 24.28, 24.16, 23.76, 23.92, 24.36, 24.4, 24.52, 24.84, 24.76, 24.44, 24.44, 24.24, 24.32, 24.04, 24.08, 24.28, 24.08, 24.08, 24.0, 23.88, 24.04, 24.28, 24.16, 24.4, 24.48, 24.4, 24.48, 24.6, 24.52, 24.52, 24.52]
+76.5765733718872
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.99070644378662, 'TIME_S_1KI': 629.9070644378662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1817.8575790786747, 'W': 23.739082320260188}
+[20.4, 20.36, 20.44, 20.44, 20.72, 20.96, 20.88, 21.12, 20.72, 20.36, 20.28, 20.84, 20.84, 20.8, 21.0, 21.12, 21.0, 20.52, 20.56, 20.32]
+373.0
+18.65
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.99070644378662, 'TIME_S_1KI': 629.9070644378662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1817.8575790786747, 'W': 23.739082320260188, 'J_1KI': 18178.57579078675, 'W_1KI': 237.3908232026019, 'W_D': 5.089082320260189, 'J_D': 389.7044856929784, 'W_D_1KI': 50.890823202601894, 'J_D_1KI': 508.908232026019}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
index e2472aa..256b7e3 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 142368, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.641618490219116, "TIME_S_1KI": 0.07474726406368788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.11982940673823, "W": 23.739150118754246, "J_1KI": 2.445211209026876, "W_1KI": 0.16674498566218704, "W_D": 4.927150118754245, "J_D": 72.25358322525018, "W_D_1KI": 0.03460855050821986, "J_D_1KI": 0.00024309220125463487}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 147223, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.998100280761719, "TIME_S_1KI": 0.07470368271779354, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2257844543456, "W": 22.66095026687019, "J_1KI": 2.256616048133414, "W_1KI": 0.15392262259884795, "W_D": 4.3619502668701955, "J_D": 63.94931951642035, "W_D_1KI": 0.029628184909084827, "J_D_1KI": 0.00020124698524744657}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
index 4bc129d..a769041 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,266 +1,917 @@
-['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": 0.0838630199432373}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015372514724731445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
+       col_indices=tensor([3209, 9868, 3248, 6619,  340, 2292, 7833, 3392, 6373,
+                           1926, 8761, 7309, 7662, 7112, 9220, 3460, 9210, 9337,
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+                      0.1434, 0.1482, 0.2179, 0.8422, 0.6150, 0.7603, 0.2665,
+                      0.8673, 0.3035, 0.1706, 0.2689, 0.9151, 0.1045, 0.0141,
+                      0.4861, 0.1566, 0.2750, 0.3958, 0.0336, 0.7854, 0.0838,
+                      0.7202, 0.5357, 0.1356, 0.8048, 0.1340, 0.0716, 0.5308,
+                      0.4959, 0.3349, 0.8036, 0.6913, 0.6107, 0.4431, 0.5386,
+                      0.3116, 0.0546, 0.1399, 0.8659, 0.1354, 0.9518]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4774, 0.5622, 0.8471,  ..., 0.2935, 0.6471, 0.0068])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.015372514724731445 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68303 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.871366500854492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([6744, 9183, 5203, 6638, 1533, 7743,  539, 8215, 8490,
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+                      8.8388e-01, 5.0231e-01, 8.8959e-01, 1.3500e-01,
+                      5.5147e-01, 3.4307e-01, 8.1439e-01, 5.6923e-01,
+                      1.5864e-01, 2.8270e-01, 7.7082e-01, 9.5930e-01,
+                      6.5183e-01, 2.8440e-01, 8.4987e-01, 4.0660e-01,
+                      4.5850e-01, 7.9000e-01, 7.4111e-01, 9.7289e-02,
+                      7.1325e-01, 4.8387e-01, 8.8020e-01, 9.2394e-01,
+                      2.2972e-01, 1.5188e-01, 7.6577e-01, 9.9898e-01,
+                      3.1108e-01, 6.0390e-01, 1.0443e-01, 2.6637e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5445, 0.4387, 0.2146,  ..., 0.9330, 0.4366, 0.2965])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 4.871366500854492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 147223 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.998100280761719}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.2336, 0.7811, 0.2916,  ..., 0.9209, 0.8685, 0.4951])
+tensor([0.8452, 0.1047, 0.0563,  ..., 0.6079, 0.4820, 0.4351])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,650 +919,268 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.0838630199432373 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 125204 -ss 10000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.234081745147705}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
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-                      0.8735, 0.3875, 0.0528, 0.7009, 0.5289, 0.2944, 0.4509,
-                      0.5369, 0.6869, 0.2268, 0.2641, 0.2154, 0.3362, 0.6552,
-                      0.1327, 0.1218, 0.6611, 0.0654, 0.0444, 0.7917, 0.2227,
-                      0.5871, 0.9300, 0.2691, 0.6683, 0.2163, 0.8183, 0.6909,
-                      0.0176, 0.0647, 0.9873, 0.6484, 0.3203, 0.2129, 0.9498,
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-                      0.3238, 0.4139, 0.4523, 0.9314, 0.2645, 0.3099]),
-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.9394, 0.5361, 0.0926,  ..., 0.1333, 0.7033, 0.7122])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 9.234081745147705 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 142368 -ss 10000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.641618490219116}
+Time: 10.998100280761719 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([1350, 1465, 4190, 6900, 6571, 5844, 4736,  324, 9249,
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-                           5572, 1722, 4856, 9429, 1499, 3117, 6370, 8257, 4390,
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5424, 0.9332, 0.7035,  ..., 0.9872, 0.5484, 0.9353])
+tensor([0.8452, 0.1047, 0.0563,  ..., 0.6079, 0.4820, 0.4351])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -919,389 +1188,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.641618490219116 seconds
+Time: 10.998100280761719 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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-tensor([0.5424, 0.9332, 0.7035,  ..., 0.9872, 0.5484, 0.9353])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 10.641618490219116 seconds
-
-[21.44, 21.48, 21.48, 21.12, 21.2, 21.24, 21.16, 21.48, 21.6, 21.92]
-[21.92, 21.88, 24.96, 26.8, 28.08, 28.64, 29.24, 29.24, 26.08, 24.52, 23.6, 23.56, 23.36, 23.36]
-14.664376258850098
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 142368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.641618490219116, 'TIME_S_1KI': 0.07474726406368788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.11982940673823, 'W': 23.739150118754246}
-[21.44, 21.48, 21.48, 21.12, 21.2, 21.24, 21.16, 21.48, 21.6, 21.92, 20.56, 20.56, 20.2, 20.2, 20.08, 20.04, 20.44, 20.72, 20.72, 21.12]
-376.24
-18.812
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 142368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.641618490219116, 'TIME_S_1KI': 0.07474726406368788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.11982940673823, 'W': 23.739150118754246, 'J_1KI': 2.445211209026876, 'W_1KI': 0.16674498566218704, 'W_D': 4.927150118754245, 'J_D': 72.25358322525018, 'W_D_1KI': 0.03460855050821986, 'J_D_1KI': 0.00024309220125463487}
+[20.36, 20.16, 20.2, 20.32, 20.32, 20.36, 20.56, 20.6, 20.52, 20.56]
+[20.68, 20.8, 21.2, 24.88, 26.6, 27.2, 27.72, 25.88, 24.68, 23.88, 23.88, 23.8, 23.88, 23.68]
+14.660717248916626
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 147223, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.998100280761719, 'TIME_S_1KI': 0.07470368271779354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2257844543456, 'W': 22.66095026687019}
+[20.36, 20.16, 20.2, 20.32, 20.32, 20.36, 20.56, 20.6, 20.52, 20.56, 20.32, 20.36, 20.24, 20.2, 20.24, 20.36, 20.24, 20.2, 20.36, 20.24]
+365.9799999999999
+18.298999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 147223, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.998100280761719, 'TIME_S_1KI': 0.07470368271779354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2257844543456, 'W': 22.66095026687019, 'J_1KI': 2.256616048133414, 'W_1KI': 0.15392262259884795, 'W_D': 4.3619502668701955, 'J_D': 63.94931951642035, 'W_D_1KI': 0.029628184909084827, 'J_D_1KI': 0.00020124698524744657}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..f94bbe2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 52408, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.675631761550903, "TIME_S_1KI": 0.2037023309714338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.76721565246584, "W": 22.07477701860741, "J_1KI": 6.1587394224634755, "W_1KI": 0.42121006370415603, "W_D": 3.4607770186074056, "J_D": 50.6018865489959, "W_D_1KI": 0.06603528122819809, "J_D_1KI": 0.0012600229207029095}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..a9bfe94
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02814483642578125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    3,  ..., 4997, 4998, 5000]),
+       col_indices=tensor([7223,  597, 5381,  ..., 4437, 2871, 7175]),
+       values=tensor([0.8424, 0.9605, 0.7186,  ..., 0.3316, 0.2968, 0.8125]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.9947, 0.8149, 0.3597,  ..., 0.7445, 0.4060, 0.0098])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.02814483642578125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37307 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.474437952041626}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([7873, 9438, 5376,  ..., 1254, 8934, 6510]),
+       values=tensor([0.8139, 0.0055, 0.6843,  ..., 0.4362, 0.9226, 0.6386]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.9683, 0.4961, 0.7880,  ..., 0.7466, 0.9086, 0.6990])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.474437952041626 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52408 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.675631761550903}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6316, 4387, 8598,  ...,  977, 3012, 3071]),
+       values=tensor([0.0249, 0.1066, 0.4899,  ..., 0.3057, 0.2915, 0.5832]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6250, 0.8754, 0.6636,  ..., 0.3831, 0.1537, 0.5147])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.675631761550903 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6316, 4387, 8598,  ...,  977, 3012, 3071]),
+       values=tensor([0.0249, 0.1066, 0.4899,  ..., 0.3057, 0.2915, 0.5832]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6250, 0.8754, 0.6636,  ..., 0.3831, 0.1537, 0.5147])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.675631761550903 seconds
+
+[20.6, 20.52, 20.48, 20.48, 20.44, 20.28, 20.56, 20.6, 20.76, 21.08]
+[21.08, 20.76, 21.6, 22.6, 23.96, 24.64, 25.4, 24.84, 24.84, 24.72, 23.92, 24.04, 24.08, 23.92]
+14.621539115905762
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 52408, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.675631761550903, 'TIME_S_1KI': 0.2037023309714338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.76721565246584, 'W': 22.07477701860741}
+[20.6, 20.52, 20.48, 20.48, 20.44, 20.28, 20.56, 20.6, 20.76, 21.08, 20.48, 20.64, 20.8, 20.76, 20.92, 21.0, 20.88, 20.76, 20.84, 20.96]
+372.2800000000001
+18.614000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 52408, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.675631761550903, 'TIME_S_1KI': 0.2037023309714338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.76721565246584, 'W': 22.07477701860741, 'J_1KI': 6.1587394224634755, 'W_1KI': 0.42121006370415603, 'W_D': 3.4607770186074056, 'J_D': 50.6018865489959, 'W_D_1KI': 0.06603528122819809, 'J_D_1KI': 0.0012600229207029095}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..118d60f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.35910129547119, "TIME_S_1KI": 983.5910129547119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2706.022798595428, "W": 24.09983015121324, "J_1KI": 27060.22798595428, "W_1KI": 240.9983015121324, "W_D": 5.635830151213241, "J_D": 632.8129610252375, "W_D_1KI": 56.358301512132414, "J_D_1KI": 563.5830151213241}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..22bc540
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.35910129547119}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       62,      113,  ..., 24999916,
+                            24999964, 25000000]),
+       col_indices=tensor([ 13628,  17541,  24252,  ..., 467551, 469636,
+                           477818]),
+       values=tensor([0.8374, 0.1433, 0.7046,  ..., 0.7606, 0.4438, 0.1648]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5427, 0.9990, 0.7165,  ..., 0.2818, 0.2990, 0.5329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 98.35910129547119 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       62,      113,  ..., 24999916,
+                            24999964, 25000000]),
+       col_indices=tensor([ 13628,  17541,  24252,  ..., 467551, 469636,
+                           477818]),
+       values=tensor([0.8374, 0.1433, 0.7046,  ..., 0.7606, 0.4438, 0.1648]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5427, 0.9990, 0.7165,  ..., 0.2818, 0.2990, 0.5329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 98.35910129547119 seconds
+
+[20.28, 20.32, 20.44, 20.32, 20.6, 20.64, 20.64, 20.52, 20.76, 20.8]
+[20.68, 20.52, 20.6, 22.08, 24.8, 25.76, 28.52, 28.48, 30.56, 30.0, 29.28, 29.8, 28.92, 28.32, 28.32, 27.32, 26.84, 25.96, 25.16, 25.08, 24.72, 24.92, 24.96, 25.12, 25.32, 25.44, 25.32, 25.24, 25.2, 25.08, 25.12, 25.28, 25.16, 25.32, 25.2, 25.04, 25.04, 25.04, 25.28, 25.16, 25.28, 25.36, 25.36, 25.36, 25.44, 25.48, 25.28, 25.24, 25.12, 25.04, 25.04, 25.2, 25.16, 25.36, 25.24, 25.36, 25.12, 25.12, 25.12, 25.2, 25.32, 25.36, 25.16, 25.16, 25.2, 25.28, 25.4, 25.44, 25.48, 25.28, 25.24, 25.36, 25.36, 25.24, 25.36, 25.2, 25.24, 25.52, 25.52, 25.52, 25.52, 25.4, 25.08, 25.2, 25.24, 25.44, 25.12, 25.12, 24.92, 24.8, 24.8, 25.08, 25.12, 25.32, 25.28, 25.2, 25.04, 25.08, 25.16, 25.4, 25.44, 25.36, 25.36, 25.36, 25.28, 25.04, 25.16]
+112.28389501571655
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.35910129547119, 'TIME_S_1KI': 983.5910129547119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2706.022798595428, 'W': 24.09983015121324}
+[20.28, 20.32, 20.44, 20.32, 20.6, 20.64, 20.64, 20.52, 20.76, 20.8, 20.48, 20.64, 20.56, 20.44, 20.36, 20.4, 20.36, 20.52, 20.64, 20.68]
+369.28
+18.464
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.35910129547119, 'TIME_S_1KI': 983.5910129547119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2706.022798595428, 'W': 24.09983015121324, 'J_1KI': 27060.22798595428, 'W_1KI': 240.9983015121324, 'W_D': 5.635830151213241, 'J_D': 632.8129610252375, 'W_D_1KI': 56.358301512132414, 'J_D_1KI': 563.5830151213241}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
index 7ae266f..36dce3a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 101.81685495376587, "TIME_S_1KI": 101.81685495376587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2509.431913032532, "W": 23.91615643501538, "J_1KI": 2509.431913032532, "W_1KI": 23.91615643501538, "W_D": 5.455156435015379, "J_D": 572.3889491109848, "W_D_1KI": 5.455156435015379, "J_D_1KI": 5.455156435015379}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078357696533203, "TIME_S_1KI": 100.78357696533203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 355.02523105621333, "W": 24.261238712111865, "J_1KI": 3550.2523105621335, "W_1KI": 242.61238712111864, "W_D": 5.657238712111866, "J_D": 82.78482829093929, "W_D_1KI": 56.57238712111866, "J_D_1KI": 565.7238712111866}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
index 06c1b57..8996689 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 101.81685495376587}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078357696533203}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499992,
+tensor(crow_indices=tensor([      0,       0,       6,  ..., 2499989,
                             2499997, 2500000]),
-       col_indices=tensor([  7138,  74289, 101345,  ...,  58125, 215534,
-                           230533]),
-       values=tensor([0.6785, 0.9079, 0.1725,  ..., 0.1754, 0.6680, 0.6302]),
+       col_indices=tensor([ 10944, 177257, 201447,  ..., 125511, 168548,
+                           443200]),
+       values=tensor([0.0549, 0.4670, 0.3111,  ..., 0.0129, 0.0661, 0.9327]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7654, 0.8855, 0.1287,  ..., 0.1047, 0.9719, 0.8120])
+tensor([0.3951, 0.3409, 0.2222,  ..., 0.4533, 0.5999, 0.5088])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 101.81685495376587 seconds
+Time: 10.078357696533203 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499992,
+tensor(crow_indices=tensor([      0,       0,       6,  ..., 2499989,
                             2499997, 2500000]),
-       col_indices=tensor([  7138,  74289, 101345,  ...,  58125, 215534,
-                           230533]),
-       values=tensor([0.6785, 0.9079, 0.1725,  ..., 0.1754, 0.6680, 0.6302]),
+       col_indices=tensor([ 10944, 177257, 201447,  ..., 125511, 168548,
+                           443200]),
+       values=tensor([0.0549, 0.4670, 0.3111,  ..., 0.0129, 0.0661, 0.9327]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7654, 0.8855, 0.1287,  ..., 0.1047, 0.9719, 0.8120])
+tensor([0.3951, 0.3409, 0.2222,  ..., 0.4533, 0.5999, 0.5088])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +35,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 101.81685495376587 seconds
+Time: 10.078357696533203 seconds
 
-[20.36, 20.36, 20.32, 20.32, 20.32, 20.56, 20.64, 20.8, 21.0, 21.0]
-[20.96, 20.8, 21.96, 21.96, 22.8, 24.92, 25.88, 26.52, 26.32, 25.68, 25.2, 25.44, 25.36, 25.12, 25.04, 25.08, 25.04, 25.24, 25.36, 25.12, 25.28, 25.28, 25.08, 24.96, 25.04, 25.04, 25.2, 25.32, 25.2, 25.36, 25.08, 24.88, 24.96, 25.12, 25.24, 25.4, 25.4, 25.4, 25.2, 25.16, 25.08, 25.04, 25.24, 25.28, 25.36, 25.48, 25.48, 25.4, 25.36, 25.48, 25.52, 25.4, 25.28, 25.04, 24.84, 24.88, 25.12, 25.68, 26.12, 26.12, 25.76, 25.32, 24.96, 24.84, 24.92, 24.96, 24.92, 24.92, 24.92, 25.28, 25.08, 25.08, 25.16, 25.12, 25.04, 25.12, 25.2, 24.92, 24.92, 24.96, 25.08, 25.4, 25.4, 25.48, 25.44, 25.24, 25.28, 25.48, 25.72, 25.56, 25.56, 25.52, 25.44, 25.2, 25.08, 25.12, 25.08, 25.28, 25.44, 25.32]
-104.92622089385986
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 101.81685495376587, 'TIME_S_1KI': 101.81685495376587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2509.431913032532, 'W': 23.91615643501538}
-[20.36, 20.36, 20.32, 20.32, 20.32, 20.56, 20.64, 20.8, 21.0, 21.0, 20.52, 20.48, 20.28, 20.48, 20.52, 20.64, 20.52, 20.44, 20.4, 20.4]
-369.22
-18.461000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 101.81685495376587, 'TIME_S_1KI': 101.81685495376587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2509.431913032532, 'W': 23.91615643501538, 'J_1KI': 2509.431913032532, 'W_1KI': 23.91615643501538, 'W_D': 5.455156435015379, 'J_D': 572.3889491109848, 'W_D_1KI': 5.455156435015379, 'J_D_1KI': 5.455156435015379}
+[20.6, 20.6, 20.76, 20.88, 20.68, 20.64, 20.36, 20.76, 20.88, 20.96]
+[21.0, 20.96, 24.12, 26.84, 26.84, 28.92, 30.04, 30.92, 26.56, 25.12, 24.8, 25.2, 25.28, 25.52]
+14.633433818817139
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.078357696533203, 'TIME_S_1KI': 100.78357696533203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.02523105621333, 'W': 24.261238712111865}
+[20.6, 20.6, 20.76, 20.88, 20.68, 20.64, 20.36, 20.76, 20.88, 20.96, 20.52, 20.48, 20.56, 20.6, 20.6, 20.64, 20.64, 20.76, 20.8, 20.8]
+372.08
+18.604
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.078357696533203, 'TIME_S_1KI': 100.78357696533203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.02523105621333, 'W': 24.261238712111865, 'J_1KI': 3550.2523105621335, 'W_1KI': 242.61238712111864, 'W_D': 5.657238712111866, 'J_D': 82.78482829093929, 'W_D_1KI': 56.57238712111866, 'J_D_1KI': 565.7238712111866}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..741a72d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.092703104019165, "TIME_S_1KI": 500.92703104019165, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1421.3682743358613, "W": 24.212233919725165, "J_1KI": 14213.682743358613, "W_1KI": 242.12233919725165, "W_D": 5.561233919725165, "J_D": 326.46972955346126, "W_D_1KI": 55.61233919725165, "J_D_1KI": 556.1233919725165}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..2787c93
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.092703104019165}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       19,       45,  ..., 12499947,
+                            12499973, 12500000]),
+       col_indices=tensor([ 17397,  55872, 132943,  ..., 437400, 464141,
+                           486359]),
+       values=tensor([0.6537, 0.5151, 0.3039,  ..., 0.7629, 0.2656, 0.5446]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4135, 0.5444, 0.9798,  ..., 0.8106, 0.6562, 0.9974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 50.092703104019165 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       19,       45,  ..., 12499947,
+                            12499973, 12500000]),
+       col_indices=tensor([ 17397,  55872, 132943,  ..., 437400, 464141,
+                           486359]),
+       values=tensor([0.6537, 0.5151, 0.3039,  ..., 0.7629, 0.2656, 0.5446]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4135, 0.5444, 0.9798,  ..., 0.8106, 0.6562, 0.9974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 50.092703104019165 seconds
+
+[20.76, 20.64, 20.64, 20.64, 20.48, 20.64, 20.6, 20.8, 21.08, 20.96]
+[20.92, 20.92, 23.96, 24.8, 26.64, 28.44, 28.44, 30.32, 28.64, 28.92, 27.6, 26.6, 25.88, 25.0, 25.04, 25.08, 25.08, 25.16, 25.16, 25.12, 25.08, 24.88, 25.08, 25.12, 25.2, 25.28, 25.36, 25.32, 25.32, 25.04, 25.04, 25.28, 25.16, 25.12, 25.24, 25.16, 25.16, 25.36, 25.24, 25.2, 25.28, 25.12, 24.96, 25.12, 25.2, 25.32, 25.36, 25.48, 25.56, 25.52, 25.52, 25.36, 25.4, 25.2, 25.28, 25.56]
+58.704549074172974
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.092703104019165, 'TIME_S_1KI': 500.92703104019165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.3682743358613, 'W': 24.212233919725165}
+[20.76, 20.64, 20.64, 20.64, 20.48, 20.64, 20.6, 20.8, 21.08, 20.96, 20.76, 20.4, 20.48, 20.92, 20.8, 20.92, 20.88, 20.72, 20.64, 21.0]
+373.02
+18.651
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.092703104019165, 'TIME_S_1KI': 500.92703104019165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.3682743358613, 'W': 24.212233919725165, 'J_1KI': 14213.682743358613, 'W_1KI': 242.12233919725165, 'W_D': 5.561233919725165, 'J_D': 326.46972955346126, 'W_D_1KI': 55.61233919725165, 'J_D_1KI': 556.1233919725165}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
index 99da0bf..8bb383c 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1750, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.16480827331543, "TIME_S_1KI": 5.80846187046596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 350.615839214325, "W": 23.96705841957935, "J_1KI": 200.35190812247143, "W_1KI": 13.695461954045342, "W_D": 5.563058419579352, "J_D": 81.38238586616524, "W_D_1KI": 3.178890525473916, "J_D_1KI": 1.8165088716993805}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1701, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.195863723754883, "TIME_S_1KI": 5.994040989861777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.5689013671875, "W": 22.02886990511616, "J_1KI": 175.5255152070473, "W_1KI": 12.950540802537425, "W_D": 3.5278699051161624, "J_D": 47.81508294677735, "W_D_1KI": 2.0739975926608833, "J_D_1KI": 1.2192813595889966}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
index 38bf734..7e66808 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.999283075332642}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6172366142272949}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     11,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      2,     11,  ..., 249984, 249992,
                             250000]),
-       col_indices=tensor([13962, 18394, 22949,  ..., 14595, 37415, 49220]),
-       values=tensor([0.3721, 0.9393, 0.0895,  ..., 0.9714, 0.3434, 0.8212]),
+       col_indices=tensor([   55, 33912,  7825,  ..., 25553, 31300, 45367]),
+       values=tensor([0.2156, 0.3825, 0.1471,  ..., 0.6075, 0.9514, 0.6641]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7511, 0.6955, 0.0801,  ..., 0.5808, 0.0034, 0.8132])
+tensor([0.8972, 0.9948, 0.0628,  ..., 0.4950, 0.5589, 0.8119])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 5.999283075332642 seconds
+Time: 0.6172366142272949 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1750 -ss 50000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.16480827331543}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1701 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.195863723754883}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     10,  ..., 249989, 249996,
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249986, 249995,
                             250000]),
-       col_indices=tensor([  581, 19518, 20111,  ..., 13396, 34309, 44743]),
-       values=tensor([0.2810, 0.4140, 0.9885,  ..., 0.7044, 0.0704, 0.4209]),
+       col_indices=tensor([ 4095,  7631, 26458,  ..., 36946, 37655, 49733]),
+       values=tensor([0.3588, 0.4994, 0.4557,  ..., 0.6547, 0.8163, 0.6645]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2162, 0.8403, 0.5346,  ..., 0.6143, 0.9627, 0.5199])
+tensor([0.0295, 0.0838, 0.1870,  ..., 0.2542, 0.3969, 0.7673])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.16480827331543 seconds
+Time: 10.195863723754883 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     10,  ..., 249989, 249996,
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249986, 249995,
                             250000]),
-       col_indices=tensor([  581, 19518, 20111,  ..., 13396, 34309, 44743]),
-       values=tensor([0.2810, 0.4140, 0.9885,  ..., 0.7044, 0.0704, 0.4209]),
+       col_indices=tensor([ 4095,  7631, 26458,  ..., 36946, 37655, 49733]),
+       values=tensor([0.3588, 0.4994, 0.4557,  ..., 0.6547, 0.8163, 0.6645]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2162, 0.8403, 0.5346,  ..., 0.6143, 0.9627, 0.5199])
+tensor([0.0295, 0.0838, 0.1870,  ..., 0.2542, 0.3969, 0.7673])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.16480827331543 seconds
+Time: 10.195863723754883 seconds
 
-[20.36, 20.36, 20.36, 20.4, 20.24, 20.28, 20.32, 20.8, 21.08, 20.92]
-[21.16, 21.04, 24.12, 26.48, 28.28, 28.28, 29.04, 29.84, 25.88, 24.76, 24.76, 24.72, 24.88, 24.76]
-14.629072666168213
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.16480827331543, 'TIME_S_1KI': 5.80846187046596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.615839214325, 'W': 23.96705841957935}
-[20.36, 20.36, 20.36, 20.4, 20.24, 20.28, 20.32, 20.8, 21.08, 20.92, 20.4, 20.24, 20.24, 20.08, 20.2, 20.32, 20.56, 20.68, 20.72, 20.72]
-368.0799999999999
-18.403999999999996
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.16480827331543, 'TIME_S_1KI': 5.80846187046596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.615839214325, 'W': 23.96705841957935, 'J_1KI': 200.35190812247143, 'W_1KI': 13.695461954045342, 'W_D': 5.563058419579352, 'J_D': 81.38238586616524, 'W_D_1KI': 3.178890525473916, 'J_D_1KI': 1.8165088716993805}
+[20.6, 20.6, 20.68, 20.56, 20.68, 20.6, 20.6, 20.48, 20.36, 20.44]
+[20.44, 20.16, 20.24, 22.04, 22.92, 24.8, 25.72, 25.84, 25.56, 24.68, 24.84, 25.0, 25.2]
+13.55352783203125
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.195863723754883, 'TIME_S_1KI': 5.994040989861777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.5689013671875, 'W': 22.02886990511616}
+[20.6, 20.6, 20.68, 20.56, 20.68, 20.6, 20.6, 20.48, 20.36, 20.44, 20.56, 20.52, 20.52, 20.44, 20.44, 20.44, 20.44, 20.56, 20.88, 20.84]
+370.02
+18.500999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.195863723754883, 'TIME_S_1KI': 5.994040989861777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.5689013671875, 'W': 22.02886990511616, 'J_1KI': 175.5255152070473, 'W_1KI': 12.950540802537425, 'W_D': 3.5278699051161624, 'J_D': 47.81508294677735, 'W_D_1KI': 2.0739975926608833, 'J_D_1KI': 1.2192813595889966}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
index 295c278..911c49b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.368531465530396, "TIME_S_1KI": 54.368531465530396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1384.197800512314, "W": 23.562014123499935, "J_1KI": 1384.197800512314, "W_1KI": 23.562014123499935, "W_D": 4.987014123499936, "J_D": 292.97215190053004, "W_D_1KI": 4.987014123499936, "J_D_1KI": 4.987014123499936}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.676417112350464, "TIME_S_1KI": 55.318223380054214, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.75970675468443, "W": 22.393652951640597, "J_1KI": 1698.2368225631317, "W_1KI": 116.02928990487356, "W_D": 4.002652951640599, "J_D": 58.58393717646598, "W_D_1KI": 20.73913446445906, "J_D_1KI": 107.45665525626457}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
index ea77cdc..7534e40 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.368531465530396}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.431778192520142}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499903,
-                            2499953, 2500000]),
-       col_indices=tensor([  180,   933,  1739,  ..., 48224, 48432, 48665]),
-       values=tensor([0.2331, 0.6137, 0.9488,  ..., 0.3126, 0.9414, 0.7411]),
+tensor(crow_indices=tensor([      0,      57,      95,  ..., 2499895,
+                            2499944, 2500000]),
+       col_indices=tensor([   14,  1180,  1352,  ..., 49220, 49912, 49936]),
+       values=tensor([0.8618, 0.4205, 0.6419,  ..., 0.4989, 0.5508, 0.1652]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7249, 0.6013, 0.3531,  ..., 0.7563, 0.1447, 0.0341])
+tensor([0.3965, 0.7585, 0.8670,  ..., 0.1152, 0.9413, 0.0865])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 54.368531465530396 seconds
+Time: 5.431778192520142 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 193 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.676417112350464}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499903,
-                            2499953, 2500000]),
-       col_indices=tensor([  180,   933,  1739,  ..., 48224, 48432, 48665]),
-       values=tensor([0.2331, 0.6137, 0.9488,  ..., 0.3126, 0.9414, 0.7411]),
+tensor(crow_indices=tensor([      0,      57,     106,  ..., 2499905,
+                            2499942, 2500000]),
+       col_indices=tensor([  275,  2452,  2625,  ..., 47289, 48937, 49987]),
+       values=tensor([0.8108, 0.0031, 0.6812,  ..., 0.9899, 0.5982, 0.1156]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7249, 0.6013, 0.3531,  ..., 0.7563, 0.1447, 0.0341])
+tensor([0.1893, 0.7932, 0.1409,  ..., 0.0408, 0.4757, 0.3205])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +36,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 54.368531465530396 seconds
+Time: 10.676417112350464 seconds
 
-[20.28, 20.28, 20.28, 20.52, 20.52, 20.52, 20.84, 20.84, 20.8, 20.84]
-[20.8, 20.8, 24.12, 26.04, 26.04, 28.44, 29.44, 30.08, 26.76, 25.32, 24.56, 24.68, 24.84, 24.84, 24.76, 24.52, 24.68, 24.72, 24.96, 24.76, 24.96, 24.84, 25.0, 24.88, 24.88, 24.64, 24.64, 24.44, 24.28, 24.24, 24.28, 24.28, 24.52, 24.72, 24.84, 24.64, 24.68, 24.68, 24.72, 24.72, 24.8, 24.76, 24.56, 24.52, 24.2, 24.24, 24.24, 24.24, 24.24, 24.4, 24.52, 24.52, 24.4, 24.44, 24.4, 24.12]
-58.74700665473938
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.368531465530396, 'TIME_S_1KI': 54.368531465530396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.197800512314, 'W': 23.562014123499935}
-[20.28, 20.28, 20.28, 20.52, 20.52, 20.52, 20.84, 20.84, 20.8, 20.84, 20.4, 20.36, 20.4, 20.28, 20.56, 20.56, 20.96, 21.12, 21.28, 21.24]
-371.5
-18.575
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.368531465530396, 'TIME_S_1KI': 54.368531465530396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.197800512314, 'W': 23.562014123499935, 'J_1KI': 1384.197800512314, 'W_1KI': 23.562014123499935, 'W_D': 4.987014123499936, 'J_D': 292.97215190053004, 'W_D_1KI': 4.987014123499936, 'J_D_1KI': 4.987014123499936}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      57,     106,  ..., 2499905,
+                            2499942, 2500000]),
+       col_indices=tensor([  275,  2452,  2625,  ..., 47289, 48937, 49987]),
+       values=tensor([0.8108, 0.0031, 0.6812,  ..., 0.9899, 0.5982, 0.1156]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1893, 0.7932, 0.1409,  ..., 0.0408, 0.4757, 0.3205])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.676417112350464 seconds
+
+[20.32, 20.4, 20.88, 20.88, 20.88, 20.88, 20.64, 20.24, 20.48, 20.52]
+[20.48, 20.64, 20.88, 22.0, 23.72, 25.32, 26.2, 26.2, 26.28, 25.08, 24.6, 24.56, 24.36, 24.36]
+14.636276960372925
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.676417112350464, 'TIME_S_1KI': 55.318223380054214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.75970675468443, 'W': 22.393652951640597}
+[20.32, 20.4, 20.88, 20.88, 20.88, 20.88, 20.64, 20.24, 20.48, 20.52, 20.24, 20.24, 20.24, 20.4, 20.4, 20.2, 20.16, 20.08, 20.16, 20.24]
+367.81999999999994
+18.391
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.676417112350464, 'TIME_S_1KI': 55.318223380054214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.75970675468443, 'W': 22.393652951640597, 'J_1KI': 1698.2368225631317, 'W_1KI': 116.02928990487356, 'W_D': 4.002652951640599, 'J_D': 58.58393717646598, 'W_D_1KI': 20.73913446445906, 'J_D_1KI': 107.45665525626457}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..0d711cb
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.1586058139801, "TIME_S_1KI": 531.586058139801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1662.8207085037227, "W": 23.65320095688087, "J_1KI": 16628.20708503723, "W_1KI": 236.53200956880872, "W_D": 4.901200956880867, "J_D": 344.55456842803903, "W_D_1KI": 49.01200956880867, "J_D_1KI": 490.12009568808674}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..142816b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.1586058139801}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      489,      973,  ..., 24998974,
+                            24999478, 25000000]),
+       col_indices=tensor([  275,   454,   699,  ..., 49715, 49729, 49796]),
+       values=tensor([0.3350, 0.9556, 0.9308,  ..., 0.7756, 0.4208, 0.8843]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3743, 0.4258, 0.0327,  ..., 0.7931, 0.5462, 0.4257])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 53.1586058139801 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      489,      973,  ..., 24998974,
+                            24999478, 25000000]),
+       col_indices=tensor([  275,   454,   699,  ..., 49715, 49729, 49796]),
+       values=tensor([0.3350, 0.9556, 0.9308,  ..., 0.7756, 0.4208, 0.8843]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3743, 0.4258, 0.0327,  ..., 0.7931, 0.5462, 0.4257])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 53.1586058139801 seconds
+
+[20.44, 20.76, 21.08, 21.12, 21.36, 21.16, 21.04, 21.0, 20.96, 20.84]
+[21.0, 20.84, 20.84, 23.72, 24.72, 26.8, 28.0, 29.48, 28.44, 28.04, 28.08, 27.92, 28.6, 27.68, 27.36, 26.52, 25.72, 24.68, 24.64, 24.72, 24.64, 24.64, 24.68, 24.68, 24.56, 24.76, 24.76, 24.64, 24.68, 24.64, 24.8, 24.64, 25.04, 24.96, 24.84, 24.88, 24.68, 24.68, 24.64, 24.8, 24.72, 24.72, 24.68, 24.56, 24.36, 24.36, 24.48, 24.4, 24.44, 24.4, 24.6, 24.56, 24.72, 24.96, 24.92, 24.84, 24.76, 24.72, 24.76, 24.8, 24.88, 24.92, 24.76, 24.68, 24.56, 24.64, 24.6]
+70.30002880096436
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.1586058139801, 'TIME_S_1KI': 531.586058139801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1662.8207085037227, 'W': 23.65320095688087}
+[20.44, 20.76, 21.08, 21.12, 21.36, 21.16, 21.04, 21.0, 20.96, 20.84, 20.6, 20.6, 20.72, 20.72, 20.68, 20.56, 20.76, 20.64, 20.6, 20.68]
+375.04
+18.752000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.1586058139801, 'TIME_S_1KI': 531.586058139801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1662.8207085037227, 'W': 23.65320095688087, 'J_1KI': 16628.20708503723, 'W_1KI': 236.53200956880872, 'W_D': 4.901200956880867, 'J_D': 344.55456842803903, 'W_D_1KI': 49.01200956880867, 'J_D_1KI': 490.12009568808674}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
index 79e75a2..458d682 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10740, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.677687883377075, "TIME_S_1KI": 0.9941981269438619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.0521117687225, "W": 22.70459223140718, "J_1KI": 30.91732884252537, "W_1KI": 2.11402162303605, "W_D": 4.278592231407178, "J_D": 62.57393091917032, "W_D_1KI": 0.3983791649354914, "J_D_1KI": 0.03709303211689864}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10429, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.529776096343994, "TIME_S_1KI": 1.0096630641810331, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 331.7018713665009, "W": 22.733124806095596, "J_1KI": 31.80572167671885, "W_1KI": 2.1797990992516634, "W_D": 4.373124806095593, "J_D": 63.8088117790222, "W_D_1KI": 0.41932350235838456, "J_D_1KI": 0.040207450604888735}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
index 0bf1f45..f7f583d 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.031747817993164}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1077582836151123}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([40215,  7884, 10043,  ..., 30495, 28697, 40914]),
-       values=tensor([0.0776, 0.0144, 0.1627,  ..., 0.8046, 0.8736, 0.3953]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([30956, 25020,  4290,  ...,  1571,  5930, 34059]),
+       values=tensor([0.1925, 0.5429, 0.7430,  ..., 0.0669, 0.5504, 0.8934]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9279, 0.0068, 0.0286,  ..., 0.3265, 0.6131, 0.7632])
+tensor([0.9564, 0.4579, 0.9465,  ..., 0.7236, 0.9546, 0.7676])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 1.031747817993164 seconds
+Time: 0.1077582836151123 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10176 -ss 50000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.948124408721924}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9744 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.809481859207153}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([36670,  6571, 29568,  ..., 18627, 41427, 17079]),
-       values=tensor([0.2785, 0.5861, 0.6450,  ..., 0.6094, 0.8660, 0.4536]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([47588, 45161, 40455,  ..., 30522, 42036,  2005]),
+       values=tensor([0.2055, 0.2802, 0.2448,  ..., 0.0926, 0.8451, 0.9361]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5003, 0.3455, 0.7125,  ..., 0.5405, 0.2393, 0.4201])
+tensor([0.5605, 0.1853, 0.0043,  ..., 0.6007, 0.1968, 0.9775])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.948124408721924 seconds
+Time: 9.809481859207153 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10740 -ss 50000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.677687883377075}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10429 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.529776096343994}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 25000, 25000]),
-       col_indices=tensor([16841, 18429, 37212,  ..., 30943,  4364, 38003]),
-       values=tensor([0.6614, 0.5763, 0.4032,  ..., 0.0279, 0.2406, 0.7956]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([20158, 23859, 20874,  ..., 41939, 15422, 41283]),
+       values=tensor([0.7225, 0.1851, 0.6655,  ..., 0.6086, 0.8791, 0.8414]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8404, 0.4278, 0.6904,  ..., 0.0651, 0.6749, 0.6556])
+tensor([0.1130, 0.7539, 0.9598,  ..., 0.4914, 0.7455, 0.6539])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.677687883377075 seconds
+Time: 10.529776096343994 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 25000, 25000]),
-       col_indices=tensor([16841, 18429, 37212,  ..., 30943,  4364, 38003]),
-       values=tensor([0.6614, 0.5763, 0.4032,  ..., 0.0279, 0.2406, 0.7956]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([20158, 23859, 20874,  ..., 41939, 15422, 41283]),
+       values=tensor([0.7225, 0.1851, 0.6655,  ..., 0.6086, 0.8791, 0.8414]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8404, 0.4278, 0.6904,  ..., 0.0651, 0.6749, 0.6556])
+tensor([0.1130, 0.7539, 0.9598,  ..., 0.4914, 0.7455, 0.6539])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.677687883377075 seconds
+Time: 10.529776096343994 seconds
 
-[20.72, 20.68, 20.64, 20.72, 20.72, 20.64, 20.68, 20.52, 20.52, 20.6]
-[21.04, 21.2, 21.2, 21.92, 23.48, 24.12, 25.48, 25.92, 26.2, 25.8, 25.88, 25.6, 25.8, 25.72]
-14.624887704849243
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.677687883377075, 'TIME_S_1KI': 0.9941981269438619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.0521117687225, 'W': 22.70459223140718}
-[20.72, 20.68, 20.64, 20.72, 20.72, 20.64, 20.68, 20.52, 20.52, 20.6, 20.2, 20.12, 20.24, 20.24, 20.28, 20.28, 20.4, 20.4, 20.44, 20.48]
-368.52000000000004
-18.426000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.677687883377075, 'TIME_S_1KI': 0.9941981269438619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.0521117687225, 'W': 22.70459223140718, 'J_1KI': 30.91732884252537, 'W_1KI': 2.11402162303605, 'W_D': 4.278592231407178, 'J_D': 62.57393091917032, 'W_D_1KI': 0.3983791649354914, 'J_D_1KI': 0.03709303211689864}
+[20.44, 20.44, 20.32, 20.64, 20.6, 20.48, 20.6, 20.36, 20.32, 20.16]
+[20.36, 20.16, 20.76, 22.32, 23.88, 23.88, 25.12, 26.36, 26.64, 26.44, 26.08, 26.2, 25.88, 25.48]
+14.591125249862671
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.529776096343994, 'TIME_S_1KI': 1.0096630641810331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.7018713665009, 'W': 22.733124806095596}
+[20.44, 20.44, 20.32, 20.64, 20.6, 20.48, 20.6, 20.36, 20.32, 20.16, 20.56, 20.32, 20.36, 20.56, 20.36, 20.28, 20.44, 20.16, 20.16, 20.44]
+367.20000000000005
+18.360000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.529776096343994, 'TIME_S_1KI': 1.0096630641810331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.7018713665009, 'W': 22.733124806095596, 'J_1KI': 31.80572167671885, 'W_1KI': 2.1797990992516634, 'W_D': 4.373124806095593, 'J_D': 63.8088117790222, 'W_D_1KI': 0.41932350235838456, 'J_D_1KI': 0.040207450604888735}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..fea28ac
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3217, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.183324813842773, "TIME_S_1KI": 3.1654724320307035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 313.5762239074707, "W": 23.142141761096784, "J_1KI": 97.47473543906457, "W_1KI": 7.193702754459679, "W_D": 4.7631417610967866, "J_D": 64.54061265373231, "W_D_1KI": 1.4806160276956128, "J_D_1KI": 0.46024744410805496}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..dbda68a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.3263556957244873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124996, 124997,
+                            125000]),
+       col_indices=tensor([ 8999, 37078,  2648,  ..., 24880, 43913, 47673]),
+       values=tensor([0.7939, 0.1706, 0.9831,  ..., 0.2838, 0.4924, 0.0921]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2834, 0.1318, 0.3567,  ..., 0.3503, 0.0519, 0.6169])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.3263556957244873 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3217 -ss 50000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.183324813842773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 125000, 125000,
+                            125000]),
+       col_indices=tensor([ 9508, 26799,  1812,  ..., 32912, 38580, 39384]),
+       values=tensor([0.1038, 0.2683, 0.7729,  ..., 0.6337, 0.2232, 0.8870]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7924, 0.9644, 0.0933,  ..., 0.2945, 0.3904, 0.9557])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.183324813842773 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 125000, 125000,
+                            125000]),
+       col_indices=tensor([ 9508, 26799,  1812,  ..., 32912, 38580, 39384]),
+       values=tensor([0.1038, 0.2683, 0.7729,  ..., 0.6337, 0.2232, 0.8870]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7924, 0.9644, 0.0933,  ..., 0.2945, 0.3904, 0.9557])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.183324813842773 seconds
+
+[20.36, 20.32, 20.32, 20.32, 20.36, 20.28, 20.48, 20.52, 20.64, 20.52]
+[20.52, 20.48, 21.56, 23.32, 25.2, 26.32, 27.16, 27.16, 26.96, 26.76, 25.76, 25.84, 25.52]
+13.550008773803711
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.183324813842773, 'TIME_S_1KI': 3.1654724320307035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.5762239074707, 'W': 23.142141761096784}
+[20.36, 20.32, 20.32, 20.32, 20.36, 20.28, 20.48, 20.52, 20.64, 20.52, 20.52, 20.6, 20.44, 20.6, 20.48, 20.32, 20.2, 20.36, 20.4, 20.48]
+367.5799999999999
+18.378999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.183324813842773, 'TIME_S_1KI': 3.1654724320307035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.5762239074707, 'W': 23.142141761096784, 'J_1KI': 97.47473543906457, 'W_1KI': 7.193702754459679, 'W_D': 4.7631417610967866, 'J_D': 64.54061265373231, 'W_D_1KI': 1.4806160276956128, 'J_D_1KI': 0.46024744410805496}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
index f8f615f..769abe1 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 96826, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.588202953338623, "TIME_S_1KI": 0.10935289027057425, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 358.2542047309875, "W": 24.50573052785985, "J_1KI": 3.699979393251683, "W_1KI": 0.2530903943967514, "W_D": 4.6827305278598494, "J_D": 68.45777967405314, "W_D_1KI": 0.04836232548963966, "J_D_1KI": 0.000499476643563089}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 97993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.781827449798584, "TIME_S_1KI": 0.1100265064831017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.41705104827884, "W": 21.541217597275665, "J_1KI": 3.218771249459439, "W_1KI": 0.21982404454681115, "W_D": 2.808217597275668, "J_D": 41.119296494484, "W_D_1KI": 0.02865732855689353, "J_D_1KI": 0.00029244260872606745}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
index 4f006ee..bb7f54b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1145937442779541}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0191800594329834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2495, 2499, 2500]),
-       col_indices=tensor([2458, 4485, 3264,  ..., 1767, 2577, 3633]),
-       values=tensor([0.5111, 0.1865, 0.4486,  ..., 0.9187, 0.4905, 0.6857]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3313, 1621, 3812,  ..., 4525, 1664, 4698]),
+       values=tensor([0.0941, 0.2796, 0.9707,  ..., 0.4661, 0.7642, 0.2416]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.1036, 0.8585, 0.3762,  ..., 0.6219, 0.4226, 0.3195])
+tensor([0.5336, 0.9402, 0.6361,  ..., 0.0126, 0.4753, 0.7232])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.1145937442779541 seconds
+Time: 0.0191800594329834 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 91628 -ss 5000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.936307668685913}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 54744 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.86583399772644}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([1700, 3040, 4129,  ..., 4083, 2058, 3930]),
-       values=tensor([0.1350, 0.7186, 0.2594,  ..., 0.2124, 0.0344, 0.1244]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 440, 3019, 2397,  ..., 2648, 4224, 1471]),
+       values=tensor([0.9686, 0.9548, 0.6770,  ..., 0.0683, 0.1247, 0.7029]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7260, 0.9332, 0.2146,  ..., 0.1697, 0.4017, 0.1867])
+tensor([0.3648, 0.8360, 0.9424,  ..., 0.5773, 0.5768, 0.8650])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 9.936307668685913 seconds
+Time: 5.86583399772644 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 96826 -ss 5000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.588202953338623}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97993 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.781827449798584}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([3997, 2967, 4931,  ..., 3835, 1314, 3597]),
-       values=tensor([0.7356, 0.2235, 0.2006,  ..., 0.5232, 0.0695, 0.2889]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2498, 2500, 2500]),
+       col_indices=tensor([3713, 3378, 4473,  ..., 4286, 2104, 3764]),
+       values=tensor([0.2566, 0.6316, 0.0221,  ..., 0.9864, 0.6559, 0.8912]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7175, 0.9581, 0.9907,  ..., 0.9548, 0.8349, 0.6616])
+tensor([0.3213, 0.3541, 0.7168,  ..., 0.5598, 0.7087, 0.6560])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.588202953338623 seconds
+Time: 10.781827449798584 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([3997, 2967, 4931,  ..., 3835, 1314, 3597]),
-       values=tensor([0.7356, 0.2235, 0.2006,  ..., 0.5232, 0.0695, 0.2889]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2498, 2500, 2500]),
+       col_indices=tensor([3713, 3378, 4473,  ..., 4286, 2104, 3764]),
+       values=tensor([0.2566, 0.6316, 0.0221,  ..., 0.9864, 0.6559, 0.8912]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7175, 0.9581, 0.9907,  ..., 0.9548, 0.8349, 0.6616])
+tensor([0.3213, 0.3541, 0.7168,  ..., 0.5598, 0.7087, 0.6560])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.588202953338623 seconds
+Time: 10.781827449798584 seconds
 
-[20.4, 20.56, 20.56, 20.6, 20.44, 20.64, 20.68, 20.96, 21.52, 22.56]
-[23.28, 23.64, 26.88, 28.2, 29.6, 29.52, 29.52, 29.72, 25.72, 24.92, 23.8, 23.72, 24.2, 24.28]
-14.619201183319092
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 96826, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.588202953338623, 'TIME_S_1KI': 0.10935289027057425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.2542047309875, 'W': 24.50573052785985}
-[20.4, 20.56, 20.56, 20.6, 20.44, 20.64, 20.68, 20.96, 21.52, 22.56, 22.96, 23.28, 23.32, 23.36, 23.32, 23.28, 23.36, 23.12, 23.0, 23.0]
-396.46
-19.823
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 96826, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.588202953338623, 'TIME_S_1KI': 0.10935289027057425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.2542047309875, 'W': 24.50573052785985, 'J_1KI': 3.699979393251683, 'W_1KI': 0.2530903943967514, 'W_D': 4.6827305278598494, 'J_D': 68.45777967405314, 'W_D_1KI': 0.04836232548963966, 'J_D_1KI': 0.000499476643563089}
+[20.44, 20.48, 20.88, 20.96, 20.96, 20.92, 20.72, 20.72, 20.56, 20.48]
+[20.44, 20.32, 20.6, 21.56, 23.32, 24.04, 24.72, 24.96, 24.56, 23.56, 23.68, 23.44, 23.52, 23.56]
+14.642489433288574
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 97993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.781827449798584, 'TIME_S_1KI': 0.1100265064831017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.41705104827884, 'W': 21.541217597275665}
+[20.44, 20.48, 20.88, 20.96, 20.96, 20.92, 20.72, 20.72, 20.56, 20.48, 20.36, 20.44, 20.44, 20.32, 20.4, 20.68, 21.08, 21.76, 21.76, 21.88]
+374.65999999999997
+18.732999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 97993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.781827449798584, 'TIME_S_1KI': 0.1100265064831017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.41705104827884, 'W': 21.541217597275665, 'J_1KI': 3.218771249459439, 'W_1KI': 0.21982404454681115, 'W_D': 2.808217597275668, 'J_D': 41.119296494484, 'W_D_1KI': 0.02865732855689353, 'J_D_1KI': 0.00029244260872606745}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
index 7e96f33..1de7362 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.667346239089966, "TIME_S_1KI": 0.6143722996653784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.18905796051024, "W": 22.76067676218484, "J_1KI": 19.1896019098376, "W_1KI": 1.3108723585892323, "W_D": 3.0516767621848366, "J_D": 44.67289422965043, "W_D_1KI": 0.17575745909029755, "J_D_1KI": 0.010122528312520737}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.451899290084839, "TIME_S_1KI": 0.5871523672875029, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.3643196678161, "W": 22.918247866906853, "J_1KI": 18.839633709781257, "W_1KI": 1.2874696852371694, "W_D": 4.382247866906855, "J_D": 64.12573871421813, "W_D_1KI": 0.24617987005824704, "J_D_1KI": 0.013829552837382564}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
index ba00b65..6e9f06a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6047096252441406}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06674790382385254}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     7,    11,  ..., 24983, 24992, 25000]),
-       col_indices=tensor([ 225,  408, 1943,  ..., 2555, 2651, 2712]),
-       values=tensor([0.7906, 0.4816, 0.2276,  ..., 0.2718, 0.8003, 0.8712]),
+tensor(crow_indices=tensor([    0,     6,    11,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 153, 1166, 1591,  ..., 4476, 1654, 3013]),
+       values=tensor([0.9133, 0.8479, 0.0929,  ..., 0.2328, 0.6185, 0.0308]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6660, 0.8709, 0.7078,  ..., 0.4840, 0.5828, 0.2928])
+tensor([0.9627, 0.4329, 0.3045,  ..., 0.2813, 0.7730, 0.0924])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.6047096252441406 seconds
+Time: 0.06674790382385254 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17363 -ss 5000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.667346239089966}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15730 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.27815866470337}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     7,  ..., 24986, 24994, 25000]),
-       col_indices=tensor([  85,  195, 4187,  ..., 2991, 3287, 4675]),
-       values=tensor([0.1915, 0.0298, 0.9128,  ..., 0.0482, 0.8260, 0.8063]),
+tensor(crow_indices=tensor([    0,     1,     5,  ..., 24988, 24995, 25000]),
+       col_indices=tensor([1294,  410,  634,  ..., 1096, 2182, 3875]),
+       values=tensor([0.7576, 0.8466, 0.6529,  ..., 0.8373, 0.3120, 0.9707]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9668, 0.6018, 0.4153,  ..., 0.6117, 0.1974, 0.6733])
+tensor([0.6367, 0.3440, 0.8123,  ..., 0.8035, 0.7344, 0.3858])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,15 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.667346239089966 seconds
+Time: 9.27815866470337 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17801 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.451899290084839}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     7,  ..., 24986, 24994, 25000]),
-       col_indices=tensor([  85,  195, 4187,  ..., 2991, 3287, 4675]),
-       values=tensor([0.1915, 0.0298, 0.9128,  ..., 0.0482, 0.8260, 0.8063]),
+tensor(crow_indices=tensor([    0,     2,     5,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 139, 2091, 2694,  ..., 3635, 3692, 4401]),
+       values=tensor([0.7198, 0.5125, 0.0166,  ..., 0.6335, 0.1279, 0.4059]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9668, 0.6018, 0.4153,  ..., 0.6117, 0.1974, 0.6733])
+tensor([0.0330, 0.5472, 0.9005,  ..., 0.3693, 0.0673, 0.4597])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -50,13 +53,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.667346239089966 seconds
+Time: 10.451899290084839 seconds
 
-[22.96, 22.64, 22.44, 22.84, 23.2, 23.48, 24.08, 23.88, 23.04, 22.2]
-[21.52, 21.52, 20.88, 24.32, 25.36, 27.04, 27.68, 28.12, 25.12, 23.88, 23.84, 23.84, 23.68, 23.84]
-14.638802766799927
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.667346239089966, 'TIME_S_1KI': 0.6143722996653784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.18905796051024, 'W': 22.76067676218484}
-[22.96, 22.64, 22.44, 22.84, 23.2, 23.48, 24.08, 23.88, 23.04, 22.2, 20.48, 20.6, 20.52, 20.76, 20.72, 20.68, 20.68, 20.76, 20.64, 20.8]
-394.18000000000006
-19.709000000000003
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.667346239089966, 'TIME_S_1KI': 0.6143722996653784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.18905796051024, 'W': 22.76067676218484, 'J_1KI': 19.1896019098376, 'W_1KI': 1.3108723585892323, 'W_D': 3.0516767621848366, 'J_D': 44.67289422965043, 'W_D_1KI': 0.17575745909029755, 'J_D_1KI': 0.010122528312520737}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     5,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 139, 2091, 2694,  ..., 3635, 3692, 4401]),
+       values=tensor([0.7198, 0.5125, 0.0166,  ..., 0.6335, 0.1279, 0.4059]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0330, 0.5472, 0.9005,  ..., 0.3693, 0.0673, 0.4597])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.451899290084839 seconds
+
+[20.28, 20.36, 20.4, 20.6, 20.68, 20.64, 20.52, 20.48, 20.32, 20.24]
+[20.4, 20.48, 23.8, 23.8, 25.36, 27.2, 28.0, 28.44, 25.2, 23.88, 23.72, 23.92, 24.08, 24.08]
+14.63306975364685
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.451899290084839, 'TIME_S_1KI': 0.5871523672875029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3643196678161, 'W': 22.918247866906853}
+[20.28, 20.36, 20.4, 20.6, 20.68, 20.64, 20.52, 20.48, 20.32, 20.24, 20.44, 20.64, 20.96, 20.68, 20.56, 20.68, 20.72, 20.76, 20.76, 20.96]
+370.71999999999997
+18.535999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.451899290084839, 'TIME_S_1KI': 0.5871523672875029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3643196678161, 'W': 22.918247866906853, 'J_1KI': 18.839633709781257, 'W_1KI': 1.2874696852371694, 'W_D': 4.382247866906855, 'J_D': 64.12573871421813, 'W_D_1KI': 0.24617987005824704, 'J_D_1KI': 0.013829552837382564}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
index a5d181f..d1eb1c9 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1948, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5094473361969, "TIME_S_1KI": 5.394993499074384, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 321.46233737945556, "W": 22.060099154306354, "J_1KI": 165.02173376768766, "W_1KI": 11.324486218843099, "W_D": 3.783099154306356, "J_D": 55.127762036561975, "W_D_1KI": 1.9420426870155834, "J_D_1KI": 0.9969418311168293}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1927, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.278133630752563, "TIME_S_1KI": 5.333748640764174, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.9295339012146, "W": 21.68469080536072, "J_1KI": 163.94890186881918, "W_1KI": 11.253082929611168, "W_D": 3.326690805360723, "J_D": 48.46736737012868, "W_D_1KI": 1.7263574495904115, "J_D_1KI": 0.8958782820915472}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output
index 80c2fbe..9788e34 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.389868259429932}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5448694229125977}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     46,     96,  ..., 249907, 249949,
+tensor(crow_indices=tensor([     0,     55,    104,  ..., 249896, 249951,
                             250000]),
-       col_indices=tensor([ 123,  345,  399,  ..., 4711, 4879, 4988]),
-       values=tensor([0.4250, 0.5468, 0.7620,  ..., 0.1883, 0.2040, 0.8985]),
+       col_indices=tensor([ 128,  142,  245,  ..., 4657, 4734, 4838]),
+       values=tensor([0.1820, 0.1438, 0.1562,  ..., 0.6881, 0.0081, 0.4382]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2612, 0.9268, 0.9416,  ..., 0.0698, 0.1077, 0.5090])
+tensor([0.7604, 0.0772, 0.6951,  ..., 0.4926, 0.6864, 0.3702])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 5.389868259429932 seconds
+Time: 0.5448694229125977 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1948 -ss 5000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5094473361969}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1927 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.278133630752563}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     57,    115,  ..., 249893, 249944,
+tensor(crow_indices=tensor([     0,     52,    109,  ..., 249898, 249957,
                             250000]),
-       col_indices=tensor([  73,  135,  475,  ..., 4575, 4723, 4971]),
-       values=tensor([0.1739, 0.5180, 0.0955,  ..., 0.3924, 0.5566, 0.2573]),
+       col_indices=tensor([  25,  140,  158,  ..., 4486, 4823, 4835]),
+       values=tensor([0.8176, 0.1521, 0.6094,  ..., 0.2740, 0.3181, 0.5161]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5075, 0.6044, 0.6141,  ..., 0.4161, 0.9554, 0.0515])
+tensor([0.4533, 0.4508, 0.3256,  ..., 0.6556, 0.1742, 0.9221])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.5094473361969 seconds
+Time: 10.278133630752563 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     57,    115,  ..., 249893, 249944,
+tensor(crow_indices=tensor([     0,     52,    109,  ..., 249898, 249957,
                             250000]),
-       col_indices=tensor([  73,  135,  475,  ..., 4575, 4723, 4971]),
-       values=tensor([0.1739, 0.5180, 0.0955,  ..., 0.3924, 0.5566, 0.2573]),
+       col_indices=tensor([  25,  140,  158,  ..., 4486, 4823, 4835]),
+       values=tensor([0.8176, 0.1521, 0.6094,  ..., 0.2740, 0.3181, 0.5161]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5075, 0.6044, 0.6141,  ..., 0.4161, 0.9554, 0.0515])
+tensor([0.4533, 0.4508, 0.3256,  ..., 0.6556, 0.1742, 0.9221])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.5094473361969 seconds
+Time: 10.278133630752563 seconds
 
-[20.56, 20.36, 20.36, 20.92, 20.64, 20.52, 20.44, 20.2, 20.0, 20.24]
-[20.4, 20.4, 20.96, 22.24, 24.0, 24.56, 25.32, 25.32, 25.28, 24.8, 24.08, 24.24, 24.16, 24.16]
-14.572116613388062
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1948, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5094473361969, 'TIME_S_1KI': 5.394993499074384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.46233737945556, 'W': 22.060099154306354}
-[20.56, 20.36, 20.36, 20.92, 20.64, 20.52, 20.44, 20.2, 20.0, 20.24, 20.16, 20.08, 20.04, 20.12, 20.12, 20.16, 20.24, 20.2, 20.44, 20.44]
-365.53999999999996
-18.276999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1948, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5094473361969, 'TIME_S_1KI': 5.394993499074384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.46233737945556, 'W': 22.060099154306354, 'J_1KI': 165.02173376768766, 'W_1KI': 11.324486218843099, 'W_D': 3.783099154306356, 'J_D': 55.127762036561975, 'W_D_1KI': 1.9420426870155834, 'J_D_1KI': 0.9969418311168293}
+[20.28, 20.32, 20.56, 20.6, 20.48, 20.56, 20.76, 20.68, 20.88, 20.88]
+[20.72, 20.48, 20.56, 21.28, 22.24, 24.0, 24.68, 25.2, 25.04, 23.88, 23.88, 24.04, 24.2, 24.4]
+14.56924319267273
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1927, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.278133630752563, 'TIME_S_1KI': 5.333748640764174, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.9295339012146, 'W': 21.68469080536072}
+[20.28, 20.32, 20.56, 20.6, 20.48, 20.56, 20.76, 20.68, 20.88, 20.88, 20.16, 20.08, 20.16, 20.16, 20.24, 20.36, 20.08, 20.2, 20.28, 20.2]
+367.15999999999997
+18.357999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1927, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.278133630752563, 'TIME_S_1KI': 5.333748640764174, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.9295339012146, 'W': 21.68469080536072, 'J_1KI': 163.94890186881918, 'W_1KI': 11.253082929611168, 'W_D': 3.326690805360723, 'J_D': 48.46736737012868, 'W_D_1KI': 1.7263574495904115, 'J_D_1KI': 0.8958782820915472}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
index 496e602..942bb7d 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 28.093817949295044, "TIME_S_1KI": 28.093817949295044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 689.2963391876219, "W": 22.82953703239495, "J_1KI": 689.2963391876219, "W_1KI": 22.82953703239495, "W_D": 4.334537032394952, "J_D": 130.87346030116072, "W_D_1KI": 4.334537032394952, "J_D_1KI": 4.334537032394952}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 393, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.605815887451172, "TIME_S_1KI": 26.986808873921557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.57593024253845, "W": 23.547531420592268, "J_1KI": 874.239008250734, "W_1KI": 59.917382749598644, "W_D": 5.065531420592269, "J_D": 73.90985657548903, "W_D_1KI": 12.889392927715695, "J_D_1KI": 32.79743747510355}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output
index 9c2fd18..5869b7e 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 28.093817949295044}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6673474311828613}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     249,     484,  ..., 1249498,
-                            1249755, 1250000]),
-       col_indices=tensor([   8,   31,   46,  ..., 4934, 4976, 4984]),
-       values=tensor([0.2044, 0.4643, 0.3912,  ..., 0.8352, 0.2191, 0.4950]),
+tensor(crow_indices=tensor([      0,     248,     507,  ..., 1249488,
+                            1249771, 1250000]),
+       col_indices=tensor([   0,   22,   35,  ..., 4958, 4983, 4999]),
+       values=tensor([0.4233, 0.1325, 0.2059,  ..., 0.9744, 0.8399, 0.1366]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7092, 0.7480, 0.2063,  ..., 0.9775, 0.7055, 0.9981])
+tensor([0.6304, 0.5951, 0.1863,  ..., 0.0552, 0.3796, 0.7701])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 28.093817949295044 seconds
+Time: 2.6673474311828613 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 393 -ss 5000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.605815887451172}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     249,     484,  ..., 1249498,
-                            1249755, 1250000]),
-       col_indices=tensor([   8,   31,   46,  ..., 4934, 4976, 4984]),
-       values=tensor([0.2044, 0.4643, 0.3912,  ..., 0.8352, 0.2191, 0.4950]),
+tensor(crow_indices=tensor([      0,     284,     548,  ..., 1249494,
+                            1249762, 1250000]),
+       col_indices=tensor([   9,   27,   28,  ..., 4894, 4914, 4954]),
+       values=tensor([0.8223, 0.3728, 0.3102,  ..., 0.8633, 0.4361, 0.2072]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7092, 0.7480, 0.2063,  ..., 0.9775, 0.7055, 0.9981])
+tensor([0.4176, 0.5149, 0.4165,  ..., 0.2240, 0.9505, 0.5242])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +36,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 28.093817949295044 seconds
+Time: 10.605815887451172 seconds
 
-[20.56, 20.6, 20.48, 20.56, 20.4, 20.48, 20.68, 20.72, 20.92, 21.08]
-[20.92, 20.92, 20.56, 23.36, 25.52, 27.28, 28.24, 28.72, 25.44, 24.36, 24.6, 24.76, 24.64, 24.52, 24.36, 24.44, 24.24, 24.4, 24.44, 24.16, 24.16, 24.12, 24.2, 24.2, 24.08, 24.12, 24.24, 24.12, 24.0]
-30.193180799484253
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 28.093817949295044, 'TIME_S_1KI': 28.093817949295044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.2963391876219, 'W': 22.82953703239495}
-[20.56, 20.6, 20.48, 20.56, 20.4, 20.48, 20.68, 20.72, 20.92, 21.08, 20.52, 20.6, 20.36, 20.4, 20.52, 20.44, 20.6, 20.44, 20.44, 20.36]
-369.9
-18.494999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 28.093817949295044, 'TIME_S_1KI': 28.093817949295044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.2963391876219, 'W': 22.82953703239495, 'J_1KI': 689.2963391876219, 'W_1KI': 22.82953703239495, 'W_D': 4.334537032394952, 'J_D': 130.87346030116072, 'W_D_1KI': 4.334537032394952, 'J_D_1KI': 4.334537032394952}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     284,     548,  ..., 1249494,
+                            1249762, 1250000]),
+       col_indices=tensor([   9,   27,   28,  ..., 4894, 4914, 4954]),
+       values=tensor([0.8223, 0.3728, 0.3102,  ..., 0.8633, 0.4361, 0.2072]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4176, 0.5149, 0.4165,  ..., 0.2240, 0.9505, 0.5242])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.605815887451172 seconds
+
+[20.32, 20.32, 20.44, 20.4, 20.52, 20.52, 20.64, 20.8, 20.92, 20.88]
+[20.92, 21.04, 24.24, 26.28, 27.72, 27.72, 28.36, 29.08, 25.2, 24.24, 24.28, 24.28, 24.28, 24.2]
+14.59074091911316
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.605815887451172, 'TIME_S_1KI': 26.986808873921557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.57593024253845, 'W': 23.547531420592268}
+[20.32, 20.32, 20.44, 20.4, 20.52, 20.52, 20.64, 20.8, 20.92, 20.88, 20.24, 20.28, 20.52, 20.64, 20.6, 20.56, 20.52, 20.48, 20.48, 20.56]
+369.64
+18.482
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.605815887451172, 'TIME_S_1KI': 26.986808873921557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.57593024253845, 'W': 23.547531420592268, 'J_1KI': 874.239008250734, 'W_1KI': 59.917382749598644, 'W_D': 5.065531420592269, 'J_D': 73.90985657548903, 'W_D_1KI': 12.889392927715695, 'J_D_1KI': 32.79743747510355}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
index fff9557..5939696 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.78093886375427, "TIME_S_1KI": 53.78093886375427, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1352.5172874259947, "W": 23.03061139017129, "J_1KI": 1352.5172874259947, "W_1KI": 23.03061139017129, "W_D": 4.417611390171292, "J_D": 259.4327902595995, "W_D_1KI": 4.417611390171292, "J_D_1KI": 4.417611390171292}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 194, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.912250995635986, "TIME_S_1KI": 56.248716472350445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.6964877033234, "W": 22.305376949262342, "J_1KI": 1684.0025139346567, "W_1KI": 114.97616984155846, "W_D": 3.7133769492623436, "J_D": 54.38810604286199, "W_D_1KI": 19.14111829516672, "J_D_1KI": 98.66555822250888}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output
index c5ce197..469c726 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.1 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.78093886375427}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.392450332641602}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     534,    1044,  ..., 2498992,
-                            2499517, 2500000]),
-       col_indices=tensor([   3,   19,   25,  ..., 4971, 4983, 4990]),
-       values=tensor([0.2124, 0.6762, 0.6770,  ..., 0.5380, 0.6783, 0.2658]),
+tensor(crow_indices=tensor([      0,     508,    1024,  ..., 2499064,
+                            2499534, 2500000]),
+       col_indices=tensor([   1,    4,   10,  ..., 4973, 4986, 4993]),
+       values=tensor([0.4448, 0.2935, 0.6096,  ..., 0.6772, 0.8304, 0.1969]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0160, 0.9125, 0.7128,  ..., 0.4183, 0.3158, 0.5797])
+tensor([0.5801, 0.6662, 0.3258,  ..., 0.9572, 0.7518, 0.3845])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 53.78093886375427 seconds
+Time: 5.392450332641602 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 194 -ss 5000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.912250995635986}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     534,    1044,  ..., 2498992,
-                            2499517, 2500000]),
-       col_indices=tensor([   3,   19,   25,  ..., 4971, 4983, 4990]),
-       values=tensor([0.2124, 0.6762, 0.6770,  ..., 0.5380, 0.6783, 0.2658]),
+tensor(crow_indices=tensor([      0,     510,    1021,  ..., 2499033,
+                            2499527, 2500000]),
+       col_indices=tensor([  27,   33,   84,  ..., 4958, 4963, 4982]),
+       values=tensor([0.5404, 0.4129, 0.3312,  ..., 0.4218, 0.5770, 0.4495]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0160, 0.9125, 0.7128,  ..., 0.4183, 0.3158, 0.5797])
+tensor([0.8518, 0.9058, 0.3829,  ..., 0.5160, 0.0011, 0.3108])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +36,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 53.78093886375427 seconds
+Time: 10.912250995635986 seconds
 
-[20.56, 20.72, 20.76, 20.76, 20.64, 20.88, 20.6, 20.52, 20.72, 20.48]
-[20.36, 20.24, 20.8, 22.0, 24.0, 25.2, 26.08, 25.8, 25.8, 25.44, 24.16, 24.32, 24.32, 24.4, 24.36, 24.4, 24.52, 24.88, 24.68, 24.64, 24.52, 24.28, 24.04, 24.08, 24.08, 24.08, 24.36, 24.4, 24.48, 24.6, 24.6, 24.64, 24.56, 24.52, 24.56, 24.32, 24.04, 24.32, 24.36, 24.24, 24.28, 24.28, 24.28, 24.48, 24.52, 24.76, 24.56, 24.24, 24.16, 24.04, 24.12, 24.12, 24.44, 24.48, 24.52, 24.4]
-58.726938009262085
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.78093886375427, 'TIME_S_1KI': 53.78093886375427, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.5172874259947, 'W': 23.03061139017129}
-[20.56, 20.72, 20.76, 20.76, 20.64, 20.88, 20.6, 20.52, 20.72, 20.48, 20.84, 20.6, 20.72, 20.72, 20.72, 20.52, 20.6, 20.72, 20.76, 20.72]
-372.26
-18.613
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.78093886375427, 'TIME_S_1KI': 53.78093886375427, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.5172874259947, 'W': 23.03061139017129, 'J_1KI': 1352.5172874259947, 'W_1KI': 23.03061139017129, 'W_D': 4.417611390171292, 'J_D': 259.4327902595995, 'W_D_1KI': 4.417611390171292, 'J_D_1KI': 4.417611390171292}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     510,    1021,  ..., 2499033,
+                            2499527, 2500000]),
+       col_indices=tensor([  27,   33,   84,  ..., 4958, 4963, 4982]),
+       values=tensor([0.5404, 0.4129, 0.3312,  ..., 0.4218, 0.5770, 0.4495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8518, 0.9058, 0.3829,  ..., 0.5160, 0.0011, 0.3108])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.912250995635986 seconds
+
+[20.28, 20.36, 20.6, 20.76, 20.96, 20.8, 20.76, 20.64, 20.48, 20.76]
+[20.88, 20.88, 20.64, 21.88, 22.92, 24.88, 26.08, 26.2, 25.72, 24.96, 24.48, 24.56, 24.76, 24.68]
+14.646535158157349
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.912250995635986, 'TIME_S_1KI': 56.248716472350445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.6964877033234, 'W': 22.305376949262342}
+[20.28, 20.36, 20.6, 20.76, 20.96, 20.8, 20.76, 20.64, 20.48, 20.76, 20.68, 20.08, 20.2, 20.52, 20.52, 20.72, 20.72, 21.32, 21.0, 21.08]
+371.84
+18.592
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.912250995635986, 'TIME_S_1KI': 56.248716472350445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.6964877033234, 'W': 22.305376949262342, 'J_1KI': 1684.0025139346567, 'W_1KI': 114.97616984155846, 'W_D': 3.7133769492623436, 'J_D': 54.38810604286199, 'W_D_1KI': 19.14111829516672, 'J_D_1KI': 98.66555822250888}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..8d5c96a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.527036905288696, "TIME_S_1KI": 105.27036905288696, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 363.19735393524167, "W": 23.113061201495835, "J_1KI": 3631.973539352417, "W_1KI": 231.13061201495836, "W_D": 4.666061201495836, "J_D": 73.32222533869742, "W_D_1KI": 46.66061201495836, "J_D_1KI": 466.6061201495836}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..9300b4b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.527036905288696}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     996,    2006,  ..., 4997968,
+                            4998974, 5000000]),
+       col_indices=tensor([   4,    8,   12,  ..., 4976, 4983, 4993]),
+       values=tensor([0.4991, 0.7024, 0.1537,  ..., 0.4726, 0.2476, 0.0939]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1571, 0.7792, 0.7385,  ..., 0.2151, 0.4821, 0.5033])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.527036905288696 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     996,    2006,  ..., 4997968,
+                            4998974, 5000000]),
+       col_indices=tensor([   4,    8,   12,  ..., 4976, 4983, 4993]),
+       values=tensor([0.4991, 0.7024, 0.1537,  ..., 0.4726, 0.2476, 0.0939]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1571, 0.7792, 0.7385,  ..., 0.2151, 0.4821, 0.5033])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.527036905288696 seconds
+
+[20.48, 20.44, 20.48, 20.64, 20.72, 20.72, 20.68, 20.72, 20.68, 20.6]
+[20.4, 20.48, 21.0, 23.96, 25.48, 27.4, 28.52, 27.56, 26.24, 25.04, 24.48, 24.4, 24.4, 24.32, 24.48]
+15.71394419670105
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.527036905288696, 'TIME_S_1KI': 105.27036905288696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 363.19735393524167, 'W': 23.113061201495835}
+[20.48, 20.44, 20.48, 20.64, 20.72, 20.72, 20.68, 20.72, 20.68, 20.6, 20.4, 20.16, 20.32, 20.32, 20.16, 20.56, 20.48, 20.52, 20.48, 20.24]
+368.94
+18.447
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.527036905288696, 'TIME_S_1KI': 105.27036905288696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 363.19735393524167, 'W': 23.113061201495835, 'J_1KI': 3631.973539352417, 'W_1KI': 231.13061201495836, 'W_D': 4.666061201495836, 'J_D': 73.32222533869742, 'W_D_1KI': 46.66061201495836, 'J_D_1KI': 466.6061201495836}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..fef5f93
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.927062749862671, "TIME_S_1KI": 159.2706274986267, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 517.2976983642578, "W": 23.521442010494468, "J_1KI": 5172.9769836425785, "W_1KI": 235.21442010494468, "W_D": 5.048442010494465, "J_D": 111.02837280082699, "W_D_1KI": 50.484420104944654, "J_D_1KI": 504.8442010494465}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..eaea98f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.927062749862671}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1527,    3024,  ..., 7496971,
+                            7498460, 7500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4985, 4992, 4996]),
+       values=tensor([0.7552, 0.2419, 0.2481,  ..., 0.7383, 0.7786, 0.4470]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6238, 0.3406, 0.3665,  ..., 0.0040, 0.2464, 0.8126])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 15.927062749862671 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1527,    3024,  ..., 7496971,
+                            7498460, 7500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4985, 4992, 4996]),
+       values=tensor([0.7552, 0.2419, 0.2481,  ..., 0.7383, 0.7786, 0.4470]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6238, 0.3406, 0.3665,  ..., 0.0040, 0.2464, 0.8126])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 15.927062749862671 seconds
+
+[20.4, 20.32, 20.32, 20.4, 20.48, 20.52, 20.8, 20.76, 20.76, 20.64]
+[20.8, 20.8, 21.12, 22.88, 23.72, 26.16, 27.92, 27.96, 27.28, 26.72, 25.24, 24.32, 24.24, 24.16, 24.2, 24.68, 24.8, 24.68, 24.68, 24.56, 24.64]
+21.99260139465332
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.927062749862671, 'TIME_S_1KI': 159.2706274986267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 517.2976983642578, 'W': 23.521442010494468}
+[20.4, 20.32, 20.32, 20.4, 20.48, 20.52, 20.8, 20.76, 20.76, 20.64, 20.56, 20.16, 20.24, 20.6, 20.64, 20.76, 20.8, 20.6, 20.28, 20.44]
+369.46000000000004
+18.473000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.927062749862671, 'TIME_S_1KI': 159.2706274986267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 517.2976983642578, 'W': 23.521442010494468, 'J_1KI': 5172.9769836425785, 'W_1KI': 235.21442010494468, 'W_D': 5.048442010494465, 'J_D': 111.02837280082699, 'W_D_1KI': 50.484420104944654, 'J_D_1KI': 504.8442010494465}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d2af780
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.84249472618103, "TIME_S_1KI": 228.4249472618103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 682.8522075366976, "W": 24.128078827131084, "J_1KI": 6828.522075366976, "W_1KI": 241.28078827131083, "W_D": 3.4220788271310845, "J_D": 96.84874200773261, "W_D_1KI": 34.220788271310845, "J_D_1KI": 342.2078827131084}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..c75f057
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.84249472618103}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2006,     4067,  ...,  9995955,
+                             9997971, 10000000]),
+       col_indices=tensor([   1,    3,    4,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5438, 0.4529, 0.4674,  ..., 0.4313, 0.1734, 0.8643]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4316, 0.5719, 0.8319,  ..., 0.7407, 0.2442, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 22.84249472618103 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2006,     4067,  ...,  9995955,
+                             9997971, 10000000]),
+       col_indices=tensor([   1,    3,    4,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5438, 0.4529, 0.4674,  ..., 0.4313, 0.1734, 0.8643]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4316, 0.5719, 0.8319,  ..., 0.7407, 0.2442, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 22.84249472618103 seconds
+
+[26.88, 26.16, 25.48, 25.52, 24.68, 24.6, 24.36, 24.16, 24.16, 24.28]
+[24.4, 24.52, 24.52, 28.04, 29.2, 30.8, 31.68, 30.2, 29.88, 28.12, 26.84, 25.72, 24.28, 24.16, 24.04, 24.12, 24.24, 24.24, 24.4, 24.44, 24.48, 24.52, 24.68, 24.48, 24.68, 24.6, 24.68]
+28.301142930984497
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.84249472618103, 'TIME_S_1KI': 228.4249472618103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 682.8522075366976, 'W': 24.128078827131084}
+[26.88, 26.16, 25.48, 25.52, 24.68, 24.6, 24.36, 24.16, 24.16, 24.28, 20.44, 20.64, 20.68, 20.8, 20.92, 21.32, 21.36, 21.44, 21.44, 21.2]
+414.12
+20.706
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.84249472618103, 'TIME_S_1KI': 228.4249472618103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 682.8522075366976, 'W': 24.128078827131084, 'J_1KI': 6828.522075366976, 'W_1KI': 241.28078827131083, 'W_D': 3.4220788271310845, 'J_D': 96.84874200773261, 'W_D_1KI': 34.220788271310845, 'J_D_1KI': 342.2078827131084}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..1c5a213
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.49390745162964, "TIME_S_1KI": 264.9390745162964, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 833.4256306743622, "W": 24.07917164557845, "J_1KI": 8334.256306743622, "W_1KI": 240.79171645578452, "W_D": 5.603171645578453, "J_D": 193.9363584108353, "W_D_1KI": 56.031716455784526, "J_D_1KI": 560.3171645578453}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..99ea343
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.49390745162964}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2475,     4991,  ..., 12495070,
+                            12497536, 12500000]),
+       col_indices=tensor([   3,    6,    7,  ..., 4992, 4996, 4999]),
+       values=tensor([0.7861, 0.1444, 0.2009,  ..., 0.5207, 0.8919, 0.5019]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4801, 0.0235, 0.0420,  ..., 0.5930, 0.2408, 0.0610])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.49390745162964 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2475,     4991,  ..., 12495070,
+                            12497536, 12500000]),
+       col_indices=tensor([   3,    6,    7,  ..., 4992, 4996, 4999]),
+       values=tensor([0.7861, 0.1444, 0.2009,  ..., 0.5207, 0.8919, 0.5019]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4801, 0.0235, 0.0420,  ..., 0.5930, 0.2408, 0.0610])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.49390745162964 seconds
+
+[20.72, 20.56, 20.56, 20.36, 20.36, 20.36, 20.28, 20.56, 20.72, 21.08]
+[21.48, 21.52, 24.72, 26.76, 28.04, 29.8, 31.48, 31.48, 29.76, 28.44, 27.2, 25.96, 25.12, 24.52, 24.64, 24.64, 24.72, 24.52, 24.56, 24.68, 24.68, 24.6, 24.52, 24.2, 24.32, 24.24, 24.16, 24.4, 24.44, 24.44, 24.44, 24.6, 24.36]
+34.611889600753784
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.49390745162964, 'TIME_S_1KI': 264.9390745162964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.4256306743622, 'W': 24.07917164557845}
+[20.72, 20.56, 20.56, 20.36, 20.36, 20.36, 20.28, 20.56, 20.72, 21.08, 20.64, 20.64, 20.36, 20.36, 20.56, 20.64, 20.68, 20.56, 20.56, 20.36]
+369.52
+18.476
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.49390745162964, 'TIME_S_1KI': 264.9390745162964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.4256306743622, 'W': 24.07917164557845, 'J_1KI': 8334.256306743622, 'W_1KI': 240.79171645578452, 'W_D': 5.603171645578453, 'J_D': 193.9363584108353, 'W_D_1KI': 56.031716455784526, 'J_D_1KI': 560.3171645578453}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
index a523326..732c4d4 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 289284, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.336281061172485, "TIME_S_1KI": 0.035730566022222056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 302.8508556365967, "W": 22.269333190276484, "J_1KI": 1.0468980504853247, "W_1KI": 0.07698086721103305, "W_D": 3.542333190276487, "J_D": 48.17381052494056, "W_D_1KI": 0.012245174950140648, "J_D_1KI": 4.232925066765064e-05}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 289937, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.948570489883423, "TIME_S_1KI": 0.037761894790535266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 337.0917576313019, "W": 22.992700250818316, "J_1KI": 1.1626379442130597, "W_1KI": 0.07930240104166876, "W_D": 4.582700250818316, "J_D": 67.18612713575365, "W_D_1KI": 0.015805848342289243, "J_D_1KI": 5.4514768181671336e-05}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
index 2a5bc74..90b44fb 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,183 +1,75 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04395866394042969}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012730121612548828}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 439, 4351,  241,  500, 1004,  350, 1803, 3065, 3191,
-                           3136, 4095,  405, 1027,  870, 1417, 1360, 1534, 1342,
-                           3091, 3442, 2499, 1358, 1636, 3780, 2825, 2256, 4221,
-                            891, 2908, 3121, 1626, 2038,  883, 1037,  495, 2079,
-                            274,  937, 1868, 1488, 2903, 1523, 2167,  269, 3946,
-                           4053, 3008, 3702, 2193, 1563,  433, 1763, 2812, 3707,
-                           1886, 3013, 1511,  241, 1937, 2889, 1518, 4490, 4205,
-                           2026, 1673,  448,  986, 4061, 3094, 3985, 2097, 1213,
-                           4129, 3540, 2913, 1842, 3281, 3579, 2699, 1582, 1926,
-                           2137, 2888,  530, 3516, 2878,   57, 3238, 1656,  156,
-                           3904, 1121,  616, 2128,  426, 4846, 2365, 4030,  347,
-                           3690,  867, 1324, 1005, 4649, 3492, 4358,   47,  220,
-                           4307,  708, 2842, 3336, 1686, 1004, 4195, 3767,  332,
-                             43, 2809, 3452, 1463, 2172, 1464, 3770, 1880, 2042,
-                           3777, 2498, 3420, 1112, 4060, 4103, 4825, 1440, 4448,
-                             99, 2245, 3060,   27, 3733,  457, 3987, 3747, 1652,
-                           2522,  757, 4125, 2250, 2724, 3925, 2338, 3816, 1409,
-                           2282, 4242,  682, 3683, 4310, 1582, 4330,  601,  544,
-                           2289, 2874, 3966, 1136,  681, 4257, 2516, 3237, 2677,
-                           2257, 2771, 3675, 3168, 1248, 4288, 3632, 3599,  280,
-                           4551, 4047, 3577, 2662, 2281, 1968, 3402,  454, 1141,
-                           3366,  354, 2291, 4168, 3523, 2296, 2127, 2248, 4229,
-                           2140,  736, 3393,  640, 4820, 2236, 1416, 4815, 3234,
-                           1042, 1979, 4637, 2323,  138, 2380, 3226, 1859, 3342,
-                           2378,  803,  349, 3172, 4960, 4660, 4480, 3337,  245,
-                           4128, 3649, 2732,  968,  771, 3445, 3899,  644,   16,
-                           3599, 1029, 1799, 4502,  366, 4843, 2859, 2949,  545,
-                            645, 3511, 4843,  251, 2988, 2387,  946]),
-       values=tensor([9.4296e-01, 5.2301e-01, 8.9037e-01, 1.8262e-01,
-                      9.3621e-01, 5.6553e-01, 9.8721e-01, 6.5141e-01,
-                      2.8305e-01, 8.9567e-01, 7.1276e-04, 4.5788e-01,
-                      1.3154e-01, 7.7912e-01, 2.1464e-01, 9.3572e-01,
-                      4.0199e-01, 1.4579e-01, 1.5259e-01, 5.2311e-01,
-                      6.3620e-01, 8.3700e-01, 3.7813e-01, 1.4289e-01,
-                      6.8630e-01, 9.7120e-01, 7.6830e-01, 1.8723e-01,
-                      5.0392e-01, 9.2014e-01, 9.6103e-01, 7.2487e-01,
-                      3.2638e-01, 3.9838e-01, 2.7919e-01, 9.9376e-02,
-                      1.2394e-01, 1.9018e-01, 9.4573e-01, 4.8384e-02,
-                      3.3755e-01, 5.4543e-01, 6.5933e-01, 9.2931e-03,
-                      6.7184e-01, 3.3367e-01, 7.2403e-02, 1.6238e-01,
-                      7.9429e-01, 7.1594e-01, 9.3852e-01, 9.0787e-01,
-                      8.7587e-01, 2.4929e-01, 3.4089e-01, 7.4583e-01,
-                      3.6106e-01, 5.5151e-01, 6.3073e-01, 2.4689e-01,
-                      6.6122e-01, 6.2804e-01, 3.7429e-04, 5.6550e-01,
-                      5.0592e-01, 5.2248e-02, 7.1885e-01, 1.4852e-03,
-                      6.1029e-01, 4.5258e-01, 9.8998e-01, 7.7545e-03,
-                      6.8035e-01, 8.7032e-01, 2.7807e-01, 6.6854e-01,
-                      8.8838e-01, 1.5830e-02, 6.6226e-01, 1.1911e-01,
-                      1.8780e-01, 3.7508e-01, 9.2709e-01, 1.3932e-01,
-                      8.5139e-01, 2.8186e-01, 2.2711e-01, 8.2491e-01,
-                      9.3666e-01, 5.4799e-01, 8.7126e-01, 5.6305e-01,
-                      2.9909e-01, 9.8105e-02, 1.0565e-01, 9.1471e-01,
-                      9.5693e-01, 5.2767e-01, 7.5753e-01, 2.3887e-01,
-                      8.7389e-01, 2.4255e-01, 8.0756e-01, 7.2201e-01,
-                      6.6620e-01, 4.9751e-01, 5.1454e-01, 8.6001e-01,
-                      3.0834e-01, 2.2246e-01, 1.9841e-01, 8.9698e-02,
-                      9.1174e-01, 9.2243e-01, 7.7010e-01, 3.5962e-01,
-                      6.8634e-01, 9.5528e-01, 9.6147e-02, 9.3024e-02,
-                      8.3726e-01, 7.2003e-01, 6.7904e-01, 2.9273e-01,
-                      9.7464e-02, 1.5658e-02, 9.0559e-01, 3.6883e-01,
-                      7.9470e-01, 3.6450e-01, 5.7814e-03, 6.5827e-02,
-                      6.1557e-02, 3.8228e-02, 4.7705e-01, 2.6058e-01,
-                      8.0137e-01, 9.8272e-01, 8.4581e-01, 6.6501e-01,
-                      5.2583e-03, 3.0522e-01, 9.5123e-01, 2.4154e-01,
-                      6.0106e-01, 6.7170e-01, 2.1086e-01, 6.6402e-01,
-                      9.0397e-01, 3.9084e-01, 2.0324e-01, 7.2153e-01,
-                      6.7300e-01, 5.3381e-01, 2.8418e-02, 4.4506e-01,
-                      1.0782e-01, 1.9622e-01, 8.0898e-02, 5.4146e-01,
-                      8.2802e-01, 7.5722e-01, 9.2798e-04, 8.7421e-02,
-                      6.0281e-01, 1.2511e-01, 5.8418e-01, 7.7672e-01,
-                      8.2524e-01, 8.4603e-01, 6.9503e-01, 5.3184e-01,
-                      8.1918e-01, 5.6983e-01, 6.0056e-01, 1.8971e-01,
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-       layout=torch.sparse_csr)
-tensor([0.0440, 0.7352, 0.2145,  ..., 0.3780, 0.1332, 0.0924])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 250
-Density: 1e-05
-Time: 0.04395866394042969 seconds
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-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 238860 -ss 5000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.669770956039429}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
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+                      0.9943, 0.0815, 0.8720, 0.8261, 0.8846]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.3130, 0.9247, 0.8789,  ..., 0.8987, 0.6939, 0.4674])
+tensor([0.4430, 0.7360, 0.8513,  ..., 0.2058, 0.5954, 0.5363])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -185,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 8.669770956039429 seconds
+Time: 0.012730121612548828 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 289284 -ss 5000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.336281061172485}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 82481 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.9870200157165527}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4104, 0.7044, 0.9040,  ..., 0.0726, 0.3479, 0.6465])
+tensor([0.1901, 0.2382, 0.6937,  ..., 0.7632, 0.8746, 0.1540])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -266,77 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.336281061172485 seconds
+Time: 2.9870200157165527 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 289937 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.948570489883423}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4104, 0.7044, 0.9040,  ..., 0.0726, 0.3479, 0.6465])
+tensor([0.0011, 0.9357, 0.8539,  ..., 0.1995, 0.1479, 0.1616])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -344,13 +239,91 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.336281061172485 seconds
+Time: 10.948570489883423 seconds
 
-[20.72, 20.68, 20.72, 20.88, 21.04, 21.0, 21.04, 20.76, 20.76, 20.8]
-[20.88, 21.0, 21.28, 23.68, 24.48, 25.8, 26.4, 26.0, 24.92, 23.92, 24.12, 24.16, 24.24]
-13.599457740783691
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289284, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.336281061172485, 'TIME_S_1KI': 0.035730566022222056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 302.8508556365967, 'W': 22.269333190276484}
-[20.72, 20.68, 20.72, 20.88, 21.04, 21.0, 21.04, 20.76, 20.76, 20.8, 21.04, 20.92, 20.64, 20.48, 20.48, 20.48, 20.68, 20.96, 21.16, 21.16]
-374.53999999999996
-18.726999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289284, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.336281061172485, 'TIME_S_1KI': 0.035730566022222056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 302.8508556365967, 'W': 22.269333190276484, 'J_1KI': 1.0468980504853247, 'W_1KI': 0.07698086721103305, 'W_D': 3.542333190276487, 'J_D': 48.17381052494056, 'W_D_1KI': 0.012245174950140648, 'J_D_1KI': 4.232925066765064e-05}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4712, 4178, 2903,   24, 4753,   51, 2819, 4572, 4453,
+                           3780, 2899, 3226, 3780, 3989,  722, 4731, 1139, 1109,
+                           4512, 1669, 4522, 2228, 2733, 1441, 2781,  363, 3766,
+                           2188, 1770,  795, 1224, 1803, 4910, 1370, 4516, 2224,
+                           2678, 4365,  692, 1811, 1383, 2901,  749, 3344, 1016,
+                           4896, 4731,  857, 4171, 1998, 4569, 2011, 3832, 2691,
+                           1005, 4276, 2954, 4491, 2491, 2981,  645, 4461, 2128,
+                           3675, 4293, 1741, 3314, 1065, 1939, 1615, 3365, 3901,
+                            589, 3305, 4000, 4212,  790, 4927, 4076, 2238, 4107,
+                           3701, 3348, 1617, 1179, 3888, 4445, 2667, 3215, 4009,
+                           4710,  219, 2800,  233, 1521, 2319,  680, 1854, 4750,
+                           3077, 1721, 3819, 3579, 2334, 2886, 4510, 1278, 1666,
+                           4749, 4910, 1969, 2508,  532, 1736, 4315, 1491,  537,
+                           3309, 3121, 4585, 2996, 3358,  502, 4286, 4572, 2864,
+                           1049,  469,  825, 1143,  635, 2773, 2543, 3425, 3473,
+                           2174, 4228, 3516, 1137, 2463, 4638, 1994, 2452, 2065,
+                             96, 3029, 2790, 1834, 4863,  978, 4811, 3677, 2912,
+                           1938, 2797,  895, 1501, 2558, 1230,  534, 2633, 3017,
+                           4982, 4618, 4241, 2899, 2098, 2010, 1636, 2502, 2716,
+                           4980,  363,  466,   23, 1737, 1476, 1286, 4720,  833,
+                           2653,  201, 3769, 3397, 3009, 4570, 2692, 2095, 4797,
+                           3941, 2845, 1360, 1763, 3589, 3716, 2365,  196, 1112,
+                            123, 2267, 4731,  228, 4673, 1590, 3794, 3816, 2846,
+                            863, 3759, 1182,  304, 2540,   66, 4385, 3694, 3525,
+                             31, 4315, 4266, 4089, 2728, 1405, 1294, 4022, 2222,
+                            370,  101, 3253, 4145, 1994, 1358,  981, 2203, 2167,
+                           3742, 4696,  614, 2733,  396, 4399,  427, 1682, 4896,
+                           3429,  693,  870, 4939, 3305, 4250, 3680]),
+       values=tensor([0.3479, 0.5084, 0.6603, 0.8257, 0.8683, 0.9247, 0.0338,
+                      0.9486, 0.8504, 0.5745, 0.3925, 0.3196, 0.6449, 0.2119,
+                      0.0164, 0.7309, 0.7682, 0.1461, 0.7397, 0.9951, 0.7123,
+                      0.4571, 0.7549, 0.0282, 0.5968, 0.6667, 0.3749, 0.3789,
+                      0.4293, 0.3353, 0.3273, 0.0531, 0.5787, 0.8917, 0.4198,
+                      0.7695, 0.7895, 0.7926, 0.6654, 0.0192, 0.0703, 0.9096,
+                      0.9289, 0.6077, 0.6990, 0.6780, 0.1687, 0.0557, 0.0641,
+                      0.1726, 0.7968, 0.1192, 0.9982, 0.1104, 0.3778, 0.1311,
+                      0.0584, 0.9615, 0.6551, 0.7173, 0.4827, 0.9281, 0.2508,
+                      0.5901, 0.8616, 0.6261, 0.7668, 0.8880, 0.5680, 0.6476,
+                      0.9494, 0.3895, 0.7153, 0.7995, 0.4681, 0.0628, 0.0354,
+                      0.8123, 0.7147, 0.5397, 0.7785, 0.1737, 0.3550, 0.8870,
+                      0.9193, 0.0915, 0.0963, 0.4243, 0.0483, 0.3655, 0.7711,
+                      0.4395, 0.3161, 0.5266, 0.7991, 0.4530, 0.0590, 0.9302,
+                      0.7021, 0.5336, 0.6784, 0.9823, 0.0943, 0.7391, 0.7084,
+                      0.0171, 0.4786, 0.7623, 0.5776, 0.2256, 0.8698, 0.1309,
+                      0.6095, 0.6277, 0.0828, 0.3536, 0.7932, 0.1162, 0.9939,
+                      0.6893, 0.6054, 0.2963, 0.4057, 0.5571, 0.8162, 0.7161,
+                      0.6029, 0.7576, 0.8687, 0.3351, 0.8262, 0.5784, 0.6376,
+                      0.1057, 0.2968, 0.0568, 0.6646, 0.7354, 0.2403, 0.0158,
+                      0.7552, 0.5770, 0.3899, 0.7014, 0.1196, 0.2500, 0.6112,
+                      0.3203, 0.8311, 0.8445, 0.8722, 0.6620, 0.5633, 0.3401,
+                      0.0024, 0.6473, 0.3675, 0.6286, 0.4764, 0.3994, 0.7176,
+                      0.9295, 0.7610, 0.0448, 0.1910, 0.5959, 0.2410, 0.6714,
+                      0.3638, 0.8788, 0.4303, 0.8357, 0.1493, 0.7533, 0.2046,
+                      0.6241, 0.3330, 0.7519, 0.0927, 0.5403, 0.3301, 0.0842,
+                      0.3044, 0.5311, 0.1859, 0.7234, 0.6523, 0.1074, 0.7205,
+                      0.0951, 0.9394, 0.8290, 0.0965, 0.9119, 0.9547, 0.5884,
+                      0.2956, 0.6206, 0.7425, 0.9894, 0.3994, 0.9059, 0.3500,
+                      0.1825, 0.6628, 0.6687, 0.3257, 0.5028, 0.7592, 0.5362,
+                      0.2886, 0.3968, 0.4420, 0.4118, 0.6245, 0.3599, 0.5238,
+                      0.6126, 0.6306, 0.0343, 0.1672, 0.1822, 0.1255, 0.6333,
+                      0.3425, 0.1597, 0.8225, 0.7857, 0.7675, 0.1595, 0.4863,
+                      0.8578, 0.1155, 0.8038, 0.8906, 0.6082, 0.3640, 0.5820,
+                      0.4951, 0.3638, 0.3016, 0.7272, 0.7832, 0.5085, 0.1101,
+                      0.2648, 0.6399, 0.4137, 0.5843, 0.7184]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0011, 0.9357, 0.8539,  ..., 0.1995, 0.1479, 0.1616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.948570489883423 seconds
+
+[20.16, 20.24, 20.28, 20.24, 20.08, 20.16, 20.2, 20.28, 20.28, 20.36]
+[20.72, 20.56, 20.44, 24.96, 27.52, 28.24, 29.2, 26.64, 25.12, 24.04, 23.96, 23.92, 24.04, 24.2]
+14.660816431045532
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.948570489883423, 'TIME_S_1KI': 0.037761894790535266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.0917576313019, 'W': 22.992700250818316}
+[20.16, 20.24, 20.28, 20.24, 20.08, 20.16, 20.2, 20.28, 20.28, 20.36, 20.4, 20.4, 20.44, 20.84, 20.84, 20.76, 20.64, 20.8, 20.84, 20.84]
+368.2
+18.41
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.948570489883423, 'TIME_S_1KI': 0.037761894790535266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.0917576313019, 'W': 22.992700250818316, 'J_1KI': 1.1626379442130597, 'W_1KI': 0.07930240104166876, 'W_D': 4.582700250818316, 'J_D': 67.18612713575365, 'W_D_1KI': 0.015805848342289243, 'J_D_1KI': 5.4514768181671336e-05}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..8ad2558
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 154350, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.289572715759277, "TIME_S_1KI": 0.0666638983852237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 297.04585779190063, "W": 21.84801552939114, "J_1KI": 1.9244953533650835, "W_1KI": 0.14154852950690727, "W_D": 3.3270155293911436, "J_D": 45.23413947987558, "W_D_1KI": 0.0215550082888963, "J_D_1KI": 0.0001396501994745468}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..f253283
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014810323715209961}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4902, 4751,  573,  ..., 1409, 1871,  577]),
+       values=tensor([0.0874, 0.7756, 0.4965,  ..., 0.1251, 0.3364, 0.3476]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4221, 0.7918, 0.4416,  ..., 0.8475, 0.7362, 0.1103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.014810323715209961 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 70896 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.822846412658691}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([1594, 2931, 2652,  ..., 4428,  449, 1795]),
+       values=tensor([0.3058, 0.1710, 0.0965,  ..., 0.7799, 0.8373, 0.5140]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.0899, 0.4612, 0.1283,  ..., 0.7452, 0.2953, 0.1670])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 4.822846412658691 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 154350 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.289572715759277}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([ 621, 1968, 1113,  ..., 1968,  726, 3393]),
+       values=tensor([0.9316, 0.3440, 0.3874,  ..., 0.4845, 0.3520, 0.3225]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4702, 0.8122, 0.0166,  ..., 0.1291, 0.0008, 0.5220])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.289572715759277 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([ 621, 1968, 1113,  ..., 1968,  726, 3393]),
+       values=tensor([0.9316, 0.3440, 0.3874,  ..., 0.4845, 0.3520, 0.3225]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4702, 0.8122, 0.0166,  ..., 0.1291, 0.0008, 0.5220])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.289572715759277 seconds
+
+[20.4, 20.44, 20.52, 20.68, 20.64, 20.48, 20.24, 20.36, 20.2, 20.16]
+[20.36, 20.44, 21.32, 23.28, 23.28, 24.96, 25.6, 26.08, 24.88, 23.92, 23.64, 23.6, 23.6]
+13.59601092338562
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 154350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.289572715759277, 'TIME_S_1KI': 0.0666638983852237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 297.04585779190063, 'W': 21.84801552939114}
+[20.4, 20.44, 20.52, 20.68, 20.64, 20.48, 20.24, 20.36, 20.2, 20.16, 20.52, 20.64, 20.96, 21.2, 21.24, 21.04, 20.76, 20.36, 20.08, 20.08]
+370.41999999999996
+18.520999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 154350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.289572715759277, 'TIME_S_1KI': 0.0666638983852237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 297.04585779190063, 'W': 21.84801552939114, 'J_1KI': 1.9244953533650835, 'W_1KI': 0.14154852950690727, 'W_D': 3.3270155293911436, 'J_D': 45.23413947987558, 'W_D_1KI': 0.0215550082888963, 'J_D_1KI': 0.0001396501994745468}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
index 4c97264..57ab5e0 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6154, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661418676376343, "TIME_S_1KI": 1.732437223980556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.1215419101716, "W": 66.29, "J_1KI": 144.31614265683646, "W_1KI": 10.771855703607411, "W_D": 31.486250000000005, "J_D": 421.8376361286641, "W_D_1KI": 5.116387715307118, "J_D_1KI": 0.8313922189319334}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6238, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.769784212112427, "TIME_S_1KI": 1.7264803161449866, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 915.7679015994072, "W": 67.11, "J_1KI": 146.8047293362307, "W_1KI": 10.75825585123437, "W_D": 31.7095, "J_D": 432.70067465007304, "W_D_1KI": 5.083279897403013, "J_D_1KI": 0.8148893711771422}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
index 3c6753a..fe560b9 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,34 @@
-['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": 1.7059962749481201}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.18373441696166992}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      10,      28,  ...,  999981,
+tensor(crow_indices=tensor([      0,       9,      18,  ...,  999979,
+                             999991, 1000000]),
+       col_indices=tensor([ 9419, 17690, 19775,  ..., 65904, 78906, 97730]),
+       values=tensor([0.1002, 0.0063, 0.1334,  ..., 0.8477, 0.2339, 0.2955]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9261, 0.9567, 0.5751,  ..., 0.3199, 0.0262, 0.3042])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.18373441696166992 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5714', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.617395401000977}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      15,  ...,  999976,
                              999989, 1000000]),
-       col_indices=tensor([10839, 13780, 19162,  ..., 70763, 71204, 84111]),
-       values=tensor([0.3862, 0.3703, 0.4692,  ..., 0.8959, 0.7094, 0.8230]),
+       col_indices=tensor([12342, 20602, 31374,  ..., 83399, 88988, 97850]),
+       values=tensor([0.3594, 0.1684, 0.5761,  ..., 0.4601, 0.3694, 0.1608]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6738, 0.0568, 0.8510,  ..., 0.5567, 0.5192, 0.1431])
+tensor([0.5848, 0.1566, 0.7046,  ..., 0.5634, 0.8550, 0.2097])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 1.7059962749481201 seconds
+Time: 9.617395401000977 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6154', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661418676376343}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6238', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.769784212112427}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      19,      22,  ...,  999983,
-                             999992, 1000000]),
-       col_indices=tensor([ 4495, 11307, 13629,  ..., 46229, 59792, 89876]),
-       values=tensor([0.8364, 0.7832, 0.5169,  ..., 0.6963, 0.9299, 0.6811]),
+tensor(crow_indices=tensor([      0,       7,      15,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([ 1977,  7363, 16479,  ..., 91067, 93957, 95744]),
+       values=tensor([0.8934, 0.4616, 0.7140,  ..., 0.3224, 0.7140, 0.9696]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2491, 0.3919, 0.1225,  ..., 0.6201, 0.7425, 0.7393])
+tensor([0.0584, 0.0097, 0.6336,  ..., 0.7366, 0.8575, 0.7006])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.661418676376343 seconds
+Time: 10.769784212112427 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      19,      22,  ...,  999983,
-                             999992, 1000000]),
-       col_indices=tensor([ 4495, 11307, 13629,  ..., 46229, 59792, 89876]),
-       values=tensor([0.8364, 0.7832, 0.5169,  ..., 0.6963, 0.9299, 0.6811]),
+tensor(crow_indices=tensor([      0,       7,      15,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([ 1977,  7363, 16479,  ..., 91067, 93957, 95744]),
+       values=tensor([0.8934, 0.4616, 0.7140,  ..., 0.3224, 0.7140, 0.9696]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2491, 0.3919, 0.1225,  ..., 0.6201, 0.7425, 0.7393])
+tensor([0.0584, 0.0097, 0.6336,  ..., 0.7366, 0.8575, 0.7006])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.661418676376343 seconds
+Time: 10.769784212112427 seconds
 
-[39.48, 39.2, 39.2, 38.44, 38.38, 38.41, 38.43, 38.34, 38.9, 38.82]
-[66.29]
-13.3975191116333
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6154, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661418676376343, 'TIME_S_1KI': 1.732437223980556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1215419101716, 'W': 66.29}
-[39.48, 39.2, 39.2, 38.44, 38.38, 38.41, 38.43, 38.34, 38.9, 38.82, 39.78, 38.22, 38.35, 38.27, 38.77, 38.41, 38.95, 38.84, 38.8, 38.25]
-696.075
-34.80375
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6154, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661418676376343, 'TIME_S_1KI': 1.732437223980556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1215419101716, 'W': 66.29, 'J_1KI': 144.31614265683646, 'W_1KI': 10.771855703607411, 'W_D': 31.486250000000005, 'J_D': 421.8376361286641, 'W_D_1KI': 5.116387715307118, 'J_D_1KI': 0.8313922189319334}
+[39.95, 40.61, 39.69, 39.43, 39.55, 38.89, 39.33, 39.12, 39.11, 38.94]
+[67.11]
+13.645774126052856
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.769784212112427, 'TIME_S_1KI': 1.7264803161449866, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 915.7679015994072, 'W': 67.11}
+[39.95, 40.61, 39.69, 39.43, 39.55, 38.89, 39.33, 39.12, 39.11, 38.94, 39.57, 38.89, 39.02, 39.08, 39.5, 39.31, 39.6, 39.05, 39.01, 39.18]
+708.01
+35.4005
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.769784212112427, 'TIME_S_1KI': 1.7264803161449866, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 915.7679015994072, 'W': 67.11, 'J_1KI': 146.8047293362307, 'W_1KI': 10.75825585123437, 'W_D': 31.7095, 'J_D': 432.70067465007304, 'W_D_1KI': 5.083279897403013, 'J_D_1KI': 0.8148893711771422}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
index 28fc497..a6d9713 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 16.51292133331299, "TIME_S_1KI": 16.51292133331299, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1675.715212612152, "W": 77.36, "J_1KI": 1675.715212612152, "W_1KI": 77.36, "W_D": 42.0475, "J_D": 910.8019054073095, "W_D_1KI": 42.0475, "J_D_1KI": 42.0475}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 631, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.591248035430908, "TIME_S_1KI": 16.78486217976372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1175.510341129303, "W": 76.44, "J_1KI": 1862.932394816645, "W_1KI": 121.14104595879556, "W_D": 40.61775, "J_D": 624.6282726112604, "W_D_1KI": 64.37044374009508, "J_D_1KI": 102.0133815215453}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
index d1f2742..4526ab4 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 16.51292133331299}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.6621592044830322}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       85,      184,  ...,  9999802,
-                             9999894, 10000000]),
-       col_indices=tensor([ 1647,  2383,  2584,  ..., 98263, 98777, 99734]),
-       values=tensor([0.1681, 0.5843, 0.2619,  ..., 0.7600, 0.0011, 0.9501]),
+tensor(crow_indices=tensor([       0,       87,      180,  ...,  9999810,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1316,  2180,  2488,  ..., 99391, 99679, 99852]),
+       values=tensor([0.4838, 0.8512, 0.8260,  ..., 0.7772, 0.9919, 0.5400]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3789, 0.7363, 0.6915,  ..., 0.8879, 0.6465, 0.7586])
+tensor([0.7127, 0.0015, 0.2736,  ..., 0.7345, 0.7377, 0.4477])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 16.51292133331299 seconds
+Time: 1.6621592044830322 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '631', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.591248035430908}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       85,      184,  ...,  9999802,
-                             9999894, 10000000]),
-       col_indices=tensor([ 1647,  2383,  2584,  ..., 98263, 98777, 99734]),
-       values=tensor([0.1681, 0.5843, 0.2619,  ..., 0.7600, 0.0011, 0.9501]),
+tensor(crow_indices=tensor([       0,      104,      198,  ...,  9999801,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1720,  2057,  4608,  ..., 98148, 99667, 99757]),
+       values=tensor([0.5091, 0.6981, 0.1321,  ..., 0.4342, 0.6647, 0.6565]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3789, 0.7363, 0.6915,  ..., 0.8879, 0.6465, 0.7586])
+tensor([0.3567, 0.4501, 0.1430,  ..., 0.3086, 0.4387, 0.0746])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 16.51292133331299 seconds
+Time: 10.591248035430908 seconds
 
-[39.67, 38.57, 40.75, 44.07, 38.52, 39.19, 39.95, 38.76, 38.89, 38.37]
-[77.36]
-21.661261796951294
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 16.51292133331299, 'TIME_S_1KI': 16.51292133331299, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1675.715212612152, 'W': 77.36}
-[39.67, 38.57, 40.75, 44.07, 38.52, 39.19, 39.95, 38.76, 38.89, 38.37, 39.54, 38.41, 38.76, 38.62, 39.0, 38.84, 38.88, 38.44, 38.6, 38.42]
-706.25
-35.3125
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 16.51292133331299, 'TIME_S_1KI': 16.51292133331299, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1675.715212612152, 'W': 77.36, 'J_1KI': 1675.715212612152, 'W_1KI': 77.36, 'W_D': 42.0475, 'J_D': 910.8019054073095, 'W_D_1KI': 42.0475, 'J_D_1KI': 42.0475}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      104,      198,  ...,  9999801,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1720,  2057,  4608,  ..., 98148, 99667, 99757]),
+       values=tensor([0.5091, 0.6981, 0.1321,  ..., 0.4342, 0.6647, 0.6565]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3567, 0.4501, 0.1430,  ..., 0.3086, 0.4387, 0.0746])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.591248035430908 seconds
+
+[39.78, 39.0, 39.24, 38.88, 39.37, 39.28, 39.34, 38.84, 41.87, 41.9]
+[76.44]
+15.378209590911865
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.591248035430908, 'TIME_S_1KI': 16.78486217976372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.510341129303, 'W': 76.44}
+[39.78, 39.0, 39.24, 38.88, 39.37, 39.28, 39.34, 38.84, 41.87, 41.9, 39.74, 39.44, 39.02, 44.31, 40.1, 38.93, 39.71, 39.12, 39.62, 39.33]
+716.4449999999999
+35.82225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.591248035430908, 'TIME_S_1KI': 16.78486217976372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.510341129303, 'W': 76.44, 'J_1KI': 1862.932394816645, 'W_1KI': 121.14104595879556, 'W_D': 40.61775, 'J_D': 624.6282726112604, 'W_D_1KI': 64.37044374009508, 'J_D_1KI': 102.0133815215453}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
index 8f7419f..87f4ecf 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.322007656097412, "TIME_S_1KI": 0.8546830881922176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 838.5464090538024, "W": 64.47, "J_1KI": 69.43333684307382, "W_1KI": 5.3382462532085775, "W_D": 29.621750000000006, "J_D": 385.28326496648793, "W_D_1KI": 2.452740746874224, "J_D_1KI": 0.20309188928328428}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12301, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471284627914429, "TIME_S_1KI": 0.8512547457860685, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.4546840524674, "W": 65.29, "J_1KI": 69.70609576883729, "W_1KI": 5.307698561092595, "W_D": 29.40700000000001, "J_D": 386.2026327757837, "W_D_1KI": 2.3906186488903347, "J_D_1KI": 0.19434343946755017}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
index d689d8c..e82259e 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8693974018096924}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09777641296386719}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([57795, 90642, 37628,  ..., 28610,   559, 98027]),
-       values=tensor([0.1696, 0.5341, 0.5606,  ..., 0.7529, 0.5749, 0.6066]),
+       col_indices=tensor([10415, 34481, 41161,  ..., 69185,  8793, 68858]),
+       values=tensor([0.7697, 0.4410, 0.3075,  ..., 0.8657, 0.1828, 0.6667]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7238, 0.7083, 0.7900,  ..., 0.2093, 0.5825, 0.4482])
+tensor([0.2533, 0.9138, 0.2717,  ..., 0.2019, 0.7103, 0.0862])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.8693974018096924 seconds
+Time: 0.09777641296386719 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12077', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.322007656097412}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10738', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.165135860443115}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99996,  99996,
                             100000]),
-       col_indices=tensor([ 3486, 41765,  3206,  ..., 33238, 50080, 42417]),
-       values=tensor([0.6049, 0.2829, 0.2416,  ..., 0.9238, 0.5292, 0.5723]),
+       col_indices=tensor([17140, 55127, 70380,  ...,  9005, 21920, 77148]),
+       values=tensor([0.4913, 0.5196, 0.1867,  ..., 0.0903, 0.8718, 0.1023]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4597, 0.0749, 0.9185,  ..., 0.4582, 0.2319, 0.2322])
+tensor([0.0174, 0.3477, 0.7027,  ..., 0.9312, 0.2138, 0.3974])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.322007656097412 seconds
+Time: 9.165135860443115 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12301', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471284627914429}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([ 3486, 41765,  3206,  ..., 33238, 50080, 42417]),
-       values=tensor([0.6049, 0.2829, 0.2416,  ..., 0.9238, 0.5292, 0.5723]),
+       col_indices=tensor([46597,   403, 54918,  ..., 58141, 94085, 20979]),
+       values=tensor([0.5040, 0.7325, 0.7996,  ..., 0.9839, 0.2631, 0.4936]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4597, 0.0749, 0.9185,  ..., 0.4582, 0.2319, 0.2322])
+tensor([0.2071, 0.7418, 0.9347,  ..., 0.4731, 0.1489, 0.5724])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.322007656097412 seconds
+Time: 10.471284627914429 seconds
 
-[39.73, 38.57, 38.91, 38.66, 38.65, 38.36, 38.94, 39.73, 38.42, 38.47]
-[64.47]
-13.006769180297852
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.322007656097412, 'TIME_S_1KI': 0.8546830881922176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 838.5464090538024, 'W': 64.47}
-[39.73, 38.57, 38.91, 38.66, 38.65, 38.36, 38.94, 39.73, 38.42, 38.47, 39.26, 38.5, 38.85, 38.41, 38.98, 38.35, 38.75, 38.41, 38.37, 38.75]
-696.9649999999999
-34.84824999999999
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.322007656097412, 'TIME_S_1KI': 0.8546830881922176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 838.5464090538024, 'W': 64.47, 'J_1KI': 69.43333684307382, 'W_1KI': 5.3382462532085775, 'W_D': 29.621750000000006, 'J_D': 385.28326496648793, 'W_D_1KI': 2.452740746874224, 'J_D_1KI': 0.20309188928328428}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([46597,   403, 54918,  ..., 58141, 94085, 20979]),
+       values=tensor([0.5040, 0.7325, 0.7996,  ..., 0.9839, 0.2631, 0.4936]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2071, 0.7418, 0.9347,  ..., 0.4731, 0.1489, 0.5724])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.471284627914429 seconds
+
+[39.92, 39.64, 39.53, 39.59, 39.42, 39.38, 39.39, 39.02, 44.4, 39.0]
+[65.29]
+13.13301706314087
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471284627914429, 'TIME_S_1KI': 0.8512547457860685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.4546840524674, 'W': 65.29}
+[39.92, 39.64, 39.53, 39.59, 39.42, 39.38, 39.39, 39.02, 44.4, 39.0, 39.77, 38.96, 39.1, 39.29, 38.97, 44.08, 39.5, 39.1, 39.5, 38.89]
+717.66
+35.882999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471284627914429, 'TIME_S_1KI': 0.8512547457860685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.4546840524674, 'W': 65.29, 'J_1KI': 69.70609576883729, 'W_1KI': 5.307698561092595, 'W_D': 29.40700000000001, 'J_D': 386.2026327757837, 'W_D_1KI': 2.3906186488903347, 'J_D_1KI': 0.19434343946755017}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..96abffc
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 7670, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.499921083450317, "TIME_S_1KI": 1.3689597240482814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 873.9648419952392, "W": 65.94, "J_1KI": 113.94587248960094, "W_1KI": 8.597131681877444, "W_D": 30.4285, "J_D": 403.29753100776674, "W_D_1KI": 3.9672099087353327, "J_D_1KI": 0.5172372762366796}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..7c108ed
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.15105009078979492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 499997, 499999,
+                            500000]),
+       col_indices=tensor([ 4363, 49954, 63940,  ...,   740, 19551, 36085]),
+       values=tensor([0.7532, 0.6946, 0.3669,  ..., 0.0744, 0.6590, 0.6868]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0672, 0.6383, 0.6761,  ..., 0.1188, 0.9489, 0.0863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.15105009078979492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6951', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.51450252532959}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     13,  ..., 499996, 499997,
+                            500000]),
+       col_indices=tensor([ 4260, 42899, 54575,  ...,  5425, 31756, 61151]),
+       values=tensor([0.4952, 0.8247, 0.2969,  ..., 0.2331, 0.9267, 0.2319]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7470, 0.2926, 0.2731,  ..., 0.9830, 0.8295, 0.9958])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 9.51450252532959 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7670', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.499921083450317}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ..., 499989, 499996,
+                            500000]),
+       col_indices=tensor([16754, 23077, 28797,  ..., 22620, 46442, 72952]),
+       values=tensor([0.6737, 0.8129, 0.9335,  ..., 0.4581, 0.1021, 0.2391]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.9975, 0.9245, 0.4309,  ..., 0.4303, 0.6144, 0.3183])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.499921083450317 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ..., 499989, 499996,
+                            500000]),
+       col_indices=tensor([16754, 23077, 28797,  ..., 22620, 46442, 72952]),
+       values=tensor([0.6737, 0.8129, 0.9335,  ..., 0.4581, 0.1021, 0.2391]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.9975, 0.9245, 0.4309,  ..., 0.4303, 0.6144, 0.3183])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.499921083450317 seconds
+
+[39.66, 38.93, 40.25, 39.53, 39.7, 39.41, 40.1, 39.55, 39.0, 39.05]
+[65.94]
+13.25394058227539
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7670, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.499921083450317, 'TIME_S_1KI': 1.3689597240482814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 873.9648419952392, 'W': 65.94}
+[39.66, 38.93, 40.25, 39.53, 39.7, 39.41, 40.1, 39.55, 39.0, 39.05, 39.72, 39.51, 39.22, 39.24, 39.32, 39.23, 40.03, 39.22, 39.23, 39.09]
+710.23
+35.5115
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7670, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.499921083450317, 'TIME_S_1KI': 1.3689597240482814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 873.9648419952392, 'W': 65.94, 'J_1KI': 113.94587248960094, 'W_1KI': 8.597131681877444, 'W_D': 30.4285, 'J_D': 403.29753100776674, 'W_D_1KI': 3.9672099087353327, 'J_D_1KI': 0.5172372762366796}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
index 9dc0eed..a0173f2 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 238697, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480466604232788, "TIME_S_1KI": 0.04390698921324017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1152.02081230402, "W": 58.89, "J_1KI": 4.826289447726699, "W_1KI": 0.24671445388924035, "W_D": 23.781499999999994, "J_D": 465.21961195123185, "W_D_1KI": 0.09963049388974303, "J_D_1KI": 0.0004173931548772839}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 237172, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.292231321334839, "TIME_S_1KI": 0.04339564249293693, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.8344623732567, "W": 65.91, "J_1KI": 3.638011495342016, "W_1KI": 0.2778995834246875, "W_D": 30.572500000000005, "J_D": 400.22768321812157, "W_D_1KI": 0.12890433946671614, "J_D_1KI": 0.0005435057235538602}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
index 9b8fd23..104c518 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05536341667175293}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.016118526458740234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     3,  ...,  9998,  9998, 10000]),
-       col_indices=tensor([8403, 1214, 9126,  ..., 1351, 3891, 9766]),
-       values=tensor([0.6664, 0.5402, 0.6356,  ..., 0.4443, 0.7393, 0.7343]),
+tensor(crow_indices=tensor([    0,     3,     3,  ...,  9995,  9998, 10000]),
+       col_indices=tensor([3770, 7218, 7901,  ..., 7147, 2189, 2422]),
+       values=tensor([0.0682, 0.4925, 0.4932,  ..., 0.9859, 0.2682, 0.5675]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.3881, 0.9820, 0.4323,  ..., 0.4549, 0.5025, 0.0926])
+tensor([0.1703, 0.4753, 0.7272,  ..., 0.9852, 0.8357, 0.1698])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.05536341667175293 seconds
+Time: 0.016118526458740234 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '189655', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.342687606811523}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65142', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8839359283447266}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9997, 10000]),
-       col_indices=tensor([1328, 2584, 2989,  ..., 4729, 4835, 7640]),
-       values=tensor([0.4337, 0.1976, 0.1440,  ..., 0.2725, 0.2860, 0.2817]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([6160, 1315,  448,  ..., 9882, 6598, 7658]),
+       values=tensor([0.4764, 0.2622, 0.7017,  ..., 0.9860, 0.1866, 0.7529]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.7295, 0.2766, 0.3418,  ..., 0.0114, 0.7550, 0.8307])
+tensor([0.4338, 0.9515, 0.6308,  ..., 0.9365, 0.1556, 0.4912])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 8.342687606811523 seconds
+Time: 2.8839359283447266 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '238697', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480466604232788}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237172', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.292231321334839}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9997, 10000]),
-       col_indices=tensor([9286, 7396,  732,  ..., 1484, 5299, 9027]),
-       values=tensor([0.3440, 0.6043, 0.5062,  ..., 0.2355, 0.1186, 0.4561]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1278, 7265, 6993,  ..., 9863, 6468, 3133]),
+       values=tensor([0.6288, 0.8682, 0.0748,  ..., 0.3062, 0.2031, 0.3525]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0996, 0.8226, 0.2068,  ..., 0.2572, 0.9962, 0.0083])
+tensor([0.1078, 0.8244, 0.8698,  ..., 0.0830, 0.2322, 0.6518])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.480466604232788 seconds
+Time: 10.292231321334839 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9997, 10000]),
-       col_indices=tensor([9286, 7396,  732,  ..., 1484, 5299, 9027]),
-       values=tensor([0.3440, 0.6043, 0.5062,  ..., 0.2355, 0.1186, 0.4561]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1278, 7265, 6993,  ..., 9863, 6468, 3133]),
+       values=tensor([0.6288, 0.8682, 0.0748,  ..., 0.3062, 0.2031, 0.3525]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0996, 0.8226, 0.2068,  ..., 0.2572, 0.9962, 0.0083])
+tensor([0.1078, 0.8244, 0.8698,  ..., 0.0830, 0.2322, 0.6518])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.480466604232788 seconds
+Time: 10.292231321334839 seconds
 
-[40.64, 38.78, 39.02, 39.34, 38.69, 38.38, 38.42, 38.49, 38.47, 38.38]
-[58.89]
-19.562248468399048
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 238697, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480466604232788, 'TIME_S_1KI': 0.04390698921324017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.02081230402, 'W': 58.89}
-[40.64, 38.78, 39.02, 39.34, 38.69, 38.38, 38.42, 38.49, 38.47, 38.38, 44.89, 39.96, 38.48, 39.7, 39.01, 38.35, 38.7, 38.38, 38.58, 38.93]
-702.1700000000001
-35.10850000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 238697, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480466604232788, 'TIME_S_1KI': 0.04390698921324017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.02081230402, 'W': 58.89, 'J_1KI': 4.826289447726699, 'W_1KI': 0.24671445388924035, 'W_D': 23.781499999999994, 'J_D': 465.21961195123185, 'W_D_1KI': 0.09963049388974303, 'J_D_1KI': 0.0004173931548772839}
+[40.27, 39.31, 39.28, 38.86, 41.16, 39.32, 39.02, 39.09, 38.81, 39.11]
+[65.91]
+13.091100931167603
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.292231321334839, 'TIME_S_1KI': 0.04339564249293693, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8344623732567, 'W': 65.91}
+[40.27, 39.31, 39.28, 38.86, 41.16, 39.32, 39.02, 39.09, 38.81, 39.11, 39.9, 39.01, 39.35, 39.15, 39.0, 38.86, 39.09, 38.89, 38.89, 40.04]
+706.7499999999999
+35.33749999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.292231321334839, 'TIME_S_1KI': 0.04339564249293693, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8344623732567, 'W': 65.91, 'J_1KI': 3.638011495342016, 'W_1KI': 0.2778995834246875, 'W_D': 30.572500000000005, 'J_D': 400.22768321812157, 'W_D_1KI': 0.12890433946671614, 'J_D_1KI': 0.0005435057235538602}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
index 80c52ca..deb9985 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75618, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.541181087493896, "TIME_S_1KI": 0.13940042169184447, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.5316159009933, "W": 66.47, "J_1KI": 12.437932977611062, "W_1KI": 0.8790235129202042, "W_D": 31.600500000000004, "J_D": 447.1380973112584, "W_D_1KI": 0.41789653257160997, "J_D_1KI": 0.005526416098965987}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75716, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.424914360046387, "TIME_S_1KI": 0.1376844307682179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.2084917426108, "W": 66.49, "J_1KI": 12.41756685169067, "W_1KI": 0.8781499286808601, "W_D": 31.17374999999999, "J_D": 440.81552819162596, "W_D_1KI": 0.4117194516350572, "J_D_1KI": 0.005437680960894093}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
index 9b49c17..169706c 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.16539597511291504}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02692556381225586}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     17,  ...,  99980,  99988,
+tensor(crow_indices=tensor([     0,      8,     22,  ...,  99984,  99990,
                             100000]),
-       col_indices=tensor([2312, 2519, 3298,  ..., 9035, 9400, 9910]),
-       values=tensor([0.1410, 0.2218, 0.1849,  ..., 0.4652, 0.0649, 0.3640]),
+       col_indices=tensor([ 947, 1869, 5338,  ..., 6268, 7050, 7942]),
+       values=tensor([0.2237, 0.7540, 0.0617,  ..., 0.6862, 0.3906, 0.7890]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4363, 0.0084, 0.9005,  ..., 0.6999, 0.4782, 0.9424])
+tensor([0.6838, 0.4222, 0.9597,  ..., 0.5474, 0.0680, 0.5394])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.16539597511291504 seconds
+Time: 0.02692556381225586 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63484', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.8150315284729}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '38996', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.952186584472656}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     21,  ...,  99981,  99989,
+tensor(crow_indices=tensor([     0,     11,     19,  ...,  99978,  99990,
                             100000]),
-       col_indices=tensor([ 457, 1232, 2417,  ..., 8600, 9856, 9966]),
-       values=tensor([0.5653, 0.7705, 0.0640,  ..., 0.9989, 0.3761, 0.2052]),
+       col_indices=tensor([   7,  556,  703,  ..., 8117, 8865, 9056]),
+       values=tensor([0.2495, 0.4435, 0.2550,  ..., 0.5409, 0.7823, 0.3947]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7731, 0.4840, 0.8355,  ..., 0.4086, 0.2552, 0.3939])
+tensor([0.8603, 0.6651, 0.2785,  ..., 0.2036, 0.7755, 0.1415])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 8.8150315284729 seconds
+Time: 5.952186584472656 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75618', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.541181087493896}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68791', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.539567232131958}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     15,  ...,  99981,  99989,
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99987,  99995,
                             100000]),
-       col_indices=tensor([ 812, 4021, 6538,  ..., 8729, 9196, 9676]),
-       values=tensor([0.8795, 0.6481, 0.9606,  ..., 0.0277, 0.7911, 0.3727]),
+       col_indices=tensor([ 696,  997, 2062,  ..., 1211, 1590, 9690]),
+       values=tensor([0.0377, 0.1568, 0.2160,  ..., 0.8237, 0.6309, 0.0587]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1908, 0.4704, 0.2059,  ..., 0.1529, 0.3275, 0.9276])
+tensor([0.0823, 0.1873, 0.3356,  ..., 0.2591, 0.5771, 0.7059])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.541181087493896 seconds
+Time: 9.539567232131958 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75716', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.424914360046387}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     15,  ...,  99981,  99989,
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99986,  99994,
                             100000]),
-       col_indices=tensor([ 812, 4021, 6538,  ..., 8729, 9196, 9676]),
-       values=tensor([0.8795, 0.6481, 0.9606,  ..., 0.0277, 0.7911, 0.3727]),
+       col_indices=tensor([1284, 3776, 5103,  ..., 6955, 7171, 8445]),
+       values=tensor([0.9684, 0.2053, 0.3935,  ..., 0.8592, 0.0314, 0.3677]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1908, 0.4704, 0.2059,  ..., 0.1529, 0.3275, 0.9276])
+tensor([0.4306, 0.9725, 0.6597,  ..., 0.5969, 0.7821, 0.5134])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +76,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.541181087493896 seconds
+Time: 10.424914360046387 seconds
 
-[38.98, 38.52, 38.36, 38.43, 38.45, 38.57, 38.52, 38.55, 38.55, 38.4]
-[66.47]
-14.149715900421143
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75618, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.541181087493896, 'TIME_S_1KI': 0.13940042169184447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.5316159009933, 'W': 66.47}
-[38.98, 38.52, 38.36, 38.43, 38.45, 38.57, 38.52, 38.55, 38.55, 38.4, 39.0, 38.48, 38.51, 39.07, 38.73, 38.62, 38.94, 38.66, 38.36, 43.76]
-697.3899999999999
-34.869499999999995
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75618, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.541181087493896, 'TIME_S_1KI': 0.13940042169184447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.5316159009933, 'W': 66.47, 'J_1KI': 12.437932977611062, 'W_1KI': 0.8790235129202042, 'W_D': 31.600500000000004, 'J_D': 447.1380973112584, 'W_D_1KI': 0.41789653257160997, 'J_D_1KI': 0.005526416098965987}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99986,  99994,
+                            100000]),
+       col_indices=tensor([1284, 3776, 5103,  ..., 6955, 7171, 8445]),
+       values=tensor([0.9684, 0.2053, 0.3935,  ..., 0.8592, 0.0314, 0.3677]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4306, 0.9725, 0.6597,  ..., 0.5969, 0.7821, 0.5134])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.424914360046387 seconds
+
+[40.22, 38.81, 38.98, 39.63, 38.95, 39.23, 38.84, 39.29, 39.31, 38.77]
+[66.49]
+14.140599966049194
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.424914360046387, 'TIME_S_1KI': 0.1376844307682179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.2084917426108, 'W': 66.49}
+[40.22, 38.81, 38.98, 39.63, 38.95, 39.23, 38.84, 39.29, 39.31, 38.77, 41.54, 39.08, 39.06, 38.87, 39.0, 38.96, 39.2, 38.82, 39.06, 41.94]
+706.325
+35.316250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.424914360046387, 'TIME_S_1KI': 0.1376844307682179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.2084917426108, 'W': 66.49, 'J_1KI': 12.41756685169067, 'W_1KI': 0.8781499286808601, 'W_D': 31.17374999999999, 'J_D': 440.81552819162596, 'W_D_1KI': 0.4117194516350572, 'J_D_1KI': 0.005437680960894093}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
index e580351..db6444c 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10094, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.424894571304321, "TIME_S_1KI": 1.0327813127902044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 874.2127872061731, "W": 66.43, "J_1KI": 86.60717131030047, "W_1KI": 6.581137309292649, "W_D": 31.351250000000007, "J_D": 412.5796122971178, "W_D_1KI": 3.1059292649098484, "J_D_1KI": 0.3077005414018078}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10206, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48923373222351, "TIME_S_1KI": 1.0277516884404774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.1208229660987, "W": 67.21, "J_1KI": 87.01948098825189, "W_1KI": 6.585341955712326, "W_D": 31.27349999999999, "J_D": 413.25169702470293, "W_D_1KI": 3.0642269253380356, "J_D_1KI": 0.30023779397785966}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
index c679aff..191d2ac 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.040170669555664}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.1162419319152832}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      84,     184,  ...,  999814,
-                             999899, 1000000]),
-       col_indices=tensor([ 171,  251,  472,  ..., 9843, 9880, 9941]),
-       values=tensor([0.4805, 0.3615, 0.2747,  ..., 0.6607, 0.4074, 0.0301]),
+tensor(crow_indices=tensor([      0,     100,     185,  ...,  999774,
+                             999893, 1000000]),
+       col_indices=tensor([  36,  100,  149,  ..., 9802, 9836, 9872]),
+       values=tensor([0.2938, 0.2320, 0.9118,  ..., 0.8681, 0.8272, 0.2716]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4780, 0.2256, 0.5818,  ..., 0.3209, 0.4621, 0.5747])
+tensor([0.9985, 0.1887, 0.5488,  ..., 0.6608, 0.9222, 0.7055])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 1.040170669555664 seconds
+Time: 0.1162419319152832 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10094', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.424894571304321}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '9032', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.29158329963684}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     106,     205,  ...,  999816,
-                             999911, 1000000]),
-       col_indices=tensor([  83,   89,  669,  ..., 9640, 9974, 9983]),
-       values=tensor([0.6432, 0.8453, 0.7190,  ..., 0.8302, 0.0770, 0.7390]),
+tensor(crow_indices=tensor([      0,      95,     205,  ...,  999782,
+                             999891, 1000000]),
+       col_indices=tensor([  54,  212,  264,  ..., 9693, 9804, 9961]),
+       values=tensor([0.9421, 0.7916, 0.1774,  ..., 0.7420, 0.5713, 0.3525]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4670, 0.3263, 0.5346,  ..., 0.9779, 0.3626, 0.9957])
+tensor([0.0899, 0.7410, 0.9990,  ..., 0.5022, 0.0295, 0.8248])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.424894571304321 seconds
+Time: 9.29158329963684 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10206', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48923373222351}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     106,     205,  ...,  999816,
-                             999911, 1000000]),
-       col_indices=tensor([  83,   89,  669,  ..., 9640, 9974, 9983]),
-       values=tensor([0.6432, 0.8453, 0.7190,  ..., 0.8302, 0.0770, 0.7390]),
+tensor(crow_indices=tensor([      0,     108,     196,  ...,  999774,
+                             999884, 1000000]),
+       col_indices=tensor([  16,  259,  309,  ..., 9528, 9603, 9788]),
+       values=tensor([0.1649, 0.9890, 0.6907,  ..., 0.8956, 0.0145, 0.7596]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4670, 0.3263, 0.5346,  ..., 0.9779, 0.3626, 0.9957])
+tensor([0.7287, 0.8351, 0.4943,  ..., 0.5583, 0.1274, 0.9823])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.424894571304321 seconds
+Time: 10.48923373222351 seconds
 
-[39.35, 40.11, 43.57, 38.24, 39.46, 38.59, 38.37, 38.38, 38.28, 38.71]
-[66.43]
-13.15990948677063
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10094, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.424894571304321, 'TIME_S_1KI': 1.0327813127902044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.2127872061731, 'W': 66.43}
-[39.35, 40.11, 43.57, 38.24, 39.46, 38.59, 38.37, 38.38, 38.28, 38.71, 40.11, 38.48, 38.37, 38.48, 38.37, 38.22, 38.39, 39.48, 38.33, 38.74]
-701.575
-35.07875
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10094, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.424894571304321, 'TIME_S_1KI': 1.0327813127902044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.2127872061731, 'W': 66.43, 'J_1KI': 86.60717131030047, 'W_1KI': 6.581137309292649, 'W_D': 31.351250000000007, 'J_D': 412.5796122971178, 'W_D_1KI': 3.1059292649098484, 'J_D_1KI': 0.3077005414018078}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     108,     196,  ...,  999774,
+                             999884, 1000000]),
+       col_indices=tensor([  16,  259,  309,  ..., 9528, 9603, 9788]),
+       values=tensor([0.1649, 0.9890, 0.6907,  ..., 0.8956, 0.0145, 0.7596]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7287, 0.8351, 0.4943,  ..., 0.5583, 0.1274, 0.9823])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.48923373222351 seconds
+
+[39.9, 39.07, 39.52, 38.94, 39.35, 44.79, 39.52, 39.98, 39.47, 39.28]
+[67.21]
+13.214117288589478
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10206, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48923373222351, 'TIME_S_1KI': 1.0277516884404774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1208229660987, 'W': 67.21}
+[39.9, 39.07, 39.52, 38.94, 39.35, 44.79, 39.52, 39.98, 39.47, 39.28, 40.29, 39.27, 43.98, 38.92, 39.16, 39.39, 39.32, 39.34, 39.08, 39.79]
+718.73
+35.9365
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10206, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48923373222351, 'TIME_S_1KI': 1.0277516884404774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1208229660987, 'W': 67.21, 'J_1KI': 87.01948098825189, 'W_1KI': 6.585341955712326, 'W_D': 31.27349999999999, 'J_D': 413.25169702470293, 'W_D_1KI': 3.0642269253380356, 'J_D_1KI': 0.30023779397785966}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
index 73d3b47..f94ac11 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496397256851196, "TIME_S_1KI": 5.970646903783388, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1131.7024735164641, "W": 74.58, "J_1KI": 643.7442966532789, "W_1KI": 42.42320819112628, "W_D": 39.617, "J_D": 601.1619320635796, "W_D_1KI": 22.535267349260522, "J_D_1KI": 12.818695875574814}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.28298306465149, "TIME_S_1KI": 5.961149602696516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.8662384581567, "W": 75.33, "J_1KI": 608.6181092511052, "W_1KI": 43.66956521739131, "W_D": 39.905, "J_D": 556.151762188673, "W_D_1KI": 23.133333333333336, "J_D_1KI": 13.410628019323674}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
index 78fc6b7..bf02e51 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.972491502761841}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.6085023880004883}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     519,     993,  ..., 4998959,
-                            4999496, 5000000]),
-       col_indices=tensor([  17,   61,   73,  ..., 9901, 9911, 9920]),
-       values=tensor([0.3098, 0.8299, 0.3979,  ..., 0.3415, 0.7398, 0.5378]),
+tensor(crow_indices=tensor([      0,     508,     930,  ..., 4999014,
+                            4999519, 5000000]),
+       col_indices=tensor([  33,   44,   68,  ..., 9921, 9984, 9990]),
+       values=tensor([0.7535, 0.2308, 0.9086,  ..., 0.5781, 0.9835, 0.5048]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6888, 0.9764, 0.3608,  ..., 0.4208, 0.9222, 0.1586])
+tensor([0.1644, 0.2567, 0.4067,  ..., 0.0618, 0.3860, 0.0437])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 5.972491502761841 seconds
+Time: 0.6085023880004883 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1758', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496397256851196}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1725', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.28298306465149}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     496,     998,  ..., 4999016,
-                            4999498, 5000000]),
-       col_indices=tensor([  25,   29,   69,  ..., 9894, 9911, 9997]),
-       values=tensor([0.8031, 0.3187, 0.9076,  ..., 0.2949, 0.8412, 0.6618]),
+tensor(crow_indices=tensor([      0,     453,     934,  ..., 4998993,
+                            4999474, 5000000]),
+       col_indices=tensor([  76,   82,   85,  ..., 9960, 9963, 9989]),
+       values=tensor([0.2757, 0.2788, 0.5904,  ..., 0.0782, 0.3342, 0.9799]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4316, 0.0196, 0.7556,  ..., 0.1123, 0.7172, 0.6330])
+tensor([0.0884, 0.4732, 0.8375,  ..., 0.9901, 0.5525, 0.7748])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.496397256851196 seconds
+Time: 10.28298306465149 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     496,     998,  ..., 4999016,
-                            4999498, 5000000]),
-       col_indices=tensor([  25,   29,   69,  ..., 9894, 9911, 9997]),
-       values=tensor([0.8031, 0.3187, 0.9076,  ..., 0.2949, 0.8412, 0.6618]),
+tensor(crow_indices=tensor([      0,     453,     934,  ..., 4998993,
+                            4999474, 5000000]),
+       col_indices=tensor([  76,   82,   85,  ..., 9960, 9963, 9989]),
+       values=tensor([0.2757, 0.2788, 0.5904,  ..., 0.0782, 0.3342, 0.9799]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4316, 0.0196, 0.7556,  ..., 0.1123, 0.7172, 0.6330])
+tensor([0.0884, 0.4732, 0.8375,  ..., 0.9901, 0.5525, 0.7748])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.496397256851196 seconds
+Time: 10.28298306465149 seconds
 
-[39.62, 38.84, 38.53, 38.4, 38.53, 38.95, 38.67, 38.93, 38.95, 38.34]
-[74.58]
-15.174342632293701
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496397256851196, 'TIME_S_1KI': 5.970646903783388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.7024735164641, 'W': 74.58}
-[39.62, 38.84, 38.53, 38.4, 38.53, 38.95, 38.67, 38.93, 38.95, 38.34, 39.14, 38.64, 38.84, 38.7, 38.86, 38.26, 38.63, 38.26, 38.4, 44.64]
-699.26
-34.963
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496397256851196, 'TIME_S_1KI': 5.970646903783388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.7024735164641, 'W': 74.58, 'J_1KI': 643.7442966532789, 'W_1KI': 42.42320819112628, 'W_D': 39.617, 'J_D': 601.1619320635796, 'W_D_1KI': 22.535267349260522, 'J_D_1KI': 12.818695875574814}
+[40.86, 38.91, 38.99, 39.35, 38.92, 38.87, 39.32, 39.52, 39.41, 41.56]
+[75.33]
+13.936894178390503
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.28298306465149, 'TIME_S_1KI': 5.961149602696516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.8662384581567, 'W': 75.33}
+[40.86, 38.91, 38.99, 39.35, 38.92, 38.87, 39.32, 39.52, 39.41, 41.56, 39.65, 38.96, 39.21, 38.88, 39.0, 40.01, 39.33, 39.71, 39.42, 39.31]
+708.5
+35.425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.28298306465149, 'TIME_S_1KI': 5.961149602696516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.8662384581567, 'W': 75.33, 'J_1KI': 608.6181092511052, 'W_1KI': 43.66956521739131, 'W_D': 39.905, 'J_D': 556.151762188673, 'W_D_1KI': 23.133333333333336, 'J_D_1KI': 13.410628019323674}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
index 6bba238..02b08b6 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 14.862756252288818, "TIME_S_1KI": 14.862756252288818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1509.8221988201142, "W": 77.42, "J_1KI": 1509.8221988201142, "W_1KI": 77.42, "W_D": 42.230500000000006, "J_D": 823.5668608534337, "W_D_1KI": 42.230500000000006, "J_D_1KI": 42.230500000000006}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 700, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.428780555725098, "TIME_S_1KI": 14.89825793675014, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1164.5532444572448, "W": 76.66, "J_1KI": 1663.6474920817784, "W_1KI": 109.5142857142857, "W_D": 40.888999999999996, "J_D": 621.1507645788192, "W_D_1KI": 58.412857142857135, "J_D_1KI": 83.44693877551019}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
index 550e30b..f0881c0 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 14.862756252288818}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1.498870611190796}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1022,     2054,  ...,  9998064,
-                             9999016, 10000000]),
-       col_indices=tensor([   8,   12,   13,  ..., 9969, 9975, 9983]),
-       values=tensor([0.6048, 0.0895, 0.3093,  ..., 0.2729, 0.9589, 0.2791]),
+tensor(crow_indices=tensor([       0,      987,     1979,  ...,  9997999,
+                             9999011, 10000000]),
+       col_indices=tensor([   3,    7,   20,  ..., 9954, 9962, 9986]),
+       values=tensor([0.9369, 0.1464, 0.7342,  ..., 0.7208, 0.8895, 0.6454]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8686, 0.6857, 0.7903,  ..., 0.7591, 0.3670, 0.6215])
+tensor([0.7880, 0.5272, 0.7128,  ..., 0.1762, 0.3407, 0.4321])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 14.862756252288818 seconds
+Time: 1.498870611190796 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '700', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.428780555725098}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1022,     2054,  ...,  9998064,
-                             9999016, 10000000]),
-       col_indices=tensor([   8,   12,   13,  ..., 9969, 9975, 9983]),
-       values=tensor([0.6048, 0.0895, 0.3093,  ..., 0.2729, 0.9589, 0.2791]),
+tensor(crow_indices=tensor([       0,      995,     2027,  ...,  9997983,
+                             9998981, 10000000]),
+       col_indices=tensor([  16,   21,   33,  ..., 9977, 9983, 9988]),
+       values=tensor([0.3684, 0.6722, 0.7880,  ..., 0.5048, 0.0966, 0.9792]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8686, 0.6857, 0.7903,  ..., 0.7591, 0.3670, 0.6215])
+tensor([0.4422, 0.8800, 0.2165,  ..., 0.4558, 0.6103, 0.1393])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 14.862756252288818 seconds
+Time: 10.428780555725098 seconds
 
-[44.78, 38.35, 39.08, 38.71, 38.47, 39.2, 39.52, 39.83, 39.49, 40.27]
-[77.42]
-19.501707553863525
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 14.862756252288818, 'TIME_S_1KI': 14.862756252288818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.8221988201142, 'W': 77.42}
-[44.78, 38.35, 39.08, 38.71, 38.47, 39.2, 39.52, 39.83, 39.49, 40.27, 40.17, 38.57, 38.51, 38.69, 38.5, 38.97, 38.48, 38.73, 38.88, 38.4]
-703.79
-35.189499999999995
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 14.862756252288818, 'TIME_S_1KI': 14.862756252288818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.8221988201142, 'W': 77.42, 'J_1KI': 1509.8221988201142, 'W_1KI': 77.42, 'W_D': 42.230500000000006, 'J_D': 823.5668608534337, 'W_D_1KI': 42.230500000000006, 'J_D_1KI': 42.230500000000006}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      995,     2027,  ...,  9997983,
+                             9998981, 10000000]),
+       col_indices=tensor([  16,   21,   33,  ..., 9977, 9983, 9988]),
+       values=tensor([0.3684, 0.6722, 0.7880,  ..., 0.5048, 0.0966, 0.9792]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4422, 0.8800, 0.2165,  ..., 0.4558, 0.6103, 0.1393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.428780555725098 seconds
+
+[40.18, 38.97, 39.04, 39.1, 38.99, 38.9, 38.95, 39.03, 44.22, 39.22]
+[76.66]
+15.191145896911621
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 700, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.428780555725098, 'TIME_S_1KI': 14.89825793675014, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.5532444572448, 'W': 76.66}
+[40.18, 38.97, 39.04, 39.1, 38.99, 38.9, 38.95, 39.03, 44.22, 39.22, 40.59, 39.18, 39.25, 44.66, 38.99, 39.16, 38.92, 38.8, 39.17, 40.19]
+715.42
+35.771
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 700, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.428780555725098, 'TIME_S_1KI': 14.89825793675014, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.5532444572448, 'W': 76.66, 'J_1KI': 1663.6474920817784, 'W_1KI': 109.5142857142857, 'W_D': 40.888999999999996, 'J_D': 621.1507645788192, 'W_D_1KI': 58.412857142857135, 'J_D_1KI': 83.44693877551019}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..a0b1b52
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 343, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.229193925857544, "TIME_S_1KI": 29.822722815911206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1277.0280195808411, "W": 76.08, "J_1KI": 3723.1137597109073, "W_1KI": 221.8075801749271, "W_D": 40.66575, "J_D": 682.5880939441324, "W_D_1KI": 118.55903790087464, "J_D_1KI": 345.65317172266657}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..9529016
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 3.0540103912353516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2046,     4073,  ..., 19996013,
+                            19997969, 20000000]),
+       col_indices=tensor([   3,    8,   17,  ..., 9981, 9984, 9987]),
+       values=tensor([0.5017, 0.4094, 0.1287,  ..., 0.4741, 0.2195, 0.3916]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.6824, 0.3340, 0.1820,  ..., 0.2779, 0.3641, 0.6445])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 3.0540103912353516 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '343', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.229193925857544}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1942,     3919,  ..., 19996087,
+                            19998013, 20000000]),
+       col_indices=tensor([   9,   10,   17,  ..., 9985, 9988, 9989]),
+       values=tensor([0.3594, 0.3340, 0.0020,  ..., 0.8034, 0.9201, 0.1838]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.0654, 0.1078, 0.4601,  ..., 0.8409, 0.3729, 0.1721])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.229193925857544 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1942,     3919,  ..., 19996087,
+                            19998013, 20000000]),
+       col_indices=tensor([   9,   10,   17,  ..., 9985, 9988, 9989]),
+       values=tensor([0.3594, 0.3340, 0.0020,  ..., 0.8034, 0.9201, 0.1838]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.0654, 0.1078, 0.4601,  ..., 0.8409, 0.3729, 0.1721])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.229193925857544 seconds
+
+[39.53, 39.95, 39.42, 39.3, 39.16, 38.94, 39.07, 38.8, 39.46, 38.91]
+[76.08]
+16.78533148765564
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.229193925857544, 'TIME_S_1KI': 29.822722815911206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.0280195808411, 'W': 76.08}
+[39.53, 39.95, 39.42, 39.3, 39.16, 38.94, 39.07, 38.8, 39.46, 38.91, 39.85, 38.96, 39.07, 39.05, 39.38, 38.91, 39.06, 39.15, 39.29, 44.34]
+708.285
+35.414249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.229193925857544, 'TIME_S_1KI': 29.822722815911206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.0280195808411, 'W': 76.08, 'J_1KI': 3723.1137597109073, 'W_1KI': 221.8075801749271, 'W_D': 40.66575, 'J_D': 682.5880939441324, 'W_D_1KI': 118.55903790087464, 'J_D_1KI': 345.65317172266657}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..a1f4a32
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 233, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.295273542404175, "TIME_S_1KI": 44.18572335795783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1458.0208019256593, "W": 76.48, "J_1KI": 6257.6000082646315, "W_1KI": 328.2403433476395, "W_D": 40.760999999999996, "J_D": 777.0709454405307, "W_D_1KI": 174.9399141630901, "J_D_1KI": 750.8150822450219}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..d790296
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 4.5049638748168945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3029,     6057,  ..., 29993789,
+                            29996881, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9988, 9991, 9998]),
+       values=tensor([0.8599, 0.6300, 0.6697,  ..., 0.0214, 0.0757, 0.9206]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.5404, 0.3446, 0.4295,  ..., 0.2969, 0.5137, 0.1316])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 4.5049638748168945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '233', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.295273542404175}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2988,     6007,  ..., 29993964,
+                            29997046, 30000000]),
+       col_indices=tensor([   9,   16,   24,  ..., 9996, 9997, 9999]),
+       values=tensor([0.2433, 0.7720, 0.0178,  ..., 0.3342, 0.8303, 0.6867]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4151, 0.6857, 0.4615,  ..., 0.0665, 0.4824, 0.1217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.295273542404175 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2988,     6007,  ..., 29993964,
+                            29997046, 30000000]),
+       col_indices=tensor([   9,   16,   24,  ..., 9996, 9997, 9999]),
+       values=tensor([0.2433, 0.7720, 0.0178,  ..., 0.3342, 0.8303, 0.6867]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4151, 0.6857, 0.4615,  ..., 0.0665, 0.4824, 0.1217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.295273542404175 seconds
+
+[40.6, 39.0, 39.72, 40.47, 39.9, 39.66, 39.13, 38.97, 39.1, 39.62]
+[76.48]
+19.064079523086548
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.295273542404175, 'TIME_S_1KI': 44.18572335795783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1458.0208019256593, 'W': 76.48}
+[40.6, 39.0, 39.72, 40.47, 39.9, 39.66, 39.13, 38.97, 39.1, 39.62, 40.89, 39.12, 39.47, 39.11, 38.93, 39.6, 38.9, 43.73, 39.55, 38.93]
+714.3800000000001
+35.71900000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.295273542404175, 'TIME_S_1KI': 44.18572335795783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1458.0208019256593, 'W': 76.48, 'J_1KI': 6257.6000082646315, 'W_1KI': 328.2403433476395, 'W_D': 40.760999999999996, 'J_D': 777.0709454405307, 'W_D_1KI': 174.9399141630901, 'J_D_1KI': 750.8150822450219}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
index 721cd64..9c3e1bc 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 361507, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.549095392227173, "TIME_S_1KI": 0.029180888315377497, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.0974852204322, "W": 65.27, "J_1KI": 2.3847324815852313, "W_1KI": 0.18054975422329303, "W_D": 30.177249999999994, "J_D": 398.58635415762654, "W_D_1KI": 0.08347625357185336, "J_D_1KI": 0.0002309118594435332}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 362205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420795440673828, "TIME_S_1KI": 0.02877043508696409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.7538476467132, "W": 65.88, "J_1KI": 2.3709055580312617, "W_1KI": 0.18188594856503915, "W_D": 30.515249999999995, "J_D": 397.7700113752484, "W_D_1KI": 0.08424856089783408, "J_D_1KI": 0.00023259911071860987}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
index 4ba6f6a..3b7f750 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,266 +1,1131 @@
-['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": 0.043045759201049805}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.01423192024230957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ...,  998,  999, 1000]),
+       col_indices=tensor([3651, 8143, 8284, 1201, 8802, 9084,  518, 1318, 7113,
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+                      5.3153e-01, 5.4974e-01, 9.8914e-01, 7.2178e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1442, 0.3479, 0.0736,  ..., 0.7713, 0.5566, 0.1675])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.01423192024230957 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '73777', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.1387269496917725}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
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+                      9.2371e-01, 9.8965e-02, 1.7327e-01, 7.3460e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0752, 0.0265, 0.5369,  ..., 0.1368, 0.6161, 0.4463])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 2.1387269496917725 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '362205', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420795440673828}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      5.3597e-01, 5.1768e-01, 7.6964e-01, 9.9864e-01,
+                      5.3829e-01, 3.1592e-01, 9.3386e-01, 5.8600e-01,
+                      1.2704e-01, 5.0213e-01, 6.2221e-02, 1.0695e-01,
+                      2.6995e-01, 2.6387e-01, 9.3927e-01, 2.7555e-01,
+                      3.1073e-01, 1.1755e-01, 8.1059e-01, 3.6864e-01,
+                      2.6251e-01, 5.7401e-01, 2.8597e-02, 8.6585e-02]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8647, 0.0597, 0.7485,  ..., 0.2087, 0.0137, 0.4402])
+tensor([0.8046, 0.6398, 0.4516,  ..., 0.6060, 0.1172, 0.9615])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,271 +1133,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.043045759201049805 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '243926', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.084842920303345}
+Time: 10.420795440673828 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      2.6251e-01, 5.7401e-01, 2.8597e-02, 8.6585e-02]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8197, 0.8953, 0.9337,  ..., 0.6014, 0.8565, 0.4467])
+tensor([0.8046, 0.6398, 0.4516,  ..., 0.6060, 0.1172, 0.9615])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -540,768 +1509,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 7.084842920303345 seconds
+Time: 10.420795440673828 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '361507', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.549095392227173}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
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-Time: 10.549095392227173 seconds
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-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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-                      8.1627e-01, 3.2290e-01, 7.1207e-01, 9.3336e-01,
-                      5.8264e-01, 8.6629e-01, 1.0427e-01, 4.1347e-01,
-                      1.2616e-01, 9.6273e-01, 2.5433e-01, 7.5316e-01,
-                      9.7344e-01, 2.8688e-01, 7.9705e-01, 3.3331e-01,
-                      9.6254e-01, 9.1487e-01, 2.8480e-01, 3.2055e-01,
-                      3.9523e-01, 3.6145e-01, 4.5015e-01, 9.4686e-01,
-                      3.2711e-02, 8.9001e-01, 1.9633e-02, 3.7308e-01,
-                      5.9301e-01, 8.8253e-01, 8.2784e-01, 4.4139e-01,
-                      6.6233e-01, 5.8030e-01, 7.4490e-01, 9.5820e-01]),
-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1332, 0.3872, 0.9921,  ..., 0.6563, 0.0596, 0.5136])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 10.549095392227173 seconds
-
-[45.65, 38.89, 39.88, 38.76, 38.37, 38.3, 38.7, 38.8, 39.08, 38.56]
-[65.27]
-13.208173513412476
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 361507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.549095392227173, 'TIME_S_1KI': 0.029180888315377497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.0974852204322, 'W': 65.27}
-[45.65, 38.89, 39.88, 38.76, 38.37, 38.3, 38.7, 38.8, 39.08, 38.56, 39.02, 38.54, 38.45, 38.34, 38.8, 39.14, 38.83, 39.15, 38.35, 39.72]
-701.855
-35.09275
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 361507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.549095392227173, 'TIME_S_1KI': 0.029180888315377497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.0974852204322, 'W': 65.27, 'J_1KI': 2.3847324815852313, 'W_1KI': 0.18054975422329303, 'W_D': 30.177249999999994, 'J_D': 398.58635415762654, 'W_D_1KI': 0.08347625357185336, 'J_D_1KI': 0.0002309118594435332}
+[40.1, 39.18, 39.44, 38.84, 39.26, 38.78, 38.88, 39.27, 39.5, 39.18]
+[65.88]
+13.035122156143188
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420795440673828, 'TIME_S_1KI': 0.02877043508696409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.7538476467132, 'W': 65.88}
+[40.1, 39.18, 39.44, 38.84, 39.26, 38.78, 38.88, 39.27, 39.5, 39.18, 39.55, 39.36, 39.34, 38.83, 39.02, 39.04, 38.92, 39.01, 41.73, 38.96]
+707.295
+35.36475
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420795440673828, 'TIME_S_1KI': 0.02877043508696409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.7538476467132, 'W': 65.88, 'J_1KI': 2.3709055580312617, 'W_1KI': 0.18188594856503915, 'W_D': 30.515249999999995, 'J_D': 397.7700113752484, 'W_D_1KI': 0.08424856089783408, 'J_D_1KI': 0.00023259911071860987}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..44b0019
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 288650, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.542811155319214, "TIME_S_1KI": 0.036524549299564224, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 872.9266104698181, "W": 66.0, "J_1KI": 3.0241697920312425, "W_1KI": 0.22865061493157804, "W_D": 30.442750000000004, "J_D": 402.64070561939485, "W_D_1KI": 0.10546596223800452, "J_D_1KI": 0.00036537662303136846}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..ec44bfd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014984130859375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([5455, 6096, 7620,  ..., 8334, 1515, 9556]),
+       values=tensor([0.3295, 0.9699, 0.1085,  ..., 0.1358, 0.2338, 0.9968]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0525, 0.2160, 0.5197,  ..., 0.9729, 0.0490, 0.2973])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.014984130859375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70074', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.549025535583496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([3016, 6372, 3284,  ..., 9865, 6936, 5486]),
+       values=tensor([0.2981, 0.0450, 0.6145,  ..., 0.3998, 0.9695, 0.2536]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.7165, 0.5810, 0.9668,  ..., 0.2745, 0.2690, 0.0815])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 2.549025535583496 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288650', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.542811155319214}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([4532, 8082, 1862,  ..., 2662, 2473, 4062]),
+       values=tensor([0.2290, 0.0977, 0.7273,  ..., 0.3334, 0.1586, 0.6128]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0256, 0.2861, 0.7976,  ..., 0.1212, 0.6310, 0.3680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.542811155319214 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([4532, 8082, 1862,  ..., 2662, 2473, 4062]),
+       values=tensor([0.2290, 0.0977, 0.7273,  ..., 0.3334, 0.1586, 0.6128]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0256, 0.2861, 0.7976,  ..., 0.1212, 0.6310, 0.3680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.542811155319214 seconds
+
+[40.67, 38.88, 39.03, 38.97, 39.55, 38.85, 39.41, 38.82, 39.41, 38.86]
+[66.0]
+13.226160764694214
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288650, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.542811155319214, 'TIME_S_1KI': 0.036524549299564224, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.9266104698181, 'W': 66.0}
+[40.67, 38.88, 39.03, 38.97, 39.55, 38.85, 39.41, 38.82, 39.41, 38.86, 39.72, 38.82, 38.97, 39.27, 39.27, 39.76, 38.89, 38.89, 45.01, 39.44]
+711.145
+35.557249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288650, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.542811155319214, 'TIME_S_1KI': 0.036524549299564224, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.9266104698181, 'W': 66.0, 'J_1KI': 3.0241697920312425, 'W_1KI': 0.22865061493157804, 'W_D': 30.442750000000004, 'J_D': 402.64070561939485, 'W_D_1KI': 0.10546596223800452, 'J_D_1KI': 0.00036537662303136846}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..c479701
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 203, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.8138906955719, "TIME_S_1KI": 53.27039751513251, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1609.7031775331498, "W": 81.98, "J_1KI": 7929.572303118964, "W_1KI": 403.8423645320197, "W_D": 30.52650000000002, "J_D": 599.3974633930925, "W_D_1KI": 150.37684729064048, "J_D_1KI": 740.7726467519235}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..e1438f6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.424488306045532}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       44,       99,  ..., 24999917,
+                            24999946, 25000000]),
+       col_indices=tensor([  4827,  10869,  14232,  ..., 471243, 483745,
+                           496563]),
+       values=tensor([0.8207, 0.6147, 0.2995,  ..., 0.3197, 0.5880, 0.5650]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7008, 0.9045, 0.7559,  ..., 0.2377, 0.3193, 0.3380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 5.424488306045532 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.982287168502808}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,      103,  ..., 24999914,
+                            24999954, 25000000]),
+       col_indices=tensor([   956,  25275,  30712,  ..., 470941, 489379,
+                           489461]),
+       values=tensor([0.2897, 0.9352, 0.3996,  ..., 0.3187, 0.8556, 0.7054]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.6888, 0.9226, 0.2376,  ..., 0.2155, 0.1168, 0.1817])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 9.982287168502808 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '203', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.8138906955719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999892,
+                            24999946, 25000000]),
+       col_indices=tensor([ 18694,  24514,  28811,  ..., 477104, 482132,
+                           483877]),
+       values=tensor([0.0999, 0.2209, 0.5662,  ..., 0.8643, 0.1918, 0.8434]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7305, 0.0261, 0.0866,  ..., 0.4657, 0.9743, 0.1757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.8138906955719 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999892,
+                            24999946, 25000000]),
+       col_indices=tensor([ 18694,  24514,  28811,  ..., 477104, 482132,
+                           483877]),
+       values=tensor([0.0999, 0.2209, 0.5662,  ..., 0.8643, 0.1918, 0.8434]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7305, 0.0261, 0.0866,  ..., 0.4657, 0.9743, 0.1757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.8138906955719 seconds
+
+[48.25, 66.08, 64.62, 65.48, 60.76, 53.27, 65.63, 72.69, 68.45, 64.01]
+[81.98]
+19.635315656661987
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 203, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.8138906955719, 'TIME_S_1KI': 53.27039751513251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1609.7031775331498, 'W': 81.98}
+[48.25, 66.08, 64.62, 65.48, 60.76, 53.27, 65.63, 72.69, 68.45, 64.01, 67.95, 64.4, 68.38, 60.78, 45.98, 39.37, 40.58, 39.5, 41.17, 43.65]
+1029.0699999999997
+51.453499999999984
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 203, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.8138906955719, 'TIME_S_1KI': 53.27039751513251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1609.7031775331498, 'W': 81.98, 'J_1KI': 7929.572303118964, 'W_1KI': 403.8423645320197, 'W_D': 30.52650000000002, 'J_D': 599.3974633930925, 'W_D_1KI': 150.37684729064048, 'J_D_1KI': 740.7726467519235}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
index 98e0f55..9445831 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.362020254135132, "TIME_S_1KI": 7.635976605847555, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.3382936573029, "W": 74.92, "J_1KI": 734.9582119803264, "W_1KI": 55.21002210759028, "W_D": 39.366, "J_D": 524.0419016032218, "W_D_1KI": 29.00957995578482, "J_D_1KI": 21.377730254815635}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.301345348358154, "TIME_S_1KI": 7.591264073955898, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 974.2936608052254, "W": 72.47, "J_1KI": 717.9761686110725, "W_1KI": 53.404568901989684, "W_D": 37.03175, "J_D": 497.85841415101294, "W_D_1KI": 27.28942520265291, "J_D_1KI": 20.11011437188866}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
index f772ea4..d835f69 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.737008094787598}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7734920978546143}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,       9,  ..., 2499995,
-                            2499998, 2500000]),
-       col_indices=tensor([ 13538,  14404, 124427,  ..., 299545,  64656,
-                           263709]),
-       values=tensor([0.6726, 0.7704, 0.5503,  ..., 0.8434, 0.2560, 0.2989]),
+tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([ 99860, 161360, 168008,  ..., 375780, 443860,
+                           468048]),
+       values=tensor([0.7731, 0.7975, 0.7314,  ..., 0.7653, 0.4860, 0.2739]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7902, 0.8995, 0.9133,  ..., 0.8775, 0.6765, 0.9460])
+tensor([0.9501, 0.6169, 0.8449,  ..., 0.9228, 0.9726, 0.5004])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 7.737008094787598 seconds
+Time: 0.7734920978546143 seconds
 
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1357', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.362020254135132}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.301345348358154}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      12,  ..., 2499987,
-                            2499995, 2500000]),
-       col_indices=tensor([ 74385, 156503, 312661,  ..., 102229, 341067,
-                           464580]),
-       values=tensor([0.2383, 0.0369, 0.7603,  ..., 0.0658, 0.9688, 0.3918]),
+tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499991,
+                            2499994, 2500000]),
+       col_indices=tensor([238281, 305722, 262347,  ..., 326599, 364388,
+                           410788]),
+       values=tensor([0.2261, 0.8621, 0.1222,  ..., 0.7643, 0.7262, 0.6796]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4224, 0.2766, 0.2547,  ..., 0.2726, 0.8333, 0.3690])
+tensor([0.7756, 0.5142, 0.7476,  ..., 0.1970, 0.9731, 0.3396])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.362020254135132 seconds
+Time: 10.301345348358154 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      12,  ..., 2499987,
-                            2499995, 2500000]),
-       col_indices=tensor([ 74385, 156503, 312661,  ..., 102229, 341067,
-                           464580]),
-       values=tensor([0.2383, 0.0369, 0.7603,  ..., 0.0658, 0.9688, 0.3918]),
+tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499991,
+                            2499994, 2500000]),
+       col_indices=tensor([238281, 305722, 262347,  ..., 326599, 364388,
+                           410788]),
+       values=tensor([0.2261, 0.8621, 0.1222,  ..., 0.7643, 0.7262, 0.6796]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4224, 0.2766, 0.2547,  ..., 0.2726, 0.8333, 0.3690])
+tensor([0.7756, 0.5142, 0.7476,  ..., 0.1970, 0.9731, 0.3396])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.362020254135132 seconds
+Time: 10.301345348358154 seconds
 
-[40.3, 38.79, 39.14, 38.79, 39.0, 38.98, 38.53, 44.02, 38.67, 38.42]
-[74.92]
-13.31204342842102
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.362020254135132, 'TIME_S_1KI': 7.635976605847555, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3382936573029, 'W': 74.92}
-[40.3, 38.79, 39.14, 38.79, 39.0, 38.98, 38.53, 44.02, 38.67, 38.42, 39.44, 38.36, 38.82, 38.43, 45.76, 38.31, 39.93, 38.5, 38.79, 38.36]
-711.08
-35.554
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.362020254135132, 'TIME_S_1KI': 7.635976605847555, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3382936573029, 'W': 74.92, 'J_1KI': 734.9582119803264, 'W_1KI': 55.21002210759028, 'W_D': 39.366, 'J_D': 524.0419016032218, 'W_D_1KI': 29.00957995578482, 'J_D_1KI': 21.377730254815635}
+[40.45, 39.15, 39.07, 39.07, 39.08, 39.31, 39.47, 39.12, 39.58, 39.08]
+[72.47]
+13.444096326828003
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.301345348358154, 'TIME_S_1KI': 7.591264073955898, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 974.2936608052254, 'W': 72.47}
+[40.45, 39.15, 39.07, 39.07, 39.08, 39.31, 39.47, 39.12, 39.58, 39.08, 39.87, 38.95, 39.89, 38.93, 39.13, 39.06, 39.33, 39.5, 40.16, 40.53]
+708.7649999999999
+35.43825
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.301345348358154, 'TIME_S_1KI': 7.591264073955898, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 974.2936608052254, 'W': 72.47, 'J_1KI': 717.9761686110725, 'W_1KI': 53.404568901989684, 'W_D': 37.03175, 'J_D': 497.85841415101294, 'W_D_1KI': 27.28942520265291, 'J_D_1KI': 20.11011437188866}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..016557b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 374, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481700897216797, "TIME_S_1KI": 28.025938227852397, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1245.1822829723358, "W": 76.84, "J_1KI": 3329.3643929741597, "W_1KI": 205.45454545454547, "W_D": 40.990750000000006, "J_D": 664.2498134532572, "W_D_1KI": 109.60093582887701, "J_D_1KI": 293.0506305584947}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..9fc6dc1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.8034374713897705}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       31,       56,  ..., 12499945,
+                            12499972, 12500000]),
+       col_indices=tensor([ 16534,  21956,  27589,  ..., 400032, 455487,
+                           480702]),
+       values=tensor([0.5221, 0.3710, 0.3411,  ..., 0.2701, 0.3669, 0.2928]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5983, 0.4656, 0.7235,  ..., 0.5590, 0.7340, 0.5167])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 2.8034374713897705 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '374', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481700897216797}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       20,       47,  ..., 12499941,
+                            12499971, 12500000]),
+       col_indices=tensor([ 37298,  48174,  79945,  ..., 425979, 429124,
+                           477898]),
+       values=tensor([0.8892, 0.8073, 0.3867,  ..., 0.6750, 0.3130, 0.8587]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8772, 0.7830, 0.9014,  ..., 0.3941, 0.0151, 0.6871])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.481700897216797 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       20,       47,  ..., 12499941,
+                            12499971, 12500000]),
+       col_indices=tensor([ 37298,  48174,  79945,  ..., 425979, 429124,
+                           477898]),
+       values=tensor([0.8892, 0.8073, 0.3867,  ..., 0.6750, 0.3130, 0.8587]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8772, 0.7830, 0.9014,  ..., 0.3941, 0.0151, 0.6871])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.481700897216797 seconds
+
+[39.77, 39.61, 39.09, 39.46, 39.43, 39.2, 39.07, 38.94, 44.28, 39.72]
+[76.84]
+16.20487093925476
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481700897216797, 'TIME_S_1KI': 28.025938227852397, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.1822829723358, 'W': 76.84}
+[39.77, 39.61, 39.09, 39.46, 39.43, 39.2, 39.07, 38.94, 44.28, 39.72, 40.11, 44.49, 39.49, 38.95, 39.21, 39.06, 39.21, 38.96, 39.02, 39.43]
+716.9849999999999
+35.84925
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481700897216797, 'TIME_S_1KI': 28.025938227852397, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.1822829723358, 'W': 76.84, 'J_1KI': 3329.3643929741597, 'W_1KI': 205.45454545454547, 'W_D': 40.990750000000006, 'J_D': 664.2498134532572, 'W_D_1KI': 109.60093582887701, 'J_D_1KI': 293.0506305584947}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
index 0569add..e80dbeb 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.70950984954834, "TIME_S_1KI": 0.6953775631159236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.8573481559754, "W": 65.04, "J_1KI": 54.987166298031, "W_1KI": 4.223102395948315, "W_D": 30.268000000000008, "J_D": 394.10636860370647, "W_D_1KI": 1.9653269268229343, "J_D_1KI": 0.12761034522582523}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15655, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.832781791687012, "TIME_S_1KI": 0.691969453317599, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.9169475746155, "W": 65.84, "J_1KI": 55.37636202967841, "W_1KI": 4.205685084637497, "W_D": 30.269999999999996, "J_D": 398.5658566689491, "W_D_1KI": 1.933567550303417, "J_D_1KI": 0.12351118175045782}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
index 5835407..da8990f 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6817739009857178}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.08056378364562988}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     11,  ..., 249990, 249996,
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 249990, 249996,
                             250000]),
-       col_indices=tensor([22352, 25754, 44016,  ..., 24187, 38739, 43878]),
-       values=tensor([0.9987, 0.7536, 0.3762,  ..., 0.2868, 0.8081, 0.6848]),
+       col_indices=tensor([28795, 30379, 41102,  ...,  5633,  6424, 22447]),
+       values=tensor([0.9841, 0.4564, 0.4138,  ..., 0.4352, 0.7831, 0.6427]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2548, 0.4461, 0.9076,  ..., 0.8528, 0.8836, 0.6180])
+tensor([0.6096, 0.2856, 0.5951,  ..., 0.5564, 0.0665, 0.9869])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.6817739009857178 seconds
+Time: 0.08056378364562988 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15401', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.70950984954834}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13033', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.741129398345947}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249994,
+tensor(crow_indices=tensor([     0,      7,     13,  ..., 249987, 249991,
                             250000]),
-       col_indices=tensor([21278, 27457, 27912,  ..., 25636, 33177, 40764]),
-       values=tensor([0.5508, 0.6259, 0.1639,  ..., 0.1456, 0.5920, 0.1745]),
+       col_indices=tensor([  370,  1086,  2786,  ..., 43615, 44396, 45243]),
+       values=tensor([0.9664, 0.8693, 0.7422,  ..., 0.8293, 0.8225, 0.1476]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3112, 0.1298, 0.2276,  ..., 0.1739, 0.6060, 0.6815])
+tensor([0.9116, 0.5607, 0.8635,  ..., 0.8139, 0.6651, 0.4589])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.70950984954834 seconds
+Time: 8.741129398345947 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15655', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.832781791687012}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249994,
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249998,
                             250000]),
-       col_indices=tensor([21278, 27457, 27912,  ..., 25636, 33177, 40764]),
-       values=tensor([0.5508, 0.6259, 0.1639,  ..., 0.1456, 0.5920, 0.1745]),
+       col_indices=tensor([12466, 31687, 41380,  ..., 43099, 30794, 44210]),
+       values=tensor([0.5535, 0.2801, 0.3869,  ..., 0.8607, 0.0342, 0.7001]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3112, 0.1298, 0.2276,  ..., 0.1739, 0.6060, 0.6815])
+tensor([0.1141, 0.0022, 0.7559,  ..., 0.9683, 0.9705, 0.8203])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.70950984954834 seconds
+Time: 10.832781791687012 seconds
 
-[39.37, 38.33, 39.38, 39.01, 38.5, 38.38, 38.36, 38.51, 38.52, 38.33]
-[65.04]
-13.020561933517456
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.70950984954834, 'TIME_S_1KI': 0.6953775631159236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.8573481559754, 'W': 65.04}
-[39.37, 38.33, 39.38, 39.01, 38.5, 38.38, 38.36, 38.51, 38.52, 38.33, 40.16, 38.83, 38.77, 38.25, 38.69, 38.32, 38.3, 38.47, 38.28, 39.22]
-695.4399999999999
-34.772
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.70950984954834, 'TIME_S_1KI': 0.6953775631159236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.8573481559754, 'W': 65.04, 'J_1KI': 54.987166298031, 'W_1KI': 4.223102395948315, 'W_D': 30.268000000000008, 'J_D': 394.10636860370647, 'W_D_1KI': 1.9653269268229343, 'J_D_1KI': 0.12761034522582523}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249998,
+                            250000]),
+       col_indices=tensor([12466, 31687, 41380,  ..., 43099, 30794, 44210]),
+       values=tensor([0.5535, 0.2801, 0.3869,  ..., 0.8607, 0.0342, 0.7001]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1141, 0.0022, 0.7559,  ..., 0.9683, 0.9705, 0.8203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.832781791687012 seconds
+
+[39.71, 38.91, 39.08, 39.03, 39.04, 38.89, 39.49, 39.37, 39.35, 39.31]
+[65.84]
+13.167025327682495
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15655, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.832781791687012, 'TIME_S_1KI': 0.691969453317599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.9169475746155, 'W': 65.84}
+[39.71, 38.91, 39.08, 39.03, 39.04, 38.89, 39.49, 39.37, 39.35, 39.31, 40.75, 39.44, 39.59, 39.09, 39.39, 39.11, 38.98, 38.78, 44.38, 39.19]
+711.4000000000001
+35.57000000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15655, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.832781791687012, 'TIME_S_1KI': 0.691969453317599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.9169475746155, 'W': 65.84, 'J_1KI': 55.37636202967841, 'W_1KI': 4.205685084637497, 'W_D': 30.269999999999996, 'J_D': 398.5658566689491, 'W_D_1KI': 1.933567550303417, 'J_D_1KI': 0.12351118175045782}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
index 4fe2ca5..4e67596 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3498, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.43606948852539, "TIME_S_1KI": 2.983438961842593, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 938.4889250850676, "W": 69.66, "J_1KI": 268.29300316897303, "W_1KI": 19.914236706689536, "W_D": 34.37075, "J_D": 463.0572526825667, "W_D_1KI": 9.82582904516867, "J_D_1KI": 2.8089848613975614}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135668277740479, "TIME_S_1KI": 2.9802023751074618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 929.8197621202468, "W": 70.57, "J_1KI": 273.39599003829665, "W_1KI": 20.74977947662452, "W_D": 35.09774999999999, "J_D": 462.44270307433595, "W_D_1KI": 10.319832402234633, "J_D_1KI": 3.0343523676079487}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
index 08291b1..73dd96f 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0015740394592285}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3087193965911865}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      44,     103,  ..., 2499905,
-                            2499956, 2500000]),
-       col_indices=tensor([  226,  2395,  3856,  ..., 46208, 48736, 49649]),
-       values=tensor([0.2794, 0.3289, 0.9047,  ..., 0.2004, 0.4257, 0.7682]),
+tensor(crow_indices=tensor([      0,      47,     100,  ..., 2499908,
+                            2499955, 2500000]),
+       col_indices=tensor([ 1811,  3820,  5210,  ..., 47398, 47518, 48036]),
+       values=tensor([0.8154, 0.8090, 0.3024,  ..., 0.4722, 0.9116, 0.7561]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4960, 0.6719, 0.9417,  ..., 0.9330, 0.7654, 0.9120])
+tensor([0.4260, 0.8795, 0.3202,  ..., 0.3159, 0.0406, 0.9752])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 3.0015740394592285 seconds
+Time: 0.3087193965911865 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3498', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.43606948852539}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3401', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135668277740479}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      53,     101,  ..., 2499890,
-                            2499947, 2500000]),
-       col_indices=tensor([    1,   302,   356,  ..., 47860, 48391, 48616]),
-       values=tensor([0.1949, 0.9610, 0.6433,  ..., 0.8236, 0.0074, 0.9971]),
+tensor(crow_indices=tensor([      0,      41,      88,  ..., 2499883,
+                            2499951, 2500000]),
+       col_indices=tensor([  638,  2365,  2400,  ..., 44467, 46636, 49496]),
+       values=tensor([0.8518, 0.8769, 0.3572,  ..., 0.1360, 0.1673, 0.1097]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6285, 0.6234, 0.6444,  ..., 0.5791, 0.7727, 0.1804])
+tensor([0.0408, 0.6133, 0.1624,  ..., 0.7272, 0.2583, 0.9038])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.43606948852539 seconds
+Time: 10.135668277740479 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      53,     101,  ..., 2499890,
-                            2499947, 2500000]),
-       col_indices=tensor([    1,   302,   356,  ..., 47860, 48391, 48616]),
-       values=tensor([0.1949, 0.9610, 0.6433,  ..., 0.8236, 0.0074, 0.9971]),
+tensor(crow_indices=tensor([      0,      41,      88,  ..., 2499883,
+                            2499951, 2500000]),
+       col_indices=tensor([  638,  2365,  2400,  ..., 44467, 46636, 49496]),
+       values=tensor([0.8518, 0.8769, 0.3572,  ..., 0.1360, 0.1673, 0.1097]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6285, 0.6234, 0.6444,  ..., 0.5791, 0.7727, 0.1804])
+tensor([0.0408, 0.6133, 0.1624,  ..., 0.7272, 0.2583, 0.9038])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.43606948852539 seconds
+Time: 10.135668277740479 seconds
 
-[38.98, 38.56, 38.59, 38.25, 38.75, 38.31, 38.43, 38.27, 38.33, 43.32]
-[69.66]
-13.472422122955322
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3498, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.43606948852539, 'TIME_S_1KI': 2.983438961842593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 938.4889250850676, 'W': 69.66}
-[38.98, 38.56, 38.59, 38.25, 38.75, 38.31, 38.43, 38.27, 38.33, 43.32, 39.04, 38.73, 38.9, 38.87, 38.44, 45.59, 38.97, 38.44, 40.32, 38.73]
-705.785
-35.289249999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3498, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.43606948852539, 'TIME_S_1KI': 2.983438961842593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 938.4889250850676, 'W': 69.66, 'J_1KI': 268.29300316897303, 'W_1KI': 19.914236706689536, 'W_D': 34.37075, 'J_D': 463.0572526825667, 'W_D_1KI': 9.82582904516867, 'J_D_1KI': 2.8089848613975614}
+[40.86, 44.19, 38.96, 38.91, 39.18, 38.84, 39.26, 38.86, 39.41, 39.32]
+[70.57]
+13.17585039138794
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135668277740479, 'TIME_S_1KI': 2.9802023751074618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.8197621202468, 'W': 70.57}
+[40.86, 44.19, 38.96, 38.91, 39.18, 38.84, 39.26, 38.86, 39.41, 39.32, 40.03, 39.02, 38.84, 38.98, 38.82, 40.3, 38.82, 38.8, 38.79, 38.72]
+709.445
+35.47225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135668277740479, 'TIME_S_1KI': 2.9802023751074618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.8197621202468, 'W': 70.57, 'J_1KI': 273.39599003829665, 'W_1KI': 20.74977947662452, 'W_D': 35.09774999999999, 'J_D': 462.44270307433595, 'W_D_1KI': 10.319832402234633, 'J_D_1KI': 3.0343523676079487}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..05ef34d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.601917266845703, "TIME_S_1KI": 38.27406955539965, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.4378191399574, "W": 76.47, "J_1KI": 5423.963245992626, "W_1KI": 276.0649819494585, "W_D": 40.946749999999994, "J_D": 804.497786986649, "W_D_1KI": 147.82220216606495, "J_D_1KI": 533.6541594442779}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..e414a83
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.789712905883789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      551,     1060,  ..., 24998953,
+                            24999494, 25000000]),
+       col_indices=tensor([   18,    53,    90,  ..., 49926, 49944, 49970]),
+       values=tensor([0.6546, 0.1735, 0.7966,  ..., 0.3203, 0.8871, 0.0598]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4855, 0.9619, 0.0930,  ..., 0.6959, 0.6112, 0.3764])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 3.789712905883789 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '277', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.601917266845703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      523,     1012,  ..., 24998990,
+                            24999509, 25000000]),
+       col_indices=tensor([  171,   246,   332,  ..., 49640, 49825, 49863]),
+       values=tensor([0.1620, 0.2511, 0.7784,  ..., 0.0916, 0.2856, 0.2435]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9840, 0.8151, 0.6106,  ..., 0.4542, 0.6992, 0.9833])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.601917266845703 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      523,     1012,  ..., 24998990,
+                            24999509, 25000000]),
+       col_indices=tensor([  171,   246,   332,  ..., 49640, 49825, 49863]),
+       values=tensor([0.1620, 0.2511, 0.7784,  ..., 0.0916, 0.2856, 0.2435]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9840, 0.8151, 0.6106,  ..., 0.4542, 0.6992, 0.9833])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.601917266845703 seconds
+
+[39.75, 38.88, 39.04, 38.94, 38.9, 44.6, 39.87, 39.01, 39.47, 38.87]
+[76.47]
+19.647414922714233
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.601917266845703, 'TIME_S_1KI': 38.27406955539965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.4378191399574, 'W': 76.47}
+[39.75, 38.88, 39.04, 38.94, 38.9, 44.6, 39.87, 39.01, 39.47, 38.87, 39.65, 39.46, 38.97, 39.37, 39.36, 38.88, 39.18, 38.94, 39.03, 38.86]
+710.465
+35.523250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.601917266845703, 'TIME_S_1KI': 38.27406955539965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.4378191399574, 'W': 76.47, 'J_1KI': 5423.963245992626, 'W_1KI': 276.0649819494585, 'W_D': 40.946749999999994, 'J_D': 804.497786986649, 'W_D_1KI': 147.82220216606495, 'J_D_1KI': 533.6541594442779}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
index 5c43412..41355a8 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35695, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.507647275924683, "TIME_S_1KI": 0.2943730851918947, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 842.3661928725243, "W": 64.41, "J_1KI": 23.598996858734395, "W_1KI": 1.8044544053789044, "W_D": 29.134750000000004, "J_D": 381.0297847817541, "W_D_1KI": 0.8162137554279313, "J_D_1KI": 0.02286633297178684}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35925, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.468754291534424, "TIME_S_1KI": 0.29140582579079816, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3263425445557, "W": 65.12, "J_1KI": 23.808666459138642, "W_1KI": 1.8126652748782186, "W_D": 29.625000000000007, "J_D": 389.1130666136743, "W_D_1KI": 0.8246346555323593, "J_D_1KI": 0.022954339750378826}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
index c6e8d71..77e4f27 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3123207092285156}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04216480255126953}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 1731,  4163, 39043,  ..., 48142,  1105, 32715]),
-       values=tensor([0.9730, 0.5233, 0.5883,  ..., 0.0098, 0.9466, 0.3610]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([ 8605,  8537, 29290,  ...,  9179, 13978,  1469]),
+       values=tensor([0.2780, 0.8342, 0.8502,  ..., 0.9082, 0.5496, 0.9536]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3233, 0.5001, 0.4757,  ..., 0.9452, 0.0190, 0.8013])
+tensor([0.7685, 0.9033, 0.7153,  ..., 0.8654, 0.8274, 0.9503])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.3123207092285156 seconds
+Time: 0.04216480255126953 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '33619', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.88913083076477}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24902', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.278246164321899}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([10235, 29693, 19116,  ..., 40289, 44691, 23523]),
-       values=tensor([0.1639, 0.2137, 0.2836,  ..., 0.1546, 0.8297, 0.2686]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([37031, 32096, 18727,  ..., 44552, 41451,  6296]),
+       values=tensor([0.0751, 0.3287, 0.1662,  ..., 0.5788, 0.0483, 0.4147]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0511, 0.8204, 0.3831,  ..., 0.1304, 0.0964, 0.0598])
+tensor([0.5341, 0.4675, 0.3327,  ..., 0.1193, 0.3106, 0.6128])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.88913083076477 seconds
+Time: 7.278246164321899 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35695', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.507647275924683}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35925', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.468754291534424}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([19065, 20351, 39842,  ..., 40423,  9509, 47347]),
-       values=tensor([0.9158, 0.3839, 0.2352,  ..., 0.6644, 0.6974, 0.4594]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([44391, 10770, 45928,  ...,  5594,  4079, 17032]),
+       values=tensor([0.6882, 0.4791, 0.4331,  ..., 0.9470, 0.6037, 0.4941]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0381, 0.0022, 0.0479,  ..., 0.9299, 0.2975, 0.9449])
+tensor([0.1889, 0.2559, 0.4696,  ..., 0.5278, 0.4768, 0.4458])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.507647275924683 seconds
+Time: 10.468754291534424 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([19065, 20351, 39842,  ..., 40423,  9509, 47347]),
-       values=tensor([0.9158, 0.3839, 0.2352,  ..., 0.6644, 0.6974, 0.4594]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([44391, 10770, 45928,  ...,  5594,  4079, 17032]),
+       values=tensor([0.6882, 0.4791, 0.4331,  ..., 0.9470, 0.6037, 0.4941]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0381, 0.0022, 0.0479,  ..., 0.9299, 0.2975, 0.9449])
+tensor([0.1889, 0.2559, 0.4696,  ..., 0.5278, 0.4768, 0.4458])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.507647275924683 seconds
+Time: 10.468754291534424 seconds
 
-[39.22, 44.39, 40.14, 38.47, 39.94, 38.41, 38.49, 38.91, 39.41, 38.82]
-[64.41]
-13.078189611434937
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35695, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.507647275924683, 'TIME_S_1KI': 0.2943730851918947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 842.3661928725243, 'W': 64.41}
-[39.22, 44.39, 40.14, 38.47, 39.94, 38.41, 38.49, 38.91, 39.41, 38.82, 39.08, 38.6, 38.48, 38.48, 38.48, 38.38, 38.83, 39.42, 38.84, 38.55]
-705.5049999999999
-35.27524999999999
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35695, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.507647275924683, 'TIME_S_1KI': 0.2943730851918947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 842.3661928725243, 'W': 64.41, 'J_1KI': 23.598996858734395, 'W_1KI': 1.8044544053789044, 'W_D': 29.134750000000004, 'J_D': 381.0297847817541, 'W_D_1KI': 0.8162137554279313, 'J_D_1KI': 0.02286633297178684}
+[39.59, 39.87, 39.3, 39.3, 39.78, 38.89, 39.35, 40.52, 39.13, 38.83]
+[65.12]
+13.134618282318115
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.468754291534424, 'TIME_S_1KI': 0.29140582579079816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3263425445557, 'W': 65.12}
+[39.59, 39.87, 39.3, 39.3, 39.78, 38.89, 39.35, 40.52, 39.13, 38.83, 39.59, 39.23, 39.11, 38.91, 39.77, 40.62, 39.49, 39.29, 38.91, 38.85]
+709.9
+35.495
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.468754291534424, 'TIME_S_1KI': 0.29140582579079816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3263425445557, 'W': 65.12, 'J_1KI': 23.808666459138642, 'W_1KI': 1.8126652748782186, 'W_D': 29.625000000000007, 'J_D': 389.1130666136743, 'W_D_1KI': 0.8246346555323593, 'J_D_1KI': 0.022954339750378826}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..54cfc66
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 18331, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45155644416809, "TIME_S_1KI": 0.570157462449844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 852.8894222688675, "W": 65.62, "J_1KI": 46.52716285357414, "W_1KI": 3.5797283290600626, "W_D": 29.885000000000005, "J_D": 388.42731460690504, "W_D_1KI": 1.6302984016147513, "J_D_1KI": 0.08893668657545967}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..bef46d2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0693967342376709}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124992, 124996,
+                            125000]),
+       col_indices=tensor([17438, 29688, 13553,  ..., 36532, 44163, 44855]),
+       values=tensor([0.2242, 0.0224, 0.1461,  ..., 0.6740, 0.5355, 0.8238]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2153, 0.3458, 0.3359,  ..., 0.7412, 0.9011, 0.6249])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.0693967342376709 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15130', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.666174411773682}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      4,  ..., 124994, 124997,
+                            125000]),
+       col_indices=tensor([17329, 36001, 38373,  ...,   475, 21379, 35295]),
+       values=tensor([0.0803, 0.2135, 0.1853,  ..., 0.6523, 0.5299, 0.1396]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.4435, 0.4362, 0.5554,  ..., 0.5263, 0.8506, 0.5178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 8.666174411773682 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18331', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45155644416809}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ..., 124997, 124997,
+                            125000]),
+       col_indices=tensor([14185, 16264,  7088,  ..., 33383, 46641, 46645]),
+       values=tensor([0.3059, 0.0880, 0.9320,  ..., 0.7602, 0.8512, 0.4645]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5025, 0.7968, 0.2806,  ..., 0.1024, 0.8091, 0.6972])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.45155644416809 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ..., 124997, 124997,
+                            125000]),
+       col_indices=tensor([14185, 16264,  7088,  ..., 33383, 46641, 46645]),
+       values=tensor([0.3059, 0.0880, 0.9320,  ..., 0.7602, 0.8512, 0.4645]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5025, 0.7968, 0.2806,  ..., 0.1024, 0.8091, 0.6972])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.45155644416809 seconds
+
+[39.86, 38.92, 44.85, 39.01, 39.6, 39.39, 38.97, 39.8, 41.08, 39.48]
+[65.62]
+12.997400522232056
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18331, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45155644416809, 'TIME_S_1KI': 0.570157462449844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.8894222688675, 'W': 65.62}
+[39.86, 38.92, 44.85, 39.01, 39.6, 39.39, 38.97, 39.8, 41.08, 39.48, 40.67, 39.02, 39.09, 39.05, 39.17, 38.87, 39.48, 39.3, 39.44, 39.31]
+714.7
+35.735
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18331, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45155644416809, 'TIME_S_1KI': 0.570157462449844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.8894222688675, 'W': 65.62, 'J_1KI': 46.52716285357414, 'W_1KI': 3.5797283290600626, 'W_D': 29.885000000000005, 'J_D': 388.42731460690504, 'W_D_1KI': 1.6302984016147513, 'J_D_1KI': 0.08893668657545967}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
index cbaf9ec..2d402b4 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 478217, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.855157613754272, "TIME_S_1KI": 0.022699229876299402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.2491577148438, "W": 64.87, "J_1KI": 1.7905033859416202, "W_1KI": 0.1356497155057223, "W_D": 29.804500000000004, "J_D": 393.40339172363286, "W_D_1KI": 0.06232421683043473, "J_D_1KI": 0.00013032622602382335}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 461205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.546576023101807, "TIME_S_1KI": 0.022867436439548153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 854.1005449390411, "W": 65.22, "J_1KI": 1.8518891706270337, "W_1KI": 0.1414121702930367, "W_D": 29.99125, "J_D": 392.75594861090184, "W_D_1KI": 0.065028024414306, "J_D_1KI": 0.0001409959224516343}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
index dc99150..a788520 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,51 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03418374061584473}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([3258, 3666,  785,  ...,  592, 2528, 4295]),
-       values=tensor([0.0745, 0.3346, 0.7433,  ..., 0.4561, 0.1450, 0.7729]),
-       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.6815, 0.4251, 0.0154,  ..., 0.8636, 0.4620, 0.2584])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 2500
-Density: 0.0001
-Time: 0.03418374061584473 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '307163', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.744239568710327}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    2,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([1557, 2371, 1241,  ..., 4745,  784, 3444]),
-       values=tensor([0.6224, 0.1480, 0.3479,  ..., 0.3226, 0.4259, 0.8584]),
-       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.6292, 0.0071, 0.7726,  ..., 0.8443, 0.3847, 0.4326])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 2500
-Density: 0.0001
-Time: 6.744239568710327 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '478217', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.855157613754272}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0131683349609375}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([ 537,  942, 2250,  ..., 4421, 3640, 3689]),
-       values=tensor([0.2431, 0.3591, 0.7204,  ..., 0.2868, 0.0163, 0.2334]),
+       col_indices=tensor([1743,  190, 2771,  ..., 2075, 3388, 3957]),
+       values=tensor([0.1814, 0.3494, 0.7591,  ..., 0.1503, 0.3935, 0.5274]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9258, 0.9006, 0.7252,  ..., 0.7255, 0.3779, 0.2202])
+tensor([0.3288, 0.8511, 0.6239,  ..., 0.9211, 0.6649, 0.5940])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.855157613754272 seconds
+Time: 0.0131683349609375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '79736', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8153021335601807}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([ 537,  942, 2250,  ..., 4421, 3640, 3689]),
-       values=tensor([0.2431, 0.3591, 0.7204,  ..., 0.2868, 0.0163, 0.2334]),
+       col_indices=tensor([3060, 1065, 3686,  ...,  959,  268, 4999]),
+       values=tensor([0.4441, 0.5663, 0.4237,  ..., 0.7927, 0.1815, 0.5098]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9258, 0.9006, 0.7252,  ..., 0.7255, 0.3779, 0.2202])
+tensor([0.5827, 0.3654, 0.8856,  ..., 0.0793, 0.3634, 0.4632])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +34,48 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.855157613754272 seconds
+Time: 1.8153021335601807 seconds
 
-[39.25, 38.14, 38.36, 38.18, 38.58, 38.56, 38.4, 38.47, 38.48, 38.68]
-[64.87]
-13.199462890625
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 478217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.855157613754272, 'TIME_S_1KI': 0.022699229876299402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.2491577148438, 'W': 64.87}
-[39.25, 38.14, 38.36, 38.18, 38.58, 38.56, 38.4, 38.47, 38.48, 38.68, 39.07, 38.8, 38.23, 39.53, 38.54, 38.21, 38.59, 38.49, 41.46, 47.58]
-701.31
-35.0655
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 478217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.855157613754272, 'TIME_S_1KI': 0.022699229876299402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.2491577148438, 'W': 64.87, 'J_1KI': 1.7905033859416202, 'W_1KI': 0.1356497155057223, 'W_D': 29.804500000000004, 'J_D': 393.40339172363286, 'W_D_1KI': 0.06232421683043473, 'J_D_1KI': 0.00013032622602382335}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '461205', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.546576023101807}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 400,  983, 3289,  ..., 2520,  735,  710]),
+       values=tensor([0.5782, 0.5847, 0.2189,  ..., 0.6822, 0.4901, 0.5172]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8041, 0.1477, 0.3593,  ..., 0.2372, 0.8803, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.546576023101807 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 400,  983, 3289,  ..., 2520,  735,  710]),
+       values=tensor([0.5782, 0.5847, 0.2189,  ..., 0.6822, 0.4901, 0.5172]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8041, 0.1477, 0.3593,  ..., 0.2372, 0.8803, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.546576023101807 seconds
+
+[42.91, 38.91, 39.2, 38.78, 38.91, 38.88, 39.21, 39.32, 39.19, 38.86]
+[65.22]
+13.09568452835083
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 461205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.546576023101807, 'TIME_S_1KI': 0.022867436439548153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.1005449390411, 'W': 65.22}
+[42.91, 38.91, 39.2, 38.78, 38.91, 38.88, 39.21, 39.32, 39.19, 38.86, 39.94, 38.72, 39.13, 38.84, 39.27, 38.74, 39.16, 38.84, 39.21, 38.82]
+704.5749999999999
+35.22875
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 461205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.546576023101807, 'TIME_S_1KI': 0.022867436439548153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.1005449390411, 'W': 65.22, 'J_1KI': 1.8518891706270337, 'W_1KI': 0.1414121702930367, 'W_D': 29.99125, 'J_D': 392.75594861090184, 'W_D_1KI': 0.065028024414306, 'J_D_1KI': 0.0001409959224516343}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
index 34a1b0c..89f214d 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 248678, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.619725465774536, "TIME_S_1KI": 0.04270472444596843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 878.5904247951509, "W": 65.93, "J_1KI": 3.5330444381696444, "W_1KI": 0.2651219649506591, "W_D": 31.137750000000004, "J_D": 414.9450781080723, "W_D_1KI": 0.12521312701565881, "J_D_1KI": 0.0005035150958897}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 244335, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.417778253555298, "TIME_S_1KI": 0.04263727363478543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.0210603523253, "W": 66.32, "J_1KI": 3.4993801966657467, "W_1KI": 0.27143061779933286, "W_D": 30.753, "J_D": 396.4786289055347, "W_D_1KI": 0.1258640800540242, "J_D_1KI": 0.0005151291466798624}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
index c454cf5..b5c1026 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05426168441772461}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.01594710350036621}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     9,  ..., 24991, 24997, 25000]),
-       col_indices=tensor([1287, 1316, 2359,  ..., 1751, 2298, 3529]),
-       values=tensor([0.1773, 0.9664, 0.4947,  ..., 0.2806, 0.9364, 0.2474]),
+tensor(crow_indices=tensor([    0,     8,    12,  ..., 24992, 24999, 25000]),
+       col_indices=tensor([ 675, 1985, 2689,  ..., 3047, 3313, 3022]),
+       values=tensor([0.4390, 0.7079, 0.1777,  ..., 0.9145, 0.2520, 0.4929]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5449, 0.4697, 0.1251,  ..., 0.6031, 0.3711, 0.9109])
+tensor([0.8163, 0.8334, 0.7201,  ..., 0.8897, 0.9022, 0.1765])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.05426168441772461 seconds
+Time: 0.01594710350036621 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193506', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.17044973373413}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65842', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.8294758796691895}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     7,    10,  ..., 24989, 24995, 25000]),
-       col_indices=tensor([ 563, 1432, 1628,  ..., 3910, 4925, 4964]),
-       values=tensor([0.0779, 0.2473, 0.4860,  ..., 0.8752, 0.7145, 0.0936]),
+tensor(crow_indices=tensor([    0,     3,     9,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([  33, 1908, 3594,  ...,  299,  386, 4209]),
+       values=tensor([0.2193, 0.5619, 0.9883,  ..., 0.1847, 0.2793, 0.9697]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5862, 0.8689, 0.7521,  ..., 0.3378, 0.8388, 0.0430])
+tensor([0.6444, 0.1854, 0.4415,  ..., 0.6088, 0.2901, 0.0299])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 8.17044973373413 seconds
+Time: 2.8294758796691895 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '248678', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.619725465774536}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '244335', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.417778253555298}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     5,  ..., 24990, 24993, 25000]),
-       col_indices=tensor([  49,  355,  745,  ..., 2877, 3597, 4425]),
-       values=tensor([0.2389, 0.4883, 0.4431,  ..., 0.9568, 0.0569, 0.8170]),
+tensor(crow_indices=tensor([    0,     3,     8,  ..., 24990, 24997, 25000]),
+       col_indices=tensor([2557, 3235, 4228,  ...,  486, 3364, 4712]),
+       values=tensor([0.2079, 0.9240, 0.7430,  ..., 0.9071, 0.8940, 0.1396]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9647, 0.9839, 0.1030,  ..., 0.7979, 0.9168, 0.5702])
+tensor([0.3103, 0.6909, 0.1567,  ..., 0.3064, 0.4398, 0.1480])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.619725465774536 seconds
+Time: 10.417778253555298 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     5,  ..., 24990, 24993, 25000]),
-       col_indices=tensor([  49,  355,  745,  ..., 2877, 3597, 4425]),
-       values=tensor([0.2389, 0.4883, 0.4431,  ..., 0.9568, 0.0569, 0.8170]),
+tensor(crow_indices=tensor([    0,     3,     8,  ..., 24990, 24997, 25000]),
+       col_indices=tensor([2557, 3235, 4228,  ...,  486, 3364, 4712]),
+       values=tensor([0.2079, 0.9240, 0.7430,  ..., 0.9071, 0.8940, 0.1396]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9647, 0.9839, 0.1030,  ..., 0.7979, 0.9168, 0.5702])
+tensor([0.3103, 0.6909, 0.1567,  ..., 0.3064, 0.4398, 0.1480])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.619725465774536 seconds
+Time: 10.417778253555298 seconds
 
-[39.55, 38.57, 38.5, 38.49, 39.15, 38.5, 38.44, 38.52, 38.74, 38.66]
-[65.93]
-13.326109886169434
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 248678, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.619725465774536, 'TIME_S_1KI': 0.04270472444596843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.5904247951509, 'W': 65.93}
-[39.55, 38.57, 38.5, 38.49, 39.15, 38.5, 38.44, 38.52, 38.74, 38.66, 39.58, 38.56, 38.38, 38.87, 38.31, 38.44, 38.39, 38.74, 38.99, 38.72]
-695.845
-34.79225
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 248678, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.619725465774536, 'TIME_S_1KI': 0.04270472444596843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.5904247951509, 'W': 65.93, 'J_1KI': 3.5330444381696444, 'W_1KI': 0.2651219649506591, 'W_D': 31.137750000000004, 'J_D': 414.9450781080723, 'W_D_1KI': 0.12521312701565881, 'J_D_1KI': 0.0005035150958897}
+[45.51, 38.78, 39.51, 38.99, 39.48, 39.2, 39.32, 39.33, 39.41, 38.75]
+[66.32]
+12.892356157302856
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 244335, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.417778253555298, 'TIME_S_1KI': 0.04263727363478543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.0210603523253, 'W': 66.32}
+[45.51, 38.78, 39.51, 38.99, 39.48, 39.2, 39.32, 39.33, 39.41, 38.75, 39.41, 38.8, 39.14, 39.28, 40.7, 39.8, 39.35, 39.07, 39.32, 40.05]
+711.3399999999999
+35.56699999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 244335, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.417778253555298, 'TIME_S_1KI': 0.04263727363478543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.0210603523253, 'W': 66.32, 'J_1KI': 3.4993801966657467, 'W_1KI': 0.27143061779933286, 'W_D': 30.753, 'J_D': 396.4786289055347, 'W_D_1KI': 0.1258640800540242, 'J_D_1KI': 0.0005151291466798624}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
index 163b6dc..d1e9eee 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 39651, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.074631690979004, "TIME_S_1KI": 0.25408266351363157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 909.2396620059013, "W": 71.91, "J_1KI": 22.931065093084698, "W_1KI": 1.8135734281607019, "W_D": 21.342999999999996, "J_D": 269.8637478263378, "W_D_1KI": 0.5382714181231241, "J_D_1KI": 0.013575229328973395}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 41575, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.472304821014404, "TIME_S_1KI": 0.2518894725439424, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 875.4065453624726, "W": 66.47, "J_1KI": 21.056080465723934, "W_1KI": 1.5987973541791942, "W_D": 30.839750000000002, "J_D": 406.1579510657788, "W_D_1KI": 0.7417859290438966, "J_D_1KI": 0.017842114949943394}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
index 5c37580..9d6f728 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.26480770111083984}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03741025924682617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     43,     97,  ..., 249898, 249949,
+tensor(crow_indices=tensor([     0,     50,     95,  ..., 249917, 249960,
                             250000]),
-       col_indices=tensor([  46,  106,  224,  ..., 4804, 4890, 4986]),
-       values=tensor([0.9512, 0.1564, 0.8337,  ..., 0.0764, 0.6147, 0.8806]),
+       col_indices=tensor([  56,  131,  133,  ..., 4645, 4665, 4841]),
+       values=tensor([0.2594, 0.3669, 0.3309,  ..., 0.9204, 0.7750, 0.3008]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5067, 0.1013, 0.0742,  ..., 0.2212, 0.5429, 0.9437])
+tensor([0.7286, 0.8651, 0.2961,  ..., 0.7120, 0.4132, 0.7079])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.26480770111083984 seconds
+Time: 0.03741025924682617 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '39651', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.074631690979004}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28067', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.088342666625977}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     49,    105,  ..., 249900, 249947,
+tensor(crow_indices=tensor([     0,     51,     92,  ..., 249888, 249951,
                             250000]),
-       col_indices=tensor([ 129,  155,  285,  ..., 4713, 4736, 4825]),
-       values=tensor([0.9050, 0.4779, 0.3101,  ..., 0.9077, 0.5485, 0.2382]),
+       col_indices=tensor([ 195,  232,  275,  ..., 4637, 4801, 4910]),
+       values=tensor([0.9933, 0.3255, 0.9817,  ..., 0.2679, 0.2640, 0.0554]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7723, 0.0685, 0.7362,  ..., 0.7986, 0.1054, 0.6909])
+tensor([0.2297, 0.2173, 0.6945,  ..., 0.5761, 0.5521, 0.3650])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.074631690979004 seconds
+Time: 7.088342666625977 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '41575', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.472304821014404}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     49,    105,  ..., 249900, 249947,
+tensor(crow_indices=tensor([     0,     48,     88,  ..., 249902, 249950,
                             250000]),
-       col_indices=tensor([ 129,  155,  285,  ..., 4713, 4736, 4825]),
-       values=tensor([0.9050, 0.4779, 0.3101,  ..., 0.9077, 0.5485, 0.2382]),
+       col_indices=tensor([  75,   83,  302,  ..., 4746, 4941, 4952]),
+       values=tensor([0.9930, 0.3893, 0.6584,  ..., 0.0382, 0.8338, 0.2904]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7723, 0.0685, 0.7362,  ..., 0.7986, 0.1054, 0.6909])
+tensor([0.5807, 0.7893, 0.2250,  ..., 0.2178, 0.8594, 0.2155])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.074631690979004 seconds
+Time: 10.472304821014404 seconds
 
-[39.13, 38.4, 39.4, 38.95, 39.01, 38.64, 38.39, 62.19, 64.39, 63.32]
-[71.91]
-12.644133806228638
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 39651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.074631690979004, 'TIME_S_1KI': 0.25408266351363157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 909.2396620059013, 'W': 71.91}
-[39.13, 38.4, 39.4, 38.95, 39.01, 38.64, 38.39, 62.19, 64.39, 63.32, 68.24, 64.54, 66.6, 65.57, 64.04, 68.19, 66.94, 66.24, 69.2, 70.61]
-1011.34
-50.567
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 39651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.074631690979004, 'TIME_S_1KI': 0.25408266351363157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 909.2396620059013, 'W': 71.91, 'J_1KI': 22.931065093084698, 'W_1KI': 1.8135734281607019, 'W_D': 21.342999999999996, 'J_D': 269.8637478263378, 'W_D_1KI': 0.5382714181231241, 'J_D_1KI': 0.013575229328973395}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     48,     88,  ..., 249902, 249950,
+                            250000]),
+       col_indices=tensor([  75,   83,  302,  ..., 4746, 4941, 4952]),
+       values=tensor([0.9930, 0.3893, 0.6584,  ..., 0.0382, 0.8338, 0.2904]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5807, 0.7893, 0.2250,  ..., 0.2178, 0.8594, 0.2155])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.472304821014404 seconds
+
+[39.83, 38.85, 39.09, 38.79, 39.53, 38.8, 39.37, 39.26, 39.29, 38.81]
+[66.47]
+13.169949531555176
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41575, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.472304821014404, 'TIME_S_1KI': 0.2518894725439424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 875.4065453624726, 'W': 66.47}
+[39.83, 38.85, 39.09, 38.79, 39.53, 38.8, 39.37, 39.26, 39.29, 38.81, 39.98, 38.99, 38.83, 39.07, 38.9, 38.8, 38.86, 39.29, 47.91, 39.33]
+712.6049999999999
+35.63025
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41575, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.472304821014404, 'TIME_S_1KI': 0.2518894725439424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 875.4065453624726, 'W': 66.47, 'J_1KI': 21.056080465723934, 'W_1KI': 1.5987973541791942, 'W_D': 30.839750000000002, 'J_D': 406.1579510657788, 'W_D_1KI': 0.7417859290438966, 'J_D_1KI': 0.017842114949943394}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
index c6f366b..e407283 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8104, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.399449348449707, "TIME_S_1KI": 1.2832489324345642, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 868.1994806170463, "W": 65.85, "J_1KI": 107.132216265677, "W_1KI": 8.125616979269497, "W_D": 30.541749999999993, "J_D": 402.67777505141487, "W_D_1KI": 3.768725320829219, "J_D_1KI": 0.46504507907566867}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8178, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511467218399048, "TIME_S_1KI": 1.2853347051111579, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 880.7755374526977, "W": 66.16, "J_1KI": 107.70060374818999, "W_1KI": 8.089997554414282, "W_D": 30.69249999999999, "J_D": 408.60343384623513, "W_D_1KI": 3.7530569821472226, "J_D_1KI": 0.4589211276775767}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
index 440ed84..b9e58d8 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.2955126762390137}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14169001579284668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     222,     488,  ..., 1249497,
-                            1249743, 1250000]),
-       col_indices=tensor([   0,    1,   24,  ..., 4925, 4934, 4978]),
-       values=tensor([0.4956, 0.3294, 0.5952,  ..., 0.4990, 0.9373, 0.9148]),
+tensor(crow_indices=tensor([      0,     264,     521,  ..., 1249500,
+                            1249758, 1250000]),
+       col_indices=tensor([  22,   35,   54,  ..., 4954, 4963, 4982]),
+       values=tensor([0.3715, 0.6699, 0.1465,  ..., 0.9132, 0.2376, 0.1878]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4962, 0.1920, 0.2421,  ..., 0.8601, 0.2392, 0.4151])
+tensor([0.3853, 0.7833, 0.5244,  ..., 0.5756, 0.3818, 0.8103])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 1.2955126762390137 seconds
+Time: 0.14169001579284668 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8104', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.399449348449707}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7410', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.513462543487549}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     238,     495,  ..., 1249467,
-                            1249713, 1250000]),
-       col_indices=tensor([   4,    6,   45,  ..., 4913, 4952, 4965]),
-       values=tensor([0.6573, 0.3725, 0.2540,  ..., 0.9752, 0.8782, 0.5831]),
+tensor(crow_indices=tensor([      0,     245,     477,  ..., 1249530,
+                            1249759, 1250000]),
+       col_indices=tensor([  15,   78,  164,  ..., 4968, 4978, 4992]),
+       values=tensor([0.5947, 0.7215, 0.2123,  ..., 0.7493, 0.6227, 0.0355]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7880, 0.7423, 0.8544,  ..., 0.3557, 0.5396, 0.2540])
+tensor([0.4245, 0.9857, 0.9704,  ..., 0.1643, 0.0459, 0.0545])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.399449348449707 seconds
+Time: 9.513462543487549 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8178', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511467218399048}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     238,     495,  ..., 1249467,
-                            1249713, 1250000]),
-       col_indices=tensor([   4,    6,   45,  ..., 4913, 4952, 4965]),
-       values=tensor([0.6573, 0.3725, 0.2540,  ..., 0.9752, 0.8782, 0.5831]),
+tensor(crow_indices=tensor([      0,     243,     531,  ..., 1249480,
+                            1249728, 1250000]),
+       col_indices=tensor([  10,   29,   31,  ..., 4961, 4980, 4981]),
+       values=tensor([0.2878, 0.6436, 0.4714,  ..., 0.0074, 0.1096, 0.3758]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7880, 0.7423, 0.8544,  ..., 0.3557, 0.5396, 0.2540])
+tensor([0.4226, 0.4894, 0.7354,  ..., 0.5546, 0.7888, 0.4627])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.399449348449707 seconds
+Time: 10.511467218399048 seconds
 
-[39.35, 38.88, 44.16, 39.47, 38.8, 39.17, 38.52, 38.75, 38.59, 39.07]
-[65.85]
-13.184502363204956
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.399449348449707, 'TIME_S_1KI': 1.2832489324345642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 868.1994806170463, 'W': 65.85}
-[39.35, 38.88, 44.16, 39.47, 38.8, 39.17, 38.52, 38.75, 38.59, 39.07, 39.04, 39.89, 38.99, 38.43, 38.88, 38.64, 38.45, 39.36, 39.15, 38.61]
-706.165
-35.30825
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.399449348449707, 'TIME_S_1KI': 1.2832489324345642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 868.1994806170463, 'W': 65.85, 'J_1KI': 107.132216265677, 'W_1KI': 8.125616979269497, 'W_D': 30.541749999999993, 'J_D': 402.67777505141487, 'W_D_1KI': 3.768725320829219, 'J_D_1KI': 0.46504507907566867}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     243,     531,  ..., 1249480,
+                            1249728, 1250000]),
+       col_indices=tensor([  10,   29,   31,  ..., 4961, 4980, 4981]),
+       values=tensor([0.2878, 0.6436, 0.4714,  ..., 0.0074, 0.1096, 0.3758]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4226, 0.4894, 0.7354,  ..., 0.5546, 0.7888, 0.4627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511467218399048 seconds
+
+[40.21, 39.14, 39.56, 39.02, 39.43, 41.29, 39.49, 39.07, 39.13, 39.19]
+[66.16]
+13.31281042098999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8178, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511467218399048, 'TIME_S_1KI': 1.2853347051111579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 880.7755374526977, 'W': 66.16}
+[40.21, 39.14, 39.56, 39.02, 39.43, 41.29, 39.49, 39.07, 39.13, 39.19, 40.14, 39.04, 40.64, 38.87, 39.1, 38.93, 39.37, 39.03, 38.8, 39.34]
+709.3500000000001
+35.46750000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8178, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511467218399048, 'TIME_S_1KI': 1.2853347051111579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 880.7755374526977, 'W': 66.16, 'J_1KI': 107.70060374818999, 'W_1KI': 8.089997554414282, 'W_D': 30.69249999999999, 'J_D': 408.60343384623513, 'W_D_1KI': 3.7530569821472226, 'J_D_1KI': 0.4589211276775767}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
index f20d05f..cc784c7 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3588, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.469439029693604, "TIME_S_1KI": 2.9179038544296554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 926.2283216476441, "W": 68.56, "J_1KI": 258.1461320088194, "W_1KI": 19.108138238573023, "W_D": 33.73800000000001, "J_D": 455.7918774175645, "W_D_1KI": 9.403010033444819, "J_D_1KI": 2.620682840982391}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3463, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.092161417007446, "TIME_S_1KI": 2.9142828232767677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.6800115585327, "W": 67.72, "J_1KI": 256.6214298465298, "W_1KI": 19.555298873808837, "W_D": 32.353, "J_D": 424.56385726451873, "W_D_1KI": 9.34247762056021, "J_D_1KI": 2.697798908622642}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
index 3e39a46..8f92a77 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.925701141357422}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.30318737030029297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     485,    1004,  ..., 2498982,
-                            2499482, 2500000]),
-       col_indices=tensor([  18,   27,   28,  ..., 4963, 4979, 4987]),
-       values=tensor([0.5744, 0.1591, 0.4039,  ..., 0.3146, 0.5536, 0.6554]),
+tensor(crow_indices=tensor([      0,     515,    1018,  ..., 2498999,
+                            2499493, 2500000]),
+       col_indices=tensor([   4,   21,   24,  ..., 4955, 4957, 4967]),
+       values=tensor([0.5325, 0.9134, 0.0336,  ..., 0.9679, 0.3618, 0.7033]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8562, 0.0559, 0.5751,  ..., 0.9013, 0.4689, 0.3374])
+tensor([0.8211, 0.4248, 0.8998,  ..., 0.9656, 0.3613, 0.2992])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 2.925701141357422 seconds
+Time: 0.30318737030029297 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3588', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.469439029693604}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3463', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.092161417007446}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     486,    1006,  ..., 2498987,
-                            2499524, 2500000]),
-       col_indices=tensor([   6,   12,   25,  ..., 4979, 4985, 4986]),
-       values=tensor([0.9526, 0.5714, 0.7457,  ..., 0.5995, 0.2741, 0.0768]),
+tensor(crow_indices=tensor([      0,     498,    1000,  ..., 2499016,
+                            2499523, 2500000]),
+       col_indices=tensor([   2,   43,   50,  ..., 4978, 4986, 4997]),
+       values=tensor([0.9352, 0.1649, 0.8628,  ..., 0.6965, 0.7003, 0.5455]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9940, 0.0288, 0.0030,  ..., 0.3299, 0.0903, 0.2227])
+tensor([0.0499, 0.6757, 0.4614,  ..., 0.0542, 0.7848, 0.0169])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.469439029693604 seconds
+Time: 10.092161417007446 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     486,    1006,  ..., 2498987,
-                            2499524, 2500000]),
-       col_indices=tensor([   6,   12,   25,  ..., 4979, 4985, 4986]),
-       values=tensor([0.9526, 0.5714, 0.7457,  ..., 0.5995, 0.2741, 0.0768]),
+tensor(crow_indices=tensor([      0,     498,    1000,  ..., 2499016,
+                            2499523, 2500000]),
+       col_indices=tensor([   2,   43,   50,  ..., 4978, 4986, 4997]),
+       values=tensor([0.9352, 0.1649, 0.8628,  ..., 0.6965, 0.7003, 0.5455]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9940, 0.0288, 0.0030,  ..., 0.3299, 0.0903, 0.2227])
+tensor([0.0499, 0.6757, 0.4614,  ..., 0.0542, 0.7848, 0.0169])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.469439029693604 seconds
+Time: 10.092161417007446 seconds
 
-[39.61, 38.42, 39.72, 38.79, 38.63, 38.61, 38.72, 38.24, 38.41, 38.4]
-[68.56]
-13.509747982025146
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.469439029693604, 'TIME_S_1KI': 2.9179038544296554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.2283216476441, 'W': 68.56}
-[39.61, 38.42, 39.72, 38.79, 38.63, 38.61, 38.72, 38.24, 38.41, 38.4, 39.84, 38.26, 38.74, 38.59, 38.81, 38.31, 38.94, 38.63, 38.58, 38.23]
-696.4399999999999
-34.821999999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.469439029693604, 'TIME_S_1KI': 2.9179038544296554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.2283216476441, 'W': 68.56, 'J_1KI': 258.1461320088194, 'W_1KI': 19.108138238573023, 'W_D': 33.73800000000001, 'J_D': 455.7918774175645, 'W_D_1KI': 9.403010033444819, 'J_D_1KI': 2.620682840982391}
+[40.21, 38.8, 38.93, 38.71, 39.03, 38.9, 38.74, 39.19, 39.25, 38.74]
+[67.72]
+13.122859001159668
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3463, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.092161417007446, 'TIME_S_1KI': 2.9142828232767677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.6800115585327, 'W': 67.72}
+[40.21, 38.8, 38.93, 38.71, 39.03, 38.9, 38.74, 39.19, 39.25, 38.74, 40.21, 38.79, 39.15, 38.78, 38.85, 38.73, 44.44, 39.11, 38.88, 38.96]
+707.3399999999999
+35.367
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3463, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.092161417007446, 'TIME_S_1KI': 2.9142828232767677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.6800115585327, 'W': 67.72, 'J_1KI': 256.6214298465298, 'W_1KI': 19.555298873808837, 'W_D': 32.353, 'J_D': 424.56385726451873, 'W_D_1KI': 9.34247762056021, 'J_D_1KI': 2.697798908622642}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..b65b863
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1741, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.298704862594604, "TIME_S_1KI": 5.915396245028492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1027.8759766101837, "W": 74.46, "J_1KI": 590.3940129868946, "W_1KI": 42.76852383687535, "W_D": 39.113499999999995, "J_D": 539.9385846245289, "W_D_1KI": 22.466111430212518, "J_D_1KI": 12.904142119593635}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..987a685
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.6030943393707275}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1017,    2015,  ..., 4997969,
+                            4998981, 5000000]),
+       col_indices=tensor([   0,   31,   34,  ..., 4984, 4989, 4995]),
+       values=tensor([0.9331, 0.7841, 0.5601,  ..., 0.4749, 0.8668, 0.1575]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1492, 0.3030, 0.4894,  ..., 0.2440, 0.4033, 0.0238])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.6030943393707275 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1741', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.298704862594604}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     961,    1935,  ..., 4998003,
+                            4999013, 5000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4968, 4970, 4989]),
+       values=tensor([0.3710, 0.5603, 0.2176,  ..., 0.5072, 0.2694, 0.8987]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6029, 0.2962, 0.8345,  ..., 0.0907, 0.1121, 0.2666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.298704862594604 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     961,    1935,  ..., 4998003,
+                            4999013, 5000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4968, 4970, 4989]),
+       values=tensor([0.3710, 0.5603, 0.2176,  ..., 0.5072, 0.2694, 0.8987]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6029, 0.2962, 0.8345,  ..., 0.0907, 0.1121, 0.2666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.298704862594604 seconds
+
+[39.56, 39.38, 38.97, 38.88, 38.82, 39.21, 39.23, 39.16, 39.31, 39.25]
+[74.46]
+13.804404735565186
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1741, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.298704862594604, 'TIME_S_1KI': 5.915396245028492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1027.8759766101837, 'W': 74.46}
+[39.56, 39.38, 38.97, 38.88, 38.82, 39.21, 39.23, 39.16, 39.31, 39.25, 40.48, 40.05, 39.23, 38.85, 39.62, 39.32, 38.87, 39.04, 39.64, 39.41]
+706.93
+35.3465
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1741, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.298704862594604, 'TIME_S_1KI': 5.915396245028492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1027.8759766101837, 'W': 74.46, 'J_1KI': 590.3940129868946, 'W_1KI': 42.76852383687535, 'W_D': 39.113499999999995, 'J_D': 539.9385846245289, 'W_D_1KI': 22.466111430212518, 'J_D_1KI': 12.904142119593635}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..20f95b6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1166, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.334495306015015, "TIME_S_1KI": 8.8632035214537, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1095.1619911956786, "W": 75.64, "J_1KI": 939.2469907338582, "W_1KI": 64.8713550600343, "W_D": 40.43425, "J_D": 585.4316993985176, "W_D_1KI": 34.67774442538593, "J_D_1KI": 29.74077566499651}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..be3ca1f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.9000704288482666}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1542,    2985,  ..., 7497037,
+                            7498525, 7500000]),
+       col_indices=tensor([   0,    1,    6,  ..., 4988, 4994, 4997]),
+       values=tensor([0.4057, 0.2930, 0.9549,  ..., 0.4574, 0.5414, 0.6416]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.4314, 0.0098, 0.8060,  ..., 0.6655, 0.0522, 0.0757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.9000704288482666 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1166', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.334495306015015}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1525,    3027,  ..., 7497015,
+                            7498498, 7500000]),
+       col_indices=tensor([  12,   15,   18,  ..., 4991, 4994, 4995]),
+       values=tensor([0.8648, 0.2387, 0.0206,  ..., 0.7504, 0.0755, 0.9898]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.0126, 0.2581, 0.5840,  ..., 0.5862, 0.5778, 0.3525])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.334495306015015 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1525,    3027,  ..., 7497015,
+                            7498498, 7500000]),
+       col_indices=tensor([  12,   15,   18,  ..., 4991, 4994, 4995]),
+       values=tensor([0.8648, 0.2387, 0.0206,  ..., 0.7504, 0.0755, 0.9898]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.0126, 0.2581, 0.5840,  ..., 0.5862, 0.5778, 0.3525])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.334495306015015 seconds
+
+[41.12, 38.83, 38.85, 38.96, 39.38, 38.75, 39.14, 38.88, 38.85, 38.77]
+[75.64]
+14.478609085083008
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1166, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.334495306015015, 'TIME_S_1KI': 8.8632035214537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1095.1619911956786, 'W': 75.64}
+[41.12, 38.83, 38.85, 38.96, 39.38, 38.75, 39.14, 38.88, 38.85, 38.77, 40.18, 38.79, 39.32, 38.91, 39.22, 38.92, 39.3, 39.28, 39.3, 38.8]
+704.115
+35.20575
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1166, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.334495306015015, 'TIME_S_1KI': 8.8632035214537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1095.1619911956786, 'W': 75.64, 'J_1KI': 939.2469907338582, 'W_1KI': 64.8713550600343, 'W_D': 40.43425, 'J_D': 585.4316993985176, 'W_D_1KI': 34.67774442538593, 'J_D_1KI': 29.74077566499651}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..973144c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 704, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.429930925369263, "TIME_S_1KI": 14.815242791717703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1157.2951932907106, "W": 76.7, "J_1KI": 1643.8852177424865, "W_1KI": 108.94886363636364, "W_D": 41.2785, "J_D": 622.8345454530717, "W_D_1KI": 58.63423295454546, "J_D_1KI": 83.2872627195248}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..0f653eb
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 1.4912726879119873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1951,     3944,  ...,  9995965,
+                             9997988, 10000000]),
+       col_indices=tensor([   6,   14,   15,  ..., 4996, 4997, 4998]),
+       values=tensor([0.7071, 0.0905, 0.6037,  ..., 0.4689, 0.3914, 0.3516]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7487, 0.9804, 0.4485,  ..., 0.3781, 0.3818, 0.0598])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 1.4912726879119873 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '704', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.429930925369263}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1962,     3997,  ...,  9995983,
+                             9998031, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4991, 4999]),
+       values=tensor([0.3156, 0.5704, 0.4238,  ..., 0.9413, 0.7746, 0.6098]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3215, 0.3275, 0.6323,  ..., 0.0024, 0.8893, 0.0654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.429930925369263 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1962,     3997,  ...,  9995983,
+                             9998031, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4991, 4999]),
+       values=tensor([0.3156, 0.5704, 0.4238,  ..., 0.9413, 0.7746, 0.6098]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3215, 0.3275, 0.6323,  ..., 0.0024, 0.8893, 0.0654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.429930925369263 seconds
+
+[40.15, 39.37, 39.68, 38.87, 39.22, 39.13, 41.47, 38.91, 39.0, 39.01]
+[76.7]
+15.088594436645508
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.429930925369263, 'TIME_S_1KI': 14.815242791717703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1157.2951932907106, 'W': 76.7}
+[40.15, 39.37, 39.68, 38.87, 39.22, 39.13, 41.47, 38.91, 39.0, 39.01, 40.04, 39.56, 38.96, 39.33, 38.96, 38.95, 39.21, 38.91, 38.9, 40.8]
+708.4300000000001
+35.4215
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.429930925369263, 'TIME_S_1KI': 14.815242791717703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1157.2951932907106, 'W': 76.7, 'J_1KI': 1643.8852177424865, 'W_1KI': 108.94886363636364, 'W_D': 41.2785, 'J_D': 622.8345454530717, 'W_D_1KI': 58.63423295454546, 'J_D_1KI': 83.2872627195248}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..70ebf3a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 569, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.483191013336182, "TIME_S_1KI": 18.42388578793705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1197.8375442028046, "W": 76.6, "J_1KI": 2105.162643590166, "W_1KI": 134.62214411247803, "W_D": 41.0435, "J_D": 641.8204340142012, "W_D_1KI": 72.13268892794376, "J_D_1KI": 126.77098229867093}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..d4faa36
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.8439099788665771}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2531,     5020,  ..., 12494917,
+                            12497450, 12500000]),
+       col_indices=tensor([   0,    1,    9,  ..., 4992, 4994, 4999]),
+       values=tensor([0.6676, 0.2754, 0.2712,  ..., 0.4447, 0.2547, 0.8500]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9338, 0.0413, 0.5968,  ..., 0.6366, 0.2029, 0.7249])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 1.8439099788665771 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '569', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.483191013336182}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2491,     4957,  ..., 12495037,
+                            12497545, 12500000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5758, 0.7291, 0.3910,  ..., 0.8483, 0.9816, 0.9388]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0835, 0.8623, 0.0534,  ..., 0.1116, 0.3605, 0.8512])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.483191013336182 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2491,     4957,  ..., 12495037,
+                            12497545, 12500000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5758, 0.7291, 0.3910,  ..., 0.8483, 0.9816, 0.9388]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0835, 0.8623, 0.0534,  ..., 0.1116, 0.3605, 0.8512])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.483191013336182 seconds
+
+[39.92, 38.82, 39.41, 39.19, 39.02, 39.24, 39.27, 38.82, 38.86, 38.89]
+[76.6]
+15.637565851211548
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.483191013336182, 'TIME_S_1KI': 18.42388578793705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.8375442028046, 'W': 76.6}
+[39.92, 38.82, 39.41, 39.19, 39.02, 39.24, 39.27, 38.82, 38.86, 38.89, 40.74, 39.81, 39.32, 39.32, 39.28, 44.23, 39.36, 38.92, 39.11, 38.75]
+711.1299999999999
+35.55649999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.483191013336182, 'TIME_S_1KI': 18.42388578793705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.8375442028046, 'W': 76.6, 'J_1KI': 2105.162643590166, 'W_1KI': 134.62214411247803, 'W_D': 41.0435, 'J_D': 641.8204340142012, 'W_D_1KI': 72.13268892794376, 'J_D_1KI': 126.77098229867093}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
index d170a6b..c3ed634 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 565598, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406220436096191, "TIME_S_1KI": 0.018398616041952396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.5254345631599, "W": 64.13, "J_1KI": 1.498459037272338, "W_1KI": 0.11338441790812556, "W_D": 29.180249999999987, "J_D": 385.6386100407241, "W_D_1KI": 0.05159185499241509, "J_D_1KI": 9.121647352433192e-05}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 569391, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.146198511123657, "TIME_S_1KI": 0.017819386873209546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 837.5037681818008, "W": 65.05, "J_1KI": 1.4708763717406856, "W_1KI": 0.11424486864035434, "W_D": 29.76475, "J_D": 383.2143010605574, "W_D_1KI": 0.05227471105092985, "J_D_1KI": 9.180810910416543e-05}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
index 4a0b8d8..6482c19 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.055680274963378906}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025912761688232422}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4927,  850,  790,  511,    1, 4275, 3659, 3202, 4099,
-                           3346, 1589,  716, 4620, 4989, 3861, 2882, 2487,  356,
-                           3163, 4196, 2032,  713,  507, 4615, 4269, 4035, 1320,
-                            655, 4926, 1128, 2992, 3058, 2439, 4007, 3555, 1710,
-                           2353,  655, 2875,  397, 2586, 4948,  858, 1089,  783,
-                           1767, 1975, 2378, 3541, 1407,  868, 4760, 4954, 4948,
-                           2154, 3756,  192, 4715, 2175,  343, 3413,  855, 3051,
-                           4256, 4765, 3143, 3774, 3357, 1362, 3915, 3187, 3177,
-                           3730, 4948, 4331, 2972, 3797,  963, 1487, 1791, 3014,
-                           3104, 4150, 4779, 2304, 1176, 1597, 3268, 4290, 3867,
-                           1778, 4097, 4190, 1835, 2167, 1131, 4492, 3907, 2098,
-                           4204, 4273, 3262, 2220, 4871, 3645, 4702, 4344, 3548,
-                            398, 3919,  762, 3209,  941, 2587, 4871, 1294,  846,
-                           4270, 1587,  490, 3776,  205, 4893, 4944, 3389, 1241,
-                            319, 1205,  149, 2679,  835,  185, 1679,  305,  803,
-                           3987, 4919, 1049, 2984,  150, 2222, 3548, 4559, 2082,
-                            773, 3809,  333, 4072, 2819,  773, 1940, 3544, 2429,
-                           4213, 3874, 3370, 3390, 3737, 2306, 2576, 3944, 3962,
-                           2700, 3672, 1959, 2924, 1160, 2820,  201, 3021, 1400,
-                           2786, 3009, 3104, 1799, 1722, 1307, 4435, 3240, 3490,
-                           3514, 3928, 2870,  339,  280, 3127,  278,   43, 1063,
-                           3176, 1262, 2341, 4542, 3316, 4835, 2103, 3750, 2839,
-                           1642, 4880, 4963, 1368, 4924, 2484, 1087,   26, 3186,
-                           4671, 3346, 1979,  748,  800,  144,   54, 3361, 3955,
-                           4948, 2768, 2175,  216,    0,  934, 3902, 3054,  854,
-                           1551,  310,  382, 1750,  779, 4286, 2768, 4550, 2371,
-                           2027, 2115, 2210, 4053, 3461, 4944,  349, 2236, 2467,
-                           2141, 1730,   73, 1349, 3773, 2561, 2961]),
-       values=tensor([0.0052, 0.9685, 0.5552, 0.5554, 0.3769, 0.8417, 0.2484,
-                      0.8557, 0.2810, 0.1770, 0.3815, 0.5491, 0.2804, 0.7014,
-                      0.4668, 0.6665, 0.6885, 0.4406, 0.0793, 0.0505, 0.2168,
-                      0.2768, 0.8793, 0.5292, 0.6124, 0.8331, 0.8520, 0.8953,
-                      0.2979, 0.9092, 0.1021, 0.9939, 0.8355, 0.6875, 0.6744,
-                      0.7797, 0.7132, 0.1964, 0.7787, 0.7395, 0.3653, 0.6907,
-                      0.2135, 0.4345, 0.6550, 0.1169, 0.1290, 0.6211, 0.7886,
-                      0.4978, 0.8807, 0.4515, 0.8365, 0.6929, 0.0657, 0.2646,
-                      0.3895, 0.0998, 0.4953, 0.3952, 0.3596, 0.9459, 0.2141,
-                      0.1718, 0.1717, 0.3607, 0.1199, 0.7175, 0.8124, 0.4557,
-                      0.0741, 0.2089, 0.8742, 0.1642, 0.0425, 0.9409, 0.3852,
-                      0.8648, 0.0435, 0.7984, 0.2433, 0.6033, 0.1259, 0.5531,
-                      0.2437, 0.6326, 0.4382, 0.6680, 0.3511, 0.0596, 0.0831,
-                      0.8185, 0.6864, 0.6621, 0.0203, 0.2915, 0.7632, 0.4015,
-                      0.1622, 0.5710, 0.1068, 0.3154, 0.7156, 0.1137, 0.7110,
-                      0.7922, 0.6817, 0.4208, 0.8226, 0.6751, 0.5470, 0.6580,
-                      0.9115, 0.2395, 0.8631, 0.8946, 0.8633, 0.9964, 0.1781,
-                      0.0456, 0.7692, 0.7333, 0.7567, 0.4246, 0.7150, 0.3292,
-                      0.8102, 0.3763, 0.7077, 0.9596, 0.7799, 0.8995, 0.4237,
-                      0.8044, 0.0028, 0.6094, 0.0822, 0.3516, 0.1473, 0.3747,
-                      0.2994, 0.6148, 0.9715, 0.8176, 0.8036, 0.4058, 0.2036,
-                      0.3753, 0.4509, 0.2117, 0.5735, 0.9721, 0.6964, 0.3733,
-                      0.2389, 0.5980, 0.7861, 0.1124, 0.7224, 0.2736, 0.1517,
-                      0.1578, 0.1015, 0.9540, 0.9804, 0.5457, 0.1059, 0.7649,
-                      0.7606, 0.0359, 0.3684, 0.4744, 0.3881, 0.5669, 0.6894,
-                      0.8642, 0.1190, 0.1465, 0.4614, 0.1113, 0.6697, 0.9048,
-                      0.9025, 0.8550, 0.3322, 0.9950, 0.8601, 0.6688, 0.9556,
-                      0.6649, 0.0390, 0.6075, 0.3304, 0.8947, 0.7252, 0.7691,
-                      0.7526, 0.8639, 0.4721, 0.9403, 0.4391, 0.6933, 0.1244,
-                      0.9914, 0.2708, 0.4335, 0.8597, 0.4714, 0.6817, 0.8948,
-                      0.1646, 0.6199, 0.1780, 0.7119, 0.3391, 0.9514, 0.4224,
-                      0.9358, 0.1033, 0.8786, 0.4834, 0.9743, 0.3774, 0.6356,
-                      0.0241, 0.9866, 0.3267, 0.8949, 0.2494, 0.9412, 0.8442,
-                      0.7104, 0.1721, 0.4102, 0.7763, 0.4723, 0.0485, 0.1320,
-                      0.4711, 0.1941, 0.9435, 0.7325, 0.9932, 0.9457, 0.1546,
-                      0.7522, 0.6262, 0.4856, 0.7356, 0.9269]),
+       col_indices=tensor([2955, 4682, 3449, 1969, 3854,  251, 2319,  668,  498,
+                           3572, 3854, 2322, 4794,  541, 1204, 3498,  557, 2286,
+                           3599, 1633,  263,  572,  401, 2359, 4315,  911, 3580,
+                            647, 4452, 4876, 2180, 2875, 4244, 3344, 2209,  559,
+                           1303, 3655, 2944, 2633, 4973, 1628, 4101,  143, 4999,
+                           4517, 2519, 3173, 1189, 2853, 1263, 1640, 2218, 2187,
+                            500,  639, 1141, 4234,  755,  459,  531, 3206, 4041,
+                           4859, 2211, 4404, 1761, 2224, 3743, 1719, 4799, 2055,
+                           4619, 4316, 4729, 1778, 2613, 3964, 2169,  739, 4154,
+                           1973, 1793, 4308,  801, 3310,  342, 3095,  520, 3764,
+                           4097, 3032, 1897, 3411, 2128, 1565, 3684, 2810, 2598,
+                           1157, 2540, 3590, 4555,  145, 3631, 3914,  308,   84,
+                           3223, 2928, 1083, 1157, 2416, 2092, 1789, 4234, 2339,
+                            866, 1332,  582,  480,  407, 2460,  594, 1184, 1302,
+                           4554, 3210,  212, 2368, 3876, 3742, 2272, 1393,  406,
+                             80, 3371, 4169, 2118,  452, 2986,  932,   49, 3160,
+                           2327,  713, 2877, 3484, 2257,  182, 3066, 1789,  610,
+                           3257, 2119, 4757,  577,  310, 2782,  116, 2689, 2876,
+                           2666,  144, 4643, 3955,  517, 1439, 3012, 4289, 3484,
+                           1701, 3447, 1824, 1951, 3681, 4272, 2740, 4790, 1804,
+                           3833, 1146,  672, 4424, 3920, 2173, 2155, 2090, 4174,
+                           4084,  560, 3425, 3143, 3470, 1712, 3489, 1811, 1766,
+                           4872, 3288,  771, 4485, 3269, 2695, 4584,  535,  200,
+                           2606, 3547, 3737,  102,  696,  271, 2048, 2470, 1740,
+                           2219,  829, 4785, 1264, 2283, 4517, 1344, 1726,  844,
+                           2384, 4310,   42, 3459, 2017, 2199, 3093, 4339,  313,
+                           1332,  303, 3733, 2074, 4094, 4006,  429, 1830, 3447,
+                           3735, 3446, 1158, 2156, 2171, 2458,  989]),
+       values=tensor([0.1546, 0.0338, 0.3832, 0.6845, 0.5359, 0.2115, 0.5134,
+                      0.5236, 0.8368, 0.0286, 0.8691, 0.3848, 0.7412, 0.7376,
+                      0.0492, 0.2561, 0.4285, 0.6403, 0.4264, 0.9823, 0.2432,
+                      0.9496, 0.9648, 0.1994, 0.5806, 0.9987, 0.8946, 0.3796,
+                      0.9742, 0.5421, 0.7745, 0.0707, 0.4271, 0.7771, 0.6813,
+                      0.4916, 0.5119, 0.7062, 0.2752, 0.9391, 0.5947, 0.6220,
+                      0.1073, 0.5082, 0.8956, 0.6658, 0.1040, 0.0454, 0.1560,
+                      0.7150, 0.4739, 0.5494, 0.1706, 0.6723, 0.3127, 0.2460,
+                      0.1585, 0.6938, 0.6937, 0.2200, 0.6734, 0.5021, 0.7544,
+                      0.1720, 0.3861, 0.8179, 0.3810, 0.0801, 0.6567, 0.1205,
+                      0.2925, 0.1255, 0.7882, 0.3753, 0.2290, 0.4877, 0.9183,
+                      0.7718, 0.3047, 0.6194, 0.2851, 0.3419, 0.7256, 0.2356,
+                      0.5102, 0.4050, 0.2487, 0.5983, 0.2252, 0.4330, 0.1968,
+                      0.1306, 0.4006, 0.3092, 0.2645, 0.4635, 0.3587, 0.7536,
+                      0.7019, 0.0792, 0.1678, 0.7485, 0.9817, 0.9065, 0.9994,
+                      0.3911, 0.8833, 0.5411, 0.3679, 0.9711, 0.9853, 0.6437,
+                      0.7861, 0.5048, 0.2591, 0.7726, 0.6174, 0.1004, 0.1489,
+                      0.6017, 0.8836, 0.5571, 0.7423, 0.4796, 0.5887, 0.7010,
+                      0.6198, 0.7601, 0.9790, 0.0717, 0.8254, 0.1983, 0.6497,
+                      0.9092, 0.9977, 0.7279, 0.3767, 0.5564, 0.8901, 0.2896,
+                      0.4914, 0.7288, 0.7282, 0.1569, 0.7491, 0.2024, 0.5103,
+                      0.2006, 0.2616, 0.3680, 0.8784, 0.5095, 0.3100, 0.5468,
+                      0.7473, 0.8067, 0.1566, 0.7922, 0.7836, 0.0734, 0.2895,
+                      0.0658, 0.3948, 0.1442, 0.9166, 0.4276, 0.4709, 0.0305,
+                      0.5373, 0.3415, 0.0906, 0.7116, 0.3303, 0.4381, 0.5263,
+                      0.3654, 0.9325, 0.0370, 0.6385, 0.6709, 0.3766, 0.7549,
+                      0.4298, 0.9883, 0.6034, 0.5195, 0.8067, 0.8173, 0.5194,
+                      0.5329, 0.4999, 0.5357, 0.5843, 0.4652, 0.6589, 0.6127,
+                      0.9625, 0.8533, 0.0618, 0.2250, 0.1341, 0.3851, 0.9176,
+                      0.6106, 0.0281, 0.2538, 0.5580, 0.0137, 0.4927, 0.5743,
+                      0.6268, 0.5818, 0.7719, 0.1711, 0.1084, 0.6064, 0.1367,
+                      0.6312, 0.8778, 0.2960, 0.3372, 0.8224, 0.9699, 0.6070,
+                      0.2907, 0.4693, 0.5694, 0.7710, 0.6091, 0.5452, 0.3569,
+                      0.0226, 0.4986, 0.6727, 0.5738, 0.8629, 0.9155, 0.9081,
+                      0.9105, 0.9222, 0.7776, 0.3699, 0.9402, 0.5035, 0.4769,
+                      0.4797, 0.1466, 0.6411, 0.6861, 0.6601]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4907, 0.7631, 0.4016,  ..., 0.1364, 0.7839, 0.0874])
+tensor([0.1758, 0.5990, 0.4260,  ..., 0.6457, 0.1523, 0.4408])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.055680274963378906 seconds
+Time: 0.025912761688232422 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '188576', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.5007991790771484}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '40520', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7472190856933594}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+       col_indices=tensor([4979, 4303, 4566, 4762, 4549,  190, 2004, 2509,  751,
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+       values=tensor([0.6428, 0.9934, 0.2143, 0.2775, 0.8801, 0.9209, 0.7937,
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+                      0.7374, 0.6494, 0.0513, 0.2661, 0.6485, 0.3876, 0.3399,
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+                      0.0451, 0.7262, 0.6349, 0.5852, 0.2103, 0.4468, 0.0131,
+                      0.9703, 0.2087, 0.9981, 0.0746, 0.6323, 0.0776, 0.3216,
+                      0.8062, 0.8168, 0.8982, 0.6078, 0.4816, 0.3037, 0.1198,
+                      0.4605, 0.7585, 0.3262, 0.3531, 0.6379]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1655, 0.0894, 0.3335,  ..., 0.5896, 0.4748, 0.7424])
+tensor([0.7653, 0.7067, 0.4116,  ..., 0.4300, 0.1070, 0.3611])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 3.5007991790771484 seconds
+Time: 0.7472190856933594 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '565598', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406220436096191}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '569391', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.146198511123657}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
-       col_indices=tensor([3711, 2509, 1480, 2246, 4155, 2306,  315, 3219,  781,
-                           3895, 3381, 2148, 1468, 1317, 2648, 3838,  486, 2691,
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-                           4164, 2100, 1603,  715, 4639, 1853, 3257, 1572, 4514,
-                           2943, 1003, 4748, 1038, 1012, 3061,  294]),
-       values=tensor([0.0072, 0.2895, 0.9639, 0.0057, 0.4191, 0.2094, 0.7103,
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-                      0.7089, 0.6119, 0.9976, 0.0416, 0.2792]),
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 989, 4634,  172,  296,  437,  988, 4701,   40, 3459,
+                           2902,  284, 2223, 3489,  379, 2336, 3854, 3801, 4913,
+                           1784,  189, 1121, 2886, 4344, 1044, 1507, 1629, 4228,
+                            950, 3157,  372, 4392,  249, 3355, 4991,   61, 3311,
+                            365, 3749, 2426, 4689,  420, 1130, 2303, 3276, 2058,
+                           3417, 2635, 1997, 4469,  149, 3640, 2817,  310, 1358,
+                           4005,  314, 2266,  836, 2231, 2153, 4590, 1083, 2752,
+                           2577, 2539, 3832,  170, 4839, 1662,  908, 3409,  160,
+                           1208, 2792, 1394, 3839,  404, 2657, 1041, 2651, 3377,
+                           3822, 3581, 2353, 3591, 2000, 4401, 4545, 4324, 3328,
+                           3711, 2291, 2328,  732,  536,  660, 2140, 2401,  566,
+                           1414, 1235, 4049, 1072, 4129, 3797, 3825, 3260, 1333,
+                           2653, 3617,   58, 3265, 1036, 4854,  392, 4867, 4701,
+                           3576,  771, 2398, 4330, 1034, 4559, 2708,  409, 3139,
+                           2114, 3463,  923,  763, 2766, 4868, 1142, 1459, 3024,
+                           2321, 1511, 1594, 1553,   98,  954, 4757, 1367, 2284,
+                            321, 4282, 4827,  311, 3480,  705, 1128,  255, 1664,
+                            653, 1381, 1987, 2729,  634, 2582, 4911, 3144, 1242,
+                           3821, 2906, 2900,  547,  195,  264, 1462, 3048, 2738,
+                            753, 4689,  302, 1125, 2387,  532,  854,  131, 4228,
+                           2001, 3802, 1432,  364, 2122,    3,  492,  481, 3737,
+                           3945, 2016, 4040, 4587, 3047,  678, 2781, 1044, 3568,
+                           1574, 3813, 2876, 1656, 4200, 1707, 1113, 3551, 4496,
+                           1942, 1480, 4429, 3975, 2412, 3934, 2906,  952, 4773,
+                           1043, 3314,  572, 4511, 1843, 4636, 1964, 2523,  457,
+                           3459, 2009, 1681,    9, 2459, 3710, 4173, 1493, 3773,
+                           2982, 4418, 4646, 1091,  541, 4902, 4735, 4604, 3735,
+                           3670,  955,  687, 2373, 4360, 1850, 1893]),
+       values=tensor([0.2345, 0.0289, 0.8583, 0.9123, 0.0874, 0.7501, 0.2033,
+                      0.8326, 0.8469, 0.1882, 0.3285, 0.3183, 0.8931, 0.0457,
+                      0.8868, 0.7189, 0.4379, 0.1462, 0.4719, 0.1691, 0.1099,
+                      0.8022, 0.0756, 0.2871, 0.6213, 0.4582, 0.2170, 0.3357,
+                      0.7252, 0.0149, 0.2470, 0.4898, 0.0035, 0.1331, 0.4871,
+                      0.7295, 0.2640, 0.3186, 0.3619, 0.0774, 0.2757, 0.9917,
+                      0.3749, 0.2825, 0.4846, 0.8782, 0.2242, 0.0584, 0.4269,
+                      0.3007, 0.5193, 0.9227, 0.9773, 0.6304, 0.0725, 0.4260,
+                      0.4518, 0.5456, 0.3019, 0.2067, 0.3845, 0.8768, 0.2863,
+                      0.4471, 0.0208, 0.9135, 0.0548, 0.1836, 0.9804, 0.3038,
+                      0.5045, 0.8119, 0.2476, 0.4867, 0.9780, 0.3338, 0.2853,
+                      0.7670, 0.4677, 0.5075, 0.3848, 0.5236, 0.0031, 0.3726,
+                      0.6233, 0.1936, 0.1739, 0.4139, 0.1871, 0.5920, 0.8457,
+                      0.8536, 0.8234, 0.3531, 0.8514, 0.1766, 0.5797, 0.3086,
+                      0.0545, 0.2101, 0.0864, 0.3338, 0.2356, 0.3200, 0.7401,
+                      0.4108, 0.5013, 0.5320, 0.4414, 0.7825, 0.0249, 0.2494,
+                      0.0429, 0.7080, 0.9162, 0.6423, 0.2821, 0.2742, 0.5289,
+                      0.2928, 0.0848, 0.8315, 0.7088, 0.8269, 0.3671, 0.5127,
+                      0.2282, 0.7407, 0.1379, 0.8288, 0.2763, 0.1471, 0.0918,
+                      0.7196, 0.6693, 0.6326, 0.9413, 0.1511, 0.6888, 0.3336,
+                      0.2545, 0.9984, 0.8005, 0.8337, 0.2430, 0.7476, 0.3204,
+                      0.0554, 0.5080, 0.0854, 0.1850, 0.7747, 0.5775, 0.2057,
+                      0.7868, 0.8337, 0.6964, 0.9562, 0.1725, 0.3223, 0.4786,
+                      0.5641, 0.5075, 0.5871, 0.6849, 0.6564, 0.2437, 0.1937,
+                      0.6389, 0.0952, 0.9817, 0.1000, 0.7393, 0.9387, 0.8443,
+                      0.9838, 0.1009, 0.7329, 0.9758, 0.9984, 0.0689, 0.6045,
+                      0.3081, 0.8442, 0.7079, 0.3197, 0.6314, 0.2885, 0.9946,
+                      0.0894, 0.3380, 0.0723, 0.8864, 0.2114, 0.6387, 0.7774,
+                      0.5705, 0.9374, 0.3114, 0.6458, 0.5623, 0.1687, 0.3946,
+                      0.8120, 0.4227, 0.8777, 0.4345, 0.8346, 0.0514, 0.7320,
+                      0.0137, 0.2630, 0.1970, 0.0196, 0.2035, 0.6052, 0.7403,
+                      0.6899, 0.2449, 0.2769, 0.3900, 0.8664, 0.9461, 0.5286,
+                      0.0997, 0.7438, 0.0400, 0.7885, 0.5277, 0.1693, 0.7534,
+                      0.3649, 0.5259, 0.9420, 0.2968, 0.8974, 0.5468, 0.5308,
+                      0.9748, 0.7021, 0.7026, 0.1970, 0.7386, 0.9856, 0.8826,
+                      0.6766, 0.7905, 0.8999, 0.3805, 0.8437]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0013, 0.0858, 0.8984,  ..., 0.5676, 0.8612, 0.3338])
+tensor([0.6631, 0.6256, 0.9086,  ..., 0.3830, 0.1647, 0.1472])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.406220436096191 seconds
+Time: 10.146198511123657 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
-       col_indices=tensor([3711, 2509, 1480, 2246, 4155, 2306,  315, 3219,  781,
-                           3895, 3381, 2148, 1468, 1317, 2648, 3838,  486, 2691,
-                           4269, 1833, 4130, 2494, 2935, 4534, 1404,  631, 2237,
-                           3119, 2408, 4857, 3452, 3551,  652, 1979,  294, 2907,
-                           4341,  963, 1166, 1723, 2311, 2016, 4067, 2454, 3108,
-                           4422,  594, 1090, 1798, 1231, 1189, 3083, 3007, 2134,
-                           3681,  526, 4251, 1258, 2420, 4062,  326, 2947,  386,
-                           3623, 4002, 1015, 2488, 2914,  344,  749, 2046, 3369,
-                           2183, 4810,  804, 4709, 4216, 4774, 3285, 1736, 1631,
-                           1116, 2085, 4390, 2715, 1633, 1339, 4203, 1468, 3776,
-                           4650, 1964, 1644, 3484,  556, 1113,  359, 2615, 4829,
-                           4748, 1322,  159, 1685, 3154, 4693, 4031, 1252, 1027,
-                            678, 4884,  997, 1416,  284, 2922, 2849, 4079,  606,
-                            470, 1943, 1148, 4302, 4930, 4799, 1057,  474, 2030,
-                           3336,  862, 2916, 4504, 1767, 3103, 2022, 3927, 3702,
-                           2754, 2164, 4564, 2862,  341, 1369, 1305, 4261, 2181,
-                           1646, 3936, 3010,  930, 4647, 2915, 4405, 3874, 1229,
-                           1875,  855, 1323,  963, 2816, 4148, 4829, 4066, 4913,
-                            691, 4066, 1415, 2632, 3157, 1676,  346, 4763,  246,
-                           2345, 1525, 4678, 2542, 2753, 3445, 3912, 2714, 1361,
-                            733, 3308,  420, 1698, 1705, 3596, 4607, 2749, 2452,
-                           4692,  611, 3476,  336,  999, 2085, 3920, 2039, 3357,
-                           4270, 3263, 3475, 3737,  446, 1786, 2984, 2510, 2736,
-                           3086, 1080, 3428, 4087,  375, 2103, 1319, 4228, 2727,
-                           4839,  645, 2259, 3905, 3083, 2174, 1253, 1258, 2465,
-                           3785, 2824,   24, 1918, 2335,  918, 1175, 3575, 2352,
-                           4164, 2100, 1603,  715, 4639, 1853, 3257, 1572, 4514,
-                           2943, 1003, 4748, 1038, 1012, 3061,  294]),
-       values=tensor([0.0072, 0.2895, 0.9639, 0.0057, 0.4191, 0.2094, 0.7103,
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-                      0.8333, 0.7188, 0.6342, 0.9555, 0.9103, 0.1687, 0.2984,
-                      0.7732, 0.0449, 0.0772, 0.1352, 0.5023, 0.0443, 0.4171,
-                      0.2148, 0.7142, 0.2678, 0.2649, 0.5734, 0.2586, 0.1803,
-                      0.3367, 0.7155, 0.6815, 0.6287, 0.8390, 0.5032, 0.1992,
-                      0.5162, 0.5707, 0.0670, 0.5923, 0.5384, 0.7500, 0.0960,
-                      0.4905, 0.7846, 0.7390, 0.3348, 0.9396, 0.2679, 0.8099,
-                      0.4907, 0.0176, 0.1919, 0.5036, 0.7682, 0.7675, 0.5778,
-                      0.9394, 0.8838, 0.1647, 0.2045, 0.3204, 0.5816, 0.4877,
-                      0.4316, 0.5907, 0.3880, 0.5556, 0.6079, 0.5805, 0.9477,
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-                      0.8811, 0.8437, 0.1818, 0.1680, 0.0986, 0.7979, 0.9912,
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-                      0.5824, 0.8528, 0.6888, 0.3925, 0.4263, 0.3416, 0.9010,
-                      0.2543, 0.7049, 0.8368, 0.2533, 0.1239, 0.2556, 0.3482,
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-                      0.7316, 0.8675, 0.0573, 0.5592, 0.5656, 0.1368, 0.7342,
-                      0.4891, 0.5212, 0.5980, 0.9850, 0.3144, 0.9416, 0.3586,
-                      0.5874, 0.8863, 0.8557, 0.4322, 0.3167, 0.3279, 0.7906,
-                      0.9595, 0.6426, 0.5182, 0.3380, 0.6725, 0.1898, 0.5553,
-                      0.6660, 0.7693, 0.0543, 0.1495, 0.4661, 0.0013, 0.2189,
-                      0.2756, 0.4230, 0.3033, 0.9296, 0.0600, 0.3160, 0.8967,
-                      0.7981, 0.0839, 0.1133, 0.3382, 0.5864, 0.5344, 0.5684,
-                      0.8353, 0.4735, 0.5909, 0.0547, 0.2196, 0.1029, 0.2516,
-                      0.4455, 0.6775, 0.1108, 0.8486, 0.1605, 0.0632, 0.7729,
-                      0.1033, 0.7416, 0.1100, 0.7509, 0.4420, 0.1639, 0.2794,
-                      0.8260, 0.8724, 0.3230, 0.8818, 0.5434, 0.6423, 0.5673,
-                      0.7089, 0.6119, 0.9976, 0.0416, 0.2792]),
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 989, 4634,  172,  296,  437,  988, 4701,   40, 3459,
+                           2902,  284, 2223, 3489,  379, 2336, 3854, 3801, 4913,
+                           1784,  189, 1121, 2886, 4344, 1044, 1507, 1629, 4228,
+                            950, 3157,  372, 4392,  249, 3355, 4991,   61, 3311,
+                            365, 3749, 2426, 4689,  420, 1130, 2303, 3276, 2058,
+                           3417, 2635, 1997, 4469,  149, 3640, 2817,  310, 1358,
+                           4005,  314, 2266,  836, 2231, 2153, 4590, 1083, 2752,
+                           2577, 2539, 3832,  170, 4839, 1662,  908, 3409,  160,
+                           1208, 2792, 1394, 3839,  404, 2657, 1041, 2651, 3377,
+                           3822, 3581, 2353, 3591, 2000, 4401, 4545, 4324, 3328,
+                           3711, 2291, 2328,  732,  536,  660, 2140, 2401,  566,
+                           1414, 1235, 4049, 1072, 4129, 3797, 3825, 3260, 1333,
+                           2653, 3617,   58, 3265, 1036, 4854,  392, 4867, 4701,
+                           3576,  771, 2398, 4330, 1034, 4559, 2708,  409, 3139,
+                           2114, 3463,  923,  763, 2766, 4868, 1142, 1459, 3024,
+                           2321, 1511, 1594, 1553,   98,  954, 4757, 1367, 2284,
+                            321, 4282, 4827,  311, 3480,  705, 1128,  255, 1664,
+                            653, 1381, 1987, 2729,  634, 2582, 4911, 3144, 1242,
+                           3821, 2906, 2900,  547,  195,  264, 1462, 3048, 2738,
+                            753, 4689,  302, 1125, 2387,  532,  854,  131, 4228,
+                           2001, 3802, 1432,  364, 2122,    3,  492,  481, 3737,
+                           3945, 2016, 4040, 4587, 3047,  678, 2781, 1044, 3568,
+                           1574, 3813, 2876, 1656, 4200, 1707, 1113, 3551, 4496,
+                           1942, 1480, 4429, 3975, 2412, 3934, 2906,  952, 4773,
+                           1043, 3314,  572, 4511, 1843, 4636, 1964, 2523,  457,
+                           3459, 2009, 1681,    9, 2459, 3710, 4173, 1493, 3773,
+                           2982, 4418, 4646, 1091,  541, 4902, 4735, 4604, 3735,
+                           3670,  955,  687, 2373, 4360, 1850, 1893]),
+       values=tensor([0.2345, 0.0289, 0.8583, 0.9123, 0.0874, 0.7501, 0.2033,
+                      0.8326, 0.8469, 0.1882, 0.3285, 0.3183, 0.8931, 0.0457,
+                      0.8868, 0.7189, 0.4379, 0.1462, 0.4719, 0.1691, 0.1099,
+                      0.8022, 0.0756, 0.2871, 0.6213, 0.4582, 0.2170, 0.3357,
+                      0.7252, 0.0149, 0.2470, 0.4898, 0.0035, 0.1331, 0.4871,
+                      0.7295, 0.2640, 0.3186, 0.3619, 0.0774, 0.2757, 0.9917,
+                      0.3749, 0.2825, 0.4846, 0.8782, 0.2242, 0.0584, 0.4269,
+                      0.3007, 0.5193, 0.9227, 0.9773, 0.6304, 0.0725, 0.4260,
+                      0.4518, 0.5456, 0.3019, 0.2067, 0.3845, 0.8768, 0.2863,
+                      0.4471, 0.0208, 0.9135, 0.0548, 0.1836, 0.9804, 0.3038,
+                      0.5045, 0.8119, 0.2476, 0.4867, 0.9780, 0.3338, 0.2853,
+                      0.7670, 0.4677, 0.5075, 0.3848, 0.5236, 0.0031, 0.3726,
+                      0.6233, 0.1936, 0.1739, 0.4139, 0.1871, 0.5920, 0.8457,
+                      0.8536, 0.8234, 0.3531, 0.8514, 0.1766, 0.5797, 0.3086,
+                      0.0545, 0.2101, 0.0864, 0.3338, 0.2356, 0.3200, 0.7401,
+                      0.4108, 0.5013, 0.5320, 0.4414, 0.7825, 0.0249, 0.2494,
+                      0.0429, 0.7080, 0.9162, 0.6423, 0.2821, 0.2742, 0.5289,
+                      0.2928, 0.0848, 0.8315, 0.7088, 0.8269, 0.3671, 0.5127,
+                      0.2282, 0.7407, 0.1379, 0.8288, 0.2763, 0.1471, 0.0918,
+                      0.7196, 0.6693, 0.6326, 0.9413, 0.1511, 0.6888, 0.3336,
+                      0.2545, 0.9984, 0.8005, 0.8337, 0.2430, 0.7476, 0.3204,
+                      0.0554, 0.5080, 0.0854, 0.1850, 0.7747, 0.5775, 0.2057,
+                      0.7868, 0.8337, 0.6964, 0.9562, 0.1725, 0.3223, 0.4786,
+                      0.5641, 0.5075, 0.5871, 0.6849, 0.6564, 0.2437, 0.1937,
+                      0.6389, 0.0952, 0.9817, 0.1000, 0.7393, 0.9387, 0.8443,
+                      0.9838, 0.1009, 0.7329, 0.9758, 0.9984, 0.0689, 0.6045,
+                      0.3081, 0.8442, 0.7079, 0.3197, 0.6314, 0.2885, 0.9946,
+                      0.0894, 0.3380, 0.0723, 0.8864, 0.2114, 0.6387, 0.7774,
+                      0.5705, 0.9374, 0.3114, 0.6458, 0.5623, 0.1687, 0.3946,
+                      0.8120, 0.4227, 0.8777, 0.4345, 0.8346, 0.0514, 0.7320,
+                      0.0137, 0.2630, 0.1970, 0.0196, 0.2035, 0.6052, 0.7403,
+                      0.6899, 0.2449, 0.2769, 0.3900, 0.8664, 0.9461, 0.5286,
+                      0.0997, 0.7438, 0.0400, 0.7885, 0.5277, 0.1693, 0.7534,
+                      0.3649, 0.5259, 0.9420, 0.2968, 0.8974, 0.5468, 0.5308,
+                      0.9748, 0.7021, 0.7026, 0.1970, 0.7386, 0.9856, 0.8826,
+                      0.6766, 0.7905, 0.8999, 0.3805, 0.8437]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0013, 0.0858, 0.8984,  ..., 0.5676, 0.8612, 0.3338])
+tensor([0.6631, 0.6256, 0.9086,  ..., 0.3830, 0.1647, 0.1472])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.406220436096191 seconds
+Time: 10.146198511123657 seconds
 
-[39.5, 43.3, 38.37, 38.43, 38.66, 38.52, 38.75, 38.13, 38.31, 39.28]
-[64.13]
-13.215740442276001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 565598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406220436096191, 'TIME_S_1KI': 0.018398616041952396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.5254345631599, 'W': 64.13}
-[39.5, 43.3, 38.37, 38.43, 38.66, 38.52, 38.75, 38.13, 38.31, 39.28, 40.56, 38.94, 38.24, 38.25, 38.39, 38.16, 38.85, 38.41, 38.49, 38.25]
-698.9950000000001
-34.94975000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 565598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406220436096191, 'TIME_S_1KI': 0.018398616041952396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.5254345631599, 'W': 64.13, 'J_1KI': 1.498459037272338, 'W_1KI': 0.11338441790812556, 'W_D': 29.180249999999987, 'J_D': 385.6386100407241, 'W_D_1KI': 0.05159185499241509, 'J_D_1KI': 9.121647352433192e-05}
+[39.27, 38.69, 38.63, 39.39, 38.61, 38.66, 38.59, 38.96, 38.73, 38.93]
+[65.05]
+12.874769687652588
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569391, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.146198511123657, 'TIME_S_1KI': 0.017819386873209546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 837.5037681818008, 'W': 65.05}
+[39.27, 38.69, 38.63, 39.39, 38.61, 38.66, 38.59, 38.96, 38.73, 38.93, 39.57, 38.76, 39.0, 38.65, 39.12, 39.14, 39.15, 44.31, 38.75, 39.36]
+705.7049999999999
+35.28525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569391, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.146198511123657, 'TIME_S_1KI': 0.017819386873209546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 837.5037681818008, 'W': 65.05, 'J_1KI': 1.4708763717406856, 'W_1KI': 0.11424486864035434, 'W_D': 29.76475, 'J_D': 383.2143010605574, 'W_D_1KI': 0.05227471105092985, 'J_D_1KI': 9.180810910416543e-05}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..1f26acc
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 520646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.21049165725708, "TIME_S_1KI": 0.01961119773753583, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 853.8924551653862, "W": 65.17, "J_1KI": 1.6400634119255428, "W_1KI": 0.1251714216569416, "W_D": 29.995000000000005, "J_D": 393.0106520283223, "W_D_1KI": 0.05761112156820566, "J_D_1KI": 0.0001106531531370752}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..65d6ab4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.012744903564453125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([1621, 4974, 1997,  ..., 3786, 4849,  461]),
+       values=tensor([0.2109, 0.3256, 0.0266,  ..., 0.3581, 0.6264, 0.0778]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8357, 0.0383, 0.1188,  ..., 0.4462, 0.9461, 0.1099])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.012744903564453125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '82385', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6614789962768555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1213, 1571, 1960,  ..., 2843, 4867, 4843]),
+       values=tensor([0.3029, 0.3061, 0.3000,  ..., 0.6016, 0.9759, 0.4960]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1072, 0.2899, 0.3055,  ..., 0.7146, 0.5978, 0.9959])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.6614789962768555 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '520646', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.21049165725708}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 472, 3691, 4268,  ..., 1601, 3041,  533]),
+       values=tensor([0.9317, 0.8516, 0.8376,  ..., 0.6191, 0.8435, 0.3776]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6520, 0.6755, 0.7512,  ..., 0.2262, 0.3599, 0.0025])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.21049165725708 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 472, 3691, 4268,  ..., 1601, 3041,  533]),
+       values=tensor([0.9317, 0.8516, 0.8376,  ..., 0.6191, 0.8435, 0.3776]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6520, 0.6755, 0.7512,  ..., 0.2262, 0.3599, 0.0025])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.21049165725708 seconds
+
+[39.48, 39.4, 39.17, 39.09, 40.79, 38.97, 38.82, 38.72, 38.81, 38.71]
+[65.17]
+13.102538824081421
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 520646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.21049165725708, 'TIME_S_1KI': 0.01961119773753583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.8924551653862, 'W': 65.17}
+[39.48, 39.4, 39.17, 39.09, 40.79, 38.97, 38.82, 38.72, 38.81, 38.71, 39.49, 39.17, 38.98, 38.86, 39.13, 38.88, 38.85, 38.81, 38.77, 38.88]
+703.5
+35.175
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 520646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.21049165725708, 'TIME_S_1KI': 0.01961119773753583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.8924551653862, 'W': 65.17, 'J_1KI': 1.6400634119255428, 'W_1KI': 0.1251714216569416, 'W_D': 29.995000000000005, 'J_D': 393.0106520283223, 'W_D_1KI': 0.05761112156820566, 'J_D_1KI': 0.0001106531531370752}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
index 027433b..3a94ff3 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3626, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.40371823310852, "TIME_S_1KI": 2.869199733344876, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 756.9219121170044, "W": 52.54, "J_1KI": 208.74845894015564, "W_1KI": 14.489795918367347, "W_D": 36.50025, "J_D": 525.8439098353386, "W_D_1KI": 10.066257584114727, "J_D_1KI": 2.776132814151883}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3659, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.513276815414429, "TIME_S_1KI": 2.8732650493070317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 699.5738222312928, "W": 48.46, "J_1KI": 191.19262701046534, "W_1KI": 13.244055752937962, "W_D": 31.944000000000003, "J_D": 461.147052772522, "W_D_1KI": 8.73025416780541, "J_D_1KI": 2.3859672500151436}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
index 8a43e29..3030a8d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.89510178565979}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3051443099975586}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      17,  ...,  999968,
-                             999983, 1000000]),
-       col_indices=tensor([23348, 35658, 56723,  ..., 82423, 86979, 88187]),
-       values=tensor([0.8917, 0.1559, 0.5748,  ..., 0.5915, 0.7647, 0.8715]),
+tensor(crow_indices=tensor([      0,       9,      20,  ...,  999978,
+                             999988, 1000000]),
+       col_indices=tensor([10874, 16180, 25759,  ..., 85120, 90595, 97571]),
+       values=tensor([0.6980, 0.1450, 0.2222,  ..., 0.0442, 0.2876, 0.2305]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4707, 0.9474, 0.3412,  ..., 0.5588, 0.8812, 0.4153])
+tensor([0.9393, 0.9771, 0.4381,  ..., 0.2097, 0.8256, 0.0395])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 2.89510178565979 seconds
+Time: 0.3051443099975586 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3626', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.40371823310852}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3440', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.870911598205566}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,      15,  ...,  999979,
-                             999990, 1000000]),
-       col_indices=tensor([16760, 54124, 62778,  ..., 86983, 90495, 98787]),
-       values=tensor([0.0638, 0.0650, 0.2338,  ..., 0.3776, 0.7465, 0.0262]),
+tensor(crow_indices=tensor([      0,      16,      29,  ...,  999980,
+                             999991, 1000000]),
+       col_indices=tensor([ 5523, 13716, 16446,  ..., 89337, 96388, 97674]),
+       values=tensor([0.9883, 0.0360, 0.0063,  ..., 0.9506, 0.8956, 0.5971]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7059, 0.4263, 0.8303,  ..., 0.6514, 0.5791, 0.5612])
+tensor([0.2735, 0.3671, 0.2374,  ..., 0.9952, 0.6404, 0.3809])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.40371823310852 seconds
+Time: 9.870911598205566 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3659', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.513276815414429}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,      15,  ...,  999979,
-                             999990, 1000000]),
-       col_indices=tensor([16760, 54124, 62778,  ..., 86983, 90495, 98787]),
-       values=tensor([0.0638, 0.0650, 0.2338,  ..., 0.3776, 0.7465, 0.0262]),
+tensor(crow_indices=tensor([      0,      15,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 3420, 12508, 17596,  ..., 74140, 75972, 84324]),
+       values=tensor([0.2457, 0.5369, 0.4041,  ..., 0.2835, 0.7746, 0.0854]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7059, 0.4263, 0.8303,  ..., 0.6514, 0.5791, 0.5612])
+tensor([0.1485, 0.5045, 0.7097,  ..., 0.5250, 0.4505, 0.4467])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.40371823310852 seconds
+Time: 10.513276815414429 seconds
 
-[18.45, 17.61, 17.77, 17.55, 17.61, 17.99, 17.58, 18.4, 17.81, 17.62]
-[52.54]
-14.406583786010742
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.40371823310852, 'TIME_S_1KI': 2.869199733344876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9219121170044, 'W': 52.54}
-[18.45, 17.61, 17.77, 17.55, 17.61, 17.99, 17.58, 18.4, 17.81, 17.62, 18.49, 17.87, 17.62, 17.77, 17.72, 17.81, 18.01, 17.57, 17.69, 18.27]
-320.79499999999996
-16.039749999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.40371823310852, 'TIME_S_1KI': 2.869199733344876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9219121170044, 'W': 52.54, 'J_1KI': 208.74845894015564, 'W_1KI': 14.489795918367347, 'W_D': 36.50025, 'J_D': 525.8439098353386, 'W_D_1KI': 10.066257584114727, 'J_D_1KI': 2.776132814151883}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      15,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 3420, 12508, 17596,  ..., 74140, 75972, 84324]),
+       values=tensor([0.2457, 0.5369, 0.4041,  ..., 0.2835, 0.7746, 0.0854]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1485, 0.5045, 0.7097,  ..., 0.5250, 0.4505, 0.4467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.513276815414429 seconds
+
+[19.03, 17.89, 18.11, 18.19, 17.92, 19.96, 20.76, 18.13, 18.16, 18.25]
+[48.46]
+14.436108589172363
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3659, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.513276815414429, 'TIME_S_1KI': 2.8732650493070317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 699.5738222312928, 'W': 48.46}
+[19.03, 17.89, 18.11, 18.19, 17.92, 19.96, 20.76, 18.13, 18.16, 18.25, 18.14, 18.17, 18.05, 17.87, 18.21, 18.32, 17.88, 17.89, 18.02, 18.16]
+330.31999999999994
+16.516
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3659, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.513276815414429, 'TIME_S_1KI': 2.8732650493070317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 699.5738222312928, 'W': 48.46, 'J_1KI': 191.19262701046534, 'W_1KI': 13.244055752937962, 'W_D': 31.944000000000003, 'J_D': 461.147052772522, 'W_D_1KI': 8.73025416780541, 'J_D_1KI': 2.3859672500151436}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
index 46cf4d4..253d722 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.417505741119385, "TIME_S_1KI": 27.417505741119385, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1870.535600104332, "W": 53.17, "J_1KI": 1870.535600104332, "W_1KI": 53.17, "W_D": 36.779250000000005, "J_D": 1293.9043910125495, "W_D_1KI": 36.779250000000005, "J_D_1KI": 36.779250000000005}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 376, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.408750772476196, "TIME_S_1KI": 27.68284779913882, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 864.391910111904, "W": 48.41, "J_1KI": 2298.914654552936, "W_1KI": 128.75, "W_D": 32.1215, "J_D": 573.5501908832788, "W_D_1KI": 85.42952127659574, "J_D_1KI": 227.20617360796737}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
index 523884d..8c93db0 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.417505741119385}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.7904272079467773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       87,      206,  ...,  9999814,
-                             9999907, 10000000]),
-       col_indices=tensor([  430,  1206,  1283,  ..., 96095, 96254, 99884]),
-       values=tensor([0.0855, 0.2486, 0.3160,  ..., 0.5781, 0.8085, 0.1274]),
+tensor(crow_indices=tensor([       0,       83,      173,  ...,  9999788,
+                             9999895, 10000000]),
+       col_indices=tensor([  687,  1990,  2832,  ..., 93491, 98909, 99713]),
+       values=tensor([0.2182, 0.4312, 0.1873,  ..., 0.5994, 0.5663, 0.3895]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4876, 0.8099, 0.9530,  ..., 0.3051, 0.4863, 0.6986])
+tensor([0.6218, 0.2935, 0.0099,  ..., 0.4944, 0.2399, 0.4191])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 27.417505741119385 seconds
+Time: 2.7904272079467773 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '376', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.408750772476196}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       87,      206,  ...,  9999814,
-                             9999907, 10000000]),
-       col_indices=tensor([  430,  1206,  1283,  ..., 96095, 96254, 99884]),
-       values=tensor([0.0855, 0.2486, 0.3160,  ..., 0.5781, 0.8085, 0.1274]),
+tensor(crow_indices=tensor([       0,      101,      179,  ...,  9999785,
+                             9999885, 10000000]),
+       col_indices=tensor([  974,  1017,  1175,  ..., 97865, 98322, 99037]),
+       values=tensor([0.8598, 0.2680, 0.8943,  ..., 0.1763, 0.5676, 0.4916]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4876, 0.8099, 0.9530,  ..., 0.3051, 0.4863, 0.6986])
+tensor([0.4049, 0.5573, 0.2557,  ..., 0.0615, 0.7671, 0.4849])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 27.417505741119385 seconds
+Time: 10.408750772476196 seconds
 
-[18.51, 17.88, 18.06, 17.74, 17.69, 18.37, 18.17, 17.77, 18.14, 17.72]
-[53.17]
-35.18028211593628
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.417505741119385, 'TIME_S_1KI': 27.417505741119385, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.535600104332, 'W': 53.17}
-[18.51, 17.88, 18.06, 17.74, 17.69, 18.37, 18.17, 17.77, 18.14, 17.72, 18.92, 17.72, 17.91, 22.37, 18.39, 17.62, 17.83, 17.88, 17.9, 17.6]
-327.815
-16.39075
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.417505741119385, 'TIME_S_1KI': 27.417505741119385, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.535600104332, 'W': 53.17, 'J_1KI': 1870.535600104332, 'W_1KI': 53.17, 'W_D': 36.779250000000005, 'J_D': 1293.9043910125495, 'W_D_1KI': 36.779250000000005, 'J_D_1KI': 36.779250000000005}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      101,      179,  ...,  9999785,
+                             9999885, 10000000]),
+       col_indices=tensor([  974,  1017,  1175,  ..., 97865, 98322, 99037]),
+       values=tensor([0.8598, 0.2680, 0.8943,  ..., 0.1763, 0.5676, 0.4916]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4049, 0.5573, 0.2557,  ..., 0.0615, 0.7671, 0.4849])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.408750772476196 seconds
+
+[18.19, 17.86, 18.15, 17.76, 18.57, 17.97, 17.88, 17.91, 18.5, 18.33]
+[48.41]
+17.855647802352905
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 376, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.408750772476196, 'TIME_S_1KI': 27.68284779913882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 864.391910111904, 'W': 48.41}
+[18.19, 17.86, 18.15, 17.76, 18.57, 17.97, 17.88, 17.91, 18.5, 18.33, 18.68, 17.76, 17.89, 18.01, 18.9, 17.73, 18.37, 17.97, 18.0, 17.88]
+325.77
+16.2885
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 376, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.408750772476196, 'TIME_S_1KI': 27.68284779913882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 864.391910111904, 'W': 48.41, 'J_1KI': 2298.914654552936, 'W_1KI': 128.75, 'W_D': 32.1215, 'J_D': 573.5501908832788, 'W_D_1KI': 85.42952127659574, 'J_D_1KI': 227.20617360796737}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
index fb45506..9c4b03d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 7957, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.378219842910767, "TIME_S_1KI": 1.3042880285171252, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 722.9498359966278, "W": 51.18, "J_1KI": 90.85708633864871, "W_1KI": 6.432072389091366, "W_D": 34.9585, "J_D": 493.81089960312846, "W_D_1KI": 4.3934271710443635, "J_D_1KI": 0.5521461821093834}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8087, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.519959688186646, "TIME_S_1KI": 1.300848236451916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 670.7503997087479, "W": 46.91, "J_1KI": 82.94180780372794, "W_1KI": 5.800667738345492, "W_D": 30.164499999999997, "J_D": 431.3120961844921, "W_D_1KI": 3.729998763447508, "J_D_1KI": 0.46123392648046346}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
index f2950b1..cc342ed 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.319572925567627}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14809489250183105}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      4,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99993,  99994,
                             100000]),
-       col_indices=tensor([ 8050, 18600, 47626,  ..., 72573,  7071, 11396]),
-       values=tensor([0.6679, 0.8144, 0.2788,  ..., 0.2480, 0.1170, 0.9852]),
+       col_indices=tensor([11566,  2001, 14819,  ..., 49184, 52555, 95716]),
+       values=tensor([0.6903, 0.1382, 0.4591,  ..., 0.3067, 0.8088, 0.6364]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3322, 0.6851, 0.8140,  ..., 0.1719, 0.4686, 0.0560])
+tensor([0.9563, 0.6034, 0.0890,  ..., 0.8548, 0.6115, 0.7911])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 1.319572925567627 seconds
+Time: 0.14809489250183105 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7957', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.378219842910767}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7090', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.205029487609863}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([79139, 34438, 57240,  ..., 99522, 68399,  1834]),
-       values=tensor([0.8717, 0.0754, 0.3550,  ..., 0.4586, 0.3508, 0.4372]),
+       col_indices=tensor([ 7711, 16815, 22150,  ..., 77554, 50594, 27282]),
+       values=tensor([0.3735, 0.5582, 0.2278,  ..., 0.2317, 0.9623, 0.5188]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1033, 0.1471, 0.4199,  ..., 0.6623, 0.5752, 0.0388])
+tensor([0.8847, 0.2063, 0.0570,  ..., 0.4149, 0.1346, 0.4208])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.378219842910767 seconds
+Time: 9.205029487609863 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8087', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.519959688186646}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([79139, 34438, 57240,  ..., 99522, 68399,  1834]),
-       values=tensor([0.8717, 0.0754, 0.3550,  ..., 0.4586, 0.3508, 0.4372]),
+       col_indices=tensor([95443, 50058, 77222,  ..., 43317,  3451, 10339]),
+       values=tensor([0.1078, 0.8522, 0.7935,  ..., 0.8133, 0.3945, 0.6126]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1033, 0.1471, 0.4199,  ..., 0.6623, 0.5752, 0.0388])
+tensor([0.8231, 0.3876, 0.1205,  ..., 0.1479, 0.8608, 0.1605])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.378219842910767 seconds
+Time: 10.519959688186646 seconds
 
-[19.98, 17.81, 17.8, 18.03, 17.89, 17.86, 17.65, 18.02, 17.79, 17.59]
-[51.18]
-14.12563180923462
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7957, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.378219842910767, 'TIME_S_1KI': 1.3042880285171252, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.9498359966278, 'W': 51.18}
-[19.98, 17.81, 17.8, 18.03, 17.89, 17.86, 17.65, 18.02, 17.79, 17.59, 18.5, 18.06, 17.99, 17.58, 17.86, 17.88, 17.85, 17.69, 17.49, 22.29]
-324.43
-16.2215
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7957, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.378219842910767, 'TIME_S_1KI': 1.3042880285171252, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.9498359966278, 'W': 51.18, 'J_1KI': 90.85708633864871, 'W_1KI': 6.432072389091366, 'W_D': 34.9585, 'J_D': 493.81089960312846, 'W_D_1KI': 4.3934271710443635, 'J_D_1KI': 0.5521461821093834}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([95443, 50058, 77222,  ..., 43317,  3451, 10339]),
+       values=tensor([0.1078, 0.8522, 0.7935,  ..., 0.8133, 0.3945, 0.6126]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8231, 0.3876, 0.1205,  ..., 0.1479, 0.8608, 0.1605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.519959688186646 seconds
+
+[18.45, 18.35, 18.06, 17.99, 17.93, 18.06, 18.08, 17.93, 22.44, 18.22]
+[46.91]
+14.298665523529053
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8087, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.519959688186646, 'TIME_S_1KI': 1.300848236451916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 670.7503997087479, 'W': 46.91}
+[18.45, 18.35, 18.06, 17.99, 17.93, 18.06, 18.08, 17.93, 22.44, 18.22, 19.6, 17.81, 18.45, 17.9, 22.14, 18.02, 18.39, 17.96, 18.34, 17.85]
+334.90999999999997
+16.7455
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8087, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.519959688186646, 'TIME_S_1KI': 1.300848236451916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 670.7503997087479, 'W': 46.91, 'J_1KI': 82.94180780372794, 'W_1KI': 5.800667738345492, 'W_D': 30.164499999999997, 'J_D': 431.3120961844921, 'W_D_1KI': 3.729998763447508, 'J_D_1KI': 0.46123392648046346}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..0e52b1a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.509625434875488, "TIME_S_1KI": 2.2079045031251026, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 685.1951848602295, "W": 48.04, "J_1KI": 143.94856824794735, "W_1KI": 10.092436974789916, "W_D": 31.590249999999997, "J_D": 450.57217295026777, "W_D_1KI": 6.636607142857142, "J_D_1KI": 1.3942451980792314}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..e5c487f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.2381742000579834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 499988, 499996,
+                            500000]),
+       col_indices=tensor([ 7829, 21471, 22951,  ..., 29509, 41224, 66852]),
+       values=tensor([0.9739, 0.6225, 0.8607,  ..., 0.0619, 0.3093, 0.0510]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6694, 0.1094, 0.7903,  ..., 0.4860, 0.7386, 0.2172])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.2381742000579834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4408', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.722268104553223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      8,  ..., 499991, 499997,
+                            500000]),
+       col_indices=tensor([ 8468, 11831, 46487,  ..., 65418, 70471, 71020]),
+       values=tensor([0.5611, 0.2625, 0.0139,  ..., 0.7643, 0.0263, 0.5630]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6021, 0.3259, 0.4454,  ..., 0.1291, 0.3066, 0.3093])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 9.722268104553223 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4760', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.509625434875488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 499986, 499991,
+                            500000]),
+       col_indices=tensor([37454, 51813, 86506,  ..., 62954, 73906, 92773]),
+       values=tensor([0.8256, 0.7091, 0.8154,  ..., 0.4160, 0.7952, 0.1689]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1880, 0.2475, 0.0895,  ..., 0.2917, 0.5906, 0.9519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.509625434875488 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 499986, 499991,
+                            500000]),
+       col_indices=tensor([37454, 51813, 86506,  ..., 62954, 73906, 92773]),
+       values=tensor([0.8256, 0.7091, 0.8154,  ..., 0.4160, 0.7952, 0.1689]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1880, 0.2475, 0.0895,  ..., 0.2917, 0.5906, 0.9519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.509625434875488 seconds
+
+[18.07, 18.03, 18.14, 17.98, 17.8, 18.31, 18.47, 18.23, 18.16, 17.92]
+[48.04]
+14.26301383972168
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.509625434875488, 'TIME_S_1KI': 2.2079045031251026, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 685.1951848602295, 'W': 48.04}
+[18.07, 18.03, 18.14, 17.98, 17.8, 18.31, 18.47, 18.23, 18.16, 17.92, 18.33, 17.81, 18.24, 18.05, 18.09, 17.75, 17.78, 21.35, 18.67, 17.95]
+328.995
+16.44975
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.509625434875488, 'TIME_S_1KI': 2.2079045031251026, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 685.1951848602295, 'W': 48.04, 'J_1KI': 143.94856824794735, 'W_1KI': 10.092436974789916, 'W_D': 31.590249999999997, 'J_D': 450.57217295026777, 'W_D_1KI': 6.636607142857142, 'J_D_1KI': 1.3942451980792314}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
index 641470f..20b7d93 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 83764, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.409894704818726, "TIME_S_1KI": 0.12427647563175977, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 711.6688519239426, "W": 50.42, "J_1KI": 8.49611828379665, "W_1KI": 0.6019292297407001, "W_D": 34.06175, "J_D": 480.7752185049654, "W_D_1KI": 0.40663948713050957, "J_D_1KI": 0.004854585348485144}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 84718, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.541555881500244, "TIME_S_1KI": 0.12443112303761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.3758532905579, "W": 46.44, "J_1KI": 7.80679257407585, "W_1KI": 0.5481715810099388, "W_D": 30.123999999999995, "J_D": 429.01133084678645, "W_D_1KI": 0.3555796879057579, "J_D_1KI": 0.004197215325028422}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
index 168b413..79fd885 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.13988041877746582}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.028100967407226562}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     5,  ...,  9997,  9998, 10000]),
-       col_indices=tensor([5444, 7298, 2758,  ..., 5406,  201, 2159]),
-       values=tensor([0.2785, 0.9301, 0.1173,  ..., 0.6105, 0.0625, 0.6073]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1696, 2591, 3015,  ..., 1730, 8585, 3790]),
+       values=tensor([0.6837, 0.7697, 0.7550,  ..., 0.1323, 0.4514, 0.4553]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9117, 0.7600, 0.5676,  ..., 0.4107, 0.0296, 0.3559])
+tensor([0.1557, 0.9230, 0.6401,  ..., 0.3725, 0.8926, 0.6402])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.13988041877746582 seconds
+Time: 0.028100967407226562 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '75064', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.409368753433228}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '37365', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.631014823913574}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([ 559, 1691, 3057,  ..., 6770,  161, 9445]),
-       values=tensor([0.2390, 0.7843, 0.4833,  ..., 0.8916, 0.1224, 0.1645]),
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([2386, 8388, 9261,  ..., 1344, 2569, 4425]),
+       values=tensor([0.6000, 0.2415, 0.7139,  ..., 0.1197, 0.4001, 0.0791]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9833, 0.3493, 0.9306,  ..., 0.5004, 0.5453, 0.7909])
+tensor([0.9194, 0.6903, 0.0708,  ..., 0.1917, 0.6424, 0.6800])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 9.409368753433228 seconds
+Time: 4.631014823913574 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '83764', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.409894704818726}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '84718', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.541555881500244}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([1791, 2178, 4941,  ..., 8437, 8977, 5726]),
-       values=tensor([0.7542, 0.7473, 0.0826,  ..., 0.7863, 0.2178, 0.9123]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([6630, 3375,  966,  ..., 5451,   76,  624]),
+       values=tensor([0.0314, 0.6841, 0.2123,  ..., 0.3011, 0.8872, 0.9156]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0349, 0.7342, 0.7720,  ..., 0.6458, 0.8179, 0.1428])
+tensor([0.4846, 0.1841, 0.4323,  ..., 0.0718, 0.1957, 0.5902])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.409894704818726 seconds
+Time: 10.541555881500244 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([1791, 2178, 4941,  ..., 8437, 8977, 5726]),
-       values=tensor([0.7542, 0.7473, 0.0826,  ..., 0.7863, 0.2178, 0.9123]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([6630, 3375,  966,  ..., 5451,   76,  624]),
+       values=tensor([0.0314, 0.6841, 0.2123,  ..., 0.3011, 0.8872, 0.9156]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0349, 0.7342, 0.7720,  ..., 0.6458, 0.8179, 0.1428])
+tensor([0.4846, 0.1841, 0.4323,  ..., 0.0718, 0.1957, 0.5902])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.409894704818726 seconds
+Time: 10.541555881500244 seconds
 
-[18.39, 17.85, 18.37, 17.98, 17.9, 18.07, 21.28, 18.76, 18.12, 17.67]
-[50.42]
-14.11481261253357
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 83764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.409894704818726, 'TIME_S_1KI': 0.12427647563175977, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 711.6688519239426, 'W': 50.42}
-[18.39, 17.85, 18.37, 17.98, 17.9, 18.07, 21.28, 18.76, 18.12, 17.67, 18.33, 17.97, 17.87, 17.66, 17.77, 17.96, 17.86, 17.77, 17.81, 17.94]
-327.16499999999996
-16.358249999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 83764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.409894704818726, 'TIME_S_1KI': 0.12427647563175977, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 711.6688519239426, 'W': 50.42, 'J_1KI': 8.49611828379665, 'W_1KI': 0.6019292297407001, 'W_D': 34.06175, 'J_D': 480.7752185049654, 'W_D_1KI': 0.40663948713050957, 'J_D_1KI': 0.004854585348485144}
+[18.42, 18.02, 18.15, 18.67, 18.24, 18.06, 18.05, 17.99, 18.11, 18.07]
+[46.44]
+14.241512775421143
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84718, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.541555881500244, 'TIME_S_1KI': 0.12443112303761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.3758532905579, 'W': 46.44}
+[18.42, 18.02, 18.15, 18.67, 18.24, 18.06, 18.05, 17.99, 18.11, 18.07, 18.36, 18.1, 17.99, 18.16, 18.02, 18.13, 18.12, 18.03, 18.02, 18.07]
+326.32000000000005
+16.316000000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84718, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.541555881500244, 'TIME_S_1KI': 0.12443112303761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.3758532905579, 'W': 46.44, 'J_1KI': 7.80679257407585, 'W_1KI': 0.5481715810099388, 'W_D': 30.123999999999995, 'J_D': 429.01133084678645, 'W_D_1KI': 0.3555796879057579, 'J_D_1KI': 0.004197215325028422}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
index b3d9f1e..e07fffc 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 33076, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.000443935394287, "TIME_S_1KI": 0.30234744030095195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 713.14643699646, "W": 51.71, "J_1KI": 21.560842816436693, "W_1KI": 1.5633692103035435, "W_D": 35.256, "J_D": 486.2249232788086, "W_D_1KI": 1.065908816059983, "J_D_1KI": 0.03222604958459254}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 34651, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.471668004989624, "TIME_S_1KI": 0.30220391922281103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 677.0323536157608, "W": 47.45, "J_1KI": 19.53860937969354, "W_1KI": 1.369368849383856, "W_D": 30.828750000000007, "J_D": 439.8748402851821, "W_D_1KI": 0.8896929381547433, "J_D_1KI": 0.025675822866720825}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
index 108ea7e..ff06711 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.31745004653930664}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.04611039161682129}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     13,     25,  ...,  99974,  99988,
+tensor(crow_indices=tensor([     0,      9,     22,  ...,  99983,  99992,
                             100000]),
-       col_indices=tensor([ 189, 1046, 1680,  ..., 7652, 7822, 9876]),
-       values=tensor([0.3200, 0.6172, 0.8426,  ..., 0.6310, 0.2892, 0.4983]),
+       col_indices=tensor([ 845, 1153, 1508,  ..., 8313, 9367, 9854]),
+       values=tensor([0.8746, 0.4039, 0.9243,  ..., 0.5657, 0.2713, 0.6449]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5979, 0.0691, 0.5787,  ..., 0.1637, 0.0173, 0.7657])
+tensor([0.3540, 0.2628, 0.4314,  ..., 0.0912, 0.3507, 0.8651])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.31745004653930664 seconds
+Time: 0.04611039161682129 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33076', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.000443935394287}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22771', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.900022268295288}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     16,  ...,  99975,  99984,
+tensor(crow_indices=tensor([     0,      8,     19,  ...,  99978,  99991,
                             100000]),
-       col_indices=tensor([2058, 2088, 2648,  ..., 8443, 9183, 9230]),
-       values=tensor([0.6058, 0.3120, 0.6569,  ..., 0.6120, 0.0868, 0.9498]),
+       col_indices=tensor([ 917, 1959, 2965,  ..., 8075, 8263, 9058]),
+       values=tensor([0.2271, 0.8712, 0.9636,  ..., 0.2167, 0.1262, 0.7253]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4574, 0.0884, 0.9388,  ..., 0.4572, 0.8159, 0.8640])
+tensor([0.8029, 0.1325, 0.2655,  ..., 0.5832, 0.4718, 0.3144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.000443935394287 seconds
+Time: 6.900022268295288 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '34651', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.471668004989624}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     16,  ...,  99975,  99984,
+tensor(crow_indices=tensor([     0,      7,     14,  ...,  99994,  99997,
                             100000]),
-       col_indices=tensor([2058, 2088, 2648,  ..., 8443, 9183, 9230]),
-       values=tensor([0.6058, 0.3120, 0.6569,  ..., 0.6120, 0.0868, 0.9498]),
+       col_indices=tensor([  24, 1396, 2236,  ..., 5590, 6310, 9874]),
+       values=tensor([0.4982, 0.6812, 0.1465,  ..., 0.3747, 0.0311, 0.9162]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4574, 0.0884, 0.9388,  ..., 0.4572, 0.8159, 0.8640])
+tensor([0.7533, 0.0703, 0.6276,  ..., 0.6008, 0.2603, 0.3256])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.000443935394287 seconds
+Time: 10.471668004989624 seconds
 
-[18.4, 17.89, 17.86, 18.15, 21.93, 17.6, 17.74, 17.84, 17.81, 17.8]
-[51.71]
-13.791267395019531
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33076, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.000443935394287, 'TIME_S_1KI': 0.30234744030095195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 713.14643699646, 'W': 51.71}
-[18.4, 17.89, 17.86, 18.15, 21.93, 17.6, 17.74, 17.84, 17.81, 17.8, 22.42, 17.97, 17.98, 17.67, 18.14, 18.06, 18.09, 18.36, 17.77, 17.82]
-329.08000000000004
-16.454
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33076, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.000443935394287, 'TIME_S_1KI': 0.30234744030095195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 713.14643699646, 'W': 51.71, 'J_1KI': 21.560842816436693, 'W_1KI': 1.5633692103035435, 'W_D': 35.256, 'J_D': 486.2249232788086, 'W_D_1KI': 1.065908816059983, 'J_D_1KI': 0.03222604958459254}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     14,  ...,  99994,  99997,
+                            100000]),
+       col_indices=tensor([  24, 1396, 2236,  ..., 5590, 6310, 9874]),
+       values=tensor([0.4982, 0.6812, 0.1465,  ..., 0.3747, 0.0311, 0.9162]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7533, 0.0703, 0.6276,  ..., 0.6008, 0.2603, 0.3256])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.471668004989624 seconds
+
+[18.34, 18.19, 18.22, 18.02, 22.16, 18.64, 18.12, 17.87, 17.95, 18.81]
+[47.45]
+14.26833200454712
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.471668004989624, 'TIME_S_1KI': 0.30220391922281103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 677.0323536157608, 'W': 47.45}
+[18.34, 18.19, 18.22, 18.02, 22.16, 18.64, 18.12, 17.87, 17.95, 18.81, 22.38, 17.93, 18.41, 17.77, 18.07, 17.88, 18.52, 17.88, 18.1, 17.86]
+332.42499999999995
+16.621249999999996
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.471668004989624, 'TIME_S_1KI': 0.30220391922281103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 677.0323536157608, 'W': 47.45, 'J_1KI': 19.53860937969354, 'W_1KI': 1.369368849383856, 'W_D': 30.828750000000007, 'J_D': 439.8748402851821, 'W_D_1KI': 0.8896929381547433, 'J_D_1KI': 0.025675822866720825}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
index ec86a79..89c422b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5536, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43858790397644, "TIME_S_1KI": 1.885583075140253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.4033546972274, "W": 52.18, "J_1KI": 136.81418979357431, "W_1KI": 9.425578034682081, "W_D": 35.855000000000004, "J_D": 520.4426462757588, "W_D_1KI": 6.476697976878613, "J_D_1KI": 1.1699237674997496}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5583, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.513006925582886, "TIME_S_1KI": 1.8830390337780558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.6418607521057, "W": 47.919999999999995, "J_1KI": 124.06266536845884, "W_1KI": 8.58319899695504, "W_D": 31.175249999999995, "J_D": 450.611084503591, "W_D_1KI": 5.583960236432024, "J_D_1KI": 1.0001719929127753}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
index cdf759d..d152642 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.8966615200042725}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.2040116786956787}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      98,     196,  ...,  999798,
-                             999896, 1000000]),
-       col_indices=tensor([ 136,  346,  355,  ..., 9896, 9907, 9979]),
-       values=tensor([0.5884, 0.9037, 0.2601,  ..., 0.4944, 0.5993, 0.9598]),
+tensor(crow_indices=tensor([      0,     106,     193,  ...,  999811,
+                             999905, 1000000]),
+       col_indices=tensor([ 107,  139,  344,  ..., 9485, 9560, 9767]),
+       values=tensor([0.2657, 0.7219, 0.2773,  ..., 0.6022, 0.5377, 0.2291]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5307, 0.6978, 0.6134,  ..., 0.5179, 0.0970, 0.9420])
+tensor([0.8604, 0.1859, 0.3719,  ..., 0.6286, 0.9460, 0.3185])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 1.8966615200042725 seconds
+Time: 0.2040116786956787 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5536', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43858790397644}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5146', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.677676439285278}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      90,     180,  ...,  999793,
-                             999892, 1000000]),
-       col_indices=tensor([  10,   80,  127,  ..., 9954, 9956, 9988]),
-       values=tensor([0.2975, 0.8577, 0.6251,  ..., 0.1783, 0.1753, 0.5886]),
+tensor(crow_indices=tensor([      0,      90,     186,  ...,  999811,
+                             999907, 1000000]),
+       col_indices=tensor([  74,  208,  311,  ..., 9654, 9863, 9976]),
+       values=tensor([0.0395, 0.4059, 0.0831,  ..., 0.6188, 0.9591, 0.8953]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4757, 0.0822, 0.0813,  ..., 0.4411, 0.1352, 0.6104])
+tensor([0.6360, 0.9265, 0.4313,  ..., 0.6926, 0.7242, 0.0651])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.43858790397644 seconds
+Time: 9.677676439285278 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5583', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.513006925582886}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      90,     180,  ...,  999793,
-                             999892, 1000000]),
-       col_indices=tensor([  10,   80,  127,  ..., 9954, 9956, 9988]),
-       values=tensor([0.2975, 0.8577, 0.6251,  ..., 0.1783, 0.1753, 0.5886]),
+tensor(crow_indices=tensor([      0,     104,     207,  ...,  999792,
+                             999898, 1000000]),
+       col_indices=tensor([ 168,  206,  240,  ..., 9827, 9842, 9996]),
+       values=tensor([0.8276, 0.5768, 0.6424,  ..., 0.0752, 0.7475, 0.3129]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4757, 0.0822, 0.0813,  ..., 0.4411, 0.1352, 0.6104])
+tensor([0.7571, 0.4178, 0.1860,  ..., 0.0563, 0.6255, 0.7203])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.43858790397644 seconds
+Time: 10.513006925582886 seconds
 
-[18.3, 18.15, 17.72, 17.62, 17.65, 18.51, 19.15, 17.59, 17.78, 18.09]
-[52.18]
-14.515204191207886
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5536, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43858790397644, 'TIME_S_1KI': 1.885583075140253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.4033546972274, 'W': 52.18}
-[18.3, 18.15, 17.72, 17.62, 17.65, 18.51, 19.15, 17.59, 17.78, 18.09, 18.11, 18.06, 18.06, 18.01, 19.44, 18.82, 18.14, 17.87, 17.86, 17.64]
-326.5
-16.325
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5536, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43858790397644, 'TIME_S_1KI': 1.885583075140253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.4033546972274, 'W': 52.18, 'J_1KI': 136.81418979357431, 'W_1KI': 9.425578034682081, 'W_D': 35.855000000000004, 'J_D': 520.4426462757588, 'W_D_1KI': 6.476697976878613, 'J_D_1KI': 1.1699237674997496}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     104,     207,  ...,  999792,
+                             999898, 1000000]),
+       col_indices=tensor([ 168,  206,  240,  ..., 9827, 9842, 9996]),
+       values=tensor([0.8276, 0.5768, 0.6424,  ..., 0.0752, 0.7475, 0.3129]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7571, 0.4178, 0.1860,  ..., 0.0563, 0.6255, 0.7203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.513006925582886 seconds
+
+[18.38, 17.86, 18.24, 17.87, 19.06, 17.88, 18.29, 21.74, 18.54, 18.08]
+[47.92]
+14.454128980636597
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5583, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.513006925582886, 'TIME_S_1KI': 1.8830390337780558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6418607521057, 'W': 47.919999999999995}
+[18.38, 17.86, 18.24, 17.87, 19.06, 17.88, 18.29, 21.74, 18.54, 18.08, 18.58, 17.99, 17.96, 22.09, 18.22, 17.97, 17.93, 18.49, 18.13, 18.23]
+334.895
+16.74475
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5583, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.513006925582886, 'TIME_S_1KI': 1.8830390337780558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6418607521057, 'W': 47.919999999999995, 'J_1KI': 124.06266536845884, 'W_1KI': 8.58319899695504, 'W_D': 31.175249999999995, 'J_D': 450.611084503591, 'W_D_1KI': 5.583960236432024, 'J_D_1KI': 1.0001719929127753}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
index 4abbf3f..20cc2bc 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.659594058990479, "TIME_S_1KI": 10.659594058990479, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 944.4377518177032, "W": 52.04, "J_1KI": 944.4377518177032, "W_1KI": 52.04, "W_D": 22.411249999999995, "J_D": 406.72618304044, "W_D_1KI": 22.411249999999995, "J_D_1KI": 22.411249999999995}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 959, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.294347047805786, "TIME_S_1KI": 10.73445990386422, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.9360737204552, "W": 48.35, "J_1KI": 826.8363646720074, "W_1KI": 50.417101147028156, "W_D": 31.9585, "J_D": 524.1168047982454, "W_D_1KI": 33.324817518248175, "J_D_1KI": 34.74954902841311}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
index e447139..643494f 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.659594058990479}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.0941581726074219}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     491,     987,  ..., 4999032,
-                            4999549, 5000000]),
-       col_indices=tensor([   2,   62,   63,  ..., 9943, 9957, 9997]),
-       values=tensor([0.7700, 0.3306, 0.4646,  ..., 0.1296, 0.2152, 0.2390]),
+tensor(crow_indices=tensor([      0,     514,    1009,  ..., 4998988,
+                            4999478, 5000000]),
+       col_indices=tensor([   8,   23,   83,  ..., 9969, 9982, 9990]),
+       values=tensor([0.1393, 0.4453, 0.1108,  ..., 0.3215, 0.7885, 0.8444]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3463, 0.4470, 0.4445,  ..., 0.6886, 0.1263, 0.4488])
+tensor([0.3317, 0.0622, 0.5595,  ..., 0.2290, 0.2268, 0.9236])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.659594058990479 seconds
+Time: 1.0941581726074219 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '959', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.294347047805786}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     491,     987,  ..., 4999032,
-                            4999549, 5000000]),
-       col_indices=tensor([   2,   62,   63,  ..., 9943, 9957, 9997]),
-       values=tensor([0.7700, 0.3306, 0.4646,  ..., 0.1296, 0.2152, 0.2390]),
+tensor(crow_indices=tensor([      0,     514,     994,  ..., 4999000,
+                            4999500, 5000000]),
+       col_indices=tensor([  13,   16,   23,  ..., 9955, 9988, 9993]),
+       values=tensor([0.2414, 0.9977, 0.9772,  ..., 0.9200, 0.6029, 0.8714]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3463, 0.4470, 0.4445,  ..., 0.6886, 0.1263, 0.4488])
+tensor([0.0530, 0.1017, 0.7510,  ..., 0.2543, 0.0728, 0.9686])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.659594058990479 seconds
+Time: 10.294347047805786 seconds
 
-[18.53, 17.91, 17.8, 17.55, 17.82, 18.2, 17.7, 17.96, 22.04, 18.01]
-[52.04]
-18.148304224014282
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.659594058990479, 'TIME_S_1KI': 10.659594058990479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 944.4377518177032, 'W': 52.04}
-[18.53, 17.91, 17.8, 17.55, 17.82, 18.2, 17.7, 17.96, 22.04, 18.01, 43.17, 47.09, 51.95, 51.61, 51.61, 46.81, 50.13, 50.7, 46.45, 18.78]
-592.575
-29.628750000000004
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.659594058990479, 'TIME_S_1KI': 10.659594058990479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 944.4377518177032, 'W': 52.04, 'J_1KI': 944.4377518177032, 'W_1KI': 52.04, 'W_D': 22.411249999999995, 'J_D': 406.72618304044, 'W_D_1KI': 22.411249999999995, 'J_D_1KI': 22.411249999999995}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     514,     994,  ..., 4999000,
+                            4999500, 5000000]),
+       col_indices=tensor([  13,   16,   23,  ..., 9955, 9988, 9993]),
+       values=tensor([0.2414, 0.9977, 0.9772,  ..., 0.9200, 0.6029, 0.8714]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0530, 0.1017, 0.7510,  ..., 0.2543, 0.0728, 0.9686])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.294347047805786 seconds
+
+[18.47, 18.05, 17.89, 17.81, 18.54, 18.75, 17.99, 17.87, 18.49, 18.81]
+[48.35]
+16.399918794631958
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.294347047805786, 'TIME_S_1KI': 10.73445990386422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.9360737204552, 'W': 48.35}
+[18.47, 18.05, 17.89, 17.81, 18.54, 18.75, 17.99, 17.87, 18.49, 18.81, 18.47, 17.8, 17.9, 18.12, 18.16, 18.55, 18.0, 18.63, 18.45, 17.91]
+327.83
+16.3915
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.294347047805786, 'TIME_S_1KI': 10.73445990386422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.9360737204552, 'W': 48.35, 'J_1KI': 826.8363646720074, 'W_1KI': 50.417101147028156, 'W_D': 31.9585, 'J_D': 524.1168047982454, 'W_D_1KI': 33.324817518248175, 'J_D_1KI': 34.74954902841311}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
index 3316ce4..a3bba53 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 26.374675512313843, "TIME_S_1KI": 26.374675512313843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1870.3769276547432, "W": 52.87, "J_1KI": 1870.3769276547432, "W_1KI": 52.87, "W_D": 36.2005, "J_D": 1280.661622272849, "W_D_1KI": 36.2005, "J_D_1KI": 36.2005}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 389, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.411779165267944, "TIME_S_1KI": 26.765499139506282, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 876.1174001097679, "W": 47.85, "J_1KI": 2252.2298203335936, "W_1KI": 123.00771208226222, "W_D": 31.457250000000002, "J_D": 575.971663210094, "W_D_1KI": 80.86696658097686, "J_D_1KI": 207.88423285598165}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
index fb57672..086f941 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 26.374675512313843}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.693603992462158}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1071,     2053,  ...,  9998024,
-                             9999000, 10000000]),
-       col_indices=tensor([   3,    4,    5,  ..., 9980, 9985, 9995]),
-       values=tensor([0.3665, 0.1961, 0.0802,  ..., 0.1951, 0.2808, 0.5332]),
+tensor(crow_indices=tensor([       0,     1004,     2049,  ...,  9997955,
+                             9998949, 10000000]),
+       col_indices=tensor([   1,    3,   35,  ..., 9984, 9987, 9993]),
+       values=tensor([0.3631, 0.8073, 0.7190,  ..., 0.1286, 0.7057, 0.1104]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6172, 0.4719, 0.5685,  ..., 0.7751, 0.3390, 0.5446])
+tensor([0.7885, 0.1169, 0.1101,  ..., 0.4416, 0.5822, 0.3212])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 26.374675512313843 seconds
+Time: 2.693603992462158 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '389', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.411779165267944}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1071,     2053,  ...,  9998024,
-                             9999000, 10000000]),
-       col_indices=tensor([   3,    4,    5,  ..., 9980, 9985, 9995]),
-       values=tensor([0.3665, 0.1961, 0.0802,  ..., 0.1951, 0.2808, 0.5332]),
+tensor(crow_indices=tensor([       0,      966,     1941,  ...,  9997989,
+                             9998973, 10000000]),
+       col_indices=tensor([   2,   11,   26,  ..., 9956, 9965, 9978]),
+       values=tensor([0.0342, 0.9218, 0.6993,  ..., 0.4506, 0.1146, 0.2093]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6172, 0.4719, 0.5685,  ..., 0.7751, 0.3390, 0.5446])
+tensor([0.6037, 0.3505, 0.1319,  ..., 0.1315, 0.2126, 0.8791])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 26.374675512313843 seconds
+Time: 10.411779165267944 seconds
 
-[18.71, 18.02, 18.12, 18.01, 22.7, 19.43, 18.17, 18.48, 18.54, 18.36]
-[52.87]
-35.376904249191284
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 26.374675512313843, 'TIME_S_1KI': 26.374675512313843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.3769276547432, 'W': 52.87}
-[18.71, 18.02, 18.12, 18.01, 22.7, 19.43, 18.17, 18.48, 18.54, 18.36, 18.41, 17.86, 17.86, 18.01, 17.87, 17.66, 17.87, 17.6, 18.07, 22.76]
-333.39
-16.6695
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 26.374675512313843, 'TIME_S_1KI': 26.374675512313843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.3769276547432, 'W': 52.87, 'J_1KI': 1870.3769276547432, 'W_1KI': 52.87, 'W_D': 36.2005, 'J_D': 1280.661622272849, 'W_D_1KI': 36.2005, 'J_D_1KI': 36.2005}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      966,     1941,  ...,  9997989,
+                             9998973, 10000000]),
+       col_indices=tensor([   2,   11,   26,  ..., 9956, 9965, 9978]),
+       values=tensor([0.0342, 0.9218, 0.6993,  ..., 0.4506, 0.1146, 0.2093]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6037, 0.3505, 0.1319,  ..., 0.1315, 0.2126, 0.8791])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.411779165267944 seconds
+
+[18.87, 18.17, 18.37, 17.93, 17.97, 18.29, 18.55, 17.92, 18.13, 17.84]
+[47.85]
+18.30966353416443
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.411779165267944, 'TIME_S_1KI': 26.765499139506282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 876.1174001097679, 'W': 47.85}
+[18.87, 18.17, 18.37, 17.93, 17.97, 18.29, 18.55, 17.92, 18.13, 17.84, 19.36, 18.07, 17.99, 17.91, 18.82, 18.49, 18.1, 18.04, 18.09, 17.96]
+327.855
+16.39275
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.411779165267944, 'TIME_S_1KI': 26.765499139506282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 876.1174001097679, 'W': 47.85, 'J_1KI': 2252.2298203335936, 'W_1KI': 123.00771208226222, 'W_D': 31.457250000000002, 'J_D': 575.971663210094, 'W_D_1KI': 80.86696658097686, 'J_D_1KI': 207.88423285598165}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..9e5a000
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.419133424758911, "TIME_S_1KI": 53.98514727854358, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1480.8600453448294, "W": 45.41, "J_1KI": 7672.849975879945, "W_1KI": 235.28497409326422, "W_D": 29.142749999999996, "J_D": 950.3707132013438, "W_D_1KI": 150.9987046632124, "J_D_1KI": 782.3767080995461}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..f051b42
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.42013144493103}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2043,     4027,  ..., 19995921,
+                            19998006, 20000000]),
+       col_indices=tensor([   9,   15,   16,  ..., 9989, 9991, 9993]),
+       values=tensor([0.8685, 0.2737, 0.0800,  ..., 0.3440, 0.3550, 0.7008]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.2841, 0.6439, 0.8852,  ..., 0.0124, 0.9656, 0.3759])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 5.42013144493103 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '193', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.419133424758911}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1925,     3946,  ..., 19996050,
+                            19998038, 20000000]),
+       col_indices=tensor([   4,    9,   22,  ..., 9994, 9995, 9997]),
+       values=tensor([0.4143, 0.0158, 0.3991,  ..., 0.4975, 0.2189, 0.5132]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4618, 0.1550, 0.6479,  ..., 0.8342, 0.8619, 0.7737])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.419133424758911 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1925,     3946,  ..., 19996050,
+                            19998038, 20000000]),
+       col_indices=tensor([   4,    9,   22,  ..., 9994, 9995, 9997]),
+       values=tensor([0.4143, 0.0158, 0.3991,  ..., 0.4975, 0.2189, 0.5132]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4618, 0.1550, 0.6479,  ..., 0.8342, 0.8619, 0.7737])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.419133424758911 seconds
+
+[18.6, 18.13, 18.25, 17.88, 17.94, 17.94, 18.23, 18.23, 18.37, 17.79]
+[45.41]
+32.61087965965271
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.419133424758911, 'TIME_S_1KI': 53.98514727854358, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1480.8600453448294, 'W': 45.41}
+[18.6, 18.13, 18.25, 17.88, 17.94, 17.94, 18.23, 18.23, 18.37, 17.79, 18.29, 17.94, 17.98, 17.82, 18.04, 18.23, 17.96, 17.98, 18.11, 17.95]
+325.345
+16.26725
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.419133424758911, 'TIME_S_1KI': 53.98514727854358, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1480.8600453448294, 'W': 45.41, 'J_1KI': 7672.849975879945, 'W_1KI': 235.28497409326422, 'W_D': 29.142749999999996, 'J_D': 950.3707132013438, 'W_D_1KI': 150.9987046632124, 'J_D_1KI': 782.3767080995461}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..28c2d57
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.80802845954895, "TIME_S_1KI": 108.0802845954895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3840.7128683352466, "W": 37.19, "J_1KI": 38407.128683352465, "W_1KI": 371.9, "W_D": 20.811749999999996, "J_D": 2149.286260757625, "W_D_1KI": 208.11749999999995, "J_D_1KI": 2081.1749999999997}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..5b24363
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.80802845954895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2964,     5988,  ..., 29994017,
+                            29996992, 30000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 9990, 9993, 9995]),
+       values=tensor([0.5510, 0.0884, 0.7125,  ..., 0.7844, 0.3492, 0.1801]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7648, 0.2589, 0.8570,  ..., 0.0438, 0.7014, 0.5513])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.80802845954895 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2964,     5988,  ..., 29994017,
+                            29996992, 30000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 9990, 9993, 9995]),
+       values=tensor([0.5510, 0.0884, 0.7125,  ..., 0.7844, 0.3492, 0.1801]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7648, 0.2589, 0.8570,  ..., 0.0438, 0.7014, 0.5513])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.80802845954895 seconds
+
+[18.36, 18.76, 18.08, 17.8, 18.22, 18.13, 18.92, 17.85, 18.59, 18.05]
+[37.19]
+103.27273106575012
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.80802845954895, 'TIME_S_1KI': 108.0802845954895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3840.7128683352466, 'W': 37.19}
+[18.36, 18.76, 18.08, 17.8, 18.22, 18.13, 18.92, 17.85, 18.59, 18.05, 18.39, 18.01, 18.68, 17.8, 18.23, 17.94, 17.9, 17.92, 18.33, 18.01]
+327.56500000000005
+16.37825
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.80802845954895, 'TIME_S_1KI': 108.0802845954895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3840.7128683352466, 'W': 37.19, 'J_1KI': 38407.128683352465, 'W_1KI': 371.9, 'W_D': 20.811749999999996, 'J_D': 2149.286260757625, 'W_D_1KI': 208.11749999999995, 'J_D_1KI': 2081.1749999999997}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
index bb313c5..d3d85cb 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 225815, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.501307487487793, "TIME_S_1KI": 0.046504029792032386, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 725.5277880001069, "W": 50.86, "J_1KI": 3.2129300002218932, "W_1KI": 0.22522861634523836, "W_D": 34.5345, "J_D": 492.64135656094555, "W_D_1KI": 0.15293271040453468, "J_D_1KI": 0.0006772477931250567}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 223318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.272708654403687, "TIME_S_1KI": 0.046000361163917314, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.7633163213729, "W": 46.54, "J_1KI": 2.963322778823798, "W_1KI": 0.2084023679237679, "W_D": 29.777749999999997, "J_D": 423.41690143078563, "W_D_1KI": 0.13334236380408207, "J_D_1KI": 0.0005970963549919042}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
index 9f5c396..404ad45 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,752 +1,373 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06352877616882324}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([8818, 4997, 6295, 3180, 5518, 5172,  746, 5244, 2210,
-                            743,  966, 3996, 5220, 1403, 3538, 8509, 4502, 3785,
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-                           6915, 5850, 9888,  478, 1163, 4552, 2977, 4780, 2098,
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-                           9854]),
-       values=tensor([3.3413e-01, 9.6154e-01, 9.2428e-01, 4.3740e-01,
-                      5.0672e-01, 6.7183e-01, 1.7642e-01, 6.2591e-01,
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-Matrix Type: synthetic
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-Time: 0.06352877616882324 seconds
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-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '165279', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.6851487159729}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02031111717224121}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      6.2252e-01, 5.9412e-01, 5.8400e-01, 4.7015e-01,
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+                      6.2691e-01, 4.0234e-01, 8.7349e-01, 9.0944e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5425, 0.1860, 0.7804,  ..., 0.3375, 0.7494, 0.4073])
+tensor([0.3846, 0.0429, 0.8390,  ..., 0.4444, 0.4671, 0.5693])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,271 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 7.6851487159729 seconds
+Time: 0.02031111717224121 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '225815', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.501307487487793}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51695', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.430596351623535}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      8.1670e-01, 5.0614e-01, 9.7279e-01, 3.1225e-01,
+                      7.8442e-01, 7.3051e-01, 5.8183e-01, 3.3468e-01,
+                      8.6277e-01, 1.6129e-01, 3.9534e-01, 5.0412e-01,
+                      9.3144e-02, 6.8435e-01, 1.5667e-01, 4.1079e-01,
+                      2.4207e-01, 4.8373e-01, 4.2507e-01, 6.1906e-01,
+                      2.8594e-01, 9.8090e-01, 8.5909e-01, 7.8064e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5928, 0.5923, 0.2769,  ..., 0.7868, 0.2495, 0.0989])
+tensor([0.8755, 0.4171, 0.4007,  ..., 0.3638, 0.0663, 0.0983])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,268 +754,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.501307487487793 seconds
+Time: 2.430596351623535 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '223318', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.272708654403687}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.3080, 0.3895, 0.3059, 0.0633, 0.6122, 0.1175, 0.6572,
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+                      0.5608, 0.0706, 0.4835, 0.2505, 0.9363, 0.3741, 0.3124,
+                      0.4634, 0.9844, 0.4189, 0.3458, 0.6504, 0.0883, 0.9154,
+                      0.1987, 0.8102, 0.2403, 0.6352, 0.9134, 0.0740, 0.6217,
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+                      0.3531, 0.3579, 0.3335, 0.4941, 0.0937, 0.0190, 0.7400,
+                      0.6048, 0.0736, 0.8480, 0.7056, 0.9624, 0.8952, 0.2590,
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+                      0.6823, 0.0554, 0.6007, 0.8647, 0.0307, 0.2993, 0.2476,
+                      0.7318, 0.8917, 0.9643, 0.6157, 0.2184, 0.8408, 0.3345,
+                      0.0712, 0.8159, 0.2459, 0.0991, 0.7444, 0.2222, 0.0014,
+                      0.1305, 0.8914, 0.0089, 0.5321, 0.7917, 0.7163, 0.9580,
+                      0.3624, 0.0142, 0.8937, 0.5115, 0.5049, 0.8434, 0.7234,
+                      0.7161, 0.2634, 0.8592, 0.3961, 0.5586, 0.2620, 0.0375,
+                      0.1665, 0.2915, 0.9139, 0.7009, 0.5095, 0.4519, 0.1213,
+                      0.3561, 0.0066, 0.4379, 0.3522, 0.6225, 0.6900, 0.8216,
+                      0.8841, 0.6553, 0.8193, 0.7688, 0.5104, 0.3926, 0.7388,
+                      0.4735, 0.1897, 0.7788, 0.8825, 0.9103, 0.2988, 0.1239,
+                      0.1792, 0.1266, 0.4818, 0.8893, 0.6604, 0.1883, 0.9700,
+                      0.5469, 0.0958, 0.2762, 0.2054, 0.3215, 0.7664]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5928, 0.5923, 0.2769,  ..., 0.7868, 0.2495, 0.0989])
+tensor([0.5980, 0.9421, 0.9493,  ..., 0.6518, 0.5202, 0.4457])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +1026,282 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.501307487487793 seconds
+Time: 10.272708654403687 seconds
 
-[18.25, 18.14, 17.95, 17.87, 17.91, 17.96, 17.87, 17.59, 18.03, 17.97]
-[50.86]
-14.265194416046143
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225815, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.501307487487793, 'TIME_S_1KI': 0.046504029792032386, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 725.5277880001069, 'W': 50.86}
-[18.25, 18.14, 17.95, 17.87, 17.91, 17.96, 17.87, 17.59, 18.03, 17.97, 17.9, 17.6, 18.05, 17.8, 18.18, 17.66, 17.82, 21.98, 18.23, 17.62]
-326.51
-16.325499999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225815, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.501307487487793, 'TIME_S_1KI': 0.046504029792032386, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 725.5277880001069, 'W': 50.86, 'J_1KI': 3.2129300002218932, 'W_1KI': 0.22522861634523836, 'W_D': 34.5345, 'J_D': 492.64135656094555, 'W_D_1KI': 0.15293271040453468, 'J_D_1KI': 0.0006772477931250567}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([1701, 5200,  151, 7106, 6780, 2055, 1375, 8242,  376,
+                           4556, 1864, 1124, 4828,   55, 5866, 9752, 5516, 7381,
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+                           3122, 6337, 1417, 1292, 5004, 6019, 1196, 5382, 6389,
+                           3275]),
+       values=tensor([0.2237, 0.0937, 0.9630, 0.1477, 0.5729, 0.1998, 0.9498,
+                      0.4379, 0.8034, 0.0235, 0.9902, 0.3432, 0.0700, 0.1825,
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+                      0.5469, 0.0958, 0.2762, 0.2054, 0.3215, 0.7664]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5980, 0.9421, 0.9493,  ..., 0.6518, 0.5202, 0.4457])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.272708654403687 seconds
+
+[18.39, 17.85, 18.29, 17.9, 18.11, 17.8, 18.16, 22.08, 18.79, 18.14]
+[46.54]
+14.219237565994263
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 223318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.272708654403687, 'TIME_S_1KI': 0.046000361163917314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.7633163213729, 'W': 46.54}
+[18.39, 17.85, 18.29, 17.9, 18.11, 17.8, 18.16, 22.08, 18.79, 18.14, 20.06, 18.33, 18.25, 21.91, 18.14, 18.0, 18.29, 18.13, 17.88, 18.08]
+335.245
+16.76225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 223318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.272708654403687, 'TIME_S_1KI': 0.046000361163917314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.7633163213729, 'W': 46.54, 'J_1KI': 2.963322778823798, 'W_1KI': 0.2084023679237679, 'W_D': 29.777749999999997, 'J_D': 423.41690143078563, 'W_D_1KI': 0.13334236380408207, 'J_D_1KI': 0.0005970963549919042}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..d34e261
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 115566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.362370014190674, "TIME_S_1KI": 0.08966625144238508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 657.2445391845703, "W": 46.58, "J_1KI": 5.687179094063741, "W_1KI": 0.40305972344807295, "W_D": 30.2825, "J_D": 427.28655555725095, "W_D_1KI": 0.2620364120935223, "J_D_1KI": 0.002267417857272228}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..30badbd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.024602413177490234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    3,  ..., 4997, 4999, 5000]),
+       col_indices=tensor([5115, 9337, 5262,  ..., 1244, 4227, 2124]),
+       values=tensor([0.7036, 0.8839, 0.8989,  ..., 0.3409, 0.8377, 0.4572]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4155, 0.9580, 0.1653,  ..., 0.8843, 0.0512, 0.6581])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.024602413177490234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '42678', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.8775720596313477}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([ 717, 6679, 2344,  ..., 3928, 4219, 6236]),
+       values=tensor([0.9595, 0.5891, 0.3421,  ..., 0.3760, 0.2961, 0.2336]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6496, 0.5288, 0.3835,  ..., 0.2038, 0.3313, 0.3083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 3.8775720596313477 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '115566', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.362370014190674}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6593, 2332, 3653,  ..., 6447, 6839, 4175]),
+       values=tensor([0.4277, 0.1691, 0.2657,  ..., 0.2731, 0.4419, 0.1553]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3155, 0.6085, 0.7514,  ..., 0.0185, 0.1956, 0.3828])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.362370014190674 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6593, 2332, 3653,  ..., 6447, 6839, 4175]),
+       values=tensor([0.4277, 0.1691, 0.2657,  ..., 0.2731, 0.4419, 0.1553]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3155, 0.6085, 0.7514,  ..., 0.0185, 0.1956, 0.3828])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.362370014190674 seconds
+
+[18.44, 17.79, 18.13, 18.16, 18.19, 17.86, 18.01, 17.82, 18.05, 18.06]
+[46.58]
+14.110015869140625
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 115566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.362370014190674, 'TIME_S_1KI': 0.08966625144238508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 657.2445391845703, 'W': 46.58}
+[18.44, 17.79, 18.13, 18.16, 18.19, 17.86, 18.01, 17.82, 18.05, 18.06, 18.35, 17.92, 18.08, 18.77, 17.97, 18.23, 18.59, 17.91, 18.14, 17.81]
+325.95
+16.2975
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 115566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.362370014190674, 'TIME_S_1KI': 0.08966625144238508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 657.2445391845703, 'W': 46.58, 'J_1KI': 5.687179094063741, 'W_1KI': 0.40305972344807295, 'W_D': 30.2825, 'J_D': 427.28655555725095, 'W_D_1KI': 0.2620364120935223, 'J_D_1KI': 0.002267417857272228}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..5b0e976
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.447505474090576, "TIME_S_1KI": 154.47505474090576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2621.8117760252953, "W": 44.61, "J_1KI": 26218.117760252953, "W_1KI": 446.1, "W_D": 28.050250000000002, "J_D": 1648.56480095166, "W_D_1KI": 280.50250000000005, "J_D_1KI": 2805.0250000000005}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..55f3dfa
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.447505474090576}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       57,      102,  ..., 24999899,
+                            24999940, 25000000]),
+       col_indices=tensor([  5577,  28835,  47310,  ..., 481805, 486701,
+                           494412]),
+       values=tensor([0.4828, 0.8396, 0.7554,  ..., 0.0896, 0.7495, 0.8303]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9266, 0.8773, 0.4193,  ..., 0.0231, 0.6267, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 15.447505474090576 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       57,      102,  ..., 24999899,
+                            24999940, 25000000]),
+       col_indices=tensor([  5577,  28835,  47310,  ..., 481805, 486701,
+                           494412]),
+       values=tensor([0.4828, 0.8396, 0.7554,  ..., 0.0896, 0.7495, 0.8303]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9266, 0.8773, 0.4193,  ..., 0.0231, 0.6267, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 15.447505474090576 seconds
+
+[18.81, 17.9, 18.4, 17.9, 18.2, 17.84, 18.01, 22.17, 18.54, 18.17]
+[44.61]
+58.77183985710144
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.447505474090576, 'TIME_S_1KI': 154.47505474090576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2621.8117760252953, 'W': 44.61}
+[18.81, 17.9, 18.4, 17.9, 18.2, 17.84, 18.01, 22.17, 18.54, 18.17, 18.44, 18.17, 17.99, 18.08, 18.12, 18.6, 18.11, 18.22, 18.18, 18.11]
+331.19499999999994
+16.559749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.447505474090576, 'TIME_S_1KI': 154.47505474090576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2621.8117760252953, 'W': 44.61, 'J_1KI': 26218.117760252953, 'W_1KI': 446.1, 'W_D': 28.050250000000002, 'J_D': 1648.56480095166, 'W_D_1KI': 280.50250000000005, 'J_D_1KI': 2805.0250000000005}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
index f1d93dd..8a43ad7 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.435759782791138, "TIME_S_1KI": 13.435759782791138, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 971.6288854122162, "W": 53.4, "J_1KI": 971.6288854122162, "W_1KI": 53.4, "W_D": 37.34824999999999, "J_D": 679.562519093573, "W_D_1KI": 37.34824999999999, "J_D_1KI": 37.34824999999999}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 751, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.154199123382568, "TIME_S_1KI": 13.520904292120598, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 705.406801700592, "W": 48.84, "J_1KI": 939.290015580016, "W_1KI": 65.03328894806924, "W_D": 32.38000000000001, "J_D": 467.67142176628124, "W_D_1KI": 43.115845539280976, "J_D_1KI": 57.41124572474165}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
index 35674e7..9480055 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 13.435759782791138}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3980276584625244}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       3,       8,  ..., 2499995,
+tensor(crow_indices=tensor([      0,       7,      14,  ..., 2499987,
                             2499996, 2500000]),
-       col_indices=tensor([ 61754, 291279, 469696,  ..., 173785, 177543,
-                           423232]),
-       values=tensor([0.5269, 0.9088, 0.4901,  ..., 0.3381, 0.9016, 0.0517]),
+       col_indices=tensor([ 56026, 195485, 327540,  ...,  74351, 467081,
+                           495492]),
+       values=tensor([0.8691, 0.8234, 0.1160,  ..., 0.0380, 0.6115, 0.8262]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7575, 0.8230, 0.6656,  ..., 0.2327, 0.7437, 0.7040])
+tensor([0.8652, 0.4148, 0.1413,  ..., 0.7873, 0.1950, 0.8001])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 13.435759782791138 seconds
+Time: 1.3980276584625244 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '751', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.154199123382568}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       3,       8,  ..., 2499995,
-                            2499996, 2500000]),
-       col_indices=tensor([ 61754, 291279, 469696,  ..., 173785, 177543,
-                           423232]),
-       values=tensor([0.5269, 0.9088, 0.4901,  ..., 0.3381, 0.9016, 0.0517]),
+tensor(crow_indices=tensor([      0,       3,      11,  ..., 2499987,
+                            2499993, 2500000]),
+       col_indices=tensor([159259, 352180, 455738,  ..., 361655, 368506,
+                           421546]),
+       values=tensor([0.7015, 0.3878, 0.3559,  ..., 0.2417, 0.3895, 0.7278]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7575, 0.8230, 0.6656,  ..., 0.2327, 0.7437, 0.7040])
+tensor([0.4879, 0.7909, 0.7587,  ..., 0.1983, 0.9582, 0.5253])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 13.435759782791138 seconds
+Time: 10.154199123382568 seconds
 
-[18.26, 17.5, 18.12, 17.51, 17.77, 17.68, 17.73, 17.56, 17.86, 17.52]
-[53.4]
-18.195297479629517
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.435759782791138, 'TIME_S_1KI': 13.435759782791138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 971.6288854122162, 'W': 53.4}
-[18.26, 17.5, 18.12, 17.51, 17.77, 17.68, 17.73, 17.56, 17.86, 17.52, 18.07, 17.37, 18.42, 19.11, 17.73, 17.65, 18.23, 17.55, 17.59, 17.46]
-321.035
-16.051750000000002
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.435759782791138, 'TIME_S_1KI': 13.435759782791138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 971.6288854122162, 'W': 53.4, 'J_1KI': 971.6288854122162, 'W_1KI': 53.4, 'W_D': 37.34824999999999, 'J_D': 679.562519093573, 'W_D_1KI': 37.34824999999999, 'J_D_1KI': 37.34824999999999}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,      11,  ..., 2499987,
+                            2499993, 2500000]),
+       col_indices=tensor([159259, 352180, 455738,  ..., 361655, 368506,
+                           421546]),
+       values=tensor([0.7015, 0.3878, 0.3559,  ..., 0.2417, 0.3895, 0.7278]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4879, 0.7909, 0.7587,  ..., 0.1983, 0.9582, 0.5253])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.154199123382568 seconds
+
+[18.79, 17.69, 18.39, 21.58, 18.07, 18.02, 18.17, 17.87, 17.87, 18.03]
+[48.84]
+14.44321870803833
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.154199123382568, 'TIME_S_1KI': 13.520904292120598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 705.406801700592, 'W': 48.84}
+[18.79, 17.69, 18.39, 21.58, 18.07, 18.02, 18.17, 17.87, 17.87, 18.03, 18.42, 17.82, 17.95, 18.61, 17.96, 18.22, 17.97, 18.42, 18.04, 17.86]
+329.19999999999993
+16.459999999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.154199123382568, 'TIME_S_1KI': 13.520904292120598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 705.406801700592, 'W': 48.84, 'J_1KI': 939.290015580016, 'W_1KI': 65.03328894806924, 'W_D': 32.38000000000001, 'J_D': 467.67142176628124, 'W_D_1KI': 43.115845539280976, 'J_D_1KI': 57.41124572474165}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..e9ce521
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.563251495361328, "TIME_S_1KI": 71.85885370994102, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 973.9529043245316, "W": 47.49, "J_1KI": 6625.529961391371, "W_1KI": 323.0612244897959, "W_D": 30.996250000000003, "J_D": 635.6893600898982, "W_D_1KI": 210.858843537415, "J_D_1KI": 1434.4139016150682}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..b78ba4c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.09878396987915}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       58,  ..., 12499941,
+                            12499962, 12500000]),
+       col_indices=tensor([  1470,   2567,   5271,  ..., 471166, 490246,
+                           499700]),
+       values=tensor([0.1668, 0.7788, 0.4321,  ..., 0.7966, 0.9450, 0.5105]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9128, 0.0898, 0.7303,  ..., 0.7724, 0.8343, 0.8680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 7.09878396987915 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '147', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.563251495361328}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       16,       39,  ..., 12499961,
+                            12499982, 12500000]),
+       col_indices=tensor([ 42415,  50722,  59820,  ..., 419133, 436999,
+                           480407]),
+       values=tensor([0.4848, 0.7890, 0.2846,  ..., 0.4428, 0.7066, 0.1150]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.7955, 0.8202, 0.0668,  ..., 0.2866, 0.4586, 0.4680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.563251495361328 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       16,       39,  ..., 12499961,
+                            12499982, 12500000]),
+       col_indices=tensor([ 42415,  50722,  59820,  ..., 419133, 436999,
+                           480407]),
+       values=tensor([0.4848, 0.7890, 0.2846,  ..., 0.4428, 0.7066, 0.1150]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.7955, 0.8202, 0.0668,  ..., 0.2866, 0.4586, 0.4680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.563251495361328 seconds
+
+[18.64, 21.68, 17.93, 17.89, 17.96, 18.08, 18.05, 17.94, 17.92, 17.72]
+[47.49]
+20.508589267730713
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.563251495361328, 'TIME_S_1KI': 71.85885370994102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.9529043245316, 'W': 47.49}
+[18.64, 21.68, 17.93, 17.89, 17.96, 18.08, 18.05, 17.94, 17.92, 17.72, 18.36, 17.99, 18.51, 17.9, 18.05, 18.98, 18.56, 18.01, 18.0, 18.13]
+329.875
+16.49375
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.563251495361328, 'TIME_S_1KI': 71.85885370994102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.9529043245316, 'W': 47.49, 'J_1KI': 6625.529961391371, 'W_1KI': 323.0612244897959, 'W_D': 30.996250000000003, 'J_D': 635.6893600898982, 'W_D_1KI': 210.858843537415, 'J_D_1KI': 1434.4139016150682}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
index af9c51f..4d243f2 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9021, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.444438695907593, "TIME_S_1KI": 1.1577916745269474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.9580717468261, "W": 51.98, "J_1KI": 81.58275931125442, "W_1KI": 5.762110630750471, "W_D": 35.674749999999996, "J_D": 505.1004274730682, "W_D_1KI": 3.9546336326349625, "J_D_1KI": 0.43838084831337576}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.50059986114502, "TIME_S_1KI": 1.1479829300475588, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 681.5257016324997, "W": 47.63, "J_1KI": 74.50811212774677, "W_1KI": 5.207171750300645, "W_D": 31.135, "J_D": 445.50289146184923, "W_D_1KI": 3.4038482562588825, "J_D_1KI": 0.37212728285327235}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
index 945b881..2f48a00 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1638367176055908}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.13143563270568848}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     10,  ..., 249989, 249992,
+tensor(crow_indices=tensor([     0,      1,     12,  ..., 249990, 249995,
                             250000]),
-       col_indices=tensor([ 5085, 27218, 28258,  ..., 33170, 33475, 34242]),
-       values=tensor([0.4699, 0.9594, 0.0965,  ..., 0.7443, 0.7286, 0.0273]),
+       col_indices=tensor([33764,   781,  3609,  ..., 16676, 21435, 31146]),
+       values=tensor([0.7781, 0.6572, 0.9120,  ..., 0.8330, 0.7571, 0.7121]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3938, 0.4910, 0.8553,  ..., 0.5913, 0.5925, 0.7936])
+tensor([0.7934, 0.6307, 0.2590,  ..., 0.3547, 0.1547, 0.8460])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 1.1638367176055908 seconds
+Time: 0.13143563270568848 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9021', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.444438695907593}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7988', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.168656826019287}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     13,  ..., 249988, 249994,
+tensor(crow_indices=tensor([     0,     10,     18,  ..., 249992, 249995,
                             250000]),
-       col_indices=tensor([ 3969, 14280, 16197,  ..., 14337, 15782, 32993]),
-       values=tensor([0.2139, 0.2141, 0.1060,  ..., 0.9818, 0.6790, 0.2416]),
+       col_indices=tensor([ 6728,  8437,  8523,  ..., 40465, 44043, 46138]),
+       values=tensor([0.4640, 0.7108, 0.7346,  ..., 0.1761, 0.2770, 0.7056]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8858, 0.9490, 0.2990,  ..., 0.1473, 0.1815, 0.8776])
+tensor([0.0571, 0.2747, 0.4590,  ..., 0.7273, 0.2570, 0.3128])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.444438695907593 seconds
+Time: 9.168656826019287 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9147', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.50059986114502}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     13,  ..., 249988, 249994,
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249987, 249996,
                             250000]),
-       col_indices=tensor([ 3969, 14280, 16197,  ..., 14337, 15782, 32993]),
-       values=tensor([0.2139, 0.2141, 0.1060,  ..., 0.9818, 0.6790, 0.2416]),
+       col_indices=tensor([ 4811,  5188, 33226,  ..., 17568, 20020, 26384]),
+       values=tensor([0.5580, 0.6578, 0.5141,  ..., 0.8482, 0.1339, 0.2046]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8858, 0.9490, 0.2990,  ..., 0.1473, 0.1815, 0.8776])
+tensor([0.2505, 0.9140, 0.1873,  ..., 0.2385, 0.4644, 0.1302])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.444438695907593 seconds
+Time: 10.50059986114502 seconds
 
-[18.28, 18.06, 17.98, 18.51, 17.98, 18.02, 19.12, 19.17, 17.99, 18.0]
-[51.98]
-14.158485412597656
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9021, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.444438695907593, 'TIME_S_1KI': 1.1577916745269474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.9580717468261, 'W': 51.98}
-[18.28, 18.06, 17.98, 18.51, 17.98, 18.02, 19.12, 19.17, 17.99, 18.0, 18.49, 17.73, 18.15, 17.89, 18.02, 17.91, 17.85, 17.6, 17.85, 17.78]
-326.105
-16.30525
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9021, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.444438695907593, 'TIME_S_1KI': 1.1577916745269474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.9580717468261, 'W': 51.98, 'J_1KI': 81.58275931125442, 'W_1KI': 5.762110630750471, 'W_D': 35.674749999999996, 'J_D': 505.1004274730682, 'W_D_1KI': 3.9546336326349625, 'J_D_1KI': 0.43838084831337576}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249987, 249996,
+                            250000]),
+       col_indices=tensor([ 4811,  5188, 33226,  ..., 17568, 20020, 26384]),
+       values=tensor([0.5580, 0.6578, 0.5141,  ..., 0.8482, 0.1339, 0.2046]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2505, 0.9140, 0.1873,  ..., 0.2385, 0.4644, 0.1302])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.50059986114502 seconds
+
+[18.33, 18.14, 21.59, 19.0, 18.11, 18.16, 17.98, 18.16, 18.03, 18.31]
+[47.63]
+14.308748722076416
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.50059986114502, 'TIME_S_1KI': 1.1479829300475588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 681.5257016324997, 'W': 47.63}
+[18.33, 18.14, 21.59, 19.0, 18.11, 18.16, 17.98, 18.16, 18.03, 18.31, 18.43, 17.74, 17.82, 18.07, 18.59, 18.18, 17.93, 18.01, 17.93, 17.85]
+329.90000000000003
+16.495
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.50059986114502, 'TIME_S_1KI': 1.1479829300475588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 681.5257016324997, 'W': 47.63, 'J_1KI': 74.50811212774677, 'W_1KI': 5.207171750300645, 'W_D': 31.135, 'J_D': 445.50289146184923, 'W_D_1KI': 3.4038482562588825, 'J_D_1KI': 0.37212728285327235}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
index 0738cf0..db8a72e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1973, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.479424238204956, "TIME_S_1KI": 5.311416238319795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 791.6620005321503, "W": 53.24, "J_1KI": 401.2478461896352, "W_1KI": 26.984287886467307, "W_D": 37.20700000000001, "J_D": 553.2563496205807, "W_D_1KI": 18.858084135833757, "J_D_1KI": 9.558076095202107}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1997, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.44909930229187, "TIME_S_1KI": 5.232398248518714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.6282246422767, "W": 48.73, "J_1KI": 367.3651600612302, "W_1KI": 24.40160240360541, "W_D": 32.179249999999996, "J_D": 484.4573373244404, "W_D_1KI": 16.113795693540307, "J_D_1KI": 8.069001348793345}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
index 3bd1995..a863f3a 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,54 @@
-['apptainer', 'run', '--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": 5.321045160293579}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.5546464920043945}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      54,      97,  ..., 2499896,
+tensor(crow_indices=tensor([      0,      55,     117,  ..., 2499903,
+                            2499953, 2500000]),
+       col_indices=tensor([  566,  1603,  2858,  ..., 47622, 48780, 49985]),
+       values=tensor([0.9915, 0.7849, 0.7900,  ..., 0.9170, 0.4625, 0.4875]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6866, 0.0421, 0.1446,  ..., 0.6566, 0.6603, 0.7026])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.5546464920043945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1893', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.948294878005981}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      33,      68,  ..., 2499891,
+                            2499938, 2500000]),
+       col_indices=tensor([ 3534,  3824,  4376,  ..., 49368, 49484, 49571]),
+       values=tensor([0.2824, 0.5783, 0.2215,  ..., 0.8826, 0.1249, 0.2741]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5004, 0.8496, 0.6985,  ..., 0.2602, 0.0299, 0.5346])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 9.948294878005981 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1997', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.44909930229187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499904,
                             2499948, 2500000]),
-       col_indices=tensor([  176,   180,   853,  ..., 47415, 47956, 49304]),
-       values=tensor([0.4358, 0.1204, 0.8362,  ..., 0.7793, 0.3332, 0.4077]),
+       col_indices=tensor([ 1519,  4331,  6515,  ..., 42103, 42230, 49135]),
+       values=tensor([0.6099, 0.9393, 0.8647,  ..., 0.8575, 0.6331, 0.4704]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9660, 0.1174, 0.2174,  ..., 0.0235, 0.8944, 0.4447])
+tensor([0.1131, 0.9150, 0.5556,  ..., 0.6033, 0.7715, 0.6124])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 5.321045160293579 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1973', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.479424238204956}
+Time: 10.44909930229187 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      43,     100,  ..., 2499912,
-                            2499964, 2500000]),
-       col_indices=tensor([  471,   539,  1515,  ..., 46324, 49367, 49678]),
-       values=tensor([0.0688, 0.1954, 0.6278,  ..., 0.4403, 0.6708, 0.8543]),
+tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499904,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1519,  4331,  6515,  ..., 42103, 42230, 49135]),
+       values=tensor([0.6099, 0.9393, 0.8647,  ..., 0.8575, 0.6331, 0.4704]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7713, 0.8001, 0.0882,  ..., 0.6644, 0.4702, 0.2491])
+tensor([0.1131, 0.9150, 0.5556,  ..., 0.6033, 0.7715, 0.6124])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,30 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.479424238204956 seconds
+Time: 10.44909930229187 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      43,     100,  ..., 2499912,
-                            2499964, 2500000]),
-       col_indices=tensor([  471,   539,  1515,  ..., 46324, 49367, 49678]),
-       values=tensor([0.0688, 0.1954, 0.6278,  ..., 0.4403, 0.6708, 0.8543]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7713, 0.8001, 0.0882,  ..., 0.6644, 0.4702, 0.2491])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 10.479424238204956 seconds
-
-[18.09, 17.68, 17.78, 17.6, 17.88, 17.97, 17.49, 17.67, 17.75, 17.8]
-[53.24]
-14.86968445777893
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1973, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.479424238204956, 'TIME_S_1KI': 5.311416238319795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.6620005321503, 'W': 53.24}
-[18.09, 17.68, 17.78, 17.6, 17.88, 17.97, 17.49, 17.67, 17.75, 17.8, 18.63, 17.66, 18.2, 17.66, 17.95, 17.62, 18.05, 17.65, 17.58, 18.42]
-320.65999999999997
-16.032999999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1973, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.479424238204956, 'TIME_S_1KI': 5.311416238319795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.6620005321503, 'W': 53.24, 'J_1KI': 401.2478461896352, 'W_1KI': 26.984287886467307, 'W_D': 37.20700000000001, 'J_D': 553.2563496205807, 'W_D_1KI': 18.858084135833757, 'J_D_1KI': 9.558076095202107}
+[18.64, 18.18, 18.44, 22.12, 18.9, 18.07, 17.86, 17.86, 18.53, 18.0]
+[48.73]
+15.054960489273071
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.44909930229187, 'TIME_S_1KI': 5.232398248518714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.6282246422767, 'W': 48.73}
+[18.64, 18.18, 18.44, 22.12, 18.9, 18.07, 17.86, 17.86, 18.53, 18.0, 18.34, 18.09, 18.08, 17.94, 18.14, 18.44, 18.04, 17.91, 17.9, 18.05]
+331.015
+16.55075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.44909930229187, 'TIME_S_1KI': 5.232398248518714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.6282246422767, 'W': 48.73, 'J_1KI': 367.3651600612302, 'W_1KI': 24.40160240360541, 'W_D': 32.179249999999996, 'J_D': 484.4573373244404, 'W_D_1KI': 16.113795693540307, 'J_D_1KI': 8.069001348793345}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..981b4ab
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 134, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.514585256576538, "TIME_S_1KI": 78.46705415355626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2494.187549471855, "W": 43.5, "J_1KI": 18613.339921431754, "W_1KI": 324.6268656716418, "W_D": 26.962, "J_D": 1545.9375795140265, "W_D_1KI": 201.2089552238806, "J_D_1KI": 1501.5593673423925}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..7744b5c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.827495813369751}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      509,     1029,  ..., 24999021,
+                            24999525, 25000000]),
+       col_indices=tensor([   77,   168,   174,  ..., 49716, 49743, 49917]),
+       values=tensor([0.0871, 0.3865, 0.3717,  ..., 0.4376, 0.0483, 0.0994]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1988, 0.8388, 0.2584,  ..., 0.5965, 0.5005, 0.7795])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 7.827495813369751 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '134', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.514585256576538}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      525,     1042,  ..., 24999041,
+                            24999512, 25000000]),
+       col_indices=tensor([  163,   320,   387,  ..., 49821, 49828, 49920]),
+       values=tensor([0.8765, 0.3303, 0.5777,  ..., 0.2129, 0.9852, 0.0873]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2121, 0.5034, 0.7106,  ..., 0.6677, 0.7232, 0.0645])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.514585256576538 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      525,     1042,  ..., 24999041,
+                            24999512, 25000000]),
+       col_indices=tensor([  163,   320,   387,  ..., 49821, 49828, 49920]),
+       values=tensor([0.8765, 0.3303, 0.5777,  ..., 0.2129, 0.9852, 0.0873]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2121, 0.5034, 0.7106,  ..., 0.6677, 0.7232, 0.0645])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.514585256576538 seconds
+
+[18.5, 18.14, 19.17, 17.93, 18.04, 17.95, 18.11, 18.93, 18.07, 18.37]
+[43.5]
+57.337644815444946
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.514585256576538, 'TIME_S_1KI': 78.46705415355626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2494.187549471855, 'W': 43.5}
+[18.5, 18.14, 19.17, 17.93, 18.04, 17.95, 18.11, 18.93, 18.07, 18.37, 22.44, 18.33, 18.23, 18.16, 18.38, 17.89, 18.61, 17.96, 18.23, 17.95]
+330.76
+16.538
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.514585256576538, 'TIME_S_1KI': 78.46705415355626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2494.187549471855, 'W': 43.5, 'J_1KI': 18613.339921431754, 'W_1KI': 324.6268656716418, 'W_D': 26.962, 'J_D': 1545.9375795140265, 'W_D_1KI': 201.2089552238806, 'J_D_1KI': 1501.5593673423925}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
index 0990f32..01c9d2b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21464, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.162778615951538, "TIME_S_1KI": 0.47348018151097365, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 708.2274965262413, "W": 50.99, "J_1KI": 32.99606301370859, "W_1KI": 2.3756056653000375, "W_D": 25.345, "J_D": 352.0303176987171, "W_D_1KI": 1.1808143868803578, "J_D_1KI": 0.055013715378324536}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 22242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.517975091934204, "TIME_S_1KI": 0.472888008809199, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 668.1220734119415, "W": 47.0, "J_1KI": 30.03875880819807, "W_1KI": 2.113119323801816, "W_D": 30.704, "J_D": 436.4685136604309, "W_D_1KI": 1.3804513982555526, "J_D_1KI": 0.062065075004745646}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
index eca4709..f23093c 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.4891834259033203}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06578350067138672}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([16409, 39665, 45486,  ..., 40216, 44015, 30698]),
-       values=tensor([0.3828, 0.2137, 0.3194,  ..., 0.5609, 0.6557, 0.9594]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16918,  1143,  1724,  ..., 48553, 41363, 39308]),
+       values=tensor([0.9238, 0.1195, 0.2813,  ..., 0.9276, 0.6113, 0.0798]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1367, 0.4150, 0.8251,  ..., 0.6451, 0.2178, 0.9645])
+tensor([0.0373, 0.8428, 0.6841,  ..., 0.3333, 0.7324, 0.6824])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.4891834259033203 seconds
+Time: 0.06578350067138672 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21464', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.162778615951538}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15961', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.534844875335693}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24999, 25000]),
-       col_indices=tensor([27591, 28713, 10997,  ...,  3373, 26495, 43984]),
-       values=tensor([0.0595, 0.2219, 0.9508,  ..., 0.7420, 0.6896, 0.8252]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([29190, 43986, 25006,  ..., 44362, 15421,  8070]),
+       values=tensor([0.3395, 0.8970, 0.1159,  ..., 0.8275, 0.4942, 0.3559]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4890, 0.2230, 0.0247,  ..., 0.5863, 0.9029, 0.3113])
+tensor([0.7532, 0.7737, 0.3401,  ..., 0.4031, 0.1788, 0.5939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,15 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.162778615951538 seconds
+Time: 7.534844875335693 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22242', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.517975091934204}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24999, 25000]),
-       col_indices=tensor([27591, 28713, 10997,  ...,  3373, 26495, 43984]),
-       values=tensor([0.0595, 0.2219, 0.9508,  ..., 0.7420, 0.6896, 0.8252]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([22981, 23025, 47875,  ..., 28752, 43497,  8642]),
+       values=tensor([0.4359, 0.4232, 0.6349,  ..., 0.3036, 0.5759, 0.5327]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4890, 0.2230, 0.0247,  ..., 0.5863, 0.9029, 0.3113])
+tensor([0.8882, 0.3621, 0.9798,  ..., 0.1733, 0.2748, 0.2728])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -50,13 +53,29 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.162778615951538 seconds
+Time: 10.517975091934204 seconds
 
-[22.1, 18.96, 18.21, 17.89, 17.76, 17.62, 17.96, 18.44, 17.92, 17.8]
-[50.99]
-13.88953709602356
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.162778615951538, 'TIME_S_1KI': 0.47348018151097365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.2274965262413, 'W': 50.99}
-[22.1, 18.96, 18.21, 17.89, 17.76, 17.62, 17.96, 18.44, 17.92, 17.8, 27.02, 48.47, 52.31, 52.11, 52.93, 45.58, 32.74, 23.06, 18.26, 18.44]
-512.9000000000001
-25.645000000000003
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.162778615951538, 'TIME_S_1KI': 0.47348018151097365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.2274965262413, 'W': 50.99, 'J_1KI': 32.99606301370859, 'W_1KI': 2.3756056653000375, 'W_D': 25.345, 'J_D': 352.0303176987171, 'W_D_1KI': 1.1808143868803578, 'J_D_1KI': 0.055013715378324536}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([22981, 23025, 47875,  ..., 28752, 43497,  8642]),
+       values=tensor([0.4359, 0.4232, 0.6349,  ..., 0.3036, 0.5759, 0.5327]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8882, 0.3621, 0.9798,  ..., 0.1733, 0.2748, 0.2728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.517975091934204 seconds
+
+[18.31, 17.89, 18.04, 17.91, 18.19, 18.28, 17.95, 17.98, 18.92, 18.1]
+[47.0]
+14.215363264083862
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.517975091934204, 'TIME_S_1KI': 0.472888008809199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.1220734119415, 'W': 47.0}
+[18.31, 17.89, 18.04, 17.91, 18.19, 18.28, 17.95, 17.98, 18.92, 18.1, 18.24, 18.64, 18.01, 17.83, 18.02, 18.06, 18.04, 17.88, 17.99, 17.93]
+325.92
+16.296
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.517975091934204, 'TIME_S_1KI': 0.472888008809199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.1220734119415, 'W': 47.0, 'J_1KI': 30.03875880819807, 'W_1KI': 2.113119323801816, 'W_D': 30.704, 'J_D': 436.4685136604309, 'W_D_1KI': 1.3804513982555526, 'J_D_1KI': 0.062065075004745646}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..7de68b9
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 11256, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481152534484863, "TIME_S_1KI": 0.9311613836607022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 671.3460792446136, "W": 47.13, "J_1KI": 59.64339723210853, "W_1KI": 4.187100213219616, "W_D": 30.78675, "J_D": 438.54368566060066, "W_D_1KI": 2.735141257995736, "J_D_1KI": 0.24299407053977753}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..d1f10c6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1096796989440918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124996, 124997,
+                            125000]),
+       col_indices=tensor([38708, 28625, 11454,  ...,   884, 22723, 30800]),
+       values=tensor([0.0038, 0.3289, 0.1581,  ..., 0.9719, 0.8303, 0.9998]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0207, 0.8237, 0.8176,  ..., 0.3561, 0.4550, 0.3366])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.1096796989440918 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9573', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.929835081100464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      4,  ..., 124997, 124998,
+                            125000]),
+       col_indices=tensor([ 5176, 42593, 37500,  ...,  3219, 12793, 38658]),
+       values=tensor([0.6803, 0.7664, 0.9859,  ..., 0.5422, 0.9603, 0.0980]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2299, 0.8541, 0.6200,  ..., 0.4981, 0.6521, 0.8502])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 8.929835081100464 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '11256', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481152534484863}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 124996, 124999,
+                            125000]),
+       col_indices=tensor([ 3517, 32781, 39284,  ..., 16837, 28625, 12663]),
+       values=tensor([0.4051, 0.3118, 0.0683,  ..., 0.4752, 0.1421, 0.6822]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2287, 0.1097, 0.9835,  ..., 0.8729, 0.0701, 0.5217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.481152534484863 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 124996, 124999,
+                            125000]),
+       col_indices=tensor([ 3517, 32781, 39284,  ..., 16837, 28625, 12663]),
+       values=tensor([0.4051, 0.3118, 0.0683,  ..., 0.4752, 0.1421, 0.6822]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2287, 0.1097, 0.9835,  ..., 0.8729, 0.0701, 0.5217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.481152534484863 seconds
+
+[18.46, 17.73, 18.38, 18.1, 18.29, 18.18, 18.04, 18.01, 18.16, 17.9]
+[47.13]
+14.244559288024902
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481152534484863, 'TIME_S_1KI': 0.9311613836607022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 671.3460792446136, 'W': 47.13}
+[18.46, 17.73, 18.38, 18.1, 18.29, 18.18, 18.04, 18.01, 18.16, 17.9, 18.94, 17.91, 17.96, 17.99, 18.41, 18.07, 18.2, 17.95, 18.46, 18.75]
+326.865
+16.34325
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481152534484863, 'TIME_S_1KI': 0.9311613836607022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 671.3460792446136, 'W': 47.13, 'J_1KI': 59.64339723210853, 'W_1KI': 4.187100213219616, 'W_D': 30.78675, 'J_D': 438.54368566060066, 'W_D_1KI': 2.735141257995736, 'J_D_1KI': 0.24299407053977753}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
index cdd4446..b6b292e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220548, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.518115997314453, "TIME_S_1KI": 0.04769082466091034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 712.8332035136224, "W": 50.77000000000001, "J_1KI": 3.2321000576456025, "W_1KI": 0.23019932168960958, "W_D": 34.49475000000001, "J_D": 484.32151165848984, "W_D_1KI": 0.15640472822242782, "J_D_1KI": 0.0007091641194770654}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.611992359161377, "TIME_S_1KI": 0.04803893256419701, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 660.6772035217285, "W": 46.78, "J_1KI": 2.990788774860249, "W_1KI": 0.211766197081085, "W_D": 30.2935, "J_D": 427.83721387100223, "W_D_1KI": 0.13713423025386592, "J_D_1KI": 0.0006207865419090009}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
index 0444e69..bcbbe28 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.06373429298400879}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.021222829818725586}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2496, 2500, 2500]),
-       col_indices=tensor([ 225,  423, 3600,  ..., 1030, 3468, 3660]),
-       values=tensor([0.7007, 0.4494, 0.9248,  ..., 0.2922, 0.0433, 0.9500]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2865, 4172, 3505,  ..., 1471, 1829, 2284]),
+       values=tensor([0.9472, 0.7106, 0.6508,  ..., 0.6327, 0.4564, 0.0632]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8445, 0.6906, 0.6660,  ..., 0.8648, 0.6232, 0.6893])
+tensor([0.0811, 0.3767, 0.7595,  ..., 0.7571, 0.6856, 0.3676])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.06373429298400879 seconds
+Time: 0.021222829818725586 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '164746', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.8433122634887695}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49475', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.351640462875366}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([3043, 3415, 2314,  ..., 4144,   83, 2442]),
-       values=tensor([0.9885, 0.5870, 0.9255,  ..., 0.0554, 0.8705, 0.0319]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([1365, 3411,  751,  ..., 1715, 4182, 3544]),
+       values=tensor([0.2168, 0.4073, 0.8209,  ..., 0.1504, 0.5765, 0.8829]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7612, 0.3828, 0.3624,  ..., 0.7209, 0.0836, 0.1248])
+tensor([0.3766, 0.6993, 0.2098,  ..., 0.7754, 0.7068, 0.6832])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 7.8433122634887695 seconds
+Time: 2.351640462875366 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '220548', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.518115997314453}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '220904', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.611992359161377}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    4,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([1110, 1648, 1178,  ..., 3403,  882, 3863]),
-       values=tensor([0.7053, 0.9818, 0.3657,  ..., 0.7070, 0.0906, 0.0064]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 469, 3066, 4238,  ..., 2570, 4418, 4413]),
+       values=tensor([0.6778, 0.7938, 0.7053,  ..., 0.2703, 0.2957, 0.5133]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7696, 0.8663, 0.2054,  ..., 0.2110, 0.6343, 0.9754])
+tensor([0.1744, 0.7493, 0.4982,  ..., 0.1073, 0.6650, 0.9357])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.518115997314453 seconds
+Time: 10.611992359161377 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    4,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([1110, 1648, 1178,  ..., 3403,  882, 3863]),
-       values=tensor([0.7053, 0.9818, 0.3657,  ..., 0.7070, 0.0906, 0.0064]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 469, 3066, 4238,  ..., 2570, 4418, 4413]),
+       values=tensor([0.6778, 0.7938, 0.7053,  ..., 0.2703, 0.2957, 0.5133]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7696, 0.8663, 0.2054,  ..., 0.2110, 0.6343, 0.9754])
+tensor([0.1744, 0.7493, 0.4982,  ..., 0.1073, 0.6650, 0.9357])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.518115997314453 seconds
+Time: 10.611992359161377 seconds
 
-[18.38, 17.72, 18.11, 17.9, 17.72, 18.84, 18.26, 17.75, 18.1, 19.21]
-[50.77]
-14.040441274642944
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.518115997314453, 'TIME_S_1KI': 0.04769082466091034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.8332035136224, 'W': 50.77000000000001}
-[18.38, 17.72, 18.11, 17.9, 17.72, 18.84, 18.26, 17.75, 18.1, 19.21, 18.75, 19.27, 17.62, 17.61, 18.13, 17.64, 17.82, 17.78, 18.21, 17.71]
-325.505
-16.27525
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.518115997314453, 'TIME_S_1KI': 0.04769082466091034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.8332035136224, 'W': 50.77000000000001, 'J_1KI': 3.2321000576456025, 'W_1KI': 0.23019932168960958, 'W_D': 34.49475000000001, 'J_D': 484.32151165848984, 'W_D_1KI': 0.15640472822242782, 'J_D_1KI': 0.0007091641194770654}
+[18.28, 17.78, 18.16, 18.31, 18.21, 17.89, 17.98, 18.38, 18.01, 17.96]
+[46.78]
+14.123069763183594
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.611992359161377, 'TIME_S_1KI': 0.04803893256419701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6772035217285, 'W': 46.78}
+[18.28, 17.78, 18.16, 18.31, 18.21, 17.89, 17.98, 18.38, 18.01, 17.96, 18.63, 18.15, 17.86, 18.23, 18.11, 18.07, 18.06, 17.82, 21.74, 19.07]
+329.73
+16.4865
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.611992359161377, 'TIME_S_1KI': 0.04803893256419701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6772035217285, 'W': 46.78, 'J_1KI': 2.990788774860249, 'W_1KI': 0.211766197081085, 'W_D': 30.2935, 'J_D': 427.83721387100223, 'W_D_1KI': 0.13713423025386592, 'J_D_1KI': 0.0006207865419090009}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
index 8603573..96eeba5 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 110820, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.466707706451416, "TIME_S_1KI": 0.0944478226534147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.0924870371819, "W": 51.05, "J_1KI": 6.606140471369625, "W_1KI": 0.46065692113336937, "W_D": 34.78399999999999, "J_D": 498.82673984527577, "W_D_1KI": 0.3138783613066233, "J_D_1KI": 0.0028323259457374416}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 111257, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7375807762146, "TIME_S_1KI": 0.09651150737674573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 673.9428442955017, "W": 47.23, "J_1KI": 6.057532059065961, "W_1KI": 0.42451261493658826, "W_D": 30.839999999999996, "J_D": 440.0676967620849, "W_D_1KI": 0.2771960415973826, "J_D_1KI": 0.0024914930440096588}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
index d9f1f9f..1589984 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.10903120040893555}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.025800704956054688}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    12,  ..., 24993, 24997, 25000]),
-       col_indices=tensor([ 238, 1233, 1853,  ..., 2176, 2430, 4262]),
-       values=tensor([0.6643, 0.7436, 0.3106,  ..., 0.6873, 0.4400, 0.9022]),
+tensor(crow_indices=tensor([    0,     3,    11,  ..., 24988, 24993, 25000]),
+       col_indices=tensor([  36,  564, 3279,  ..., 4511, 4767, 4922]),
+       values=tensor([0.2797, 0.2996, 0.9239,  ..., 0.1899, 0.3417, 0.3512]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1554, 0.8998, 0.5501,  ..., 0.9645, 0.8024, 0.0587])
+tensor([0.3254, 0.3602, 0.2662,  ..., 0.3074, 0.8226, 0.4658])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.10903120040893555 seconds
+Time: 0.025800704956054688 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '96302', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.124423503875732}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40696', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.8407018184661865}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     5,     5,  ..., 24990, 24996, 25000]),
-       col_indices=tensor([ 172,  514, 1428,  ..., 3067, 4065, 4821]),
-       values=tensor([0.3942, 0.3525, 0.9893,  ..., 0.1091, 0.2236, 0.5194]),
+tensor(crow_indices=tensor([    0,     7,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([ 298,  713, 1200,  ..., 1957, 3799, 4153]),
+       values=tensor([0.8486, 0.9770, 0.8154,  ..., 0.4467, 0.7513, 0.9966]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3072, 0.9146, 0.5714,  ..., 0.0055, 0.2166, 0.7033])
+tensor([0.8767, 0.4273, 0.1763,  ..., 0.9403, 0.3580, 0.5902])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 9.124423503875732 seconds
+Time: 3.8407018184661865 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '110820', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.466707706451416}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '111257', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7375807762146}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     9,  ..., 24990, 24995, 25000]),
-       col_indices=tensor([ 254, 2428, 3765,  ..., 2763, 3021, 4452]),
-       values=tensor([0.4991, 0.2229, 0.1709,  ..., 0.9765, 0.1191, 0.1560]),
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 575, 1907, 4405,  ..., 1224, 3086, 3740]),
+       values=tensor([0.1597, 0.6483, 0.2533,  ..., 0.7760, 0.1307, 0.6720]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0044, 0.7640, 0.5767,  ..., 0.5512, 0.1474, 0.2527])
+tensor([0.0745, 0.7131, 0.6004,  ..., 0.2535, 0.5073, 0.4932])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.466707706451416 seconds
+Time: 10.7375807762146 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     9,  ..., 24990, 24995, 25000]),
-       col_indices=tensor([ 254, 2428, 3765,  ..., 2763, 3021, 4452]),
-       values=tensor([0.4991, 0.2229, 0.1709,  ..., 0.9765, 0.1191, 0.1560]),
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 575, 1907, 4405,  ..., 1224, 3086, 3740]),
+       values=tensor([0.1597, 0.6483, 0.2533,  ..., 0.7760, 0.1307, 0.6720]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0044, 0.7640, 0.5767,  ..., 0.5512, 0.1474, 0.2527])
+tensor([0.0745, 0.7131, 0.6004,  ..., 0.2535, 0.5073, 0.4932])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.466707706451416 seconds
+Time: 10.7375807762146 seconds
 
-[18.71, 18.12, 17.8, 18.17, 17.79, 18.66, 18.24, 17.82, 17.96, 18.33]
-[51.05]
-14.340695142745972
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 110820, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.466707706451416, 'TIME_S_1KI': 0.0944478226534147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.0924870371819, 'W': 51.05}
-[18.71, 18.12, 17.8, 18.17, 17.79, 18.66, 18.24, 17.82, 17.96, 18.33, 18.24, 17.71, 17.88, 18.05, 17.89, 18.02, 18.2, 18.01, 17.96, 18.8]
-325.32000000000005
-16.266000000000002
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 110820, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.466707706451416, 'TIME_S_1KI': 0.0944478226534147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.0924870371819, 'W': 51.05, 'J_1KI': 6.606140471369625, 'W_1KI': 0.46065692113336937, 'W_D': 34.78399999999999, 'J_D': 498.82673984527577, 'W_D_1KI': 0.3138783613066233, 'J_D_1KI': 0.0028323259457374416}
+[18.26, 17.96, 17.92, 18.07, 18.39, 18.16, 18.05, 18.66, 19.34, 18.38]
+[47.23]
+14.269380569458008
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 111257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7375807762146, 'TIME_S_1KI': 0.09651150737674573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 673.9428442955017, 'W': 47.23}
+[18.26, 17.96, 17.92, 18.07, 18.39, 18.16, 18.05, 18.66, 19.34, 18.38, 18.78, 18.12, 17.96, 17.92, 17.93, 18.7, 18.05, 17.87, 17.97, 18.04]
+327.8
+16.39
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 111257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7375807762146, 'TIME_S_1KI': 0.09651150737674573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 673.9428442955017, 'W': 47.23, 'J_1KI': 6.057532059065961, 'W_1KI': 0.42451261493658826, 'W_D': 30.839999999999996, 'J_D': 440.0676967620849, 'W_D_1KI': 0.2771960415973826, 'J_D_1KI': 0.0024914930440096588}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
index 480b88c..a469b78 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20672, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.150692462921143, "TIME_S_1KI": 0.4910358196072534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 730.3927840781212, "W": 52.39, "J_1KI": 35.332468270032955, "W_1KI": 2.534345975232198, "W_D": 36.085, "J_D": 503.07737380146983, "W_D_1KI": 1.7455979102167183, "J_D_1KI": 0.08444262336574683}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21150, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43382978439331, "TIME_S_1KI": 0.4933252853141046, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 678.8009396362305, "W": 47.74, "J_1KI": 32.09460707499908, "W_1KI": 2.257210401891253, "W_D": 31.383250000000004, "J_D": 446.22914932632455, "W_D_1KI": 1.483841607565012, "J_D_1KI": 0.0701579956295514}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
index a5c980d..c2c0006 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5079245567321777}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.06486701965332031}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     49,     95,  ..., 249890, 249948,
+tensor(crow_indices=tensor([     0,     47,     88,  ..., 249915, 249957,
                             250000]),
-       col_indices=tensor([  55,   65,  142,  ..., 4926, 4940, 4998]),
-       values=tensor([0.9119, 0.0018, 0.8572,  ..., 0.6690, 0.1772, 0.9395]),
+       col_indices=tensor([  72,  118,  180,  ..., 4779, 4849, 4984]),
+       values=tensor([0.8923, 0.3860, 0.0290,  ..., 0.0532, 0.0516, 0.8464]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5150, 0.8940, 0.4191,  ..., 0.2946, 0.8617, 0.5629])
+tensor([0.1487, 0.9450, 0.3254,  ..., 0.6866, 0.3989, 0.7268])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.5079245567321777 seconds
+Time: 0.06486701965332031 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '20672', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.150692462921143}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16186', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.035360336303711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     43,     97,  ..., 249889, 249944,
+tensor(crow_indices=tensor([     0,     44,     82,  ..., 249908, 249962,
                             250000]),
-       col_indices=tensor([   6,   85,  316,  ..., 4939, 4964, 4997]),
-       values=tensor([0.9982, 0.1843, 0.4498,  ..., 0.0146, 0.5221, 0.3769]),
+       col_indices=tensor([  36,   43,   78,  ..., 4796, 4867, 4932]),
+       values=tensor([0.3758, 0.9832, 0.1983,  ..., 0.0743, 0.8633, 0.0592]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3609, 0.3004, 0.4171,  ..., 0.6127, 0.3616, 0.7085])
+tensor([0.5985, 0.2492, 0.1240,  ..., 0.8930, 0.7764, 0.3200])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.150692462921143 seconds
+Time: 8.035360336303711 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21150', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43382978439331}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     43,     97,  ..., 249889, 249944,
+tensor(crow_indices=tensor([     0,     47,     95,  ..., 249894, 249948,
                             250000]),
-       col_indices=tensor([   6,   85,  316,  ..., 4939, 4964, 4997]),
-       values=tensor([0.9982, 0.1843, 0.4498,  ..., 0.0146, 0.5221, 0.3769]),
+       col_indices=tensor([ 129,  143,  228,  ..., 4613, 4768, 4965]),
+       values=tensor([0.2601, 0.0327, 0.3118,  ..., 0.8257, 0.2689, 0.3965]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3609, 0.3004, 0.4171,  ..., 0.6127, 0.3616, 0.7085])
+tensor([0.3768, 0.4725, 0.8369,  ..., 0.3357, 0.4139, 0.5546])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.150692462921143 seconds
+Time: 10.43382978439331 seconds
 
-[18.49, 17.73, 17.92, 17.82, 17.93, 17.75, 17.92, 17.66, 18.2, 17.78]
-[52.39]
-13.9414541721344
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20672, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.150692462921143, 'TIME_S_1KI': 0.4910358196072534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 730.3927840781212, 'W': 52.39}
-[18.49, 17.73, 17.92, 17.82, 17.93, 17.75, 17.92, 17.66, 18.2, 17.78, 18.3, 17.44, 17.89, 17.79, 17.89, 17.6, 18.39, 22.34, 17.77, 17.55]
-326.1
-16.305
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20672, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.150692462921143, 'TIME_S_1KI': 0.4910358196072534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 730.3927840781212, 'W': 52.39, 'J_1KI': 35.332468270032955, 'W_1KI': 2.534345975232198, 'W_D': 36.085, 'J_D': 503.07737380146983, 'W_D_1KI': 1.7455979102167183, 'J_D_1KI': 0.08444262336574683}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     47,     95,  ..., 249894, 249948,
+                            250000]),
+       col_indices=tensor([ 129,  143,  228,  ..., 4613, 4768, 4965]),
+       values=tensor([0.2601, 0.0327, 0.3118,  ..., 0.8257, 0.2689, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3768, 0.4725, 0.8369,  ..., 0.3357, 0.4139, 0.5546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.43382978439331 seconds
+
+[18.56, 17.95, 18.07, 18.36, 18.1, 18.2, 18.17, 18.16, 18.01, 17.83]
+[47.74]
+14.218704223632812
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21150, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43382978439331, 'TIME_S_1KI': 0.4933252853141046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 678.8009396362305, 'W': 47.74}
+[18.56, 17.95, 18.07, 18.36, 18.1, 18.2, 18.17, 18.16, 18.01, 17.83, 19.27, 18.21, 17.98, 17.93, 18.07, 18.27, 18.29, 17.78, 18.3, 18.91]
+327.135
+16.356749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21150, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43382978439331, 'TIME_S_1KI': 0.4933252853141046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 678.8009396362305, 'W': 47.74, 'J_1KI': 32.09460707499908, 'W_1KI': 2.257210401891253, 'W_D': 31.383250000000004, 'J_D': 446.22914932632455, 'W_D_1KI': 1.483841607565012, 'J_D_1KI': 0.0701579956295514}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
index e52e6e6..c9a64dd 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4507, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511178016662598, "TIME_S_1KI": 2.332189486723452, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.7805129575729, "W": 51.93, "J_1KI": 168.13412756990746, "W_1KI": 11.522076769469715, "W_D": 35.70625, "J_D": 521.0379441708326, "W_D_1KI": 7.922398491235855, "J_D_1KI": 1.7577986446052485}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4516, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.427689790725708, "TIME_S_1KI": 2.309054426644311, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 694.9637099742889, "W": 47.9, "J_1KI": 153.88921832911623, "W_1KI": 10.606731620903455, "W_D": 31.338749999999997, "J_D": 454.682546262145, "W_D_1KI": 6.939492914083259, "J_D_1KI": 1.5366459065729094}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
index a885cd8..ce3acc5 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.3295679092407227}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.2486708164215088}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     254,     504,  ..., 1249528,
-                            1249756, 1250000]),
-       col_indices=tensor([   6,   36,   59,  ..., 4952, 4989, 4991]),
-       values=tensor([0.0659, 0.7749, 0.0668,  ..., 0.7589, 0.1810, 0.5312]),
+tensor(crow_indices=tensor([      0,     247,     481,  ..., 1249493,
+                            1249753, 1250000]),
+       col_indices=tensor([   6,   31,   64,  ..., 4955, 4959, 4978]),
+       values=tensor([0.8259, 0.7056, 0.5562,  ..., 0.4513, 0.5248, 0.2272]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.3067, 0.0072, 0.7740,  ..., 0.2122, 0.3107, 0.3197])
+tensor([0.7144, 0.3655, 0.7208,  ..., 0.9456, 0.6678, 0.5049])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 2.3295679092407227 seconds
+Time: 0.2486708164215088 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4507', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511178016662598}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4222', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.815936088562012}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     219,     483,  ..., 1249530,
-                            1249766, 1250000]),
-       col_indices=tensor([  20,   32,  102,  ..., 4974, 4977, 4994]),
-       values=tensor([0.6920, 0.8171, 0.6223,  ..., 0.4625, 0.9983, 0.7249]),
+tensor(crow_indices=tensor([      0,     269,     519,  ..., 1249537,
+                            1249769, 1250000]),
+       col_indices=tensor([  42,   49,   74,  ..., 4955, 4973, 4990]),
+       values=tensor([0.0021, 0.9721, 0.1598,  ..., 0.6170, 0.8086, 0.1248]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.2561, 0.5591, 0.5400,  ..., 0.8818, 0.4529, 0.6860])
+tensor([0.9473, 0.7157, 0.7204,  ..., 0.2702, 0.4361, 0.8753])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.511178016662598 seconds
+Time: 9.815936088562012 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4516', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.427689790725708}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     219,     483,  ..., 1249530,
-                            1249766, 1250000]),
-       col_indices=tensor([  20,   32,  102,  ..., 4974, 4977, 4994]),
-       values=tensor([0.6920, 0.8171, 0.6223,  ..., 0.4625, 0.9983, 0.7249]),
+tensor(crow_indices=tensor([      0,     262,     521,  ..., 1249547,
+                            1249780, 1250000]),
+       col_indices=tensor([   5,   16,   32,  ..., 4965, 4966, 4994]),
+       values=tensor([0.6294, 0.1213, 0.8577,  ..., 0.8057, 0.3565, 0.7731]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.2561, 0.5591, 0.5400,  ..., 0.8818, 0.4529, 0.6860])
+tensor([0.5561, 0.2020, 0.8277,  ..., 0.0800, 0.4571, 0.4718])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.511178016662598 seconds
+Time: 10.427689790725708 seconds
 
-[18.39, 17.88, 18.12, 17.92, 17.75, 18.25, 17.78, 17.64, 17.79, 18.46]
-[51.93]
-14.592345714569092
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511178016662598, 'TIME_S_1KI': 2.332189486723452, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.7805129575729, 'W': 51.93}
-[18.39, 17.88, 18.12, 17.92, 17.75, 18.25, 17.78, 17.64, 17.79, 18.46, 18.69, 17.8, 17.97, 17.87, 17.86, 17.58, 17.99, 19.45, 18.2, 17.71]
-324.47499999999997
-16.22375
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511178016662598, 'TIME_S_1KI': 2.332189486723452, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.7805129575729, 'W': 51.93, 'J_1KI': 168.13412756990746, 'W_1KI': 11.522076769469715, 'W_D': 35.70625, 'J_D': 521.0379441708326, 'W_D_1KI': 7.922398491235855, 'J_D_1KI': 1.7577986446052485}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     262,     521,  ..., 1249547,
+                            1249780, 1250000]),
+       col_indices=tensor([   5,   16,   32,  ..., 4965, 4966, 4994]),
+       values=tensor([0.6294, 0.1213, 0.8577,  ..., 0.8057, 0.3565, 0.7731]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.5561, 0.2020, 0.8277,  ..., 0.0800, 0.4571, 0.4718])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.427689790725708 seconds
+
+[18.38, 21.41, 18.53, 17.98, 18.13, 17.94, 18.18, 18.19, 18.64, 17.87]
+[47.9]
+14.508636951446533
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.427689790725708, 'TIME_S_1KI': 2.309054426644311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 694.9637099742889, 'W': 47.9}
+[18.38, 21.41, 18.53, 17.98, 18.13, 17.94, 18.18, 18.19, 18.64, 17.87, 18.41, 18.3, 18.02, 18.19, 18.18, 18.28, 18.35, 17.94, 18.16, 18.95]
+331.225
+16.56125
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.427689790725708, 'TIME_S_1KI': 2.309054426644311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 694.9637099742889, 'W': 47.9, 'J_1KI': 153.88921832911623, 'W_1KI': 10.606731620903455, 'W_D': 31.338749999999997, 'J_D': 454.682546262145, 'W_D_1KI': 6.939492914083259, 'J_D_1KI': 1.5366459065729094}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
index 635a552..6d226de 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2058, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.45784878730774, "TIME_S_1KI": 5.08155917750619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 785.1421909189224, "W": 52.34, "J_1KI": 381.5073813988933, "W_1KI": 25.432458697764822, "W_D": 35.778000000000006, "J_D": 536.6988404030801, "W_D_1KI": 17.384839650145775, "J_D_1KI": 8.44744395050815}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.487555503845215, "TIME_S_1KI": 5.04937674715706, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 724.931280415058, "W": 48.23, "J_1KI": 349.0280599013279, "W_1KI": 23.220991815117955, "W_D": 31.779249999999998, "J_D": 477.6647811140418, "W_D_1KI": 15.300553683196917, "J_D_1KI": 7.366660415597938}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
index 4a3b3d8..ec991c2 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.10130500793457}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.53643798828125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     521,     995,  ..., 2499020,
-                            2499527, 2500000]),
-       col_indices=tensor([  19,   49,   51,  ..., 4986, 4987, 4995]),
-       values=tensor([0.7936, 0.5375, 0.7301,  ..., 0.7605, 0.2307, 0.9856]),
+tensor(crow_indices=tensor([      0,     485,     953,  ..., 2499027,
+                            2499524, 2500000]),
+       col_indices=tensor([   4,    6,   18,  ..., 4982, 4984, 4999]),
+       values=tensor([0.6950, 0.7335, 0.8547,  ..., 0.7303, 0.2740, 0.2643]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5487, 0.7747, 0.8035,  ..., 0.5625, 0.3730, 0.5706])
+tensor([0.4725, 0.9871, 0.6689,  ..., 0.5705, 0.1526, 0.5563])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 5.10130500793457 seconds
+Time: 0.53643798828125 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2058', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.45784878730774}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1957', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.889901638031006}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     509,    1017,  ..., 2498984,
-                            2499497, 2500000]),
-       col_indices=tensor([   6,   22,   24,  ..., 4979, 4998, 4999]),
-       values=tensor([0.4917, 0.1142, 0.9293,  ..., 0.2344, 0.9124, 0.9917]),
+tensor(crow_indices=tensor([      0,     453,     921,  ..., 2498978,
+                            2499478, 2500000]),
+       col_indices=tensor([   4,   20,   34,  ..., 4974, 4986, 4991]),
+       values=tensor([0.7939, 0.2865, 0.3388,  ..., 0.3715, 0.9532, 0.1224]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7101, 0.7759, 0.4138,  ..., 0.4795, 0.2601, 0.9117])
+tensor([0.8979, 0.8998, 0.6031,  ..., 0.0686, 0.1119, 0.6753])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.45784878730774 seconds
+Time: 9.889901638031006 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2077', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.487555503845215}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     509,    1017,  ..., 2498984,
-                            2499497, 2500000]),
-       col_indices=tensor([   6,   22,   24,  ..., 4979, 4998, 4999]),
-       values=tensor([0.4917, 0.1142, 0.9293,  ..., 0.2344, 0.9124, 0.9917]),
+tensor(crow_indices=tensor([      0,     534,    1011,  ..., 2499001,
+                            2499492, 2500000]),
+       col_indices=tensor([  12,   16,   26,  ..., 4984, 4992, 4995]),
+       values=tensor([0.3129, 0.5758, 0.2112,  ..., 0.6208, 0.5668, 0.8482]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7101, 0.7759, 0.4138,  ..., 0.4795, 0.2601, 0.9117])
+tensor([0.7114, 0.1437, 0.5452,  ..., 0.6795, 0.1114, 0.8178])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.45784878730774 seconds
+Time: 10.487555503845215 seconds
 
-[17.93, 17.71, 18.06, 17.99, 17.73, 17.68, 22.42, 18.22, 17.93, 17.72]
-[52.34]
-15.000806093215942
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2058, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.45784878730774, 'TIME_S_1KI': 5.08155917750619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 785.1421909189224, 'W': 52.34}
-[17.93, 17.71, 18.06, 17.99, 17.73, 17.68, 22.42, 18.22, 17.93, 17.72, 17.97, 22.23, 18.27, 18.03, 17.94, 17.65, 17.66, 17.82, 18.34, 17.5]
-331.24
-16.562
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2058, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.45784878730774, 'TIME_S_1KI': 5.08155917750619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 785.1421909189224, 'W': 52.34, 'J_1KI': 381.5073813988933, 'W_1KI': 25.432458697764822, 'W_D': 35.778000000000006, 'J_D': 536.6988404030801, 'W_D_1KI': 17.384839650145775, 'J_D_1KI': 8.44744395050815}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1011,  ..., 2499001,
+                            2499492, 2500000]),
+       col_indices=tensor([  12,   16,   26,  ..., 4984, 4992, 4995]),
+       values=tensor([0.3129, 0.5758, 0.2112,  ..., 0.6208, 0.5668, 0.8482]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7114, 0.1437, 0.5452,  ..., 0.6795, 0.1114, 0.8178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.487555503845215 seconds
+
+[18.45, 17.83, 21.85, 18.4, 18.31, 17.94, 17.97, 18.32, 18.18, 17.83]
+[48.23]
+15.030712842941284
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.487555503845215, 'TIME_S_1KI': 5.04937674715706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.931280415058, 'W': 48.23}
+[18.45, 17.83, 21.85, 18.4, 18.31, 17.94, 17.97, 18.32, 18.18, 17.83, 18.49, 17.82, 18.18, 18.17, 18.15, 17.82, 18.07, 17.68, 17.99, 17.9]
+329.015
+16.45075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.487555503845215, 'TIME_S_1KI': 5.04937674715706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.931280415058, 'W': 48.23, 'J_1KI': 349.0280599013279, 'W_1KI': 23.220991815117955, 'W_D': 31.779249999999998, 'J_D': 477.6647811140418, 'W_D_1KI': 15.300553683196917, 'J_D_1KI': 7.366660415597938}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..e6d6ed2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 962, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.386102437973022, "TIME_S_1KI": 10.796364280637237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 760.2223528313637, "W": 48.37, "J_1KI": 790.2519260201285, "W_1KI": 50.280665280665275, "W_D": 31.813249999999996, "J_D": 500.00297221857306, "W_D_1KI": 33.069906444906444, "J_D_1KI": 34.376202125682376}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..c8b98a6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.090480089187622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     979,    1989,  ..., 4997947,
+                            4998978, 5000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4989, 4991, 4992]),
+       values=tensor([0.4629, 0.8349, 0.1230,  ..., 0.3254, 0.2010, 0.4262]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8820, 0.9283, 0.2134,  ..., 0.8569, 0.5183, 0.3465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 1.090480089187622 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '962', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.386102437973022}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1020,    2003,  ..., 4997983,
+                            4998972, 5000000]),
+       col_indices=tensor([   2,   12,   14,  ..., 4982, 4988, 4994]),
+       values=tensor([0.5268, 0.0953, 0.2601,  ..., 0.2366, 0.8226, 0.2641]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6710, 0.0398, 0.1150,  ..., 0.1943, 0.5070, 0.9802])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.386102437973022 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1020,    2003,  ..., 4997983,
+                            4998972, 5000000]),
+       col_indices=tensor([   2,   12,   14,  ..., 4982, 4988, 4994]),
+       values=tensor([0.5268, 0.0953, 0.2601,  ..., 0.2366, 0.8226, 0.2641]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6710, 0.0398, 0.1150,  ..., 0.1943, 0.5070, 0.9802])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.386102437973022 seconds
+
+[18.34, 18.12, 18.35, 17.91, 17.99, 18.03, 18.28, 18.28, 17.95, 18.09]
+[48.37]
+15.71681523323059
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 962, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.386102437973022, 'TIME_S_1KI': 10.796364280637237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.2223528313637, 'W': 48.37}
+[18.34, 18.12, 18.35, 17.91, 17.99, 18.03, 18.28, 18.28, 17.95, 18.09, 18.27, 18.18, 18.12, 17.89, 18.15, 21.61, 18.11, 17.8, 18.1, 21.83]
+331.13500000000005
+16.55675
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 962, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.386102437973022, 'TIME_S_1KI': 10.796364280637237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.2223528313637, 'W': 48.37, 'J_1KI': 790.2519260201285, 'W_1KI': 50.280665280665275, 'W_D': 31.813249999999996, 'J_D': 500.00297221857306, 'W_D_1KI': 33.069906444906444, 'J_D_1KI': 34.376202125682376}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..6efa366
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 640, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.40328574180603, "TIME_S_1KI": 16.255133971571922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.4070657157898, "W": 48.54, "J_1KI": 1238.1360401809216, "W_1KI": 75.84375, "W_D": 32.294250000000005, "J_D": 527.1980198185445, "W_D_1KI": 50.45976562500001, "J_D_1KI": 78.84338378906251}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..e11bc1c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.6401786804199219}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1479,    2972,  ..., 7496976,
+                            7498517, 7500000]),
+       col_indices=tensor([   4,   10,   12,  ..., 4987, 4989, 4997]),
+       values=tensor([0.8092, 0.2326, 0.1918,  ..., 0.7537, 0.2703, 0.9406]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6079, 0.4244, 0.8803,  ..., 0.9929, 0.8834, 0.4182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 1.6401786804199219 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '640', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.40328574180603}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1469,    2947,  ..., 7496962,
+                            7498492, 7500000]),
+       col_indices=tensor([   5,    8,   10,  ..., 4979, 4981, 4995]),
+       values=tensor([0.5097, 0.4133, 0.1946,  ..., 0.6762, 0.7827, 0.5941]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3256, 0.8526, 0.3288,  ..., 0.0837, 0.1622, 0.2040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.40328574180603 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1469,    2947,  ..., 7496962,
+                            7498492, 7500000]),
+       col_indices=tensor([   5,    8,   10,  ..., 4979, 4981, 4995]),
+       values=tensor([0.5097, 0.4133, 0.1946,  ..., 0.6762, 0.7827, 0.5941]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3256, 0.8526, 0.3288,  ..., 0.0837, 0.1622, 0.2040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.40328574180603 seconds
+
+[18.61, 18.25, 18.17, 17.83, 17.98, 17.89, 17.97, 17.91, 17.89, 18.07]
+[48.54]
+16.32482624053955
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.40328574180603, 'TIME_S_1KI': 16.255133971571922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.4070657157898, 'W': 48.54}
+[18.61, 18.25, 18.17, 17.83, 17.98, 17.89, 17.97, 17.91, 17.89, 18.07, 18.46, 18.32, 17.92, 18.02, 17.98, 18.33, 17.98, 17.9, 18.02, 17.97]
+324.91499999999996
+16.245749999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.40328574180603, 'TIME_S_1KI': 16.255133971571922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.4070657157898, 'W': 48.54, 'J_1KI': 1238.1360401809216, 'W_1KI': 75.84375, 'W_D': 32.294250000000005, 'J_D': 527.1980198185445, 'W_D_1KI': 50.45976562500001, 'J_D_1KI': 78.84338378906251}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..679e37e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 393, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.361774921417236, "TIME_S_1KI": 26.365839494700346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 830.257935385704, "W": 47.87, "J_1KI": 2112.615611668458, "W_1KI": 121.80661577608141, "W_D": 31.368499999999997, "J_D": 544.0556934645175, "W_D_1KI": 79.81806615776081, "J_D_1KI": 203.09940498157965}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..4eb279b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 2.6700339317321777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1981,     4013,  ...,  9996053,
+                             9998014, 10000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4993, 4995, 4997]),
+       values=tensor([0.1269, 0.6137, 0.4927,  ..., 0.6127, 0.1027, 0.1107]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1274, 0.2875, 0.5158,  ..., 0.6638, 0.6368, 0.8182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 2.6700339317321777 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '393', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.361774921417236}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2035,     3994,  ...,  9995956,
+                             9997978, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4993, 4999]),
+       values=tensor([0.0874, 0.4595, 0.0218,  ..., 0.9380, 0.2756, 0.2464]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1585, 0.7775, 0.6260,  ..., 0.0357, 0.4122, 0.0843])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.361774921417236 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2035,     3994,  ...,  9995956,
+                             9997978, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4993, 4999]),
+       values=tensor([0.0874, 0.4595, 0.0218,  ..., 0.9380, 0.2756, 0.2464]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1585, 0.7775, 0.6260,  ..., 0.0357, 0.4122, 0.0843])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.361774921417236 seconds
+
+[18.09, 21.49, 19.13, 18.18, 17.87, 18.12, 18.49, 17.91, 18.18, 17.86]
+[47.87]
+17.344013690948486
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.361774921417236, 'TIME_S_1KI': 26.365839494700346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 830.257935385704, 'W': 47.87}
+[18.09, 21.49, 19.13, 18.18, 17.87, 18.12, 18.49, 17.91, 18.18, 17.86, 18.63, 18.02, 17.95, 17.88, 17.98, 18.08, 17.96, 17.78, 18.35, 18.74]
+330.03
+16.5015
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.361774921417236, 'TIME_S_1KI': 26.365839494700346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 830.257935385704, 'W': 47.87, 'J_1KI': 2112.615611668458, 'W_1KI': 121.80661577608141, 'W_D': 31.368499999999997, 'J_D': 544.0556934645175, 'W_D_1KI': 79.81806615776081, 'J_D_1KI': 203.09940498157965}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..afa179f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 307, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.394981145858765, "TIME_S_1KI": 33.85987343927936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 930.0089691495895, "W": 47.58, "J_1KI": 3029.3451763830276, "W_1KI": 154.98371335504885, "W_D": 31.056999999999995, "J_D": 607.0468380596636, "W_D_1KI": 101.16286644951138, "J_D_1KI": 329.5207376205583}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..91aa70e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 3.415400505065918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2477,     4964,  ..., 12494964,
+                            12497482, 12500000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4993, 4994, 4997]),
+       values=tensor([0.5791, 0.4301, 0.3570,  ..., 0.1858, 0.4639, 0.9573]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0093, 0.1244, 0.8882,  ..., 0.7606, 0.5225, 0.2163])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 3.415400505065918 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '307', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.394981145858765}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2499,     4994,  ..., 12495082,
+                            12497507, 12500000]),
+       col_indices=tensor([   0,    1,    6,  ..., 4995, 4997, 4999]),
+       values=tensor([0.6585, 0.9778, 0.9803,  ..., 0.8325, 0.0849, 0.1040]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3486, 0.4233, 0.3644,  ..., 0.4149, 0.2376, 0.4812])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.394981145858765 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2499,     4994,  ..., 12495082,
+                            12497507, 12500000]),
+       col_indices=tensor([   0,    1,    6,  ..., 4995, 4997, 4999]),
+       values=tensor([0.6585, 0.9778, 0.9803,  ..., 0.8325, 0.0849, 0.1040]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3486, 0.4233, 0.3644,  ..., 0.4149, 0.2376, 0.4812])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.394981145858765 seconds
+
+[18.48, 21.56, 18.9, 18.32, 18.22, 17.83, 18.09, 17.98, 18.55, 18.34]
+[47.58]
+19.546216249465942
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 307, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.394981145858765, 'TIME_S_1KI': 33.85987343927936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 930.0089691495895, 'W': 47.58}
+[18.48, 21.56, 18.9, 18.32, 18.22, 17.83, 18.09, 17.98, 18.55, 18.34, 18.35, 17.93, 17.78, 17.82, 18.22, 18.23, 18.5, 17.76, 18.34, 17.69]
+330.46000000000004
+16.523000000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 307, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.394981145858765, 'TIME_S_1KI': 33.85987343927936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 930.0089691495895, 'W': 47.58, 'J_1KI': 3029.3451763830276, 'W_1KI': 154.98371335504885, 'W_D': 31.056999999999995, 'J_D': 607.0468380596636, 'W_D_1KI': 101.16286644951138, 'J_D_1KI': 329.5207376205583}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
index 6c5cd21..ca05b98 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 359075, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.526627540588379, "TIME_S_1KI": 0.029315957782046587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.3910086989404, "W": 50.77, "J_1KI": 2.0257355947892233, "W_1KI": 0.14139107428810138, "W_D": 34.40475000000001, "J_D": 492.92310038477194, "W_D_1KI": 0.09581494116827963, "J_D_1KI": 0.00026683824039066943}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 355542, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.588425874710083, "TIME_S_1KI": 0.02978108317641821, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 655.0935684347153, "W": 46.57, "J_1KI": 1.8425209073322286, "W_1KI": 0.13098311873140162, "W_D": 30.1475, "J_D": 424.08059597134593, "W_D_1KI": 0.08479307648604104, "J_D_1KI": 0.00023848962003375422}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
index f806f78..a05082b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.11398792266845703}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.022435426712036133}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 241, 1973,  126, 4921, 4422, 2653, 3082, 2201,  909,
-                            773, 2476, 1101, 4124, 1149, 4932, 4150,  708, 3916,
-                           3901, 3756, 2285, 2145, 2412, 4449, 1421, 1959,  273,
-                            295,  438, 3557, 1406, 2159, 1555, 1352, 2308,  123,
-                            422,  816, 1668, 2887,  824, 2337,  308, 3497,  990,
-                            532, 4077,  543, 4572, 3537, 2814,  363, 2178,  459,
-                            194, 3590, 3027, 4470, 4045, 3521, 3600, 3448, 3378,
-                           3735, 2740, 4248, 3124, 1351, 4670,  655,   21, 1574,
-                           1992, 4925, 3906, 2630, 1378, 3476, 2249, 1157,  791,
-                           4242,  829, 3492,  751, 3125,  155, 1550, 3503, 3772,
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+                      0.1116, 0.5347, 0.0671, 0.7793, 0.1196]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1137, 0.5017, 0.2439,  ..., 0.6384, 0.0681, 0.9585])
+tensor([0.9677, 0.1967, 0.0087,  ..., 0.2565, 0.6584, 0.6200])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.11398792266845703 seconds
+Time: 0.022435426712036133 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '92115', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.6936018466949463}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46800', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.382112741470337}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([4150, 2888, 4184, 1530,  678,  479, 1107,  471, 4285,
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+                            348, 2552, 3299,  317, 1166, 2830, 4896, 4608, 3014,
+                            670, 2086, 2508, 2837, 2920,  612, 4090, 2710, 1095,
+                           1628, 1220,  274, 3831, 1535, 1786, 4549]),
+       values=tensor([0.5255, 0.0182, 0.6174, 0.1076, 0.3535, 0.7090, 0.2797,
+                      0.4131, 0.9644, 0.6573, 0.3774, 0.2463, 0.8634, 0.5392,
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+                      0.4023, 0.6070, 0.4869, 0.6448, 0.8450, 0.9154, 0.1431,
+                      0.8925, 0.4147, 0.0491, 0.3877, 0.8712, 0.5490, 0.2553,
+                      0.4929, 0.0602, 0.3093, 0.9867, 0.9857, 0.7010, 0.9249,
+                      0.3952, 0.9763, 0.0416, 0.7299, 0.7590, 0.5814, 0.9861,
+                      0.2685, 0.2403, 0.0997, 0.7290, 0.0363, 0.1796, 0.0573,
+                      0.1340, 0.1547, 0.5881, 0.5516, 0.6658, 0.9991, 0.5590,
+                      0.3010, 0.2004, 0.5300, 0.9600, 0.5439, 0.0253, 0.1689,
+                      0.7972, 0.3164, 0.6988, 0.4588, 0.6168, 0.9056, 0.7303,
+                      0.2798, 0.7978, 0.1100, 0.0574, 0.5151, 0.8940, 0.9058,
+                      0.1406, 0.2261, 0.0686, 0.3738, 0.0528]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1760, 0.3447, 0.5672,  ..., 0.4540, 0.2179, 0.2738])
+tensor([0.9505, 0.7309, 0.0755,  ..., 0.0952, 0.7737, 0.4268])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 2.6936018466949463 seconds
+Time: 1.382112741470337 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '359075', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.526627540588379}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355542', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.588425874710083}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([3778, 4984, 4122, 2676, 3957, 4059, 4909, 4911, 2572,
-                           1267, 1150, 3364, 3576, 4257, 4803, 3469, 2315, 1996,
-                           1589, 4554, 3627,  222,  735, 2019, 1196, 3402,  918,
-                            508, 1833, 3932, 3749, 3244, 4451, 1193, 3387, 2934,
-                           4933, 2676, 1892, 1253, 2562, 3303,   93, 1367, 4037,
-                            388, 4569, 3905, 2205,  438, 2955, 2830, 2546, 3603,
-                           3071, 4886, 2701, 3617, 3981, 2453, 1634,  906, 2460,
-                           4767, 4482, 2328, 3968, 2373,  709, 1470, 1396, 1265,
-                            427, 2495,   18, 4172, 3266, 4196,  702,  133, 2624,
-                           2942, 4262, 4579, 1940, 2403,   42, 1771,  590, 3624,
-                           3674, 2977, 3577, 3648,  673, 1388, 4388,   17,  194,
-                            155,  552, 2075, 1300, 4736,  849, 2848, 3737, 3431,
-                           4900, 4636,  211, 2218,  935,  599, 2948, 4874,  369,
-                            966,  947, 3488,  346, 1181, 1472, 1637,  372, 1874,
-                           4884,  172,  214,  771, 3131, 1713, 3058, 4267, 3602,
-                           2760, 3398, 3174,    9,  318, 4703,  779, 2824, 4515,
-                           2540, 3491,  647, 4310, 4641, 1357,  289,  349,   73,
-                            908, 1015, 1680,  677,  202, 1047, 1747, 4308, 1250,
-                           3160, 3099, 2970, 2272, 3209, 2339, 1660, 4649,  642,
-                           2647, 4042, 3441, 1713, 3501, 3454, 4660, 2114, 1751,
-                           4938, 3300,  396, 1888, 1868, 2474, 3021, 4177, 1556,
-                           3530,  583,  156,  782,  534,  780, 3712, 1163, 3018,
-                           2652, 2501, 1137, 3069, 4789,  548, 1908,  709, 3367,
-                           4443, 1991, 4909,  152, 2054, 2229,   14, 2251, 1027,
-                           3732,  288,  642, 4326, 2761, 4086, 1629,  946, 4083,
-                           1089, 2210, 3114, 3172,  376, 4660, 3852, 3198, 3613,
-                            592, 1388, 3114, 4183, 4318, 1850, 4771,  843, 2522,
-                           2774, 2939, 3529, 1857, 2895, 2137, 4447]),
-       values=tensor([0.4475, 0.8812, 0.1292, 0.7293, 0.6267, 0.0108, 0.5387,
-                      0.9156, 0.4928, 0.6543, 0.3448, 0.7375, 0.4487, 0.3828,
-                      0.2863, 0.2902, 0.7640, 0.5621, 0.0700, 0.7401, 0.8451,
-                      0.9099, 0.0211, 0.8004, 0.5172, 0.0685, 0.5469, 0.9562,
-                      0.9763, 0.1102, 0.0709, 0.8735, 0.6816, 0.5541, 0.7172,
-                      0.8388, 0.7596, 0.0622, 0.0743, 0.1726, 0.6490, 0.2165,
-                      0.6650, 0.7371, 0.8810, 0.8711, 0.2280, 0.6052, 0.7488,
-                      0.7562, 0.5277, 0.9948, 0.0106, 0.0299, 0.7667, 0.5618,
-                      0.6094, 0.9214, 0.6504, 0.8772, 0.7922, 0.0380, 0.8257,
-                      0.9627, 0.8457, 0.9488, 0.7481, 0.0656, 0.7384, 0.8073,
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-                      0.2911, 0.9747, 0.0465, 0.1509, 0.5817, 0.7676, 0.1559,
-                      0.4514, 0.2238, 0.9216, 0.0912, 0.0562, 0.6927, 0.2560,
-                      0.7407, 0.7561, 0.5126, 0.8908, 0.4965, 0.0086, 0.7725,
-                      0.2468, 0.7667, 0.7880, 0.6098, 0.9369, 0.5035, 0.3626,
-                      0.7343, 0.2151, 0.1827, 0.2696, 0.7224, 0.6480, 0.0746,
-                      0.6229, 0.9622, 0.8016, 0.2190, 0.3391, 0.8517, 0.1344,
-                      0.9710, 0.8151, 0.7634, 0.9047, 0.8447, 0.3478, 0.4789,
-                      0.5543, 0.6475, 0.6794, 0.8153, 0.2995, 0.6764, 0.2993,
-                      0.4440, 0.6818, 0.5702, 0.7074, 0.4488, 0.4032, 0.6268,
-                      0.7286, 0.4749, 0.3646, 0.0331, 0.4227, 0.8138, 0.3173,
-                      0.0403, 0.2636, 0.3980, 0.1390, 0.1641, 0.6671, 0.5330,
-                      0.3639, 0.7467, 0.8967, 0.7753, 0.2492, 0.1215, 0.6986,
-                      0.6107, 0.6922, 0.6270, 0.0513, 0.3708, 0.4140, 0.6870,
-                      0.6642, 0.1925, 0.0944, 0.4210, 0.5791, 0.4516, 0.5935,
-                      0.1022, 0.0482, 0.6022, 0.6705, 0.3885, 0.1005, 0.3611,
-                      0.3535, 0.1700, 0.7214, 0.8017, 0.2409, 0.4915, 0.6710,
-                      0.5749, 0.1541, 0.6514, 0.2028, 0.1566, 0.2795, 0.9275,
-                      0.1313, 0.4671, 0.8621, 0.0474, 0.9495, 0.4065, 0.1561,
-                      0.3930, 0.1891, 0.0713, 0.9951, 0.8365, 0.9415, 0.9314,
-                      0.4274, 0.7485, 0.9571, 0.9768, 0.5673, 0.4241, 0.5508,
-                      0.4033, 0.2950, 0.2855, 0.8415, 0.9844, 0.7770, 0.3923,
-                      0.5787, 0.9241, 0.3429, 0.2388, 0.7432, 0.5287, 0.4894,
-                      0.3564, 0.1539, 0.3683, 0.3338, 0.2500, 0.3763, 0.4479,
-                      0.2028, 0.8079, 0.0187, 0.3962, 0.2530, 0.6932, 0.4307,
-                      0.2510, 0.2498, 0.5817, 0.8657, 0.8402]),
+       col_indices=tensor([4976, 2999, 3531,  729, 4293, 4703,  527,  519, 1087,
+                           2588, 2503, 4621,  623, 2508, 4018, 2181, 4566,  129,
+                           1882,  141,  241, 4234, 3011, 3475, 1507, 1753, 1592,
+                             16, 1837, 2881, 4935, 1126, 1212, 1843, 3769, 1406,
+                           4716, 3662, 2501, 4960, 3438, 3187,  281, 2582, 4408,
+                           1833, 1310, 2092, 1021, 4933, 3776, 3565, 3765, 4067,
+                           3036, 4650, 3192, 1618, 3142,  599, 3093, 1420, 3804,
+                            496, 2492, 4465, 4733, 3170, 4826, 4434, 3018,  360,
+                           3823, 3346, 2952, 3585, 1580, 4171, 3383, 4158, 1729,
+                           4780,  335, 3992, 4542, 2789, 4029,  416, 4212,  913,
+                            792, 2949, 3671, 2278, 3431, 4080, 1970, 3530, 1625,
+                           1846,  123, 3801,  179, 2650, 2793, 3609, 4951, 1904,
+                           3327, 3068,  797, 2882, 3831, 1946, 4674, 2611, 3167,
+                           3799,  820, 4595, 2920, 4476,  339,   16, 1057, 3858,
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+                           3327, 1560, 1579, 4983,  674, 1259, 1259, 1614, 4040,
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+                           4840, 4044, 3897, 1211, 4242,  540,  433, 3144, 2336,
+                           4751,  151, 2282, 2250, 2548, 3515,  851, 1606, 4107,
+                            209,  996, 4881, 3062, 1226,  876, 3179,  583, 2155,
+                            304, 1823,   35,  692, 3702, 4106, 1336, 1789, 1359,
+                           3986, 1095,  312, 3819,  955, 1411, 4676, 1229, 4251,
+                            263, 3639, 4785, 3043, 1158, 1138, 2259, 2515, 3611,
+                           4898, 3676,  269,  509, 3101, 1147,  409, 3191, 1984,
+                           2546, 1127, 3710, 4610, 4906,  997, 3399, 3628,  347,
+                            847, 2034,  588, 2961, 2738, 4337, 3827, 1890, 1227,
+                           1635, 1728, 2759, 1429, 4176, 4191, 4862, 4968, 1336,
+                            119, 3406,  554,  532, 1507, 1917, 2750]),
+       values=tensor([0.5199, 0.0559, 0.9006, 0.7777, 0.2843, 0.6207, 0.6975,
+                      0.8689, 0.5742, 0.4737, 0.6755, 0.5958, 0.4384, 0.4390,
+                      0.8599, 0.7635, 0.7028, 0.4511, 0.3241, 0.0806, 0.8242,
+                      0.9053, 0.3849, 0.6596, 0.0472, 0.0762, 0.5653, 0.2663,
+                      0.8433, 0.4091, 0.7837, 0.1469, 0.0020, 0.4944, 0.8738,
+                      0.1320, 0.2431, 0.2650, 0.7193, 0.2483, 0.4028, 0.5091,
+                      0.3427, 0.6757, 0.9747, 0.5603, 0.1148, 0.1932, 0.5560,
+                      0.7920, 0.7790, 0.7814, 0.5929, 0.3079, 0.4008, 0.4233,
+                      0.4893, 0.9629, 0.6575, 0.2210, 0.9465, 0.6464, 0.0795,
+                      0.6735, 0.0196, 0.9297, 0.4843, 0.7360, 0.6757, 0.8217,
+                      0.8318, 0.9782, 0.6754, 0.1098, 0.6940, 0.8548, 0.6163,
+                      0.6934, 0.6171, 0.1041, 0.7511, 0.4878, 0.3635, 0.8620,
+                      0.3905, 0.3542, 0.8549, 0.4722, 0.9206, 0.2789, 0.9568,
+                      0.1952, 0.7173, 0.8016, 0.5436, 0.6025, 0.2628, 0.4776,
+                      0.0648, 0.4119, 0.1873, 0.5768, 0.6229, 0.4612, 0.3781,
+                      0.9685, 0.2051, 0.1702, 0.4419, 0.4538, 0.4515, 0.1665,
+                      0.0292, 0.6419, 0.1217, 0.0198, 0.2563, 0.3495, 0.0936,
+                      0.2664, 0.5259, 0.3038, 0.7533, 0.2522, 0.7253, 0.1463,
+                      0.2087, 0.7896, 0.0364, 0.6152, 0.7986, 0.8112, 0.7556,
+                      0.0756, 0.4969, 0.3790, 0.1978, 0.1043, 0.8605, 0.5011,
+                      0.8762, 0.2381, 0.0660, 0.2738, 0.0392, 0.5623, 0.1390,
+                      0.4257, 0.6102, 0.9603, 0.2834, 0.0200, 0.2511, 0.8990,
+                      0.6324, 0.9167, 0.3730, 0.6817, 0.4652, 0.8996, 0.9136,
+                      0.8631, 0.9770, 0.2565, 0.6496, 0.1799, 0.4018, 0.4388,
+                      0.9314, 0.6833, 0.7914, 0.9455, 0.7655, 0.3170, 0.6697,
+                      0.9757, 0.4151, 0.9576, 0.1567, 0.8861, 0.0170, 0.7822,
+                      0.2221, 0.7184, 0.0581, 0.4824, 0.4815, 0.2522, 0.1559,
+                      0.1892, 0.1146, 0.0347, 0.4792, 0.0555, 0.6999, 0.5092,
+                      0.0919, 0.7115, 0.9111, 0.4498, 0.4923, 0.5300, 0.3126,
+                      0.8103, 0.3276, 0.8722, 0.4823, 0.2311, 0.5957, 0.0760,
+                      0.2892, 0.3555, 0.6604, 0.7686, 0.1412, 0.8595, 0.6702,
+                      0.1119, 0.1550, 0.8493, 0.8158, 0.3714, 0.8983, 0.3484,
+                      0.6611, 0.8110, 0.2241, 0.8267, 0.9605, 0.8151, 0.1779,
+                      0.2906, 0.1723, 0.3272, 0.4678, 0.1292, 0.9514, 0.6369,
+                      0.8054, 0.2983, 0.3742, 0.8673, 0.0274, 0.1851, 0.9052,
+                      0.8742, 0.4529, 0.2266, 0.4410, 0.5323]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7545, 0.7162, 0.2861,  ..., 0.9381, 0.3630, 0.3493])
+tensor([0.4269, 0.0147, 0.1392,  ..., 0.1737, 0.9746, 0.9710])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.526627540588379 seconds
+Time: 10.588425874710083 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([3778, 4984, 4122, 2676, 3957, 4059, 4909, 4911, 2572,
-                           1267, 1150, 3364, 3576, 4257, 4803, 3469, 2315, 1996,
-                           1589, 4554, 3627,  222,  735, 2019, 1196, 3402,  918,
-                            508, 1833, 3932, 3749, 3244, 4451, 1193, 3387, 2934,
-                           4933, 2676, 1892, 1253, 2562, 3303,   93, 1367, 4037,
-                            388, 4569, 3905, 2205,  438, 2955, 2830, 2546, 3603,
-                           3071, 4886, 2701, 3617, 3981, 2453, 1634,  906, 2460,
-                           4767, 4482, 2328, 3968, 2373,  709, 1470, 1396, 1265,
-                            427, 2495,   18, 4172, 3266, 4196,  702,  133, 2624,
-                           2942, 4262, 4579, 1940, 2403,   42, 1771,  590, 3624,
-                           3674, 2977, 3577, 3648,  673, 1388, 4388,   17,  194,
-                            155,  552, 2075, 1300, 4736,  849, 2848, 3737, 3431,
-                           4900, 4636,  211, 2218,  935,  599, 2948, 4874,  369,
-                            966,  947, 3488,  346, 1181, 1472, 1637,  372, 1874,
-                           4884,  172,  214,  771, 3131, 1713, 3058, 4267, 3602,
-                           2760, 3398, 3174,    9,  318, 4703,  779, 2824, 4515,
-                           2540, 3491,  647, 4310, 4641, 1357,  289,  349,   73,
-                            908, 1015, 1680,  677,  202, 1047, 1747, 4308, 1250,
-                           3160, 3099, 2970, 2272, 3209, 2339, 1660, 4649,  642,
-                           2647, 4042, 3441, 1713, 3501, 3454, 4660, 2114, 1751,
-                           4938, 3300,  396, 1888, 1868, 2474, 3021, 4177, 1556,
-                           3530,  583,  156,  782,  534,  780, 3712, 1163, 3018,
-                           2652, 2501, 1137, 3069, 4789,  548, 1908,  709, 3367,
-                           4443, 1991, 4909,  152, 2054, 2229,   14, 2251, 1027,
-                           3732,  288,  642, 4326, 2761, 4086, 1629,  946, 4083,
-                           1089, 2210, 3114, 3172,  376, 4660, 3852, 3198, 3613,
-                            592, 1388, 3114, 4183, 4318, 1850, 4771,  843, 2522,
-                           2774, 2939, 3529, 1857, 2895, 2137, 4447]),
-       values=tensor([0.4475, 0.8812, 0.1292, 0.7293, 0.6267, 0.0108, 0.5387,
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-                      0.2028, 0.8079, 0.0187, 0.3962, 0.2530, 0.6932, 0.4307,
-                      0.2510, 0.2498, 0.5817, 0.8657, 0.8402]),
+       col_indices=tensor([4976, 2999, 3531,  729, 4293, 4703,  527,  519, 1087,
+                           2588, 2503, 4621,  623, 2508, 4018, 2181, 4566,  129,
+                           1882,  141,  241, 4234, 3011, 3475, 1507, 1753, 1592,
+                             16, 1837, 2881, 4935, 1126, 1212, 1843, 3769, 1406,
+                           4716, 3662, 2501, 4960, 3438, 3187,  281, 2582, 4408,
+                           1833, 1310, 2092, 1021, 4933, 3776, 3565, 3765, 4067,
+                           3036, 4650, 3192, 1618, 3142,  599, 3093, 1420, 3804,
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+                           3823, 3346, 2952, 3585, 1580, 4171, 3383, 4158, 1729,
+                           4780,  335, 3992, 4542, 2789, 4029,  416, 4212,  913,
+                            792, 2949, 3671, 2278, 3431, 4080, 1970, 3530, 1625,
+                           1846,  123, 3801,  179, 2650, 2793, 3609, 4951, 1904,
+                           3327, 3068,  797, 2882, 3831, 1946, 4674, 2611, 3167,
+                           3799,  820, 4595, 2920, 4476,  339,   16, 1057, 3858,
+                           1996, 2480,  211,  316, 2763, 1963, 1855,  789, 4091,
+                           3327, 1560, 1579, 4983,  674, 1259, 1259, 1614, 4040,
+                           4463, 3393,  619, 1723, 4630,  933, 1161, 3845, 4268,
+                           4840, 4044, 3897, 1211, 4242,  540,  433, 3144, 2336,
+                           4751,  151, 2282, 2250, 2548, 3515,  851, 1606, 4107,
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+                            847, 2034,  588, 2961, 2738, 4337, 3827, 1890, 1227,
+                           1635, 1728, 2759, 1429, 4176, 4191, 4862, 4968, 1336,
+                            119, 3406,  554,  532, 1507, 1917, 2750]),
+       values=tensor([0.5199, 0.0559, 0.9006, 0.7777, 0.2843, 0.6207, 0.6975,
+                      0.8689, 0.5742, 0.4737, 0.6755, 0.5958, 0.4384, 0.4390,
+                      0.8599, 0.7635, 0.7028, 0.4511, 0.3241, 0.0806, 0.8242,
+                      0.9053, 0.3849, 0.6596, 0.0472, 0.0762, 0.5653, 0.2663,
+                      0.8433, 0.4091, 0.7837, 0.1469, 0.0020, 0.4944, 0.8738,
+                      0.1320, 0.2431, 0.2650, 0.7193, 0.2483, 0.4028, 0.5091,
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+                      0.7920, 0.7790, 0.7814, 0.5929, 0.3079, 0.4008, 0.4233,
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+                      0.8318, 0.9782, 0.6754, 0.1098, 0.6940, 0.8548, 0.6163,
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+                      0.2087, 0.7896, 0.0364, 0.6152, 0.7986, 0.8112, 0.7556,
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+                      0.8762, 0.2381, 0.0660, 0.2738, 0.0392, 0.5623, 0.1390,
+                      0.4257, 0.6102, 0.9603, 0.2834, 0.0200, 0.2511, 0.8990,
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+                      0.8631, 0.9770, 0.2565, 0.6496, 0.1799, 0.4018, 0.4388,
+                      0.9314, 0.6833, 0.7914, 0.9455, 0.7655, 0.3170, 0.6697,
+                      0.9757, 0.4151, 0.9576, 0.1567, 0.8861, 0.0170, 0.7822,
+                      0.2221, 0.7184, 0.0581, 0.4824, 0.4815, 0.2522, 0.1559,
+                      0.1892, 0.1146, 0.0347, 0.4792, 0.0555, 0.6999, 0.5092,
+                      0.0919, 0.7115, 0.9111, 0.4498, 0.4923, 0.5300, 0.3126,
+                      0.8103, 0.3276, 0.8722, 0.4823, 0.2311, 0.5957, 0.0760,
+                      0.2892, 0.3555, 0.6604, 0.7686, 0.1412, 0.8595, 0.6702,
+                      0.1119, 0.1550, 0.8493, 0.8158, 0.3714, 0.8983, 0.3484,
+                      0.6611, 0.8110, 0.2241, 0.8267, 0.9605, 0.8151, 0.1779,
+                      0.2906, 0.1723, 0.3272, 0.4678, 0.1292, 0.9514, 0.6369,
+                      0.8054, 0.2983, 0.3742, 0.8673, 0.0274, 0.1851, 0.9052,
+                      0.8742, 0.4529, 0.2266, 0.4410, 0.5323]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7545, 0.7162, 0.2861,  ..., 0.9381, 0.3630, 0.3493])
+tensor([0.4269, 0.0147, 0.1392,  ..., 0.1737, 0.9746, 0.9710])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.526627540588379 seconds
+Time: 10.588425874710083 seconds
 
-[18.43, 17.7, 18.24, 17.84, 17.84, 17.8, 18.19, 17.69, 17.91, 17.97]
-[50.77]
-14.327181577682495
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 359075, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.526627540588379, 'TIME_S_1KI': 0.029315957782046587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.3910086989404, 'W': 50.77}
-[18.43, 17.7, 18.24, 17.84, 17.84, 17.8, 18.19, 17.69, 17.91, 17.97, 18.65, 17.88, 17.9, 17.81, 18.36, 17.89, 17.8, 17.9, 21.76, 18.54]
-327.305
-16.36525
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 359075, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.526627540588379, 'TIME_S_1KI': 0.029315957782046587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.3910086989404, 'W': 50.77, 'J_1KI': 2.0257355947892233, 'W_1KI': 0.14139107428810138, 'W_D': 34.40475000000001, 'J_D': 492.92310038477194, 'W_D_1KI': 0.09581494116827963, 'J_D_1KI': 0.00026683824039066943}
+[18.34, 17.96, 17.95, 19.17, 18.44, 18.12, 18.19, 19.28, 18.14, 18.1]
+[46.57]
+14.066857814788818
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 355542, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.588425874710083, 'TIME_S_1KI': 0.02978108317641821, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 655.0935684347153, 'W': 46.57}
+[18.34, 17.96, 17.95, 19.17, 18.44, 18.12, 18.19, 19.28, 18.14, 18.1, 18.29, 18.16, 18.13, 17.89, 18.15, 18.08, 18.32, 17.91, 18.06, 18.27]
+328.45
+16.4225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 355542, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.588425874710083, 'TIME_S_1KI': 0.02978108317641821, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 655.0935684347153, 'W': 46.57, 'J_1KI': 1.8425209073322286, 'W_1KI': 0.13098311873140162, 'W_D': 30.1475, 'J_D': 424.08059597134593, 'W_D_1KI': 0.08479307648604104, 'J_D_1KI': 0.00023848962003375422}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..0d245c9
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 303638, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.314902782440186, "TIME_S_1KI": 0.033971053631100805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 652.3264142632485, "W": 46.59, "J_1KI": 2.148368828220606, "W_1KI": 0.1534392928421344, "W_D": 30.048000000000002, "J_D": 420.71483356475835, "W_D_1KI": 0.0989599457248434, "J_D_1KI": 0.0003259142324901475}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..2579376
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018550395965576172}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2148, 2186, 2653,  ..., 4713,  108, 1050]),
+       values=tensor([0.2240, 0.7824, 0.4591,  ..., 0.0832, 0.2125, 0.9204]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9989, 0.0104, 0.9920,  ..., 0.8791, 0.9452, 0.9082])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.018550395965576172 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '56602', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9573328495025635}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([ 143, 2910, 3407,  ...,  360, 1598, 1598]),
+       values=tensor([0.9185, 0.3997, 0.2489,  ..., 0.0567, 0.3179, 0.0072]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5677, 0.2086, 0.3083,  ..., 0.5984, 0.8633, 0.3307])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.9573328495025635 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '303638', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.314902782440186}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4078,   67, 3564,  ..., 1146, 2529, 4353]),
+       values=tensor([0.7614, 0.2386, 0.2238,  ..., 0.8351, 0.5866, 0.5164]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2149, 0.3426, 0.8577,  ..., 0.2235, 0.1553, 0.5182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.314902782440186 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4078,   67, 3564,  ..., 1146, 2529, 4353]),
+       values=tensor([0.7614, 0.2386, 0.2238,  ..., 0.8351, 0.5866, 0.5164]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2149, 0.3426, 0.8577,  ..., 0.2235, 0.1553, 0.5182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.314902782440186 seconds
+
+[18.14, 22.27, 18.32, 18.01, 17.87, 18.05, 18.11, 18.01, 18.55, 17.98]
+[46.59]
+14.001425504684448
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 303638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.314902782440186, 'TIME_S_1KI': 0.033971053631100805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.3264142632485, 'W': 46.59}
+[18.14, 22.27, 18.32, 18.01, 17.87, 18.05, 18.11, 18.01, 18.55, 17.98, 18.58, 17.96, 18.17, 18.69, 18.11, 18.0, 18.22, 18.0, 18.06, 18.18]
+330.84000000000003
+16.542
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 303638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.314902782440186, 'TIME_S_1KI': 0.033971053631100805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.3264142632485, 'W': 46.59, 'J_1KI': 2.148368828220606, 'W_1KI': 0.1534392928421344, 'W_D': 30.048000000000002, 'J_D': 420.71483356475835, 'W_D_1KI': 0.0989599457248434, 'J_D_1KI': 0.0003259142324901475}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..59c1962
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 23.50609064102173, "TIME_S_1KI": 23.50609064102173, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 609.4492170715331, "W": 22.389903061636694, "J_1KI": 609.4492170715331, "W_1KI": 22.389903061636694, "W_D": 3.917903061636693, "J_D": 106.64463114929183, "W_D_1KI": 3.9179030616366926, "J_D_1KI": 3.9179030616366926}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
similarity index 53%
rename from pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.output
rename to pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
index f9e5f4b..81f61e7 100644
--- 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_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 24.361413717269897}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 23.50609064102173}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      21,  ...,  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]),
+tensor(crow_indices=tensor([      0,       6,      16,  ...,  999976,
+                             999990, 1000000]),
+       col_indices=tensor([35450, 44241, 45004,  ..., 57756, 61659, 92730]),
+       values=tensor([0.6041, 0.1643, 0.4254,  ..., 0.8911, 0.3600, 0.5834]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0568, 0.5322, 0.2500,  ..., 0.3574, 0.0150, 0.2325])
+tensor([0.3461, 0.8147, 0.1835,  ..., 0.7972, 0.9198, 0.6224])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 24.361413717269897 seconds
+Time: 23.50609064102173 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      21,  ...,  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]),
+tensor(crow_indices=tensor([      0,       6,      16,  ...,  999976,
+                             999990, 1000000]),
+       col_indices=tensor([35450, 44241, 45004,  ..., 57756, 61659, 92730]),
+       values=tensor([0.6041, 0.1643, 0.4254,  ..., 0.8911, 0.3600, 0.5834]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0568, 0.5322, 0.2500,  ..., 0.3574, 0.0150, 0.2325])
+tensor([0.3461, 0.8147, 0.1835,  ..., 0.7972, 0.9198, 0.6224])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 24.361413717269897 seconds
+Time: 23.50609064102173 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}
+[20.48, 20.4, 20.48, 20.48, 20.28, 20.4, 20.28, 20.28, 20.4, 20.44]
+[20.28, 20.36, 20.36, 21.44, 23.4, 25.2, 26.04, 26.28, 25.2, 24.36, 24.32, 24.36, 24.52, 24.6, 24.68, 24.76, 24.68, 24.48, 24.52, 24.52, 24.48, 24.76, 24.72, 24.72, 24.64, 24.88]
+27.219823837280273
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 23.50609064102173, 'TIME_S_1KI': 23.50609064102173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 609.4492170715331, 'W': 22.389903061636694}
+[20.48, 20.4, 20.48, 20.48, 20.28, 20.4, 20.28, 20.28, 20.4, 20.44, 20.6, 20.64, 20.72, 21.0, 20.92, 20.76, 20.68, 20.44, 20.28, 20.48]
+369.44000000000005
+18.472
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 23.50609064102173, 'TIME_S_1KI': 23.50609064102173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 609.4492170715331, 'W': 22.389903061636694, 'J_1KI': 609.4492170715331, 'W_1KI': 22.389903061636694, 'W_D': 3.917903061636693, 'J_D': 106.64463114929183, 'W_D_1KI': 3.9179030616366926, 'J_D_1KI': 3.9179030616366926}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..05631c7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 227.44817399978638, "TIME_S_1KI": 227.44817399978638, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5651.356887254718, "W": 23.406634424755232, "J_1KI": 5651.356887254718, "W_1KI": 23.406634424755232, "W_D": 5.256634424755234, "J_D": 1269.1750817751913, "W_D_1KI": 5.256634424755234, "J_D_1KI": 5.256634424755234}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..4847746
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 227.44817399978638}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      105,      212,  ...,  9999786,
+                             9999896, 10000000]),
+       col_indices=tensor([  310,  1031,  2044,  ..., 96924, 97369, 99264]),
+       values=tensor([0.4389, 0.5701, 0.8338,  ..., 0.1266, 0.7107, 0.7989]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1044, 0.8564, 0.8953,  ..., 0.3136, 0.0570, 0.9535])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 227.44817399978638 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      105,      212,  ...,  9999786,
+                             9999896, 10000000]),
+       col_indices=tensor([  310,  1031,  2044,  ..., 96924, 97369, 99264]),
+       values=tensor([0.4389, 0.5701, 0.8338,  ..., 0.1266, 0.7107, 0.7989]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1044, 0.8564, 0.8953,  ..., 0.3136, 0.0570, 0.9535])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 227.44817399978638 seconds
+
+[20.56, 20.48, 20.48, 20.36, 20.4, 20.32, 20.32, 19.96, 20.0, 20.04]
+[20.12, 20.12, 20.4, 21.88, 23.4, 25.08, 27.48, 28.24, 27.96, 27.08, 26.24, 25.68, 24.72, 24.48, 24.4, 24.28, 24.36, 24.4, 24.48, 24.68, 24.72, 24.72, 24.8, 24.88, 24.76, 24.68, 24.64, 24.48, 24.44, 24.28, 24.44, 24.64, 24.76, 24.8, 24.84, 24.72, 24.44, 24.36, 24.32, 24.48, 24.48, 24.6, 24.76, 24.76, 24.68, 24.64, 24.72, 24.64, 24.4, 24.4, 24.68, 24.52, 24.6, 24.56, 24.48, 24.28, 24.32, 24.28, 24.32, 24.52, 24.52, 24.64, 24.76, 24.76, 24.68, 24.68, 24.6, 24.56, 24.72, 24.52, 24.76, 24.76, 24.68, 24.56, 24.48, 24.24, 24.4, 24.6, 24.76, 24.76, 24.8, 24.64, 24.64, 24.56, 24.76, 24.72, 24.8, 24.8, 24.8, 24.8, 24.6, 24.56, 24.44, 24.68, 24.72, 24.72, 24.68, 24.72, 24.68, 24.88, 24.92, 24.84, 24.8, 24.8, 25.0, 25.08, 25.0, 24.92, 24.8, 24.8, 24.6, 24.72, 24.84, 25.0, 25.0, 24.88, 24.96, 24.92, 24.96, 24.92, 24.92, 24.64, 24.56, 24.44, 24.44, 24.36, 24.6, 24.44, 24.52, 24.88, 25.12, 25.12, 25.2, 25.32, 24.96, 24.96, 24.8, 24.56, 24.64, 24.52, 24.44, 24.48, 24.4, 24.28, 24.56, 24.52, 24.48, 24.6, 24.52, 24.6, 24.64, 24.88, 24.8, 24.8, 24.76, 24.76, 24.76, 24.4, 24.28, 24.28, 24.28, 24.32, 24.8, 25.04, 24.92, 24.8, 24.8, 24.56, 24.52, 24.52, 24.48, 24.64, 24.52, 24.64, 24.68, 24.68, 24.72, 24.56, 24.56, 24.96, 24.96, 24.88, 24.72, 24.4, 24.32, 24.36, 24.4, 24.64, 24.92, 24.8, 24.76, 24.72, 24.64, 24.52, 24.8, 24.72, 24.76, 24.76, 24.72, 25.04, 24.96, 24.64, 24.32, 24.16, 24.12, 24.36, 24.44, 24.32, 24.16, 24.04, 24.32, 24.68, 24.56, 24.68, 24.72, 24.28, 24.4, 24.36, 24.36, 24.36, 24.44, 24.44, 24.16, 24.32, 24.44, 24.36, 24.6, 24.68, 24.8, 24.76]
+241.44252371788025
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 227.44817399978638, 'TIME_S_1KI': 227.44817399978638, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5651.356887254718, 'W': 23.406634424755232}
+[20.56, 20.48, 20.48, 20.36, 20.4, 20.32, 20.32, 19.96, 20.0, 20.04, 20.08, 20.32, 20.08, 20.0, 19.84, 19.84, 19.92, 20.08, 20.08, 20.36]
+363.0
+18.15
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 227.44817399978638, 'TIME_S_1KI': 227.44817399978638, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5651.356887254718, 'W': 23.406634424755232, 'J_1KI': 5651.356887254718, 'W_1KI': 23.406634424755232, 'W_D': 5.256634424755234, 'J_D': 1269.1750817751913, 'W_D_1KI': 5.256634424755234, 'J_D_1KI': 5.256634424755234}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..4c6f614
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3195, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.793879747390747, "TIME_S_1KI": 3.3783661181191698, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 338.5230771541595, "W": 23.150052849773353, "J_1KI": 105.95401475873538, "W_1KI": 7.245712942026088, "W_D": 4.780052849773355, "J_D": 69.89868274450302, "W_D_1KI": 1.4961041783328186, "J_D_1KI": 0.46826421857052225}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
similarity index 59%
rename from pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.output
rename to pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
index 0c82a6f..a9ff479 100644
--- 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_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.3640708923339844}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.2854795455932617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99997,
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([49077, 61829, 75773,  ...,  9180, 24382, 73621]),
-       values=tensor([0.5511, 0.7896, 0.6815,  ..., 0.2019, 0.1356, 0.1654]),
+       col_indices=tensor([96494, 10713, 51050,  ..., 77096, 58241, 39394]),
+       values=tensor([0.9472, 0.0468, 0.6571,  ..., 0.2815, 0.5696, 0.0055]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3810, 0.9981, 0.5438,  ..., 0.4984, 0.5897, 0.5823])
+tensor([0.9254, 0.6847, 0.8457,  ..., 0.6275, 0.7476, 0.1010])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 3.3640708923339844 seconds
+Time: 3.2854795455932617 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3195 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.793879747390747}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997, 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]),
+       col_indices=tensor([41532, 61839,  3968,  ..., 19432, 54156, 77664]),
+       values=tensor([0.2018, 0.3494, 0.0819,  ..., 0.4942, 0.5843, 0.6732]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0734, 0.1949, 0.9405,  ..., 0.2088, 0.6775, 0.6290])
+tensor([0.4618, 0.8259, 0.6206,  ..., 0.7480, 0.2960, 0.5870])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 21.78993320465088 seconds
+Time: 10.793879747390747 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997, 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]),
+       col_indices=tensor([41532, 61839,  3968,  ..., 19432, 54156, 77664]),
+       values=tensor([0.2018, 0.3494, 0.0819,  ..., 0.4942, 0.5843, 0.6732]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0734, 0.1949, 0.9405,  ..., 0.2088, 0.6775, 0.6290])
+tensor([0.4618, 0.8259, 0.6206,  ..., 0.7480, 0.2960, 0.5870])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 21.78993320465088 seconds
+Time: 10.793879747390747 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}
+[20.88, 20.84, 20.52, 20.4, 20.32, 20.2, 20.2, 20.44, 20.48, 20.8]
+[20.92, 20.8, 21.0, 22.96, 24.6, 25.56, 26.6, 27.0, 26.4, 25.96, 26.12, 26.12, 25.88, 25.88]
+14.62299370765686
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.793879747390747, 'TIME_S_1KI': 3.3783661181191698, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.5230771541595, 'W': 23.150052849773353}
+[20.88, 20.84, 20.52, 20.4, 20.32, 20.2, 20.2, 20.44, 20.48, 20.8, 20.4, 20.4, 20.2, 20.36, 20.52, 20.36, 20.4, 20.36, 20.2, 20.32]
+367.4
+18.369999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.793879747390747, 'TIME_S_1KI': 3.3783661181191698, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 338.5230771541595, 'W': 23.150052849773353, 'J_1KI': 105.95401475873538, 'W_1KI': 7.245712942026088, 'W_D': 4.780052849773355, 'J_D': 69.89868274450302, 'W_D_1KI': 1.4961041783328186, 'J_D_1KI': 0.46826421857052225}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..8de09b8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32341, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.187894582748413, "TIME_S_1KI": 0.3150148289399961, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.80105960845947, "W": 22.074332714462717, "J_1KI": 9.981171256561623, "W_1KI": 0.6825494794367124, "W_D": 3.644332714462717, "J_D": 53.29241327524185, "W_D_1KI": 0.11268460203650836, "J_D_1KI": 0.0034842646187968327}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..a571cff
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 0.3246574401855469}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([ 702,  590, 2393,  ..., 5106, 4251, 5881]),
+       values=tensor([0.8131, 0.4443, 0.5032,  ..., 0.0454, 0.7892, 0.7021]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5617, 0.3540, 0.6665,  ..., 0.2887, 0.4752, 0.2274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.3246574401855469 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32341 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.187894582748413}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([5513, 4819, 4488,  ..., 7223, 1569, 1749]),
+       values=tensor([0.7502, 0.9864, 0.0219,  ..., 0.7577, 0.3030, 0.6500]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4735, 0.0629, 0.6403,  ..., 0.2218, 0.6036, 0.6062])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.187894582748413 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([5513, 4819, 4488,  ..., 7223, 1569, 1749]),
+       values=tensor([0.7502, 0.9864, 0.0219,  ..., 0.7577, 0.3030, 0.6500]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4735, 0.0629, 0.6403,  ..., 0.2218, 0.6036, 0.6062])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.187894582748413 seconds
+
+[20.16, 20.36, 20.44, 20.64, 20.64, 20.64, 20.68, 20.0, 19.96, 20.04]
+[20.04, 20.36, 20.64, 22.2, 24.32, 25.36, 25.96, 26.0, 25.44, 24.12, 24.0, 23.8, 23.8, 23.84]
+14.623366594314575
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32341, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.187894582748413, 'TIME_S_1KI': 0.3150148289399961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.80105960845947, 'W': 22.074332714462717}
+[20.16, 20.36, 20.44, 20.64, 20.64, 20.64, 20.68, 20.0, 19.96, 20.04, 19.96, 20.28, 20.36, 20.44, 20.48, 20.48, 20.72, 20.76, 20.96, 21.36]
+368.6
+18.43
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32341, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.187894582748413, 'TIME_S_1KI': 0.3150148289399961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.80105960845947, 'W': 22.074332714462717, 'J_1KI': 9.981171256561623, 'W_1KI': 0.6825494794367124, 'W_D': 3.644332714462717, 'J_D': 53.29241327524185, 'W_D_1KI': 0.11268460203650836, 'J_D_1KI': 0.0034842646187968327}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..f0f5b59
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4681, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.577015399932861, "TIME_S_1KI": 2.2595632129743346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.1403603744507, "W": 22.94669291080223, "J_1KI": 71.38226028080554, "W_1KI": 4.902092055287809, "W_D": 4.40869291080223, "J_D": 64.19758366584783, "W_D_1KI": 0.9418271546255567, "J_D_1KI": 0.20120212660234066}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..7485b00
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 2.242969274520874}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     21,  ...,  99980,  99990,
+                            100000]),
+       col_indices=tensor([ 158,  243, 1021,  ..., 9060, 9386, 9562]),
+       values=tensor([0.4026, 0.0672, 0.1618,  ..., 0.9478, 0.4676, 0.6061]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1276, 0.9367, 0.3121,  ..., 0.3681, 0.2222, 0.5819])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.242969274520874 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4681 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.577015399932861}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     23,  ...,  99978,  99989,
+                            100000]),
+       col_indices=tensor([1463, 2229, 2458,  ..., 6913, 8671, 9837]),
+       values=tensor([0.1583, 0.2191, 0.0082,  ..., 0.3537, 0.5043, 0.1355]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7982, 0.2389, 0.8535,  ..., 0.4532, 0.2540, 0.6422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.577015399932861 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     23,  ...,  99978,  99989,
+                            100000]),
+       col_indices=tensor([1463, 2229, 2458,  ..., 6913, 8671, 9837]),
+       values=tensor([0.1583, 0.2191, 0.0082,  ..., 0.3537, 0.5043, 0.1355]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7982, 0.2389, 0.8535,  ..., 0.4532, 0.2540, 0.6422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.577015399932861 seconds
+
+[20.84, 20.96, 20.84, 20.76, 20.88, 20.8, 20.72, 20.8, 20.8, 20.72]
+[20.72, 21.0, 21.04, 25.64, 26.56, 27.96, 28.44, 25.92, 25.0, 24.0, 23.88, 24.12, 24.36, 24.36]
+14.561591148376465
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.577015399932861, 'TIME_S_1KI': 2.2595632129743346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.1403603744507, 'W': 22.94669291080223}
+[20.84, 20.96, 20.84, 20.76, 20.88, 20.8, 20.72, 20.8, 20.8, 20.72, 20.52, 20.24, 20.24, 20.2, 20.44, 20.16, 20.56, 20.64, 20.52, 20.32]
+370.76
+18.538
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.577015399932861, 'TIME_S_1KI': 2.2595632129743346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.1403603744507, 'W': 22.94669291080223, 'J_1KI': 71.38226028080554, 'W_1KI': 4.902092055287809, 'W_D': 4.40869291080223, 'J_D': 64.19758366584783, 'W_D_1KI': 0.9418271546255567, 'J_D_1KI': 0.20120212660234066}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..7587c79
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 21.648348808288574, "TIME_S_1KI": 21.648348808288574, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 597.1462204551697, "W": 23.932854010192194, "J_1KI": 597.1462204551697, "W_1KI": 23.932854010192194, "W_D": 5.369854010192196, "J_D": 133.98268443942072, "W_D_1KI": 5.369854010192196, "J_D_1KI": 5.369854010192196}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..ed3ac8e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 21.648348808288574}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     181,  ...,  999807,
+                             999904, 1000000]),
+       col_indices=tensor([  20,   39,  173,  ..., 9424, 9617, 9690]),
+       values=tensor([0.7771, 0.0078, 0.5851,  ..., 0.0250, 0.0076, 0.8688]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6163, 0.0977, 0.8617,  ..., 0.7477, 0.6432, 0.7227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.648348808288574 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     181,  ...,  999807,
+                             999904, 1000000]),
+       col_indices=tensor([  20,   39,  173,  ..., 9424, 9617, 9690]),
+       values=tensor([0.7771, 0.0078, 0.5851,  ..., 0.0250, 0.0076, 0.8688]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6163, 0.0977, 0.8617,  ..., 0.7477, 0.6432, 0.7227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.648348808288574 seconds
+
+[20.48, 20.52, 20.6, 20.6, 20.6, 20.72, 20.48, 20.56, 20.64, 20.72]
+[20.72, 21.08, 23.92, 25.96, 27.92, 28.72, 29.32, 26.52, 26.52, 25.36, 24.24, 24.4, 24.24, 24.44, 24.16, 24.12, 24.16, 24.12, 24.16, 24.2, 24.32, 24.36, 24.68, 24.72]
+24.95089888572693
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.648348808288574, 'TIME_S_1KI': 21.648348808288574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.1462204551697, 'W': 23.932854010192194}
+[20.48, 20.52, 20.6, 20.6, 20.6, 20.72, 20.48, 20.56, 20.64, 20.72, 20.68, 20.72, 20.52, 20.52, 20.64, 20.76, 20.64, 20.8, 20.68, 20.64]
+371.26
+18.563
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.648348808288574, 'TIME_S_1KI': 21.648348808288574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.1462204551697, 'W': 23.932854010192194, 'J_1KI': 597.1462204551697, 'W_1KI': 23.932854010192194, 'W_D': 5.369854010192196, 'J_D': 133.98268443942072, 'W_D_1KI': 5.369854010192196, 'J_D_1KI': 5.369854010192196}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..e29f054
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 106.87029075622559, "TIME_S_1KI": 106.87029075622559, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2633.659623832703, "W": 23.232350154828893, "J_1KI": 2633.659623832703, "W_1KI": 23.232350154828893, "W_D": 4.519350154828892, "J_D": 512.3213944957257, "W_D_1KI": 4.519350154828892, "J_D_1KI": 4.519350154828892}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..5edda98
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 106.87029075622559}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     497,     970,  ..., 4998958,
+                            4999495, 5000000]),
+       col_indices=tensor([   3,   19,   30,  ..., 9933, 9939, 9986]),
+       values=tensor([0.6521, 0.8632, 0.3100,  ..., 0.6388, 0.4505, 0.0265]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1776, 0.4739, 0.9893,  ..., 0.4929, 0.9525, 0.7109])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.87029075622559 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     497,     970,  ..., 4998958,
+                            4999495, 5000000]),
+       col_indices=tensor([   3,   19,   30,  ..., 9933, 9939, 9986]),
+       values=tensor([0.6521, 0.8632, 0.3100,  ..., 0.6388, 0.4505, 0.0265]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1776, 0.4739, 0.9893,  ..., 0.4929, 0.9525, 0.7109])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.87029075622559 seconds
+
+[20.76, 20.72, 20.52, 20.48, 20.68, 20.6, 20.6, 20.56, 20.68, 20.6]
+[20.64, 20.72, 20.72, 24.72, 25.96, 28.32, 29.48, 30.12, 26.68, 25.6, 25.08, 24.6, 24.6, 24.6, 24.8, 24.8, 24.88, 24.92, 24.84, 24.8, 24.72, 24.52, 24.52, 24.52, 24.6, 24.56, 24.4, 24.48, 24.32, 24.16, 24.28, 24.36, 24.48, 24.64, 24.68, 24.64, 24.4, 24.68, 24.72, 24.72, 24.56, 24.64, 24.48, 24.32, 24.12, 24.12, 24.2, 24.52, 24.4, 24.56, 24.68, 24.48, 24.28, 24.24, 24.2, 24.04, 23.92, 24.04, 24.28, 24.12, 24.28, 24.36, 24.28, 24.44, 24.52, 24.6, 24.72, 24.72, 24.88, 24.84, 24.72, 24.44, 24.16, 24.2, 24.0, 24.2, 24.44, 24.32, 24.2, 24.2, 24.16, 24.12, 24.24, 24.2, 24.12, 24.16, 24.2, 24.16, 24.4, 24.4, 24.36, 24.2, 24.28, 24.52, 24.12, 24.36, 24.64, 24.6, 24.6, 24.52, 24.48, 24.2, 24.4, 24.4, 24.4, 24.52, 24.4, 24.16]
+113.36173939704895
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.87029075622559, 'TIME_S_1KI': 106.87029075622559, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2633.659623832703, 'W': 23.232350154828893}
+[20.76, 20.72, 20.52, 20.48, 20.68, 20.6, 20.6, 20.56, 20.68, 20.6, 20.52, 20.76, 20.76, 21.2, 21.2, 21.28, 21.12, 21.0, 20.88, 20.56]
+374.26
+18.713
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.87029075622559, 'TIME_S_1KI': 106.87029075622559, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2633.659623832703, 'W': 23.232350154828893, 'J_1KI': 2633.659623832703, 'W_1KI': 23.232350154828893, 'W_D': 4.519350154828892, 'J_D': 512.3213944957257, 'W_D_1KI': 4.519350154828892, 'J_D_1KI': 4.519350154828892}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..8cd094e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.98000812530518, "TIME_S_1KI": 210.98000812530518, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5224.394376831054, "W": 23.586192508859664, "J_1KI": 5224.394376831054, "W_1KI": 23.586192508859664, "W_D": 5.122192508859662, "J_D": 1134.5770933685287, "W_D_1KI": 5.122192508859662, "J_D_1KI": 5.122192508859662}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..d1e3dfe
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.98000812530518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      993,     1975,  ...,  9997956,
+                             9998997, 10000000]),
+       col_indices=tensor([  19,   22,   26,  ..., 9979, 9989, 9990]),
+       values=tensor([0.9746, 0.4059, 0.0503,  ..., 0.3598, 0.3506, 0.0768]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8784, 0.5931, 0.4456,  ..., 0.6081, 0.2914, 0.4121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.98000812530518 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      993,     1975,  ...,  9997956,
+                             9998997, 10000000]),
+       col_indices=tensor([  19,   22,   26,  ..., 9979, 9989, 9990]),
+       values=tensor([0.9746, 0.4059, 0.0503,  ..., 0.3598, 0.3506, 0.0768]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8784, 0.5931, 0.4456,  ..., 0.6081, 0.2914, 0.4121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.98000812530518 seconds
+
+[20.72, 20.64, 20.6, 20.6, 20.48, 20.44, 20.4, 20.36, 20.28, 20.28]
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+221.50223588943481
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.98000812530518, 'TIME_S_1KI': 210.98000812530518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5224.394376831054, 'W': 23.586192508859664}
+[20.72, 20.64, 20.6, 20.6, 20.48, 20.44, 20.4, 20.36, 20.28, 20.28, 20.24, 20.44, 20.68, 20.92, 21.04, 20.8, 20.44, 20.28, 20.2, 20.12]
+369.28000000000003
+18.464000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.98000812530518, 'TIME_S_1KI': 210.98000812530518, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5224.394376831054, 'W': 23.586192508859664, 'J_1KI': 5224.394376831054, 'W_1KI': 23.586192508859664, 'W_D': 5.122192508859662, 'J_D': 1134.5770933685287, 'W_D_1KI': 5.122192508859662, 'J_D_1KI': 5.122192508859662}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..e2472aa
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 142368, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.641618490219116, "TIME_S_1KI": 0.07474726406368788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.11982940673823, "W": 23.739150118754246, "J_1KI": 2.445211209026876, "W_1KI": 0.16674498566218704, "W_D": 4.927150118754245, "J_D": 72.25358322525018, "W_D_1KI": 0.03460855050821986, "J_D_1KI": 0.00024309220125463487}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..4bc129d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-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": 0.0838630199432373}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([2253,  476, 8386,  498, 9957, 4225, 8921, 5276, 6649,
+                           8361, 9030, 5103, 3236, 7146, 9127, 2162, 9108, 6109,
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+                      0.8399, 0.9753, 0.8351, 0.3146, 0.2432, 0.5413]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.2336, 0.7811, 0.2916,  ..., 0.9209, 0.8685, 0.4951])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.0838630199432373 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 125204 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.234081745147705}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
+       col_indices=tensor([8690, 1861, 4903, 1985, 5995, 5133, 3649, 6848, 9888,
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+                           4114]),
+       values=tensor([0.9947, 0.8142, 0.8795, 0.8201, 0.5910, 0.5774, 0.6787,
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+                      0.0176, 0.0647, 0.9873, 0.6484, 0.3203, 0.2129, 0.9498,
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+                      0.3238, 0.4139, 0.4523, 0.9314, 0.2645, 0.3099]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.9394, 0.5361, 0.0926,  ..., 0.1333, 0.7033, 0.7122])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.234081745147705 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 142368 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.641618490219116}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([1350, 1465, 4190, 6900, 6571, 5844, 4736,  324, 9249,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5424, 0.9332, 0.7035,  ..., 0.9872, 0.5484, 0.9353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.641618490219116 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      4.0205e-02, 5.1058e-03, 5.9797e-01, 3.2629e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5424, 0.9332, 0.7035,  ..., 0.9872, 0.5484, 0.9353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.641618490219116 seconds
+
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 142368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.641618490219116, 'TIME_S_1KI': 0.07474726406368788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.11982940673823, 'W': 23.739150118754246, 'J_1KI': 2.445211209026876, 'W_1KI': 0.16674498566218704, 'W_D': 4.927150118754245, 'J_D': 72.25358322525018, 'W_D_1KI': 0.03460855050821986, 'J_D_1KI': 0.00024309220125463487}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..7ae266f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 101.81685495376587, "TIME_S_1KI": 101.81685495376587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2509.431913032532, "W": 23.91615643501538, "J_1KI": 2509.431913032532, "W_1KI": 23.91615643501538, "W_D": 5.455156435015379, "J_D": 572.3889491109848, "W_D_1KI": 5.455156435015379, "J_D_1KI": 5.455156435015379}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..06c1b57
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 101.81685495376587}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499992,
+                            2499997, 2500000]),
+       col_indices=tensor([  7138,  74289, 101345,  ...,  58125, 215534,
+                           230533]),
+       values=tensor([0.6785, 0.9079, 0.1725,  ..., 0.1754, 0.6680, 0.6302]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7654, 0.8855, 0.1287,  ..., 0.1047, 0.9719, 0.8120])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 101.81685495376587 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499992,
+                            2499997, 2500000]),
+       col_indices=tensor([  7138,  74289, 101345,  ...,  58125, 215534,
+                           230533]),
+       values=tensor([0.6785, 0.9079, 0.1725,  ..., 0.1754, 0.6680, 0.6302]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7654, 0.8855, 0.1287,  ..., 0.1047, 0.9719, 0.8120])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 101.81685495376587 seconds
+
+[20.36, 20.36, 20.32, 20.32, 20.32, 20.56, 20.64, 20.8, 21.0, 21.0]
+[20.96, 20.8, 21.96, 21.96, 22.8, 24.92, 25.88, 26.52, 26.32, 25.68, 25.2, 25.44, 25.36, 25.12, 25.04, 25.08, 25.04, 25.24, 25.36, 25.12, 25.28, 25.28, 25.08, 24.96, 25.04, 25.04, 25.2, 25.32, 25.2, 25.36, 25.08, 24.88, 24.96, 25.12, 25.24, 25.4, 25.4, 25.4, 25.2, 25.16, 25.08, 25.04, 25.24, 25.28, 25.36, 25.48, 25.48, 25.4, 25.36, 25.48, 25.52, 25.4, 25.28, 25.04, 24.84, 24.88, 25.12, 25.68, 26.12, 26.12, 25.76, 25.32, 24.96, 24.84, 24.92, 24.96, 24.92, 24.92, 24.92, 25.28, 25.08, 25.08, 25.16, 25.12, 25.04, 25.12, 25.2, 24.92, 24.92, 24.96, 25.08, 25.4, 25.4, 25.48, 25.44, 25.24, 25.28, 25.48, 25.72, 25.56, 25.56, 25.52, 25.44, 25.2, 25.08, 25.12, 25.08, 25.28, 25.44, 25.32]
+104.92622089385986
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 101.81685495376587, 'TIME_S_1KI': 101.81685495376587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2509.431913032532, 'W': 23.91615643501538}
+[20.36, 20.36, 20.32, 20.32, 20.32, 20.56, 20.64, 20.8, 21.0, 21.0, 20.52, 20.48, 20.28, 20.48, 20.52, 20.64, 20.52, 20.44, 20.4, 20.4]
+369.22
+18.461000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 101.81685495376587, 'TIME_S_1KI': 101.81685495376587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2509.431913032532, 'W': 23.91615643501538, 'J_1KI': 2509.431913032532, 'W_1KI': 23.91615643501538, 'W_D': 5.455156435015379, 'J_D': 572.3889491109848, 'W_D_1KI': 5.455156435015379, 'J_D_1KI': 5.455156435015379}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..99da0bf
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1750, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.16480827331543, "TIME_S_1KI": 5.80846187046596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 350.615839214325, "W": 23.96705841957935, "J_1KI": 200.35190812247143, "W_1KI": 13.695461954045342, "W_D": 5.563058419579352, "J_D": 81.38238586616524, "W_D_1KI": 3.178890525473916, "J_D_1KI": 1.8165088716993805}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
similarity index 57%
rename from pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.output
rename to pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
index 19d6f4f..38bf734 100644
--- 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_10_10_10_synthetic_50000_0.0001.output
@@ -1,15 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.156386375427246}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.999283075332642}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     12,  ..., 249983, 249990,
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 249990, 249995,
                             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]),
+       col_indices=tensor([13962, 18394, 22949,  ..., 14595, 37415, 49220]),
+       values=tensor([0.3721, 0.9393, 0.0895,  ..., 0.9714, 0.3434, 0.8212]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6560, 0.4823, 0.8578,  ..., 0.1247, 0.5626, 0.6108])
+tensor([0.7511, 0.6955, 0.0801,  ..., 0.5808, 0.0034, 0.8132])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -17,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 6.156386375427246 seconds
+Time: 5.999283075332642 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1750 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.16480827331543}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     11,  ..., 249987, 249992,
+tensor(crow_indices=tensor([     0,      8,     10,  ..., 249989, 249996,
                             250000]),
-       col_indices=tensor([ 5492, 16093, 20671,  ..., 32727, 38238, 43452]),
-       values=tensor([0.3185, 0.0470, 0.4206,  ..., 0.2062, 0.5185, 0.9595]),
+       col_indices=tensor([  581, 19518, 20111,  ..., 13396, 34309, 44743]),
+       values=tensor([0.2810, 0.4140, 0.9885,  ..., 0.7044, 0.0704, 0.4209]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4169, 0.1713, 0.4477,  ..., 0.1220, 0.0527, 0.5400])
+tensor([0.2162, 0.8403, 0.5346,  ..., 0.6143, 0.9627, 0.5199])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -37,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 20.881499767303467 seconds
+Time: 10.16480827331543 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     11,  ..., 249987, 249992,
+tensor(crow_indices=tensor([     0,      8,     10,  ..., 249989, 249996,
                             250000]),
-       col_indices=tensor([ 5492, 16093, 20671,  ..., 32727, 38238, 43452]),
-       values=tensor([0.3185, 0.0470, 0.4206,  ..., 0.2062, 0.5185, 0.9595]),
+       col_indices=tensor([  581, 19518, 20111,  ..., 13396, 34309, 44743]),
+       values=tensor([0.2810, 0.4140, 0.9885,  ..., 0.7044, 0.0704, 0.4209]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4169, 0.1713, 0.4477,  ..., 0.1220, 0.0527, 0.5400])
+tensor([0.2162, 0.8403, 0.5346,  ..., 0.6143, 0.9627, 0.5199])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -54,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 20.881499767303467 seconds
+Time: 10.16480827331543 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}
+[20.36, 20.36, 20.36, 20.4, 20.24, 20.28, 20.32, 20.8, 21.08, 20.92]
+[21.16, 21.04, 24.12, 26.48, 28.28, 28.28, 29.04, 29.84, 25.88, 24.76, 24.76, 24.72, 24.88, 24.76]
+14.629072666168213
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.16480827331543, 'TIME_S_1KI': 5.80846187046596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.615839214325, 'W': 23.96705841957935}
+[20.36, 20.36, 20.36, 20.4, 20.24, 20.28, 20.32, 20.8, 21.08, 20.92, 20.4, 20.24, 20.24, 20.08, 20.2, 20.32, 20.56, 20.68, 20.72, 20.72]
+368.0799999999999
+18.403999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.16480827331543, 'TIME_S_1KI': 5.80846187046596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 350.615839214325, 'W': 23.96705841957935, 'J_1KI': 200.35190812247143, 'W_1KI': 13.695461954045342, 'W_D': 5.563058419579352, 'J_D': 81.38238586616524, 'W_D_1KI': 3.178890525473916, 'J_D_1KI': 1.8165088716993805}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..295c278
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 54.368531465530396, "TIME_S_1KI": 54.368531465530396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1384.197800512314, "W": 23.562014123499935, "J_1KI": 1384.197800512314, "W_1KI": 23.562014123499935, "W_D": 4.987014123499936, "J_D": 292.97215190053004, "W_D_1KI": 4.987014123499936, "J_D_1KI": 4.987014123499936}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.001.output
similarity index 51%
rename from pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.output
rename to pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_0.001.output
index 1dd53bb..ea77cdc 100644
--- 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_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.24125528335571}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 54.368531465530396}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499903,
+                            2499953, 2500000]),
+       col_indices=tensor([  180,   933,  1739,  ..., 48224, 48432, 48665]),
+       values=tensor([0.2331, 0.6137, 0.9488,  ..., 0.3126, 0.9414, 0.7411]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1491, 0.4739, 0.9733,  ..., 0.7895, 0.7265, 0.6840])
+tensor([0.7249, 0.6013, 0.3531,  ..., 0.7563, 0.1447, 0.0341])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 54.24125528335571 seconds
+Time: 54.368531465530396 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499903,
+                            2499953, 2500000]),
+       col_indices=tensor([  180,   933,  1739,  ..., 48224, 48432, 48665]),
+       values=tensor([0.2331, 0.6137, 0.9488,  ..., 0.3126, 0.9414, 0.7411]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1491, 0.4739, 0.9733,  ..., 0.7895, 0.7265, 0.6840])
+tensor([0.7249, 0.6013, 0.3531,  ..., 0.7563, 0.1447, 0.0341])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 54.24125528335571 seconds
+Time: 54.368531465530396 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}
+[20.28, 20.28, 20.28, 20.52, 20.52, 20.52, 20.84, 20.84, 20.8, 20.84]
+[20.8, 20.8, 24.12, 26.04, 26.04, 28.44, 29.44, 30.08, 26.76, 25.32, 24.56, 24.68, 24.84, 24.84, 24.76, 24.52, 24.68, 24.72, 24.96, 24.76, 24.96, 24.84, 25.0, 24.88, 24.88, 24.64, 24.64, 24.44, 24.28, 24.24, 24.28, 24.28, 24.52, 24.72, 24.84, 24.64, 24.68, 24.68, 24.72, 24.72, 24.8, 24.76, 24.56, 24.52, 24.2, 24.24, 24.24, 24.24, 24.24, 24.4, 24.52, 24.52, 24.4, 24.44, 24.4, 24.12]
+58.74700665473938
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.368531465530396, 'TIME_S_1KI': 54.368531465530396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.197800512314, 'W': 23.562014123499935}
+[20.28, 20.28, 20.28, 20.52, 20.52, 20.52, 20.84, 20.84, 20.8, 20.84, 20.4, 20.36, 20.4, 20.28, 20.56, 20.56, 20.96, 21.12, 21.28, 21.24]
+371.5
+18.575
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 54.368531465530396, 'TIME_S_1KI': 54.368531465530396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.197800512314, 'W': 23.562014123499935, 'J_1KI': 1384.197800512314, 'W_1KI': 23.562014123499935, 'W_D': 4.987014123499936, 'J_D': 292.97215190053004, 'W_D_1KI': 4.987014123499936, 'J_D_1KI': 4.987014123499936}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..79e75a2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10740, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.677687883377075, "TIME_S_1KI": 0.9941981269438619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.0521117687225, "W": 22.70459223140718, "J_1KI": 30.91732884252537, "W_1KI": 2.11402162303605, "W_D": 4.278592231407178, "J_D": 62.57393091917032, "W_D_1KI": 0.3983791649354914, "J_D_1KI": 0.03709303211689864}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..0bf1f45
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 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": 1.031747817993164}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([40215,  7884, 10043,  ..., 30495, 28697, 40914]),
+       values=tensor([0.0776, 0.0144, 0.1627,  ..., 0.8046, 0.8736, 0.3953]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9279, 0.0068, 0.0286,  ..., 0.3265, 0.6131, 0.7632])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 1.031747817993164 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10176 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.948124408721924}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([36670,  6571, 29568,  ..., 18627, 41427, 17079]),
+       values=tensor([0.2785, 0.5861, 0.6450,  ..., 0.6094, 0.8660, 0.4536]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5003, 0.3455, 0.7125,  ..., 0.5405, 0.2393, 0.4201])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.948124408721924 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10740 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.677687883377075}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([16841, 18429, 37212,  ..., 30943,  4364, 38003]),
+       values=tensor([0.6614, 0.5763, 0.4032,  ..., 0.0279, 0.2406, 0.7956]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8404, 0.4278, 0.6904,  ..., 0.0651, 0.6749, 0.6556])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.677687883377075 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([16841, 18429, 37212,  ..., 30943,  4364, 38003]),
+       values=tensor([0.6614, 0.5763, 0.4032,  ..., 0.0279, 0.2406, 0.7956]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8404, 0.4278, 0.6904,  ..., 0.0651, 0.6749, 0.6556])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.677687883377075 seconds
+
+[20.72, 20.68, 20.64, 20.72, 20.72, 20.64, 20.68, 20.52, 20.52, 20.6]
+[21.04, 21.2, 21.2, 21.92, 23.48, 24.12, 25.48, 25.92, 26.2, 25.8, 25.88, 25.6, 25.8, 25.72]
+14.624887704849243
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.677687883377075, 'TIME_S_1KI': 0.9941981269438619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.0521117687225, 'W': 22.70459223140718}
+[20.72, 20.68, 20.64, 20.72, 20.72, 20.64, 20.68, 20.52, 20.52, 20.6, 20.2, 20.12, 20.24, 20.24, 20.28, 20.28, 20.4, 20.4, 20.44, 20.48]
+368.52000000000004
+18.426000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.677687883377075, 'TIME_S_1KI': 0.9941981269438619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.0521117687225, 'W': 22.70459223140718, 'J_1KI': 30.91732884252537, 'W_1KI': 2.11402162303605, 'W_D': 4.278592231407178, 'J_D': 62.57393091917032, 'W_D_1KI': 0.3983791649354914, 'J_D_1KI': 0.03709303211689864}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..f8f615f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 96826, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.588202953338623, "TIME_S_1KI": 0.10935289027057425, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 358.2542047309875, "W": 24.50573052785985, "J_1KI": 3.699979393251683, "W_1KI": 0.2530903943967514, "W_D": 4.6827305278598494, "J_D": 68.45777967405314, "W_D_1KI": 0.04836232548963966, "J_D_1KI": 0.000499476643563089}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..4f006ee
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1145937442779541}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2495, 2499, 2500]),
+       col_indices=tensor([2458, 4485, 3264,  ..., 1767, 2577, 3633]),
+       values=tensor([0.5111, 0.1865, 0.4486,  ..., 0.9187, 0.4905, 0.6857]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1036, 0.8585, 0.3762,  ..., 0.6219, 0.4226, 0.3195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.1145937442779541 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 91628 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.936307668685913}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1700, 3040, 4129,  ..., 4083, 2058, 3930]),
+       values=tensor([0.1350, 0.7186, 0.2594,  ..., 0.2124, 0.0344, 0.1244]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7260, 0.9332, 0.2146,  ..., 0.1697, 0.4017, 0.1867])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 9.936307668685913 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 96826 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.588202953338623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3997, 2967, 4931,  ..., 3835, 1314, 3597]),
+       values=tensor([0.7356, 0.2235, 0.2006,  ..., 0.5232, 0.0695, 0.2889]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7175, 0.9581, 0.9907,  ..., 0.9548, 0.8349, 0.6616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.588202953338623 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3997, 2967, 4931,  ..., 3835, 1314, 3597]),
+       values=tensor([0.7356, 0.2235, 0.2006,  ..., 0.5232, 0.0695, 0.2889]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7175, 0.9581, 0.9907,  ..., 0.9548, 0.8349, 0.6616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.588202953338623 seconds
+
+[20.4, 20.56, 20.56, 20.6, 20.44, 20.64, 20.68, 20.96, 21.52, 22.56]
+[23.28, 23.64, 26.88, 28.2, 29.6, 29.52, 29.52, 29.72, 25.72, 24.92, 23.8, 23.72, 24.2, 24.28]
+14.619201183319092
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 96826, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.588202953338623, 'TIME_S_1KI': 0.10935289027057425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.2542047309875, 'W': 24.50573052785985}
+[20.4, 20.56, 20.56, 20.6, 20.44, 20.64, 20.68, 20.96, 21.52, 22.56, 22.96, 23.28, 23.32, 23.36, 23.32, 23.28, 23.36, 23.12, 23.0, 23.0]
+396.46
+19.823
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 96826, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.588202953338623, 'TIME_S_1KI': 0.10935289027057425, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.2542047309875, 'W': 24.50573052785985, 'J_1KI': 3.699979393251683, 'W_1KI': 0.2530903943967514, 'W_D': 4.6827305278598494, 'J_D': 68.45777967405314, 'W_D_1KI': 0.04836232548963966, 'J_D_1KI': 0.000499476643563089}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..7e96f33
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.667346239089966, "TIME_S_1KI": 0.6143722996653784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.18905796051024, "W": 22.76067676218484, "J_1KI": 19.1896019098376, "W_1KI": 1.3108723585892323, "W_D": 3.0516767621848366, "J_D": 44.67289422965043, "W_D_1KI": 0.17575745909029755, "J_D_1KI": 0.010122528312520737}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..ba00b65
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,62 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6047096252441406}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    11,  ..., 24983, 24992, 25000]),
+       col_indices=tensor([ 225,  408, 1943,  ..., 2555, 2651, 2712]),
+       values=tensor([0.7906, 0.4816, 0.2276,  ..., 0.2718, 0.8003, 0.8712]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6660, 0.8709, 0.7078,  ..., 0.4840, 0.5828, 0.2928])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.6047096252441406 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17363 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.667346239089966}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     7,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([  85,  195, 4187,  ..., 2991, 3287, 4675]),
+       values=tensor([0.1915, 0.0298, 0.9128,  ..., 0.0482, 0.8260, 0.8063]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9668, 0.6018, 0.4153,  ..., 0.6117, 0.1974, 0.6733])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.667346239089966 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     7,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([  85,  195, 4187,  ..., 2991, 3287, 4675]),
+       values=tensor([0.1915, 0.0298, 0.9128,  ..., 0.0482, 0.8260, 0.8063]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9668, 0.6018, 0.4153,  ..., 0.6117, 0.1974, 0.6733])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.667346239089966 seconds
+
+[22.96, 22.64, 22.44, 22.84, 23.2, 23.48, 24.08, 23.88, 23.04, 22.2]
+[21.52, 21.52, 20.88, 24.32, 25.36, 27.04, 27.68, 28.12, 25.12, 23.88, 23.84, 23.84, 23.68, 23.84]
+14.638802766799927
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.667346239089966, 'TIME_S_1KI': 0.6143722996653784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.18905796051024, 'W': 22.76067676218484}
+[22.96, 22.64, 22.44, 22.84, 23.2, 23.48, 24.08, 23.88, 23.04, 22.2, 20.48, 20.6, 20.52, 20.76, 20.72, 20.68, 20.68, 20.76, 20.64, 20.8]
+394.18000000000006
+19.709000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.667346239089966, 'TIME_S_1KI': 0.6143722996653784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.18905796051024, 'W': 22.76067676218484, 'J_1KI': 19.1896019098376, 'W_1KI': 1.3108723585892323, 'W_D': 3.0516767621848366, 'J_D': 44.67289422965043, 'W_D_1KI': 0.17575745909029755, 'J_D_1KI': 0.010122528312520737}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..a5d181f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1948, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5094473361969, "TIME_S_1KI": 5.394993499074384, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 321.46233737945556, "W": 22.060099154306354, "J_1KI": 165.02173376768766, "W_1KI": 11.324486218843099, "W_D": 3.783099154306356, "J_D": 55.127762036561975, "W_D_1KI": 1.9420426870155834, "J_D_1KI": 0.9969418311168293}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..80c2fbe
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.389868259429932}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     46,     96,  ..., 249907, 249949,
+                            250000]),
+       col_indices=tensor([ 123,  345,  399,  ..., 4711, 4879, 4988]),
+       values=tensor([0.4250, 0.5468, 0.7620,  ..., 0.1883, 0.2040, 0.8985]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2612, 0.9268, 0.9416,  ..., 0.0698, 0.1077, 0.5090])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 5.389868259429932 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1948 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.5094473361969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     57,    115,  ..., 249893, 249944,
+                            250000]),
+       col_indices=tensor([  73,  135,  475,  ..., 4575, 4723, 4971]),
+       values=tensor([0.1739, 0.5180, 0.0955,  ..., 0.3924, 0.5566, 0.2573]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5075, 0.6044, 0.6141,  ..., 0.4161, 0.9554, 0.0515])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.5094473361969 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     57,    115,  ..., 249893, 249944,
+                            250000]),
+       col_indices=tensor([  73,  135,  475,  ..., 4575, 4723, 4971]),
+       values=tensor([0.1739, 0.5180, 0.0955,  ..., 0.3924, 0.5566, 0.2573]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5075, 0.6044, 0.6141,  ..., 0.4161, 0.9554, 0.0515])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.5094473361969 seconds
+
+[20.56, 20.36, 20.36, 20.92, 20.64, 20.52, 20.44, 20.2, 20.0, 20.24]
+[20.4, 20.4, 20.96, 22.24, 24.0, 24.56, 25.32, 25.32, 25.28, 24.8, 24.08, 24.24, 24.16, 24.16]
+14.572116613388062
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1948, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5094473361969, 'TIME_S_1KI': 5.394993499074384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.46233737945556, 'W': 22.060099154306354}
+[20.56, 20.36, 20.36, 20.92, 20.64, 20.52, 20.44, 20.2, 20.0, 20.24, 20.16, 20.08, 20.04, 20.12, 20.12, 20.16, 20.24, 20.2, 20.44, 20.44]
+365.53999999999996
+18.276999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1948, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.5094473361969, 'TIME_S_1KI': 5.394993499074384, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.46233737945556, 'W': 22.060099154306354, 'J_1KI': 165.02173376768766, 'W_1KI': 11.324486218843099, 'W_D': 3.783099154306356, 'J_D': 55.127762036561975, 'W_D_1KI': 1.9420426870155834, 'J_D_1KI': 0.9969418311168293}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..496e602
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 28.093817949295044, "TIME_S_1KI": 28.093817949295044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 689.2963391876219, "W": 22.82953703239495, "J_1KI": 689.2963391876219, "W_1KI": 22.82953703239495, "W_D": 4.334537032394952, "J_D": 130.87346030116072, "W_D_1KI": 4.334537032394952, "J_D_1KI": 4.334537032394952}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..9c2fd18
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 28.093817949295044}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     249,     484,  ..., 1249498,
+                            1249755, 1250000]),
+       col_indices=tensor([   8,   31,   46,  ..., 4934, 4976, 4984]),
+       values=tensor([0.2044, 0.4643, 0.3912,  ..., 0.8352, 0.2191, 0.4950]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7092, 0.7480, 0.2063,  ..., 0.9775, 0.7055, 0.9981])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 28.093817949295044 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     249,     484,  ..., 1249498,
+                            1249755, 1250000]),
+       col_indices=tensor([   8,   31,   46,  ..., 4934, 4976, 4984]),
+       values=tensor([0.2044, 0.4643, 0.3912,  ..., 0.8352, 0.2191, 0.4950]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7092, 0.7480, 0.2063,  ..., 0.9775, 0.7055, 0.9981])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 28.093817949295044 seconds
+
+[20.56, 20.6, 20.48, 20.56, 20.4, 20.48, 20.68, 20.72, 20.92, 21.08]
+[20.92, 20.92, 20.56, 23.36, 25.52, 27.28, 28.24, 28.72, 25.44, 24.36, 24.6, 24.76, 24.64, 24.52, 24.36, 24.44, 24.24, 24.4, 24.44, 24.16, 24.16, 24.12, 24.2, 24.2, 24.08, 24.12, 24.24, 24.12, 24.0]
+30.193180799484253
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 28.093817949295044, 'TIME_S_1KI': 28.093817949295044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.2963391876219, 'W': 22.82953703239495}
+[20.56, 20.6, 20.48, 20.56, 20.4, 20.48, 20.68, 20.72, 20.92, 21.08, 20.52, 20.6, 20.36, 20.4, 20.52, 20.44, 20.6, 20.44, 20.44, 20.36]
+369.9
+18.494999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 28.093817949295044, 'TIME_S_1KI': 28.093817949295044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 689.2963391876219, 'W': 22.82953703239495, 'J_1KI': 689.2963391876219, 'W_1KI': 22.82953703239495, 'W_D': 4.334537032394952, 'J_D': 130.87346030116072, 'W_D_1KI': 4.334537032394952, 'J_D_1KI': 4.334537032394952}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..fff9557
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.78093886375427, "TIME_S_1KI": 53.78093886375427, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1352.5172874259947, "W": 23.03061139017129, "J_1KI": 1352.5172874259947, "W_1KI": 23.03061139017129, "W_D": 4.417611390171292, "J_D": 259.4327902595995, "W_D_1KI": 4.417611390171292, "J_D_1KI": 4.417611390171292}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..c5ce197
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.78093886375427}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1044,  ..., 2498992,
+                            2499517, 2500000]),
+       col_indices=tensor([   3,   19,   25,  ..., 4971, 4983, 4990]),
+       values=tensor([0.2124, 0.6762, 0.6770,  ..., 0.5380, 0.6783, 0.2658]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0160, 0.9125, 0.7128,  ..., 0.4183, 0.3158, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.78093886375427 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1044,  ..., 2498992,
+                            2499517, 2500000]),
+       col_indices=tensor([   3,   19,   25,  ..., 4971, 4983, 4990]),
+       values=tensor([0.2124, 0.6762, 0.6770,  ..., 0.5380, 0.6783, 0.2658]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0160, 0.9125, 0.7128,  ..., 0.4183, 0.3158, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.78093886375427 seconds
+
+[20.56, 20.72, 20.76, 20.76, 20.64, 20.88, 20.6, 20.52, 20.72, 20.48]
+[20.36, 20.24, 20.8, 22.0, 24.0, 25.2, 26.08, 25.8, 25.8, 25.44, 24.16, 24.32, 24.32, 24.4, 24.36, 24.4, 24.52, 24.88, 24.68, 24.64, 24.52, 24.28, 24.04, 24.08, 24.08, 24.08, 24.36, 24.4, 24.48, 24.6, 24.6, 24.64, 24.56, 24.52, 24.56, 24.32, 24.04, 24.32, 24.36, 24.24, 24.28, 24.28, 24.28, 24.48, 24.52, 24.76, 24.56, 24.24, 24.16, 24.04, 24.12, 24.12, 24.44, 24.48, 24.52, 24.4]
+58.726938009262085
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.78093886375427, 'TIME_S_1KI': 53.78093886375427, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.5172874259947, 'W': 23.03061139017129}
+[20.56, 20.72, 20.76, 20.76, 20.64, 20.88, 20.6, 20.52, 20.72, 20.48, 20.84, 20.6, 20.72, 20.72, 20.72, 20.52, 20.6, 20.72, 20.76, 20.72]
+372.26
+18.613
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.78093886375427, 'TIME_S_1KI': 53.78093886375427, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.5172874259947, 'W': 23.03061139017129, 'J_1KI': 1352.5172874259947, 'W_1KI': 23.03061139017129, 'W_D': 4.417611390171292, 'J_D': 259.4327902595995, 'W_D_1KI': 4.417611390171292, 'J_D_1KI': 4.417611390171292}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..a523326
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 289284, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.336281061172485, "TIME_S_1KI": 0.035730566022222056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 302.8508556365967, "W": 22.269333190276484, "J_1KI": 1.0468980504853247, "W_1KI": 0.07698086721103305, "W_D": 3.542333190276487, "J_D": 48.17381052494056, "W_D_1KI": 0.012245174950140648, "J_D_1KI": 4.232925066765064e-05}
diff --git a/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..2a5bc74
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,356 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04395866394042969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 439, 4351,  241,  500, 1004,  350, 1803, 3065, 3191,
+                           3136, 4095,  405, 1027,  870, 1417, 1360, 1534, 1342,
+                           3091, 3442, 2499, 1358, 1636, 3780, 2825, 2256, 4221,
+                            891, 2908, 3121, 1626, 2038,  883, 1037,  495, 2079,
+                            274,  937, 1868, 1488, 2903, 1523, 2167,  269, 3946,
+                           4053, 3008, 3702, 2193, 1563,  433, 1763, 2812, 3707,
+                           1886, 3013, 1511,  241, 1937, 2889, 1518, 4490, 4205,
+                           2026, 1673,  448,  986, 4061, 3094, 3985, 2097, 1213,
+                           4129, 3540, 2913, 1842, 3281, 3579, 2699, 1582, 1926,
+                           2137, 2888,  530, 3516, 2878,   57, 3238, 1656,  156,
+                           3904, 1121,  616, 2128,  426, 4846, 2365, 4030,  347,
+                           3690,  867, 1324, 1005, 4649, 3492, 4358,   47,  220,
+                           4307,  708, 2842, 3336, 1686, 1004, 4195, 3767,  332,
+                             43, 2809, 3452, 1463, 2172, 1464, 3770, 1880, 2042,
+                           3777, 2498, 3420, 1112, 4060, 4103, 4825, 1440, 4448,
+                             99, 2245, 3060,   27, 3733,  457, 3987, 3747, 1652,
+                           2522,  757, 4125, 2250, 2724, 3925, 2338, 3816, 1409,
+                           2282, 4242,  682, 3683, 4310, 1582, 4330,  601,  544,
+                           2289, 2874, 3966, 1136,  681, 4257, 2516, 3237, 2677,
+                           2257, 2771, 3675, 3168, 1248, 4288, 3632, 3599,  280,
+                           4551, 4047, 3577, 2662, 2281, 1968, 3402,  454, 1141,
+                           3366,  354, 2291, 4168, 3523, 2296, 2127, 2248, 4229,
+                           2140,  736, 3393,  640, 4820, 2236, 1416, 4815, 3234,
+                           1042, 1979, 4637, 2323,  138, 2380, 3226, 1859, 3342,
+                           2378,  803,  349, 3172, 4960, 4660, 4480, 3337,  245,
+                           4128, 3649, 2732,  968,  771, 3445, 3899,  644,   16,
+                           3599, 1029, 1799, 4502,  366, 4843, 2859, 2949,  545,
+                            645, 3511, 4843,  251, 2988, 2387,  946]),
+       values=tensor([9.4296e-01, 5.2301e-01, 8.9037e-01, 1.8262e-01,
+                      9.3621e-01, 5.6553e-01, 9.8721e-01, 6.5141e-01,
+                      2.8305e-01, 8.9567e-01, 7.1276e-04, 4.5788e-01,
+                      1.3154e-01, 7.7912e-01, 2.1464e-01, 9.3572e-01,
+                      4.0199e-01, 1.4579e-01, 1.5259e-01, 5.2311e-01,
+                      6.3620e-01, 8.3700e-01, 3.7813e-01, 1.4289e-01,
+                      6.8630e-01, 9.7120e-01, 7.6830e-01, 1.8723e-01,
+                      5.0392e-01, 9.2014e-01, 9.6103e-01, 7.2487e-01,
+                      3.2638e-01, 3.9838e-01, 2.7919e-01, 9.9376e-02,
+                      1.2394e-01, 1.9018e-01, 9.4573e-01, 4.8384e-02,
+                      3.3755e-01, 5.4543e-01, 6.5933e-01, 9.2931e-03,
+                      6.7184e-01, 3.3367e-01, 7.2403e-02, 1.6238e-01,
+                      7.9429e-01, 7.1594e-01, 9.3852e-01, 9.0787e-01,
+                      8.7587e-01, 2.4929e-01, 3.4089e-01, 7.4583e-01,
+                      3.6106e-01, 5.5151e-01, 6.3073e-01, 2.4689e-01,
+                      6.6122e-01, 6.2804e-01, 3.7429e-04, 5.6550e-01,
+                      5.0592e-01, 5.2248e-02, 7.1885e-01, 1.4852e-03,
+                      6.1029e-01, 4.5258e-01, 9.8998e-01, 7.7545e-03,
+                      6.8035e-01, 8.7032e-01, 2.7807e-01, 6.6854e-01,
+                      8.8838e-01, 1.5830e-02, 6.6226e-01, 1.1911e-01,
+                      1.8780e-01, 3.7508e-01, 9.2709e-01, 1.3932e-01,
+                      8.5139e-01, 2.8186e-01, 2.2711e-01, 8.2491e-01,
+                      9.3666e-01, 5.4799e-01, 8.7126e-01, 5.6305e-01,
+                      2.9909e-01, 9.8105e-02, 1.0565e-01, 9.1471e-01,
+                      9.5693e-01, 5.2767e-01, 7.5753e-01, 2.3887e-01,
+                      8.7389e-01, 2.4255e-01, 8.0756e-01, 7.2201e-01,
+                      6.6620e-01, 4.9751e-01, 5.1454e-01, 8.6001e-01,
+                      3.0834e-01, 2.2246e-01, 1.9841e-01, 8.9698e-02,
+                      9.1174e-01, 9.2243e-01, 7.7010e-01, 3.5962e-01,
+                      6.8634e-01, 9.5528e-01, 9.6147e-02, 9.3024e-02,
+                      8.3726e-01, 7.2003e-01, 6.7904e-01, 2.9273e-01,
+                      9.7464e-02, 1.5658e-02, 9.0559e-01, 3.6883e-01,
+                      7.9470e-01, 3.6450e-01, 5.7814e-03, 6.5827e-02,
+                      6.1557e-02, 3.8228e-02, 4.7705e-01, 2.6058e-01,
+                      8.0137e-01, 9.8272e-01, 8.4581e-01, 6.6501e-01,
+                      5.2583e-03, 3.0522e-01, 9.5123e-01, 2.4154e-01,
+                      6.0106e-01, 6.7170e-01, 2.1086e-01, 6.6402e-01,
+                      9.0397e-01, 3.9084e-01, 2.0324e-01, 7.2153e-01,
+                      6.7300e-01, 5.3381e-01, 2.8418e-02, 4.4506e-01,
+                      1.0782e-01, 1.9622e-01, 8.0898e-02, 5.4146e-01,
+                      8.2802e-01, 7.5722e-01, 9.2798e-04, 8.7421e-02,
+                      6.0281e-01, 1.2511e-01, 5.8418e-01, 7.7672e-01,
+                      8.2524e-01, 8.4603e-01, 6.9503e-01, 5.3184e-01,
+                      8.1918e-01, 5.6983e-01, 6.0056e-01, 1.8971e-01,
+                      1.0667e-01, 1.4853e-01, 3.6607e-01, 9.1330e-01,
+                      7.6093e-01, 6.6336e-01, 8.3088e-02, 8.4756e-01,
+                      5.8339e-01, 9.7773e-03, 7.7948e-02, 2.5127e-01,
+                      9.2139e-01, 3.2626e-01, 8.8502e-01, 8.8419e-01,
+                      9.3048e-01, 2.5403e-01, 7.0568e-01, 6.2669e-01,
+                      5.4774e-01, 7.1848e-01, 6.1011e-01, 7.7754e-01,
+                      8.5827e-01, 1.7827e-01, 6.2997e-01, 8.0090e-02,
+                      2.7963e-01, 9.9685e-01, 9.8342e-01, 1.9697e-01,
+                      4.5505e-01, 4.5432e-01, 2.5097e-01, 6.7016e-01,
+                      1.8891e-01, 1.1873e-01, 3.8346e-01, 2.0525e-01,
+                      7.7441e-01, 9.7489e-01, 9.5720e-01, 1.2362e-01,
+                      6.3758e-01, 4.1703e-01, 4.2223e-01, 1.8615e-01,
+                      3.6248e-02, 7.9391e-01, 2.0557e-01, 2.4331e-01,
+                      3.3957e-02, 7.9866e-01, 9.2672e-01, 7.1739e-01,
+                      4.0885e-01, 7.5316e-01, 1.3635e-01, 7.8209e-01,
+                      7.8379e-01, 8.6373e-01, 4.7931e-01, 9.1748e-01,
+                      8.8234e-01, 3.9897e-02, 1.9663e-01, 5.1895e-01,
+                      1.8534e-01, 5.8047e-01, 8.8859e-01, 6.9097e-01,
+                      9.8689e-01, 3.5349e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.0440, 0.7352, 0.2145,  ..., 0.3780, 0.1332, 0.0924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04395866394042969 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 238860 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.669770956039429}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3091, 2173, 2760, 4828, 4497, 2021, 4336, 1372, 2593,
+                           4578, 2353, 1617, 2286, 4843,  611, 3842,  780, 3798,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3130, 0.9247, 0.8789,  ..., 0.8987, 0.6939, 0.4674])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 8.669770956039429 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 289284 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.336281061172485}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1127, 2914,  651, 3868, 3478, 2616,  630, 2816, 2915,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
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+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.336281061172485 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1127, 2914,  651, 3868, 3478, 2616,  630, 2816, 2915,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4104, 0.7044, 0.9040,  ..., 0.0726, 0.3479, 0.6465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.336281061172485 seconds
+
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+[20.88, 21.0, 21.28, 23.68, 24.48, 25.8, 26.4, 26.0, 24.92, 23.92, 24.12, 24.16, 24.24]
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289284, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.336281061172485, 'TIME_S_1KI': 0.035730566022222056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 302.8508556365967, 'W': 22.269333190276484, 'J_1KI': 1.0468980504853247, 'W_1KI': 0.07698086721103305, 'W_D': 3.542333190276487, 'J_D': 48.17381052494056, 'W_D_1KI': 0.012245174950140648, 'J_D_1KI': 4.232925066765064e-05}
diff --git a/pytorch/output_synthetic_1core_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
deleted file mode 100644
index 024170e..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index def7a28..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 1edb066..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.0005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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]
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-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
deleted file mode 100644
index 78b2e2b..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 1a5d909..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.001.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 19d74a9..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index a379597..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_0.005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
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-{'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}
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-{'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_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json
deleted file mode 100644
index 044dbd0..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_100000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e69de29..0000000
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
deleted file mode 100644
index 82012e6..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_300000_1e-05.output
+++ /dev/null
@@ -1 +0,0 @@
-['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
deleted file mode 100644
index 849e147..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 9831ac1..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0001.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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
deleted file mode 100644
index cc699f5..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e29b500..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 31f0e20..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 2362809..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.001.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 883d2cb..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 9061dbb..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
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-Time: 96.94969439506531 seconds
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-[20.28, 20.48, 20.32, 20.24, 20.08, 20.28, 20.2, 20.08, 20.24, 20.04]
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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': 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
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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': 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
deleted file mode 100644
index de9aa95..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 111912f..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.01.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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]
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-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
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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': 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
deleted file mode 100644
index 8e7a90d..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 129f379..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.05.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
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-[20.36, 20.44, 20.52, 20.6, 20.8, 20.68, 20.76, 20.72, 21.08, 20.84]
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-997.7273364067078
-{'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
deleted file mode 100644
index 32cac99..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 3244029..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_0.1.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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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-{'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}
-[20.2, 20.08, 20.12, 19.96, 20.0, 20.0, 20.04, 20.04, 19.92, 20.12, 20.08, 20.04, 20.24, 20.4, 20.4, 20.44, 20.44, 20.12, 20.12, 20.2]
-362.65999999999997
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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': 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_1e-05.json b/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json
deleted file mode 100644
index e115dec..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e2a45bb..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_30000_1e-05.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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
deleted file mode 100644
index ddc6fac..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e80940c..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 5ee77f1..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.0005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 8992275..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 5b8cd5a..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index d7cdb54..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 789132f..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 209aa36..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_0.01.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
-
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-{'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
deleted file mode 100644
index 918a20d..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index bf1327e..0000000
--- a/pytorch/output_synthetic_1core_old/altra_1_csr_20_10_10_synthetic_50000_1e-05.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['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_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..4c97264
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6154, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661418676376343, "TIME_S_1KI": 1.732437223980556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.1215419101716, "W": 66.29, "J_1KI": 144.31614265683646, "W_1KI": 10.771855703607411, "W_D": 31.486250000000005, "J_D": 421.8376361286641, "W_D_1KI": 5.116387715307118, "J_D_1KI": 0.8313922189319334}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..3c6753a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 1.7059962749481201}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      28,  ...,  999981,
+                             999989, 1000000]),
+       col_indices=tensor([10839, 13780, 19162,  ..., 70763, 71204, 84111]),
+       values=tensor([0.3862, 0.3703, 0.4692,  ..., 0.8959, 0.7094, 0.8230]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6738, 0.0568, 0.8510,  ..., 0.5567, 0.5192, 0.1431])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 1.7059962749481201 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6154', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661418676376343}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      19,      22,  ...,  999983,
+                             999992, 1000000]),
+       col_indices=tensor([ 4495, 11307, 13629,  ..., 46229, 59792, 89876]),
+       values=tensor([0.8364, 0.7832, 0.5169,  ..., 0.6963, 0.9299, 0.6811]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2491, 0.3919, 0.1225,  ..., 0.6201, 0.7425, 0.7393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.661418676376343 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      19,      22,  ...,  999983,
+                             999992, 1000000]),
+       col_indices=tensor([ 4495, 11307, 13629,  ..., 46229, 59792, 89876]),
+       values=tensor([0.8364, 0.7832, 0.5169,  ..., 0.6963, 0.9299, 0.6811]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2491, 0.3919, 0.1225,  ..., 0.6201, 0.7425, 0.7393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.661418676376343 seconds
+
+[39.48, 39.2, 39.2, 38.44, 38.38, 38.41, 38.43, 38.34, 38.9, 38.82]
+[66.29]
+13.3975191116333
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6154, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661418676376343, 'TIME_S_1KI': 1.732437223980556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1215419101716, 'W': 66.29}
+[39.48, 39.2, 39.2, 38.44, 38.38, 38.41, 38.43, 38.34, 38.9, 38.82, 39.78, 38.22, 38.35, 38.27, 38.77, 38.41, 38.95, 38.84, 38.8, 38.25]
+696.075
+34.80375
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6154, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661418676376343, 'TIME_S_1KI': 1.732437223980556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1215419101716, 'W': 66.29, 'J_1KI': 144.31614265683646, 'W_1KI': 10.771855703607411, 'W_D': 31.486250000000005, 'J_D': 421.8376361286641, 'W_D_1KI': 5.116387715307118, 'J_D_1KI': 0.8313922189319334}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..28fc497
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 16.51292133331299, "TIME_S_1KI": 16.51292133331299, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1675.715212612152, "W": 77.36, "J_1KI": 1675.715212612152, "W_1KI": 77.36, "W_D": 42.0475, "J_D": 910.8019054073095, "W_D_1KI": 42.0475, "J_D_1KI": 42.0475}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..d1f2742
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 16.51292133331299}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       85,      184,  ...,  9999802,
+                             9999894, 10000000]),
+       col_indices=tensor([ 1647,  2383,  2584,  ..., 98263, 98777, 99734]),
+       values=tensor([0.1681, 0.5843, 0.2619,  ..., 0.7600, 0.0011, 0.9501]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3789, 0.7363, 0.6915,  ..., 0.8879, 0.6465, 0.7586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 16.51292133331299 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       85,      184,  ...,  9999802,
+                             9999894, 10000000]),
+       col_indices=tensor([ 1647,  2383,  2584,  ..., 98263, 98777, 99734]),
+       values=tensor([0.1681, 0.5843, 0.2619,  ..., 0.7600, 0.0011, 0.9501]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3789, 0.7363, 0.6915,  ..., 0.8879, 0.6465, 0.7586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 16.51292133331299 seconds
+
+[39.67, 38.57, 40.75, 44.07, 38.52, 39.19, 39.95, 38.76, 38.89, 38.37]
+[77.36]
+21.661261796951294
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 16.51292133331299, 'TIME_S_1KI': 16.51292133331299, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1675.715212612152, 'W': 77.36}
+[39.67, 38.57, 40.75, 44.07, 38.52, 39.19, 39.95, 38.76, 38.89, 38.37, 39.54, 38.41, 38.76, 38.62, 39.0, 38.84, 38.88, 38.44, 38.6, 38.42]
+706.25
+35.3125
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 16.51292133331299, 'TIME_S_1KI': 16.51292133331299, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1675.715212612152, 'W': 77.36, 'J_1KI': 1675.715212612152, 'W_1KI': 77.36, 'W_D': 42.0475, 'J_D': 910.8019054073095, 'W_D_1KI': 42.0475, 'J_D_1KI': 42.0475}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..8f7419f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.322007656097412, "TIME_S_1KI": 0.8546830881922176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 838.5464090538024, "W": 64.47, "J_1KI": 69.43333684307382, "W_1KI": 5.3382462532085775, "W_D": 29.621750000000006, "J_D": 385.28326496648793, "W_D_1KI": 2.452740746874224, "J_D_1KI": 0.20309188928328428}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
similarity index 58%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.output
rename to pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
index 1f19b8b..d689d8c 100644
--- 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_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8651721477508545}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8693974018096924}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 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]),
+       col_indices=tensor([57795, 90642, 37628,  ..., 28610,   559, 98027]),
+       values=tensor([0.1696, 0.5341, 0.5606,  ..., 0.7529, 0.5749, 0.6066]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7674, 0.7013, 0.3294,  ..., 0.7372, 0.8879, 0.9691])
+tensor([0.7238, 0.7083, 0.7900,  ..., 0.2093, 0.5825, 0.4482])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.8651721477508545 seconds
+Time: 0.8693974018096924 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12077', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.322007656097412}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 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]),
+       col_indices=tensor([ 3486, 41765,  3206,  ..., 33238, 50080, 42417]),
+       values=tensor([0.6049, 0.2829, 0.2416,  ..., 0.9238, 0.5292, 0.5723]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8442, 0.5820, 0.8888,  ..., 0.9824, 0.3648, 0.8783])
+tensor([0.4597, 0.0749, 0.9185,  ..., 0.4582, 0.2319, 0.2322])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 20.77256941795349 seconds
+Time: 10.322007656097412 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 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]),
+       col_indices=tensor([ 3486, 41765,  3206,  ..., 33238, 50080, 42417]),
+       values=tensor([0.6049, 0.2829, 0.2416,  ..., 0.9238, 0.5292, 0.5723]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8442, 0.5820, 0.8888,  ..., 0.9824, 0.3648, 0.8783])
+tensor([0.4597, 0.0749, 0.9185,  ..., 0.4582, 0.2319, 0.2322])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 20.77256941795349 seconds
+Time: 10.322007656097412 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}
+[39.73, 38.57, 38.91, 38.66, 38.65, 38.36, 38.94, 39.73, 38.42, 38.47]
+[64.47]
+13.006769180297852
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.322007656097412, 'TIME_S_1KI': 0.8546830881922176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 838.5464090538024, 'W': 64.47}
+[39.73, 38.57, 38.91, 38.66, 38.65, 38.36, 38.94, 39.73, 38.42, 38.47, 39.26, 38.5, 38.85, 38.41, 38.98, 38.35, 38.75, 38.41, 38.37, 38.75]
+696.9649999999999
+34.84824999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.322007656097412, 'TIME_S_1KI': 0.8546830881922176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 838.5464090538024, 'W': 64.47, 'J_1KI': 69.43333684307382, 'W_1KI': 5.3382462532085775, 'W_D': 29.621750000000006, 'J_D': 385.28326496648793, 'W_D_1KI': 2.452740746874224, 'J_D_1KI': 0.20309188928328428}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..9dc0eed
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 238697, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480466604232788, "TIME_S_1KI": 0.04390698921324017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1152.02081230402, "W": 58.89, "J_1KI": 4.826289447726699, "W_1KI": 0.24671445388924035, "W_D": 23.781499999999994, "J_D": 465.21961195123185, "W_D_1KI": 0.09963049388974303, "J_D_1KI": 0.0004173931548772839}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..9b8fd23
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 0.05536341667175293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([8403, 1214, 9126,  ..., 1351, 3891, 9766]),
+       values=tensor([0.6664, 0.5402, 0.6356,  ..., 0.4443, 0.7393, 0.7343]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.3881, 0.9820, 0.4323,  ..., 0.4549, 0.5025, 0.0926])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.05536341667175293 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '189655', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.342687606811523}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9997, 10000]),
+       col_indices=tensor([1328, 2584, 2989,  ..., 4729, 4835, 7640]),
+       values=tensor([0.4337, 0.1976, 0.1440,  ..., 0.2725, 0.2860, 0.2817]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.7295, 0.2766, 0.3418,  ..., 0.0114, 0.7550, 0.8307])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 8.342687606811523 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '238697', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.480466604232788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9997, 10000]),
+       col_indices=tensor([9286, 7396,  732,  ..., 1484, 5299, 9027]),
+       values=tensor([0.3440, 0.6043, 0.5062,  ..., 0.2355, 0.1186, 0.4561]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0996, 0.8226, 0.2068,  ..., 0.2572, 0.9962, 0.0083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.480466604232788 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9997, 10000]),
+       col_indices=tensor([9286, 7396,  732,  ..., 1484, 5299, 9027]),
+       values=tensor([0.3440, 0.6043, 0.5062,  ..., 0.2355, 0.1186, 0.4561]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0996, 0.8226, 0.2068,  ..., 0.2572, 0.9962, 0.0083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.480466604232788 seconds
+
+[40.64, 38.78, 39.02, 39.34, 38.69, 38.38, 38.42, 38.49, 38.47, 38.38]
+[58.89]
+19.562248468399048
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 238697, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480466604232788, 'TIME_S_1KI': 0.04390698921324017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.02081230402, 'W': 58.89}
+[40.64, 38.78, 39.02, 39.34, 38.69, 38.38, 38.42, 38.49, 38.47, 38.38, 44.89, 39.96, 38.48, 39.7, 39.01, 38.35, 38.7, 38.38, 38.58, 38.93]
+702.1700000000001
+35.10850000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 238697, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.480466604232788, 'TIME_S_1KI': 0.04390698921324017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.02081230402, 'W': 58.89, 'J_1KI': 4.826289447726699, 'W_1KI': 0.24671445388924035, 'W_D': 23.781499999999994, 'J_D': 465.21961195123185, 'W_D_1KI': 0.09963049388974303, 'J_D_1KI': 0.0004173931548772839}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..80c52ca
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75618, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.541181087493896, "TIME_S_1KI": 0.13940042169184447, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.5316159009933, "W": 66.47, "J_1KI": 12.437932977611062, "W_1KI": 0.8790235129202042, "W_D": 31.600500000000004, "J_D": 447.1380973112584, "W_D_1KI": 0.41789653257160997, "J_D_1KI": 0.005526416098965987}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..9b49c17
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 0.16539597511291504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99980,  99988,
+                            100000]),
+       col_indices=tensor([2312, 2519, 3298,  ..., 9035, 9400, 9910]),
+       values=tensor([0.1410, 0.2218, 0.1849,  ..., 0.4652, 0.0649, 0.3640]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4363, 0.0084, 0.9005,  ..., 0.6999, 0.4782, 0.9424])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.16539597511291504 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '63484', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.8150315284729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     21,  ...,  99981,  99989,
+                            100000]),
+       col_indices=tensor([ 457, 1232, 2417,  ..., 8600, 9856, 9966]),
+       values=tensor([0.5653, 0.7705, 0.0640,  ..., 0.9989, 0.3761, 0.2052]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7731, 0.4840, 0.8355,  ..., 0.4086, 0.2552, 0.3939])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.8150315284729 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75618', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.541181087493896}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     15,  ...,  99981,  99989,
+                            100000]),
+       col_indices=tensor([ 812, 4021, 6538,  ..., 8729, 9196, 9676]),
+       values=tensor([0.8795, 0.6481, 0.9606,  ..., 0.0277, 0.7911, 0.3727]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1908, 0.4704, 0.2059,  ..., 0.1529, 0.3275, 0.9276])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.541181087493896 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     15,  ...,  99981,  99989,
+                            100000]),
+       col_indices=tensor([ 812, 4021, 6538,  ..., 8729, 9196, 9676]),
+       values=tensor([0.8795, 0.6481, 0.9606,  ..., 0.0277, 0.7911, 0.3727]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1908, 0.4704, 0.2059,  ..., 0.1529, 0.3275, 0.9276])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.541181087493896 seconds
+
+[38.98, 38.52, 38.36, 38.43, 38.45, 38.57, 38.52, 38.55, 38.55, 38.4]
+[66.47]
+14.149715900421143
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75618, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.541181087493896, 'TIME_S_1KI': 0.13940042169184447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.5316159009933, 'W': 66.47}
+[38.98, 38.52, 38.36, 38.43, 38.45, 38.57, 38.52, 38.55, 38.55, 38.4, 39.0, 38.48, 38.51, 39.07, 38.73, 38.62, 38.94, 38.66, 38.36, 43.76]
+697.3899999999999
+34.869499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75618, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.541181087493896, 'TIME_S_1KI': 0.13940042169184447, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.5316159009933, 'W': 66.47, 'J_1KI': 12.437932977611062, 'W_1KI': 0.8790235129202042, 'W_D': 31.600500000000004, 'J_D': 447.1380973112584, 'W_D_1KI': 0.41789653257160997, 'J_D_1KI': 0.005526416098965987}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..e580351
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10094, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.424894571304321, "TIME_S_1KI": 1.0327813127902044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 874.2127872061731, "W": 66.43, "J_1KI": 86.60717131030047, "W_1KI": 6.581137309292649, "W_D": 31.351250000000007, "J_D": 412.5796122971178, "W_D_1KI": 3.1059292649098484, "J_D_1KI": 0.3077005414018078}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..c679aff
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 1.040170669555664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      84,     184,  ...,  999814,
+                             999899, 1000000]),
+       col_indices=tensor([ 171,  251,  472,  ..., 9843, 9880, 9941]),
+       values=tensor([0.4805, 0.3615, 0.2747,  ..., 0.6607, 0.4074, 0.0301]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4780, 0.2256, 0.5818,  ..., 0.3209, 0.4621, 0.5747])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 1.040170669555664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10094', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.424894571304321}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     205,  ...,  999816,
+                             999911, 1000000]),
+       col_indices=tensor([  83,   89,  669,  ..., 9640, 9974, 9983]),
+       values=tensor([0.6432, 0.8453, 0.7190,  ..., 0.8302, 0.0770, 0.7390]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4670, 0.3263, 0.5346,  ..., 0.9779, 0.3626, 0.9957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.424894571304321 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     205,  ...,  999816,
+                             999911, 1000000]),
+       col_indices=tensor([  83,   89,  669,  ..., 9640, 9974, 9983]),
+       values=tensor([0.6432, 0.8453, 0.7190,  ..., 0.8302, 0.0770, 0.7390]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4670, 0.3263, 0.5346,  ..., 0.9779, 0.3626, 0.9957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.424894571304321 seconds
+
+[39.35, 40.11, 43.57, 38.24, 39.46, 38.59, 38.37, 38.38, 38.28, 38.71]
+[66.43]
+13.15990948677063
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10094, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.424894571304321, 'TIME_S_1KI': 1.0327813127902044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.2127872061731, 'W': 66.43}
+[39.35, 40.11, 43.57, 38.24, 39.46, 38.59, 38.37, 38.38, 38.28, 38.71, 40.11, 38.48, 38.37, 38.48, 38.37, 38.22, 38.39, 39.48, 38.33, 38.74]
+701.575
+35.07875
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10094, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.424894571304321, 'TIME_S_1KI': 1.0327813127902044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.2127872061731, 'W': 66.43, 'J_1KI': 86.60717131030047, 'W_1KI': 6.581137309292649, 'W_D': 31.351250000000007, 'J_D': 412.5796122971178, 'W_D_1KI': 3.1059292649098484, 'J_D_1KI': 0.3077005414018078}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..73d3b47
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496397256851196, "TIME_S_1KI": 5.970646903783388, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1131.7024735164641, "W": 74.58, "J_1KI": 643.7442966532789, "W_1KI": 42.42320819112628, "W_D": 39.617, "J_D": 601.1619320635796, "W_D_1KI": 22.535267349260522, "J_D_1KI": 12.818695875574814}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..78fc6b7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 5.972491502761841}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     519,     993,  ..., 4998959,
+                            4999496, 5000000]),
+       col_indices=tensor([  17,   61,   73,  ..., 9901, 9911, 9920]),
+       values=tensor([0.3098, 0.8299, 0.3979,  ..., 0.3415, 0.7398, 0.5378]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6888, 0.9764, 0.3608,  ..., 0.4208, 0.9222, 0.1586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 5.972491502761841 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1758', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.496397256851196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     496,     998,  ..., 4999016,
+                            4999498, 5000000]),
+       col_indices=tensor([  25,   29,   69,  ..., 9894, 9911, 9997]),
+       values=tensor([0.8031, 0.3187, 0.9076,  ..., 0.2949, 0.8412, 0.6618]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4316, 0.0196, 0.7556,  ..., 0.1123, 0.7172, 0.6330])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.496397256851196 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     496,     998,  ..., 4999016,
+                            4999498, 5000000]),
+       col_indices=tensor([  25,   29,   69,  ..., 9894, 9911, 9997]),
+       values=tensor([0.8031, 0.3187, 0.9076,  ..., 0.2949, 0.8412, 0.6618]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4316, 0.0196, 0.7556,  ..., 0.1123, 0.7172, 0.6330])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.496397256851196 seconds
+
+[39.62, 38.84, 38.53, 38.4, 38.53, 38.95, 38.67, 38.93, 38.95, 38.34]
+[74.58]
+15.174342632293701
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496397256851196, 'TIME_S_1KI': 5.970646903783388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.7024735164641, 'W': 74.58}
+[39.62, 38.84, 38.53, 38.4, 38.53, 38.95, 38.67, 38.93, 38.95, 38.34, 39.14, 38.64, 38.84, 38.7, 38.86, 38.26, 38.63, 38.26, 38.4, 44.64]
+699.26
+34.963
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.496397256851196, 'TIME_S_1KI': 5.970646903783388, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.7024735164641, 'W': 74.58, 'J_1KI': 643.7442966532789, 'W_1KI': 42.42320819112628, 'W_D': 39.617, 'J_D': 601.1619320635796, 'W_D_1KI': 22.535267349260522, 'J_D_1KI': 12.818695875574814}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..6bba238
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 14.862756252288818, "TIME_S_1KI": 14.862756252288818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1509.8221988201142, "W": 77.42, "J_1KI": 1509.8221988201142, "W_1KI": 77.42, "W_D": 42.230500000000006, "J_D": 823.5668608534337, "W_D_1KI": 42.230500000000006, "J_D_1KI": 42.230500000000006}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..550e30b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 14.862756252288818}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1022,     2054,  ...,  9998064,
+                             9999016, 10000000]),
+       col_indices=tensor([   8,   12,   13,  ..., 9969, 9975, 9983]),
+       values=tensor([0.6048, 0.0895, 0.3093,  ..., 0.2729, 0.9589, 0.2791]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8686, 0.6857, 0.7903,  ..., 0.7591, 0.3670, 0.6215])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 14.862756252288818 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1022,     2054,  ...,  9998064,
+                             9999016, 10000000]),
+       col_indices=tensor([   8,   12,   13,  ..., 9969, 9975, 9983]),
+       values=tensor([0.6048, 0.0895, 0.3093,  ..., 0.2729, 0.9589, 0.2791]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8686, 0.6857, 0.7903,  ..., 0.7591, 0.3670, 0.6215])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 14.862756252288818 seconds
+
+[44.78, 38.35, 39.08, 38.71, 38.47, 39.2, 39.52, 39.83, 39.49, 40.27]
+[77.42]
+19.501707553863525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 14.862756252288818, 'TIME_S_1KI': 14.862756252288818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.8221988201142, 'W': 77.42}
+[44.78, 38.35, 39.08, 38.71, 38.47, 39.2, 39.52, 39.83, 39.49, 40.27, 40.17, 38.57, 38.51, 38.69, 38.5, 38.97, 38.48, 38.73, 38.88, 38.4]
+703.79
+35.189499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 14.862756252288818, 'TIME_S_1KI': 14.862756252288818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1509.8221988201142, 'W': 77.42, 'J_1KI': 1509.8221988201142, 'W_1KI': 77.42, 'W_D': 42.230500000000006, 'J_D': 823.5668608534337, 'W_D_1KI': 42.230500000000006, 'J_D_1KI': 42.230500000000006}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..721cd64
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 361507, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.549095392227173, "TIME_S_1KI": 0.029180888315377497, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.0974852204322, "W": 65.27, "J_1KI": 2.3847324815852313, "W_1KI": 0.18054975422329303, "W_D": 30.177249999999994, "J_D": 398.58635415762654, "W_D_1KI": 0.08347625357185336, "J_D_1KI": 0.0002309118594435332}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..4ba6f6a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-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": 0.043045759201049805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([1583, 2010, 5254, 7979, 6044, 1811, 7275, 5124, 1436,
+                           6977, 5579, 4446, 9531, 9948, 1649, 1369, 7922, 7653,
+                           2659, 6213,  259, 9933, 1809, 5407, 6617, 1048, 9736,
+                           8197, 4865, 8298, 9784,   82, 5539,  325, 4902, 1683,
+                           7666,  621, 9636, 2101, 9761,  740, 4832,  605, 5884,
+                           3975, 9597, 4053, 6617, 1715, 7682, 8784, 4868, 6631,
+                           4385, 6313, 6260, 3586, 9177, 2920, 7526, 5398, 7541,
+                            248, 3734, 7646, 6276, 8109, 2125, 9714, 6281, 1353,
+                           5963, 2603,  264, 3737, 9675, 9238, 2280, 9506, 9180,
+                           8024, 6153, 5553, 3522, 6695, 1640, 8954, 8297, 6626,
+                            843, 9222, 5001, 3481, 6513, 5429, 9771, 5585, 8988,
+                           5464, 3454, 6624, 6512, 7330, 6444, 6199, 5861, 4510,
+                            672, 6028, 7721, 5884, 2715, 1700, 4921, 4515, 9810,
+                            242, 3364, 5002, 1424, 3751, 9511, 8727, 7691, 6098,
+                           8102, 5389, 3846,  102, 6605, 6800, 5446, 4429, 9979,
+                           6051,  887, 6347, 5680, 5275, 2705, 9887, 8702, 7450,
+                           9613, 1435, 9601, 9924, 7389, 9934, 1462, 4302,  946,
+                            584, 8440, 8214, 4233, 5454, 4216, 6174, 9492, 1467,
+                           7415, 3149, 3551, 5073, 7523, 8960, 4752, 7424, 4426,
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+                      0.6001, 0.8208, 0.0590, 0.9928, 0.6332, 0.0738]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8647, 0.0597, 0.7485,  ..., 0.2087, 0.0137, 0.4402])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.043045759201049805 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '243926', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.084842920303345}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8197, 0.8953, 0.9337,  ..., 0.6014, 0.8565, 0.4467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.084842920303345 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '361507', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.549095392227173}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      6.6233e-01, 5.8030e-01, 7.4490e-01, 9.5820e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1332, 0.3872, 0.9921,  ..., 0.6563, 0.0596, 0.5136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.549095392227173 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
+       col_indices=tensor([3175, 1540, 6513, 4566, 9706, 3242, 7522,  361, 3563,
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+                      4.3519e-01, 4.6871e-01, 3.5600e-01, 7.0874e-01,
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+                      8.1865e-01, 6.7206e-01, 4.9234e-01, 9.2049e-01,
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+                      5.6399e-02, 1.9669e-01, 1.0907e-01, 8.8177e-01,
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+                      4.0233e-01, 4.7439e-01, 8.6903e-01, 6.4683e-01,
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+                      1.2616e-01, 9.6273e-01, 2.5433e-01, 7.5316e-01,
+                      9.7344e-01, 2.8688e-01, 7.9705e-01, 3.3331e-01,
+                      9.6254e-01, 9.1487e-01, 2.8480e-01, 3.2055e-01,
+                      3.9523e-01, 3.6145e-01, 4.5015e-01, 9.4686e-01,
+                      3.2711e-02, 8.9001e-01, 1.9633e-02, 3.7308e-01,
+                      5.9301e-01, 8.8253e-01, 8.2784e-01, 4.4139e-01,
+                      6.6233e-01, 5.8030e-01, 7.4490e-01, 9.5820e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1332, 0.3872, 0.9921,  ..., 0.6563, 0.0596, 0.5136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.549095392227173 seconds
+
+[45.65, 38.89, 39.88, 38.76, 38.37, 38.3, 38.7, 38.8, 39.08, 38.56]
+[65.27]
+13.208173513412476
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 361507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.549095392227173, 'TIME_S_1KI': 0.029180888315377497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.0974852204322, 'W': 65.27}
+[45.65, 38.89, 39.88, 38.76, 38.37, 38.3, 38.7, 38.8, 39.08, 38.56, 39.02, 38.54, 38.45, 38.34, 38.8, 39.14, 38.83, 39.15, 38.35, 39.72]
+701.855
+35.09275
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 361507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.549095392227173, 'TIME_S_1KI': 0.029180888315377497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.0974852204322, 'W': 65.27, 'J_1KI': 2.3847324815852313, 'W_1KI': 0.18054975422329303, 'W_D': 30.177249999999994, 'J_D': 398.58635415762654, 'W_D_1KI': 0.08347625357185336, 'J_D_1KI': 0.0002309118594435332}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..98e0f55
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.362020254135132, "TIME_S_1KI": 7.635976605847555, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.3382936573029, "W": 74.92, "J_1KI": 734.9582119803264, "W_1KI": 55.21002210759028, "W_D": 39.366, "J_D": 524.0419016032218, "W_D_1KI": 29.00957995578482, "J_D_1KI": 21.377730254815635}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
similarity index 56%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.output
rename to pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
index b6ef67e..f772ea4 100644
--- 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_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.669674634933472}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.737008094787598}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,       6,       9,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 13538,  14404, 124427,  ..., 299545,  64656,
+                           263709]),
+       values=tensor([0.6726, 0.7704, 0.5503,  ..., 0.8434, 0.2560, 0.2989]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6123, 0.6429, 0.4015,  ..., 0.8475, 0.6522, 0.4492])
+tensor([0.7902, 0.8995, 0.9133,  ..., 0.8775, 0.6765, 0.9460])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 7.669674634933472 seconds
+Time: 7.737008094787598 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1357', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.362020254135132}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       2,       8,  ..., 2499990,
+tensor(crow_indices=tensor([      0,       7,      12,  ..., 2499987,
                             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]),
+       col_indices=tensor([ 74385, 156503, 312661,  ..., 102229, 341067,
+                           464580]),
+       values=tensor([0.2383, 0.0369, 0.7603,  ..., 0.0658, 0.9688, 0.3918]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3985, 0.6545, 0.8794,  ..., 0.0163, 0.4728, 0.5226])
+tensor([0.4224, 0.2766, 0.2547,  ..., 0.2726, 0.8333, 0.3690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 21.017223119735718 seconds
+Time: 10.362020254135132 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       2,       8,  ..., 2499990,
+tensor(crow_indices=tensor([      0,       7,      12,  ..., 2499987,
                             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]),
+       col_indices=tensor([ 74385, 156503, 312661,  ..., 102229, 341067,
+                           464580]),
+       values=tensor([0.2383, 0.0369, 0.7603,  ..., 0.0658, 0.9688, 0.3918]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3985, 0.6545, 0.8794,  ..., 0.0163, 0.4728, 0.5226])
+tensor([0.4224, 0.2766, 0.2547,  ..., 0.2726, 0.8333, 0.3690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 21.017223119735718 seconds
+Time: 10.362020254135132 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}
+[40.3, 38.79, 39.14, 38.79, 39.0, 38.98, 38.53, 44.02, 38.67, 38.42]
+[74.92]
+13.31204342842102
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.362020254135132, 'TIME_S_1KI': 7.635976605847555, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3382936573029, 'W': 74.92}
+[40.3, 38.79, 39.14, 38.79, 39.0, 38.98, 38.53, 44.02, 38.67, 38.42, 39.44, 38.36, 38.82, 38.43, 45.76, 38.31, 39.93, 38.5, 38.79, 38.36]
+711.08
+35.554
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.362020254135132, 'TIME_S_1KI': 7.635976605847555, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3382936573029, 'W': 74.92, 'J_1KI': 734.9582119803264, 'W_1KI': 55.21002210759028, 'W_D': 39.366, 'J_D': 524.0419016032218, 'W_D_1KI': 29.00957995578482, 'J_D_1KI': 21.377730254815635}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..0569add
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.70950984954834, "TIME_S_1KI": 0.6953775631159236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.8573481559754, "W": 65.04, "J_1KI": 54.987166298031, "W_1KI": 4.223102395948315, "W_D": 30.268000000000008, "J_D": 394.10636860370647, "W_D_1KI": 1.9653269268229343, "J_D_1KI": 0.12761034522582523}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
similarity index 58%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.output
rename to pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
index 114c64b..5835407 100644
--- 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_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6844191551208496}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6817739009857178}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      9,  ..., 249993, 249996,
+tensor(crow_indices=tensor([     0,      3,     11,  ..., 249990, 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]),
+       col_indices=tensor([22352, 25754, 44016,  ..., 24187, 38739, 43878]),
+       values=tensor([0.9987, 0.7536, 0.3762,  ..., 0.2868, 0.8081, 0.6848]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0481, 0.3329, 0.6398,  ..., 0.2932, 0.9523, 0.8115])
+tensor([0.2548, 0.4461, 0.9076,  ..., 0.8528, 0.8836, 0.6180])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.6844191551208496 seconds
+Time: 0.6817739009857178 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15401', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.70950984954834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     10,  ..., 249991, 249996,
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249994,
                             250000]),
-       col_indices=tensor([ 1625, 14875, 16966,  ..., 28233, 46165, 49230]),
-       values=tensor([0.7686, 0.4498, 0.3631,  ..., 0.7737, 0.9073, 0.9265]),
+       col_indices=tensor([21278, 27457, 27912,  ..., 25636, 33177, 40764]),
+       values=tensor([0.5508, 0.6259, 0.1639,  ..., 0.1456, 0.5920, 0.1745]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0406, 0.0267, 0.6076,  ..., 0.5503, 0.5752, 0.3050])
+tensor([0.3112, 0.1298, 0.2276,  ..., 0.1739, 0.6060, 0.6815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 20.60400652885437 seconds
+Time: 10.70950984954834 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     10,  ..., 249991, 249996,
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249994,
                             250000]),
-       col_indices=tensor([ 1625, 14875, 16966,  ..., 28233, 46165, 49230]),
-       values=tensor([0.7686, 0.4498, 0.3631,  ..., 0.7737, 0.9073, 0.9265]),
+       col_indices=tensor([21278, 27457, 27912,  ..., 25636, 33177, 40764]),
+       values=tensor([0.5508, 0.6259, 0.1639,  ..., 0.1456, 0.5920, 0.1745]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0406, 0.0267, 0.6076,  ..., 0.5503, 0.5752, 0.3050])
+tensor([0.3112, 0.1298, 0.2276,  ..., 0.1739, 0.6060, 0.6815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 20.60400652885437 seconds
+Time: 10.70950984954834 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}
+[39.37, 38.33, 39.38, 39.01, 38.5, 38.38, 38.36, 38.51, 38.52, 38.33]
+[65.04]
+13.020561933517456
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.70950984954834, 'TIME_S_1KI': 0.6953775631159236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.8573481559754, 'W': 65.04}
+[39.37, 38.33, 39.38, 39.01, 38.5, 38.38, 38.36, 38.51, 38.52, 38.33, 40.16, 38.83, 38.77, 38.25, 38.69, 38.32, 38.3, 38.47, 38.28, 39.22]
+695.4399999999999
+34.772
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.70950984954834, 'TIME_S_1KI': 0.6953775631159236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.8573481559754, 'W': 65.04, 'J_1KI': 54.987166298031, 'W_1KI': 4.223102395948315, 'W_D': 30.268000000000008, 'J_D': 394.10636860370647, 'W_D_1KI': 1.9653269268229343, 'J_D_1KI': 0.12761034522582523}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..4fe2ca5
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3498, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.43606948852539, "TIME_S_1KI": 2.983438961842593, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 938.4889250850676, "W": 69.66, "J_1KI": 268.29300316897303, "W_1KI": 19.914236706689536, "W_D": 34.37075, "J_D": 463.0572526825667, "W_D_1KI": 9.82582904516867, "J_D_1KI": 2.8089848613975614}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
similarity index 55%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.output
rename to pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
index 452da36..08291b1 100644
--- 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_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0004022121429443}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.0015740394592285}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      44,     103,  ..., 2499905,
+                            2499956, 2500000]),
+       col_indices=tensor([  226,  2395,  3856,  ..., 46208, 48736, 49649]),
+       values=tensor([0.2794, 0.3289, 0.9047,  ..., 0.2004, 0.4257, 0.7682]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6467, 0.8674, 0.4268,  ..., 0.4389, 0.3661, 0.7175])
+tensor([0.4960, 0.6719, 0.9417,  ..., 0.9330, 0.7654, 0.9120])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 3.0004022121429443 seconds
+Time: 3.0015740394592285 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3498', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.43606948852539}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      53,     101,  ..., 2499890,
+                            2499947, 2500000]),
+       col_indices=tensor([    1,   302,   356,  ..., 47860, 48391, 48616]),
+       values=tensor([0.1949, 0.9610, 0.6433,  ..., 0.8236, 0.0074, 0.9971]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1681, 0.1881, 0.9636,  ..., 0.5462, 0.5477, 0.9460])
+tensor([0.6285, 0.6234, 0.6444,  ..., 0.5791, 0.7727, 0.1804])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 20.897379159927368 seconds
+Time: 10.43606948852539 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      53,     101,  ..., 2499890,
+                            2499947, 2500000]),
+       col_indices=tensor([    1,   302,   356,  ..., 47860, 48391, 48616]),
+       values=tensor([0.1949, 0.9610, 0.6433,  ..., 0.8236, 0.0074, 0.9971]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1681, 0.1881, 0.9636,  ..., 0.5462, 0.5477, 0.9460])
+tensor([0.6285, 0.6234, 0.6444,  ..., 0.5791, 0.7727, 0.1804])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 20.897379159927368 seconds
+Time: 10.43606948852539 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}
+[38.98, 38.56, 38.59, 38.25, 38.75, 38.31, 38.43, 38.27, 38.33, 43.32]
+[69.66]
+13.472422122955322
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3498, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.43606948852539, 'TIME_S_1KI': 2.983438961842593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 938.4889250850676, 'W': 69.66}
+[38.98, 38.56, 38.59, 38.25, 38.75, 38.31, 38.43, 38.27, 38.33, 43.32, 39.04, 38.73, 38.9, 38.87, 38.44, 45.59, 38.97, 38.44, 40.32, 38.73]
+705.785
+35.289249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3498, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.43606948852539, 'TIME_S_1KI': 2.983438961842593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 938.4889250850676, 'W': 69.66, 'J_1KI': 268.29300316897303, 'W_1KI': 19.914236706689536, 'W_D': 34.37075, 'J_D': 463.0572526825667, 'W_D_1KI': 9.82582904516867, 'J_D_1KI': 2.8089848613975614}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..5c43412
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35695, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.507647275924683, "TIME_S_1KI": 0.2943730851918947, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 842.3661928725243, "W": 64.41, "J_1KI": 23.598996858734395, "W_1KI": 1.8044544053789044, "W_D": 29.134750000000004, "J_D": 381.0297847817541, "W_D_1KI": 0.8162137554279313, "J_D_1KI": 0.02286633297178684}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
similarity index 58%
rename from pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.output
rename to pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
index b01c98d..c6e8d71 100644
--- 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_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.31334519386291504}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3123207092285156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     3,     3,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 1731,  4163, 39043,  ..., 48142,  1105, 32715]),
+       values=tensor([0.9730, 0.5233, 0.5883,  ..., 0.0098, 0.9466, 0.3610]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5931, 0.9062, 0.9804,  ..., 0.6131, 0.7776, 0.6003])
+tensor([0.3233, 0.5001, 0.4757,  ..., 0.9452, 0.0190, 0.8013])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.31334519386291504 seconds
+Time: 0.3123207092285156 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '33619', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.88913083076477}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     0,     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]),
+       col_indices=tensor([10235, 29693, 19116,  ..., 40289, 44691, 23523]),
+       values=tensor([0.1639, 0.2137, 0.2836,  ..., 0.1546, 0.8297, 0.2686]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2944, 0.4597, 0.6320,  ..., 0.6057, 0.1898, 0.1566])
+tensor([0.0511, 0.8204, 0.3831,  ..., 0.1304, 0.0964, 0.0598])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 19.641794443130493 seconds
+Time: 9.88913083076477 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35695', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.507647275924683}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([19065, 20351, 39842,  ..., 40423,  9509, 47347]),
+       values=tensor([0.9158, 0.3839, 0.2352,  ..., 0.6644, 0.6974, 0.4594]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6139, 0.5193, 0.8626,  ..., 0.9070, 0.3972, 0.6619])
+tensor([0.0381, 0.0022, 0.0479,  ..., 0.9299, 0.2975, 0.9449])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 21.077060222625732 seconds
+Time: 10.507647275924683 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     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]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([19065, 20351, 39842,  ..., 40423,  9509, 47347]),
+       values=tensor([0.9158, 0.3839, 0.2352,  ..., 0.6644, 0.6974, 0.4594]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6139, 0.5193, 0.8626,  ..., 0.9070, 0.3972, 0.6619])
+tensor([0.0381, 0.0022, 0.0479,  ..., 0.9299, 0.2975, 0.9449])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 21.077060222625732 seconds
+Time: 10.507647275924683 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}
+[39.22, 44.39, 40.14, 38.47, 39.94, 38.41, 38.49, 38.91, 39.41, 38.82]
+[64.41]
+13.078189611434937
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35695, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.507647275924683, 'TIME_S_1KI': 0.2943730851918947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 842.3661928725243, 'W': 64.41}
+[39.22, 44.39, 40.14, 38.47, 39.94, 38.41, 38.49, 38.91, 39.41, 38.82, 39.08, 38.6, 38.48, 38.48, 38.48, 38.38, 38.83, 39.42, 38.84, 38.55]
+705.5049999999999
+35.27524999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35695, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.507647275924683, 'TIME_S_1KI': 0.2943730851918947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 842.3661928725243, 'W': 64.41, 'J_1KI': 23.598996858734395, 'W_1KI': 1.8044544053789044, 'W_D': 29.134750000000004, 'J_D': 381.0297847817541, 'W_D_1KI': 0.8162137554279313, 'J_D_1KI': 0.02286633297178684}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..cbaf9ec
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 478217, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.855157613754272, "TIME_S_1KI": 0.022699229876299402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.2491577148438, "W": 64.87, "J_1KI": 1.7905033859416202, "W_1KI": 0.1356497155057223, "W_D": 29.804500000000004, "J_D": 393.40339172363286, "W_D_1KI": 0.06232421683043473, "J_D_1KI": 0.00013032622602382335}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..dc99150
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03418374061584473}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3258, 3666,  785,  ...,  592, 2528, 4295]),
+       values=tensor([0.0745, 0.3346, 0.7433,  ..., 0.4561, 0.1450, 0.7729]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6815, 0.4251, 0.0154,  ..., 0.8636, 0.4620, 0.2584])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.03418374061584473 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '307163', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.744239568710327}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1557, 2371, 1241,  ..., 4745,  784, 3444]),
+       values=tensor([0.6224, 0.1480, 0.3479,  ..., 0.3226, 0.4259, 0.8584]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6292, 0.0071, 0.7726,  ..., 0.8443, 0.3847, 0.4326])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.744239568710327 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '478217', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.855157613754272}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 537,  942, 2250,  ..., 4421, 3640, 3689]),
+       values=tensor([0.2431, 0.3591, 0.7204,  ..., 0.2868, 0.0163, 0.2334]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9258, 0.9006, 0.7252,  ..., 0.7255, 0.3779, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.855157613754272 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 537,  942, 2250,  ..., 4421, 3640, 3689]),
+       values=tensor([0.2431, 0.3591, 0.7204,  ..., 0.2868, 0.0163, 0.2334]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9258, 0.9006, 0.7252,  ..., 0.7255, 0.3779, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.855157613754272 seconds
+
+[39.25, 38.14, 38.36, 38.18, 38.58, 38.56, 38.4, 38.47, 38.48, 38.68]
+[64.87]
+13.199462890625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 478217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.855157613754272, 'TIME_S_1KI': 0.022699229876299402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.2491577148438, 'W': 64.87}
+[39.25, 38.14, 38.36, 38.18, 38.58, 38.56, 38.4, 38.47, 38.48, 38.68, 39.07, 38.8, 38.23, 39.53, 38.54, 38.21, 38.59, 38.49, 41.46, 47.58]
+701.31
+35.0655
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 478217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.855157613754272, 'TIME_S_1KI': 0.022699229876299402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.2491577148438, 'W': 64.87, 'J_1KI': 1.7905033859416202, 'W_1KI': 0.1356497155057223, 'W_D': 29.804500000000004, 'J_D': 393.40339172363286, 'W_D_1KI': 0.06232421683043473, 'J_D_1KI': 0.00013032622602382335}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..34a1b0c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 248678, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.619725465774536, "TIME_S_1KI": 0.04270472444596843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 878.5904247951509, "W": 65.93, "J_1KI": 3.5330444381696444, "W_1KI": 0.2651219649506591, "W_D": 31.137750000000004, "J_D": 414.9450781080723, "W_D_1KI": 0.12521312701565881, "J_D_1KI": 0.0005035150958897}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..c454cf5
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05426168441772461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([1287, 1316, 2359,  ..., 1751, 2298, 3529]),
+       values=tensor([0.1773, 0.9664, 0.4947,  ..., 0.2806, 0.9364, 0.2474]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5449, 0.4697, 0.1251,  ..., 0.6031, 0.3711, 0.9109])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05426168441772461 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193506', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.17044973373413}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    10,  ..., 24989, 24995, 25000]),
+       col_indices=tensor([ 563, 1432, 1628,  ..., 3910, 4925, 4964]),
+       values=tensor([0.0779, 0.2473, 0.4860,  ..., 0.8752, 0.7145, 0.0936]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5862, 0.8689, 0.7521,  ..., 0.3378, 0.8388, 0.0430])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 8.17044973373413 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '248678', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.619725465774536}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ..., 24990, 24993, 25000]),
+       col_indices=tensor([  49,  355,  745,  ..., 2877, 3597, 4425]),
+       values=tensor([0.2389, 0.4883, 0.4431,  ..., 0.9568, 0.0569, 0.8170]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9647, 0.9839, 0.1030,  ..., 0.7979, 0.9168, 0.5702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.619725465774536 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ..., 24990, 24993, 25000]),
+       col_indices=tensor([  49,  355,  745,  ..., 2877, 3597, 4425]),
+       values=tensor([0.2389, 0.4883, 0.4431,  ..., 0.9568, 0.0569, 0.8170]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9647, 0.9839, 0.1030,  ..., 0.7979, 0.9168, 0.5702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.619725465774536 seconds
+
+[39.55, 38.57, 38.5, 38.49, 39.15, 38.5, 38.44, 38.52, 38.74, 38.66]
+[65.93]
+13.326109886169434
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 248678, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.619725465774536, 'TIME_S_1KI': 0.04270472444596843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.5904247951509, 'W': 65.93}
+[39.55, 38.57, 38.5, 38.49, 39.15, 38.5, 38.44, 38.52, 38.74, 38.66, 39.58, 38.56, 38.38, 38.87, 38.31, 38.44, 38.39, 38.74, 38.99, 38.72]
+695.845
+34.79225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 248678, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.619725465774536, 'TIME_S_1KI': 0.04270472444596843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.5904247951509, 'W': 65.93, 'J_1KI': 3.5330444381696444, 'W_1KI': 0.2651219649506591, 'W_D': 31.137750000000004, 'J_D': 414.9450781080723, 'W_D_1KI': 0.12521312701565881, 'J_D_1KI': 0.0005035150958897}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..163b6dc
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 39651, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.074631690979004, "TIME_S_1KI": 0.25408266351363157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 909.2396620059013, "W": 71.91, "J_1KI": 22.931065093084698, "W_1KI": 1.8135734281607019, "W_D": 21.342999999999996, "J_D": 269.8637478263378, "W_D_1KI": 0.5382714181231241, "J_D_1KI": 0.013575229328973395}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..5c37580
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.26480770111083984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     43,     97,  ..., 249898, 249949,
+                            250000]),
+       col_indices=tensor([  46,  106,  224,  ..., 4804, 4890, 4986]),
+       values=tensor([0.9512, 0.1564, 0.8337,  ..., 0.0764, 0.6147, 0.8806]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5067, 0.1013, 0.0742,  ..., 0.2212, 0.5429, 0.9437])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.26480770111083984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '39651', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.074631690979004}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     49,    105,  ..., 249900, 249947,
+                            250000]),
+       col_indices=tensor([ 129,  155,  285,  ..., 4713, 4736, 4825]),
+       values=tensor([0.9050, 0.4779, 0.3101,  ..., 0.9077, 0.5485, 0.2382]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7723, 0.0685, 0.7362,  ..., 0.7986, 0.1054, 0.6909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.074631690979004 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     49,    105,  ..., 249900, 249947,
+                            250000]),
+       col_indices=tensor([ 129,  155,  285,  ..., 4713, 4736, 4825]),
+       values=tensor([0.9050, 0.4779, 0.3101,  ..., 0.9077, 0.5485, 0.2382]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7723, 0.0685, 0.7362,  ..., 0.7986, 0.1054, 0.6909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.074631690979004 seconds
+
+[39.13, 38.4, 39.4, 38.95, 39.01, 38.64, 38.39, 62.19, 64.39, 63.32]
+[71.91]
+12.644133806228638
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 39651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.074631690979004, 'TIME_S_1KI': 0.25408266351363157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 909.2396620059013, 'W': 71.91}
+[39.13, 38.4, 39.4, 38.95, 39.01, 38.64, 38.39, 62.19, 64.39, 63.32, 68.24, 64.54, 66.6, 65.57, 64.04, 68.19, 66.94, 66.24, 69.2, 70.61]
+1011.34
+50.567
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 39651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.074631690979004, 'TIME_S_1KI': 0.25408266351363157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 909.2396620059013, 'W': 71.91, 'J_1KI': 22.931065093084698, 'W_1KI': 1.8135734281607019, 'W_D': 21.342999999999996, 'J_D': 269.8637478263378, 'W_D_1KI': 0.5382714181231241, 'J_D_1KI': 0.013575229328973395}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..c6f366b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8104, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.399449348449707, "TIME_S_1KI": 1.2832489324345642, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 868.1994806170463, "W": 65.85, "J_1KI": 107.132216265677, "W_1KI": 8.125616979269497, "W_D": 30.541749999999993, "J_D": 402.67777505141487, "W_D_1KI": 3.768725320829219, "J_D_1KI": 0.46504507907566867}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..440ed84
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.2955126762390137}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     222,     488,  ..., 1249497,
+                            1249743, 1250000]),
+       col_indices=tensor([   0,    1,   24,  ..., 4925, 4934, 4978]),
+       values=tensor([0.4956, 0.3294, 0.5952,  ..., 0.4990, 0.9373, 0.9148]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4962, 0.1920, 0.2421,  ..., 0.8601, 0.2392, 0.4151])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 1.2955126762390137 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8104', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.399449348449707}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     238,     495,  ..., 1249467,
+                            1249713, 1250000]),
+       col_indices=tensor([   4,    6,   45,  ..., 4913, 4952, 4965]),
+       values=tensor([0.6573, 0.3725, 0.2540,  ..., 0.9752, 0.8782, 0.5831]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7880, 0.7423, 0.8544,  ..., 0.3557, 0.5396, 0.2540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.399449348449707 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     238,     495,  ..., 1249467,
+                            1249713, 1250000]),
+       col_indices=tensor([   4,    6,   45,  ..., 4913, 4952, 4965]),
+       values=tensor([0.6573, 0.3725, 0.2540,  ..., 0.9752, 0.8782, 0.5831]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7880, 0.7423, 0.8544,  ..., 0.3557, 0.5396, 0.2540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.399449348449707 seconds
+
+[39.35, 38.88, 44.16, 39.47, 38.8, 39.17, 38.52, 38.75, 38.59, 39.07]
+[65.85]
+13.184502363204956
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.399449348449707, 'TIME_S_1KI': 1.2832489324345642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 868.1994806170463, 'W': 65.85}
+[39.35, 38.88, 44.16, 39.47, 38.8, 39.17, 38.52, 38.75, 38.59, 39.07, 39.04, 39.89, 38.99, 38.43, 38.88, 38.64, 38.45, 39.36, 39.15, 38.61]
+706.165
+35.30825
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.399449348449707, 'TIME_S_1KI': 1.2832489324345642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 868.1994806170463, 'W': 65.85, 'J_1KI': 107.132216265677, 'W_1KI': 8.125616979269497, 'W_D': 30.541749999999993, 'J_D': 402.67777505141487, 'W_D_1KI': 3.768725320829219, 'J_D_1KI': 0.46504507907566867}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..f20d05f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3588, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.469439029693604, "TIME_S_1KI": 2.9179038544296554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 926.2283216476441, "W": 68.56, "J_1KI": 258.1461320088194, "W_1KI": 19.108138238573023, "W_D": 33.73800000000001, "J_D": 455.7918774175645, "W_D_1KI": 9.403010033444819, "J_D_1KI": 2.620682840982391}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..3e39a46
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.925701141357422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     485,    1004,  ..., 2498982,
+                            2499482, 2500000]),
+       col_indices=tensor([  18,   27,   28,  ..., 4963, 4979, 4987]),
+       values=tensor([0.5744, 0.1591, 0.4039,  ..., 0.3146, 0.5536, 0.6554]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8562, 0.0559, 0.5751,  ..., 0.9013, 0.4689, 0.3374])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 2.925701141357422 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3588', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.469439029693604}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     486,    1006,  ..., 2498987,
+                            2499524, 2500000]),
+       col_indices=tensor([   6,   12,   25,  ..., 4979, 4985, 4986]),
+       values=tensor([0.9526, 0.5714, 0.7457,  ..., 0.5995, 0.2741, 0.0768]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9940, 0.0288, 0.0030,  ..., 0.3299, 0.0903, 0.2227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.469439029693604 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     486,    1006,  ..., 2498987,
+                            2499524, 2500000]),
+       col_indices=tensor([   6,   12,   25,  ..., 4979, 4985, 4986]),
+       values=tensor([0.9526, 0.5714, 0.7457,  ..., 0.5995, 0.2741, 0.0768]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9940, 0.0288, 0.0030,  ..., 0.3299, 0.0903, 0.2227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.469439029693604 seconds
+
+[39.61, 38.42, 39.72, 38.79, 38.63, 38.61, 38.72, 38.24, 38.41, 38.4]
+[68.56]
+13.509747982025146
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.469439029693604, 'TIME_S_1KI': 2.9179038544296554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.2283216476441, 'W': 68.56}
+[39.61, 38.42, 39.72, 38.79, 38.63, 38.61, 38.72, 38.24, 38.41, 38.4, 39.84, 38.26, 38.74, 38.59, 38.81, 38.31, 38.94, 38.63, 38.58, 38.23]
+696.4399999999999
+34.821999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3588, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.469439029693604, 'TIME_S_1KI': 2.9179038544296554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.2283216476441, 'W': 68.56, 'J_1KI': 258.1461320088194, 'W_1KI': 19.108138238573023, 'W_D': 33.73800000000001, 'J_D': 455.7918774175645, 'W_D_1KI': 9.403010033444819, 'J_D_1KI': 2.620682840982391}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..d170a6b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 565598, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406220436096191, "TIME_S_1KI": 0.018398616041952396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.5254345631599, "W": 64.13, "J_1KI": 1.498459037272338, "W_1KI": 0.11338441790812556, "W_D": 29.180249999999987, "J_D": 385.6386100407241, "W_D_1KI": 0.05159185499241509, "J_D_1KI": 9.121647352433192e-05}
diff --git a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..4a0b8d8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.055680274963378906}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4927,  850,  790,  511,    1, 4275, 3659, 3202, 4099,
+                           3346, 1589,  716, 4620, 4989, 3861, 2882, 2487,  356,
+                           3163, 4196, 2032,  713,  507, 4615, 4269, 4035, 1320,
+                            655, 4926, 1128, 2992, 3058, 2439, 4007, 3555, 1710,
+                           2353,  655, 2875,  397, 2586, 4948,  858, 1089,  783,
+                           1767, 1975, 2378, 3541, 1407,  868, 4760, 4954, 4948,
+                           2154, 3756,  192, 4715, 2175,  343, 3413,  855, 3051,
+                           4256, 4765, 3143, 3774, 3357, 1362, 3915, 3187, 3177,
+                           3730, 4948, 4331, 2972, 3797,  963, 1487, 1791, 3014,
+                           3104, 4150, 4779, 2304, 1176, 1597, 3268, 4290, 3867,
+                           1778, 4097, 4190, 1835, 2167, 1131, 4492, 3907, 2098,
+                           4204, 4273, 3262, 2220, 4871, 3645, 4702, 4344, 3548,
+                            398, 3919,  762, 3209,  941, 2587, 4871, 1294,  846,
+                           4270, 1587,  490, 3776,  205, 4893, 4944, 3389, 1241,
+                            319, 1205,  149, 2679,  835,  185, 1679,  305,  803,
+                           3987, 4919, 1049, 2984,  150, 2222, 3548, 4559, 2082,
+                            773, 3809,  333, 4072, 2819,  773, 1940, 3544, 2429,
+                           4213, 3874, 3370, 3390, 3737, 2306, 2576, 3944, 3962,
+                           2700, 3672, 1959, 2924, 1160, 2820,  201, 3021, 1400,
+                           2786, 3009, 3104, 1799, 1722, 1307, 4435, 3240, 3490,
+                           3514, 3928, 2870,  339,  280, 3127,  278,   43, 1063,
+                           3176, 1262, 2341, 4542, 3316, 4835, 2103, 3750, 2839,
+                           1642, 4880, 4963, 1368, 4924, 2484, 1087,   26, 3186,
+                           4671, 3346, 1979,  748,  800,  144,   54, 3361, 3955,
+                           4948, 2768, 2175,  216,    0,  934, 3902, 3054,  854,
+                           1551,  310,  382, 1750,  779, 4286, 2768, 4550, 2371,
+                           2027, 2115, 2210, 4053, 3461, 4944,  349, 2236, 2467,
+                           2141, 1730,   73, 1349, 3773, 2561, 2961]),
+       values=tensor([0.0052, 0.9685, 0.5552, 0.5554, 0.3769, 0.8417, 0.2484,
+                      0.8557, 0.2810, 0.1770, 0.3815, 0.5491, 0.2804, 0.7014,
+                      0.4668, 0.6665, 0.6885, 0.4406, 0.0793, 0.0505, 0.2168,
+                      0.2768, 0.8793, 0.5292, 0.6124, 0.8331, 0.8520, 0.8953,
+                      0.2979, 0.9092, 0.1021, 0.9939, 0.8355, 0.6875, 0.6744,
+                      0.7797, 0.7132, 0.1964, 0.7787, 0.7395, 0.3653, 0.6907,
+                      0.2135, 0.4345, 0.6550, 0.1169, 0.1290, 0.6211, 0.7886,
+                      0.4978, 0.8807, 0.4515, 0.8365, 0.6929, 0.0657, 0.2646,
+                      0.3895, 0.0998, 0.4953, 0.3952, 0.3596, 0.9459, 0.2141,
+                      0.1718, 0.1717, 0.3607, 0.1199, 0.7175, 0.8124, 0.4557,
+                      0.0741, 0.2089, 0.8742, 0.1642, 0.0425, 0.9409, 0.3852,
+                      0.8648, 0.0435, 0.7984, 0.2433, 0.6033, 0.1259, 0.5531,
+                      0.2437, 0.6326, 0.4382, 0.6680, 0.3511, 0.0596, 0.0831,
+                      0.8185, 0.6864, 0.6621, 0.0203, 0.2915, 0.7632, 0.4015,
+                      0.1622, 0.5710, 0.1068, 0.3154, 0.7156, 0.1137, 0.7110,
+                      0.7922, 0.6817, 0.4208, 0.8226, 0.6751, 0.5470, 0.6580,
+                      0.9115, 0.2395, 0.8631, 0.8946, 0.8633, 0.9964, 0.1781,
+                      0.0456, 0.7692, 0.7333, 0.7567, 0.4246, 0.7150, 0.3292,
+                      0.8102, 0.3763, 0.7077, 0.9596, 0.7799, 0.8995, 0.4237,
+                      0.8044, 0.0028, 0.6094, 0.0822, 0.3516, 0.1473, 0.3747,
+                      0.2994, 0.6148, 0.9715, 0.8176, 0.8036, 0.4058, 0.2036,
+                      0.3753, 0.4509, 0.2117, 0.5735, 0.9721, 0.6964, 0.3733,
+                      0.2389, 0.5980, 0.7861, 0.1124, 0.7224, 0.2736, 0.1517,
+                      0.1578, 0.1015, 0.9540, 0.9804, 0.5457, 0.1059, 0.7649,
+                      0.7606, 0.0359, 0.3684, 0.4744, 0.3881, 0.5669, 0.6894,
+                      0.8642, 0.1190, 0.1465, 0.4614, 0.1113, 0.6697, 0.9048,
+                      0.9025, 0.8550, 0.3322, 0.9950, 0.8601, 0.6688, 0.9556,
+                      0.6649, 0.0390, 0.6075, 0.3304, 0.8947, 0.7252, 0.7691,
+                      0.7526, 0.8639, 0.4721, 0.9403, 0.4391, 0.6933, 0.1244,
+                      0.9914, 0.2708, 0.4335, 0.8597, 0.4714, 0.6817, 0.8948,
+                      0.1646, 0.6199, 0.1780, 0.7119, 0.3391, 0.9514, 0.4224,
+                      0.9358, 0.1033, 0.8786, 0.4834, 0.9743, 0.3774, 0.6356,
+                      0.0241, 0.9866, 0.3267, 0.8949, 0.2494, 0.9412, 0.8442,
+                      0.7104, 0.1721, 0.4102, 0.7763, 0.4723, 0.0485, 0.1320,
+                      0.4711, 0.1941, 0.9435, 0.7325, 0.9932, 0.9457, 0.1546,
+                      0.7522, 0.6262, 0.4856, 0.7356, 0.9269]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4907, 0.7631, 0.4016,  ..., 0.1364, 0.7839, 0.0874])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.055680274963378906 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '188576', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.5007991790771484}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1945, 1023, 4059, 2482, 4205,  303, 4777,  854, 2860,
+                           3128, 1003, 4735, 2788, 4977, 4888, 1184, 1747, 1500,
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+                           2619, 1855,  848, 4820,   42, 3478, 2615, 4379, 3969,
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+                           2076, 4350, 3765, 3931, 2191, 4279, 3507, 1647, 3640,
+                             24, 2376, 2290, 3244,  118, 4586, 1505, 1122, 1321,
+                           3378, 2663, 1121, 2193, 4996, 4050, 1149, 1171,  674,
+                             98,  868, 2491, 2360, 3984, 4243, 3717]),
+       values=tensor([0.8923, 0.2170, 0.4055, 0.4662, 0.6388, 0.1130, 0.3558,
+                      0.8111, 0.3477, 0.3800, 0.1079, 0.8330, 0.9521, 0.2703,
+                      0.3856, 0.0011, 0.5451, 0.8270, 0.6026, 0.6871, 0.2987,
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+                      0.0472, 0.1280, 0.3544, 0.8497, 0.3044, 0.7975, 0.4038,
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+                      0.4821, 0.5628, 0.3527, 0.5347, 0.9985, 0.4438, 0.9458,
+                      0.8619, 0.6814, 0.4148, 0.2273, 0.3882, 0.1003, 0.0543,
+                      0.4150, 0.9185, 0.0166, 0.8297, 0.5190, 0.1538, 0.6141,
+                      0.2637, 0.0598, 0.8180, 0.7469, 0.0453, 0.5538, 0.8701,
+                      0.6469, 0.0982, 0.7176, 0.0465, 0.3670, 0.5104, 0.4937,
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+                      0.4220, 0.0019, 0.8210, 0.7450, 0.1671, 0.2691, 0.2129,
+                      0.6046, 0.1184, 0.5733, 0.5791, 0.6764]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1655, 0.0894, 0.3335,  ..., 0.5896, 0.4748, 0.7424])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.5007991790771484 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '565598', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.406220436096191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([3711, 2509, 1480, 2246, 4155, 2306,  315, 3219,  781,
+                           3895, 3381, 2148, 1468, 1317, 2648, 3838,  486, 2691,
+                           4269, 1833, 4130, 2494, 2935, 4534, 1404,  631, 2237,
+                           3119, 2408, 4857, 3452, 3551,  652, 1979,  294, 2907,
+                           4341,  963, 1166, 1723, 2311, 2016, 4067, 2454, 3108,
+                           4422,  594, 1090, 1798, 1231, 1189, 3083, 3007, 2134,
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+                           4164, 2100, 1603,  715, 4639, 1853, 3257, 1572, 4514,
+                           2943, 1003, 4748, 1038, 1012, 3061,  294]),
+       values=tensor([0.0072, 0.2895, 0.9639, 0.0057, 0.4191, 0.2094, 0.7103,
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+                      0.8260, 0.8724, 0.3230, 0.8818, 0.5434, 0.6423, 0.5673,
+                      0.7089, 0.6119, 0.9976, 0.0416, 0.2792]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0013, 0.0858, 0.8984,  ..., 0.5676, 0.8612, 0.3338])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.406220436096191 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([3711, 2509, 1480, 2246, 4155, 2306,  315, 3219,  781,
+                           3895, 3381, 2148, 1468, 1317, 2648, 3838,  486, 2691,
+                           4269, 1833, 4130, 2494, 2935, 4534, 1404,  631, 2237,
+                           3119, 2408, 4857, 3452, 3551,  652, 1979,  294, 2907,
+                           4341,  963, 1166, 1723, 2311, 2016, 4067, 2454, 3108,
+                           4422,  594, 1090, 1798, 1231, 1189, 3083, 3007, 2134,
+                           3681,  526, 4251, 1258, 2420, 4062,  326, 2947,  386,
+                           3623, 4002, 1015, 2488, 2914,  344,  749, 2046, 3369,
+                           2183, 4810,  804, 4709, 4216, 4774, 3285, 1736, 1631,
+                           1116, 2085, 4390, 2715, 1633, 1339, 4203, 1468, 3776,
+                           4650, 1964, 1644, 3484,  556, 1113,  359, 2615, 4829,
+                           4748, 1322,  159, 1685, 3154, 4693, 4031, 1252, 1027,
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+                            470, 1943, 1148, 4302, 4930, 4799, 1057,  474, 2030,
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+                           2345, 1525, 4678, 2542, 2753, 3445, 3912, 2714, 1361,
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+                           4839,  645, 2259, 3905, 3083, 2174, 1253, 1258, 2465,
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+                           4164, 2100, 1603,  715, 4639, 1853, 3257, 1572, 4514,
+                           2943, 1003, 4748, 1038, 1012, 3061,  294]),
+       values=tensor([0.0072, 0.2895, 0.9639, 0.0057, 0.4191, 0.2094, 0.7103,
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+                      0.7981, 0.0839, 0.1133, 0.3382, 0.5864, 0.5344, 0.5684,
+                      0.8353, 0.4735, 0.5909, 0.0547, 0.2196, 0.1029, 0.2516,
+                      0.4455, 0.6775, 0.1108, 0.8486, 0.1605, 0.0632, 0.7729,
+                      0.1033, 0.7416, 0.1100, 0.7509, 0.4420, 0.1639, 0.2794,
+                      0.8260, 0.8724, 0.3230, 0.8818, 0.5434, 0.6423, 0.5673,
+                      0.7089, 0.6119, 0.9976, 0.0416, 0.2792]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0013, 0.0858, 0.8984,  ..., 0.5676, 0.8612, 0.3338])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.406220436096191 seconds
+
+[39.5, 43.3, 38.37, 38.43, 38.66, 38.52, 38.75, 38.13, 38.31, 39.28]
+[64.13]
+13.215740442276001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 565598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406220436096191, 'TIME_S_1KI': 0.018398616041952396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.5254345631599, 'W': 64.13}
+[39.5, 43.3, 38.37, 38.43, 38.66, 38.52, 38.75, 38.13, 38.31, 39.28, 40.56, 38.94, 38.24, 38.25, 38.39, 38.16, 38.85, 38.41, 38.49, 38.25]
+698.9950000000001
+34.94975000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 565598, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.406220436096191, 'TIME_S_1KI': 0.018398616041952396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 847.5254345631599, 'W': 64.13, 'J_1KI': 1.498459037272338, 'W_1KI': 0.11338441790812556, 'W_D': 29.180249999999987, 'J_D': 385.6386100407241, 'W_D_1KI': 0.05159185499241509, 'J_D_1KI': 9.121647352433192e-05}
diff --git a/pytorch/output_synthetic_1core_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
deleted file mode 100644
index 0c0f3d2..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 89364a9..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_1000000_1e-05.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['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
deleted file mode 100644
index 11555aa..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 0a9c6a6..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 5c997f3..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 7cc2d82..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 378f4a9..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index bd8189d..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_0.005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 8e3d7eb..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_100000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 39218a0..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 100fb4b..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_3000000_1e-05.output
+++ /dev/null
@@ -1,51 +0,0 @@
-['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
deleted file mode 100644
index 4b4dea9..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index b2c76a2..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0001.output
+++ /dev/null
@@ -1,68 +0,0 @@
-['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
deleted file mode 100644
index 9826631..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 1d00751..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.0005.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['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
deleted file mode 100644
index b74a472..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 798bebb..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_0.001.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['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
deleted file mode 100644
index e1aa60a..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 219c53d..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_300000_1e-05.output
+++ /dev/null
@@ -1,68 +0,0 @@
-['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
deleted file mode 100644
index 9bbde60..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 616b7ef..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0001.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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
deleted file mode 100644
index 80e5199..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index ccdbe37..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 6b2d57a..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 5dedece..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.001.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 465580a..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 8f37edd..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index a4676a2..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index d1812b6..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.01.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 7f038bb..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 509a893..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.05.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 9a9fc75..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 09ee6f8..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_0.1.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 1c62691..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 3d76ec3..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_30000_1e-05.output
+++ /dev/null
@@ -1,81 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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
deleted file mode 100644
index d3de446..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index c127bed..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_0.0001.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['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
deleted file mode 100644
index 19009e3..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_500000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index b0cdcc0..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e7d785e..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index ac0ade3..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 3e8eb72..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 7504f86..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 4a5e75c..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['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
deleted file mode 100644
index 446b456..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 607e614..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_0.01.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['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
deleted file mode 100644
index 116c34c..0000000
--- a/pytorch/output_synthetic_1core_old/epyc_7313p_1_csr_20_10_10_synthetic_50000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..027433b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3626, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.40371823310852, "TIME_S_1KI": 2.869199733344876, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 756.9219121170044, "W": 52.54, "J_1KI": 208.74845894015564, "W_1KI": 14.489795918367347, "W_D": 36.50025, "J_D": 525.8439098353386, "W_D_1KI": 10.066257584114727, "J_D_1KI": 2.776132814151883}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
similarity index 57%
rename from pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.output
rename to pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
index 00d043f..8a43e29 100644
--- 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_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8750712871551514}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.89510178565979}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999968,
+                             999983, 1000000]),
+       col_indices=tensor([23348, 35658, 56723,  ..., 82423, 86979, 88187]),
+       values=tensor([0.8917, 0.1559, 0.5748,  ..., 0.5915, 0.7647, 0.8715]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6025, 0.9571, 0.3756,  ..., 0.4137, 0.6855, 0.5355])
+tensor([0.4707, 0.9474, 0.3412,  ..., 0.5588, 0.8812, 0.4153])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 2.8750712871551514 seconds
+Time: 2.89510178565979 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3626', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.40371823310852}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      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]),
+tensor(crow_indices=tensor([      0,       5,      15,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([16760, 54124, 62778,  ..., 86983, 90495, 98787]),
+       values=tensor([0.0638, 0.0650, 0.2338,  ..., 0.3776, 0.7465, 0.0262]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7723, 0.9147, 0.2969,  ..., 0.3023, 0.2205, 0.3351])
+tensor([0.7059, 0.4263, 0.8303,  ..., 0.6514, 0.5791, 0.5612])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 20.99964690208435 seconds
+Time: 10.40371823310852 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      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]),
+tensor(crow_indices=tensor([      0,       5,      15,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([16760, 54124, 62778,  ..., 86983, 90495, 98787]),
+       values=tensor([0.0638, 0.0650, 0.2338,  ..., 0.3776, 0.7465, 0.0262]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7723, 0.9147, 0.2969,  ..., 0.3023, 0.2205, 0.3351])
+tensor([0.7059, 0.4263, 0.8303,  ..., 0.6514, 0.5791, 0.5612])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 20.99964690208435 seconds
+Time: 10.40371823310852 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}
+[18.45, 17.61, 17.77, 17.55, 17.61, 17.99, 17.58, 18.4, 17.81, 17.62]
+[52.54]
+14.406583786010742
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.40371823310852, 'TIME_S_1KI': 2.869199733344876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9219121170044, 'W': 52.54}
+[18.45, 17.61, 17.77, 17.55, 17.61, 17.99, 17.58, 18.4, 17.81, 17.62, 18.49, 17.87, 17.62, 17.77, 17.72, 17.81, 18.01, 17.57, 17.69, 18.27]
+320.79499999999996
+16.039749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.40371823310852, 'TIME_S_1KI': 2.869199733344876, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9219121170044, 'W': 52.54, 'J_1KI': 208.74845894015564, 'W_1KI': 14.489795918367347, 'W_D': 36.50025, 'J_D': 525.8439098353386, 'W_D_1KI': 10.066257584114727, 'J_D_1KI': 2.776132814151883}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..46cf4d4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.417505741119385, "TIME_S_1KI": 27.417505741119385, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1870.535600104332, "W": 53.17, "J_1KI": 1870.535600104332, "W_1KI": 53.17, "W_D": 36.779250000000005, "J_D": 1293.9043910125495, "W_D_1KI": 36.779250000000005, "J_D_1KI": 36.779250000000005}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
similarity index 55%
rename from pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.output
rename to pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
index 1b57712..523884d 100644
--- 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_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-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}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 27.417505741119385}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      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]),
+tensor(crow_indices=tensor([       0,       87,      206,  ...,  9999814,
+                             9999907, 10000000]),
+       col_indices=tensor([  430,  1206,  1283,  ..., 96095, 96254, 99884]),
+       values=tensor([0.0855, 0.2486, 0.3160,  ..., 0.5781, 0.8085, 0.1274]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3917, 0.2257, 0.3594,  ..., 0.6468, 0.3908, 0.8732])
+tensor([0.4876, 0.8099, 0.9530,  ..., 0.3051, 0.4863, 0.6986])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 27.55906629562378 seconds
+Time: 27.417505741119385 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      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]),
+tensor(crow_indices=tensor([       0,       87,      206,  ...,  9999814,
+                             9999907, 10000000]),
+       col_indices=tensor([  430,  1206,  1283,  ..., 96095, 96254, 99884]),
+       values=tensor([0.0855, 0.2486, 0.3160,  ..., 0.5781, 0.8085, 0.1274]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3917, 0.2257, 0.3594,  ..., 0.6468, 0.3908, 0.8732])
+tensor([0.4876, 0.8099, 0.9530,  ..., 0.3051, 0.4863, 0.6986])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 27.55906629562378 seconds
+Time: 27.417505741119385 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}
+[18.51, 17.88, 18.06, 17.74, 17.69, 18.37, 18.17, 17.77, 18.14, 17.72]
+[53.17]
+35.18028211593628
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.417505741119385, 'TIME_S_1KI': 27.417505741119385, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.535600104332, 'W': 53.17}
+[18.51, 17.88, 18.06, 17.74, 17.69, 18.37, 18.17, 17.77, 18.14, 17.72, 18.92, 17.72, 17.91, 22.37, 18.39, 17.62, 17.83, 17.88, 17.9, 17.6]
+327.815
+16.39075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 27.417505741119385, 'TIME_S_1KI': 27.417505741119385, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.535600104332, 'W': 53.17, 'J_1KI': 1870.535600104332, 'W_1KI': 53.17, 'W_D': 36.779250000000005, 'J_D': 1293.9043910125495, 'W_D_1KI': 36.779250000000005, 'J_D_1KI': 36.779250000000005}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..fb45506
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 7957, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.378219842910767, "TIME_S_1KI": 1.3042880285171252, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 722.9498359966278, "W": 51.18, "J_1KI": 90.85708633864871, "W_1KI": 6.432072389091366, "W_D": 34.9585, "J_D": 493.81089960312846, "W_D_1KI": 4.3934271710443635, "J_D_1KI": 0.5521461821093834}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
similarity index 54%
rename from pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.output
rename to pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
index 2e16cf3..f2950b1 100644
--- 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_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3139328956604004}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.319572925567627}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      3,      4,  ...,  99998, 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]),
+       col_indices=tensor([ 8050, 18600, 47626,  ..., 72573,  7071, 11396]),
+       values=tensor([0.6679, 0.8144, 0.2788,  ..., 0.2480, 0.1170, 0.9852]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5233, 0.9086, 0.0476,  ..., 0.5287, 0.8958, 0.9684])
+tensor([0.3322, 0.6851, 0.8140,  ..., 0.1719, 0.4686, 0.0560])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 1.3139328956604004 seconds
+Time: 1.319572925567627 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7957', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.378219842910767}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  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]),
+       col_indices=tensor([79139, 34438, 57240,  ..., 99522, 68399,  1834]),
+       values=tensor([0.8717, 0.0754, 0.3550,  ..., 0.4586, 0.3508, 0.4372]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6356, 0.8811, 0.4031,  ..., 0.8985, 0.9999, 0.3835])
+tensor([0.1033, 0.1471, 0.4199,  ..., 0.6623, 0.5752, 0.0388])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 20.790293216705322 seconds
+Time: 10.378219842910767 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  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]),
+       col_indices=tensor([79139, 34438, 57240,  ..., 99522, 68399,  1834]),
+       values=tensor([0.8717, 0.0754, 0.3550,  ..., 0.4586, 0.3508, 0.4372]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6356, 0.8811, 0.4031,  ..., 0.8985, 0.9999, 0.3835])
+tensor([0.1033, 0.1471, 0.4199,  ..., 0.6623, 0.5752, 0.0388])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 20.790293216705322 seconds
+Time: 10.378219842910767 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}
+[19.98, 17.81, 17.8, 18.03, 17.89, 17.86, 17.65, 18.02, 17.79, 17.59]
+[51.18]
+14.12563180923462
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7957, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.378219842910767, 'TIME_S_1KI': 1.3042880285171252, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.9498359966278, 'W': 51.18}
+[19.98, 17.81, 17.8, 18.03, 17.89, 17.86, 17.65, 18.02, 17.79, 17.59, 18.5, 18.06, 17.99, 17.58, 17.86, 17.88, 17.85, 17.69, 17.49, 22.29]
+324.43
+16.2215
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 7957, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.378219842910767, 'TIME_S_1KI': 1.3042880285171252, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.9498359966278, 'W': 51.18, 'J_1KI': 90.85708633864871, 'W_1KI': 6.432072389091366, 'W_D': 34.9585, 'J_D': 493.81089960312846, 'W_D_1KI': 4.3934271710443635, 'J_D_1KI': 0.5521461821093834}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..641470f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 83764, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.409894704818726, "TIME_S_1KI": 0.12427647563175977, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 711.6688519239426, "W": 50.42, "J_1KI": 8.49611828379665, "W_1KI": 0.6019292297407001, "W_D": 34.06175, "J_D": 480.7752185049654, "W_D_1KI": 0.40663948713050957, "J_D_1KI": 0.004854585348485144}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..168b413
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 0.13988041877746582}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     5,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([5444, 7298, 2758,  ..., 5406,  201, 2159]),
+       values=tensor([0.2785, 0.9301, 0.1173,  ..., 0.6105, 0.0625, 0.6073]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9117, 0.7600, 0.5676,  ..., 0.4107, 0.0296, 0.3559])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.13988041877746582 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '75064', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.409368753433228}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([ 559, 1691, 3057,  ..., 6770,  161, 9445]),
+       values=tensor([0.2390, 0.7843, 0.4833,  ..., 0.8916, 0.1224, 0.1645]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9833, 0.3493, 0.9306,  ..., 0.5004, 0.5453, 0.7909])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 9.409368753433228 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '83764', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.409894704818726}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1791, 2178, 4941,  ..., 8437, 8977, 5726]),
+       values=tensor([0.7542, 0.7473, 0.0826,  ..., 0.7863, 0.2178, 0.9123]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0349, 0.7342, 0.7720,  ..., 0.6458, 0.8179, 0.1428])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.409894704818726 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1791, 2178, 4941,  ..., 8437, 8977, 5726]),
+       values=tensor([0.7542, 0.7473, 0.0826,  ..., 0.7863, 0.2178, 0.9123]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0349, 0.7342, 0.7720,  ..., 0.6458, 0.8179, 0.1428])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.409894704818726 seconds
+
+[18.39, 17.85, 18.37, 17.98, 17.9, 18.07, 21.28, 18.76, 18.12, 17.67]
+[50.42]
+14.11481261253357
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 83764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.409894704818726, 'TIME_S_1KI': 0.12427647563175977, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 711.6688519239426, 'W': 50.42}
+[18.39, 17.85, 18.37, 17.98, 17.9, 18.07, 21.28, 18.76, 18.12, 17.67, 18.33, 17.97, 17.87, 17.66, 17.77, 17.96, 17.86, 17.77, 17.81, 17.94]
+327.16499999999996
+16.358249999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 83764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.409894704818726, 'TIME_S_1KI': 0.12427647563175977, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 711.6688519239426, 'W': 50.42, 'J_1KI': 8.49611828379665, 'W_1KI': 0.6019292297407001, 'W_D': 34.06175, 'J_D': 480.7752185049654, 'W_D_1KI': 0.40663948713050957, 'J_D_1KI': 0.004854585348485144}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..b3d9f1e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 33076, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.000443935394287, "TIME_S_1KI": 0.30234744030095195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 713.14643699646, "W": 51.71, "J_1KI": 21.560842816436693, "W_1KI": 1.5633692103035435, "W_D": 35.256, "J_D": 486.2249232788086, "W_D_1KI": 1.065908816059983, "J_D_1KI": 0.03222604958459254}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..108ea7e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_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-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": 0.31745004653930664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     25,  ...,  99974,  99988,
+                            100000]),
+       col_indices=tensor([ 189, 1046, 1680,  ..., 7652, 7822, 9876]),
+       values=tensor([0.3200, 0.6172, 0.8426,  ..., 0.6310, 0.2892, 0.4983]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5979, 0.0691, 0.5787,  ..., 0.1637, 0.0173, 0.7657])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.31745004653930664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33076', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.000443935394287}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     16,  ...,  99975,  99984,
+                            100000]),
+       col_indices=tensor([2058, 2088, 2648,  ..., 8443, 9183, 9230]),
+       values=tensor([0.6058, 0.3120, 0.6569,  ..., 0.6120, 0.0868, 0.9498]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4574, 0.0884, 0.9388,  ..., 0.4572, 0.8159, 0.8640])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.000443935394287 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     16,  ...,  99975,  99984,
+                            100000]),
+       col_indices=tensor([2058, 2088, 2648,  ..., 8443, 9183, 9230]),
+       values=tensor([0.6058, 0.3120, 0.6569,  ..., 0.6120, 0.0868, 0.9498]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4574, 0.0884, 0.9388,  ..., 0.4572, 0.8159, 0.8640])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.000443935394287 seconds
+
+[18.4, 17.89, 17.86, 18.15, 21.93, 17.6, 17.74, 17.84, 17.81, 17.8]
+[51.71]
+13.791267395019531
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33076, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.000443935394287, 'TIME_S_1KI': 0.30234744030095195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 713.14643699646, 'W': 51.71}
+[18.4, 17.89, 17.86, 18.15, 21.93, 17.6, 17.74, 17.84, 17.81, 17.8, 22.42, 17.97, 17.98, 17.67, 18.14, 18.06, 18.09, 18.36, 17.77, 17.82]
+329.08000000000004
+16.454
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 33076, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.000443935394287, 'TIME_S_1KI': 0.30234744030095195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 713.14643699646, 'W': 51.71, 'J_1KI': 21.560842816436693, 'W_1KI': 1.5633692103035435, 'W_D': 35.256, 'J_D': 486.2249232788086, 'W_D_1KI': 1.065908816059983, 'J_D_1KI': 0.03222604958459254}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..ec86a79
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5536, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43858790397644, "TIME_S_1KI": 1.885583075140253, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.4033546972274, "W": 52.18, "J_1KI": 136.81418979357431, "W_1KI": 9.425578034682081, "W_D": 35.855000000000004, "J_D": 520.4426462757588, "W_D_1KI": 6.476697976878613, "J_D_1KI": 1.1699237674997496}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..cdf759d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 1.8966615200042725}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      98,     196,  ...,  999798,
+                             999896, 1000000]),
+       col_indices=tensor([ 136,  346,  355,  ..., 9896, 9907, 9979]),
+       values=tensor([0.5884, 0.9037, 0.2601,  ..., 0.4944, 0.5993, 0.9598]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5307, 0.6978, 0.6134,  ..., 0.5179, 0.0970, 0.9420])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 1.8966615200042725 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5536', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43858790397644}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      90,     180,  ...,  999793,
+                             999892, 1000000]),
+       col_indices=tensor([  10,   80,  127,  ..., 9954, 9956, 9988]),
+       values=tensor([0.2975, 0.8577, 0.6251,  ..., 0.1783, 0.1753, 0.5886]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4757, 0.0822, 0.0813,  ..., 0.4411, 0.1352, 0.6104])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.43858790397644 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      90,     180,  ...,  999793,
+                             999892, 1000000]),
+       col_indices=tensor([  10,   80,  127,  ..., 9954, 9956, 9988]),
+       values=tensor([0.2975, 0.8577, 0.6251,  ..., 0.1783, 0.1753, 0.5886]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4757, 0.0822, 0.0813,  ..., 0.4411, 0.1352, 0.6104])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.43858790397644 seconds
+
+[18.3, 18.15, 17.72, 17.62, 17.65, 18.51, 19.15, 17.59, 17.78, 18.09]
+[52.18]
+14.515204191207886
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5536, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43858790397644, 'TIME_S_1KI': 1.885583075140253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.4033546972274, 'W': 52.18}
+[18.3, 18.15, 17.72, 17.62, 17.65, 18.51, 19.15, 17.59, 17.78, 18.09, 18.11, 18.06, 18.06, 18.01, 19.44, 18.82, 18.14, 17.87, 17.86, 17.64]
+326.5
+16.325
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5536, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43858790397644, 'TIME_S_1KI': 1.885583075140253, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.4033546972274, 'W': 52.18, 'J_1KI': 136.81418979357431, 'W_1KI': 9.425578034682081, 'W_D': 35.855000000000004, 'J_D': 520.4426462757588, 'W_D_1KI': 6.476697976878613, 'J_D_1KI': 1.1699237674997496}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..4abbf3f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 10.659594058990479, "TIME_S_1KI": 10.659594058990479, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 944.4377518177032, "W": 52.04, "J_1KI": 944.4377518177032, "W_1KI": 52.04, "W_D": 22.411249999999995, "J_D": 406.72618304044, "W_D_1KI": 22.411249999999995, "J_D_1KI": 22.411249999999995}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..e447139
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 10.659594058990479}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     491,     987,  ..., 4999032,
+                            4999549, 5000000]),
+       col_indices=tensor([   2,   62,   63,  ..., 9943, 9957, 9997]),
+       values=tensor([0.7700, 0.3306, 0.4646,  ..., 0.1296, 0.2152, 0.2390]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3463, 0.4470, 0.4445,  ..., 0.6886, 0.1263, 0.4488])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.659594058990479 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     491,     987,  ..., 4999032,
+                            4999549, 5000000]),
+       col_indices=tensor([   2,   62,   63,  ..., 9943, 9957, 9997]),
+       values=tensor([0.7700, 0.3306, 0.4646,  ..., 0.1296, 0.2152, 0.2390]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3463, 0.4470, 0.4445,  ..., 0.6886, 0.1263, 0.4488])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.659594058990479 seconds
+
+[18.53, 17.91, 17.8, 17.55, 17.82, 18.2, 17.7, 17.96, 22.04, 18.01]
+[52.04]
+18.148304224014282
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.659594058990479, 'TIME_S_1KI': 10.659594058990479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 944.4377518177032, 'W': 52.04}
+[18.53, 17.91, 17.8, 17.55, 17.82, 18.2, 17.7, 17.96, 22.04, 18.01, 43.17, 47.09, 51.95, 51.61, 51.61, 46.81, 50.13, 50.7, 46.45, 18.78]
+592.575
+29.628750000000004
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.659594058990479, 'TIME_S_1KI': 10.659594058990479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 944.4377518177032, 'W': 52.04, 'J_1KI': 944.4377518177032, 'W_1KI': 52.04, 'W_D': 22.411249999999995, 'J_D': 406.72618304044, 'W_D_1KI': 22.411249999999995, 'J_D_1KI': 22.411249999999995}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..3316ce4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 26.374675512313843, "TIME_S_1KI": 26.374675512313843, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1870.3769276547432, "W": 52.87, "J_1KI": 1870.3769276547432, "W_1KI": 52.87, "W_D": 36.2005, "J_D": 1280.661622272849, "W_D_1KI": 36.2005, "J_D_1KI": 36.2005}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..fb57672
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 26.374675512313843}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1071,     2053,  ...,  9998024,
+                             9999000, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9980, 9985, 9995]),
+       values=tensor([0.3665, 0.1961, 0.0802,  ..., 0.1951, 0.2808, 0.5332]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6172, 0.4719, 0.5685,  ..., 0.7751, 0.3390, 0.5446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 26.374675512313843 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1071,     2053,  ...,  9998024,
+                             9999000, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9980, 9985, 9995]),
+       values=tensor([0.3665, 0.1961, 0.0802,  ..., 0.1951, 0.2808, 0.5332]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6172, 0.4719, 0.5685,  ..., 0.7751, 0.3390, 0.5446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 26.374675512313843 seconds
+
+[18.71, 18.02, 18.12, 18.01, 22.7, 19.43, 18.17, 18.48, 18.54, 18.36]
+[52.87]
+35.376904249191284
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 26.374675512313843, 'TIME_S_1KI': 26.374675512313843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.3769276547432, 'W': 52.87}
+[18.71, 18.02, 18.12, 18.01, 22.7, 19.43, 18.17, 18.48, 18.54, 18.36, 18.41, 17.86, 17.86, 18.01, 17.87, 17.66, 17.87, 17.6, 18.07, 22.76]
+333.39
+16.6695
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 26.374675512313843, 'TIME_S_1KI': 26.374675512313843, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1870.3769276547432, 'W': 52.87, 'J_1KI': 1870.3769276547432, 'W_1KI': 52.87, 'W_D': 36.2005, 'J_D': 1280.661622272849, 'W_D_1KI': 36.2005, 'J_D_1KI': 36.2005}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..bb313c5
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 225815, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.501307487487793, "TIME_S_1KI": 0.046504029792032386, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 725.5277880001069, "W": 50.86, "J_1KI": 3.2129300002218932, "W_1KI": 0.22522861634523836, "W_D": 34.5345, "J_D": 492.64135656094555, "W_D_1KI": 0.15293271040453468, "J_D_1KI": 0.0006772477931250567}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..9f5c396
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 0.06352877616882324}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([8818, 4997, 6295, 3180, 5518, 5172,  746, 5244, 2210,
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+                      7.3056e-01, 6.0896e-01, 8.5559e-01, 8.0240e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7941, 0.9355, 0.0308,  ..., 0.8188, 0.6700, 0.4642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.06352877616882324 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '165279', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.6851487159729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      5.8933e-02, 5.3074e-01, 3.6413e-01, 2.2481e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5425, 0.1860, 0.7804,  ..., 0.3375, 0.7494, 0.4073])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.6851487159729 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', '225815', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.501307487487793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.6725, 0.8987, 0.3589, 0.1096, 0.3360, 0.7261]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5928, 0.5923, 0.2769,  ..., 0.7868, 0.2495, 0.0989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.501307487487793 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 621, 8067, 8701, 6486, 5538, 5824,  379, 1918, 5000,
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+       values=tensor([0.4878, 0.2366, 0.8733, 0.8071, 0.3473, 0.2387, 0.9164,
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+                      0.9593, 0.4610, 0.7212, 0.1418, 0.6637, 0.4566, 0.9474,
+                      0.6935, 0.5967, 0.6983, 0.4944, 0.8522, 0.4429, 0.6141,
+                      0.6725, 0.8987, 0.3589, 0.1096, 0.3360, 0.7261]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5928, 0.5923, 0.2769,  ..., 0.7868, 0.2495, 0.0989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.501307487487793 seconds
+
+[18.25, 18.14, 17.95, 17.87, 17.91, 17.96, 17.87, 17.59, 18.03, 17.97]
+[50.86]
+14.265194416046143
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225815, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.501307487487793, 'TIME_S_1KI': 0.046504029792032386, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 725.5277880001069, 'W': 50.86}
+[18.25, 18.14, 17.95, 17.87, 17.91, 17.96, 17.87, 17.59, 18.03, 17.97, 17.9, 17.6, 18.05, 17.8, 18.18, 17.66, 17.82, 21.98, 18.23, 17.62]
+326.51
+16.325499999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 225815, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.501307487487793, 'TIME_S_1KI': 0.046504029792032386, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 725.5277880001069, 'W': 50.86, 'J_1KI': 3.2129300002218932, 'W_1KI': 0.22522861634523836, 'W_D': 34.5345, 'J_D': 492.64135656094555, 'W_D_1KI': 0.15293271040453468, 'J_D_1KI': 0.0006772477931250567}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..f1d93dd
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 13.435759782791138, "TIME_S_1KI": 13.435759782791138, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 971.6288854122162, "W": 53.4, "J_1KI": 971.6288854122162, "W_1KI": 53.4, "W_D": 37.34824999999999, "J_D": 679.562519093573, "W_D_1KI": 37.34824999999999, "J_D_1KI": 37.34824999999999}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..35674e7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/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": 13.435759782791138}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,       8,  ..., 2499995,
+                            2499996, 2500000]),
+       col_indices=tensor([ 61754, 291279, 469696,  ..., 173785, 177543,
+                           423232]),
+       values=tensor([0.5269, 0.9088, 0.4901,  ..., 0.3381, 0.9016, 0.0517]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7575, 0.8230, 0.6656,  ..., 0.2327, 0.7437, 0.7040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 13.435759782791138 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,       8,  ..., 2499995,
+                            2499996, 2500000]),
+       col_indices=tensor([ 61754, 291279, 469696,  ..., 173785, 177543,
+                           423232]),
+       values=tensor([0.5269, 0.9088, 0.4901,  ..., 0.3381, 0.9016, 0.0517]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7575, 0.8230, 0.6656,  ..., 0.2327, 0.7437, 0.7040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 13.435759782791138 seconds
+
+[18.26, 17.5, 18.12, 17.51, 17.77, 17.68, 17.73, 17.56, 17.86, 17.52]
+[53.4]
+18.195297479629517
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.435759782791138, 'TIME_S_1KI': 13.435759782791138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 971.6288854122162, 'W': 53.4}
+[18.26, 17.5, 18.12, 17.51, 17.77, 17.68, 17.73, 17.56, 17.86, 17.52, 18.07, 17.37, 18.42, 19.11, 17.73, 17.65, 18.23, 17.55, 17.59, 17.46]
+321.035
+16.051750000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 13.435759782791138, 'TIME_S_1KI': 13.435759782791138, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 971.6288854122162, 'W': 53.4, 'J_1KI': 971.6288854122162, 'W_1KI': 53.4, 'W_D': 37.34824999999999, 'J_D': 679.562519093573, 'W_D_1KI': 37.34824999999999, 'J_D_1KI': 37.34824999999999}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..af9c51f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9021, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.444438695907593, "TIME_S_1KI": 1.1577916745269474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.9580717468261, "W": 51.98, "J_1KI": 81.58275931125442, "W_1KI": 5.762110630750471, "W_D": 35.674749999999996, "J_D": 505.1004274730682, "W_D_1KI": 3.9546336326349625, "J_D_1KI": 0.43838084831337576}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
similarity index 53%
rename from pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.output
rename to pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
index a874d29..945b881 100644
--- 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_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1573309898376465}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.1638367176055908}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     12,  ..., 249987, 249995,
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 249989, 249992,
                             250000]),
-       col_indices=tensor([ 2662,  3637,  9309,  ..., 20434, 25231, 37285]),
-       values=tensor([0.2333, 0.4961, 0.7423,  ..., 0.5095, 0.9257, 0.2343]),
+       col_indices=tensor([ 5085, 27218, 28258,  ..., 33170, 33475, 34242]),
+       values=tensor([0.4699, 0.9594, 0.0965,  ..., 0.7443, 0.7286, 0.0273]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2799, 0.1370, 0.8773,  ..., 0.1897, 0.8081, 0.5839])
+tensor([0.3938, 0.4910, 0.8553,  ..., 0.5913, 0.5925, 0.7936])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 1.1573309898376465 seconds
+Time: 1.1638367176055908 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9021', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.444438695907593}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      8,  ..., 249992, 249993,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 249988, 249994,
                             250000]),
-       col_indices=tensor([ 4160, 33356, 44413,  ..., 34267, 38517, 46233]),
-       values=tensor([0.6958, 0.8946, 0.2330,  ..., 0.2200, 0.1570, 0.6240]),
+       col_indices=tensor([ 3969, 14280, 16197,  ..., 14337, 15782, 32993]),
+       values=tensor([0.2139, 0.2141, 0.1060,  ..., 0.9818, 0.6790, 0.2416]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7770, 0.7832, 0.7648,  ..., 0.3539, 0.1104, 0.8005])
+tensor([0.8858, 0.9490, 0.2990,  ..., 0.1473, 0.1815, 0.8776])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 20.748866319656372 seconds
+Time: 10.444438695907593 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      8,  ..., 249992, 249993,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 249988, 249994,
                             250000]),
-       col_indices=tensor([ 4160, 33356, 44413,  ..., 34267, 38517, 46233]),
-       values=tensor([0.6958, 0.8946, 0.2330,  ..., 0.2200, 0.1570, 0.6240]),
+       col_indices=tensor([ 3969, 14280, 16197,  ..., 14337, 15782, 32993]),
+       values=tensor([0.2139, 0.2141, 0.1060,  ..., 0.9818, 0.6790, 0.2416]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7770, 0.7832, 0.7648,  ..., 0.3539, 0.1104, 0.8005])
+tensor([0.8858, 0.9490, 0.2990,  ..., 0.1473, 0.1815, 0.8776])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 20.748866319656372 seconds
+Time: 10.444438695907593 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}
+[18.28, 18.06, 17.98, 18.51, 17.98, 18.02, 19.12, 19.17, 17.99, 18.0]
+[51.98]
+14.158485412597656
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9021, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.444438695907593, 'TIME_S_1KI': 1.1577916745269474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.9580717468261, 'W': 51.98}
+[18.28, 18.06, 17.98, 18.51, 17.98, 18.02, 19.12, 19.17, 17.99, 18.0, 18.49, 17.73, 18.15, 17.89, 18.02, 17.91, 17.85, 17.6, 17.85, 17.78]
+326.105
+16.30525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9021, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.444438695907593, 'TIME_S_1KI': 1.1577916745269474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.9580717468261, 'W': 51.98, 'J_1KI': 81.58275931125442, 'W_1KI': 5.762110630750471, 'W_D': 35.674749999999996, 'J_D': 505.1004274730682, 'W_D_1KI': 3.9546336326349625, 'J_D_1KI': 0.43838084831337576}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..0738cf0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1973, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.479424238204956, "TIME_S_1KI": 5.311416238319795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 791.6620005321503, "W": 53.24, "J_1KI": 401.2478461896352, "W_1KI": 26.984287886467307, "W_D": 37.20700000000001, "J_D": 553.2563496205807, "W_D_1KI": 18.858084135833757, "J_D_1KI": 9.558076095202107}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
similarity index 56%
rename from pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.output
rename to pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
index b99ed97..3bd1995 100644
--- 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_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.365004062652588}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.321045160293579}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      54,      97,  ..., 2499896,
+                            2499948, 2500000]),
+       col_indices=tensor([  176,   180,   853,  ..., 47415, 47956, 49304]),
+       values=tensor([0.4358, 0.1204, 0.8362,  ..., 0.7793, 0.3332, 0.4077]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5585, 0.2658, 0.0418,  ..., 0.1878, 0.1276, 0.9658])
+tensor([0.9660, 0.1174, 0.2174,  ..., 0.0235, 0.8944, 0.4447])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 5.365004062652588 seconds
+Time: 5.321045160293579 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1973', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.479424238204956}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      43,     100,  ..., 2499912,
+                            2499964, 2500000]),
+       col_indices=tensor([  471,   539,  1515,  ..., 46324, 49367, 49678]),
+       values=tensor([0.0688, 0.1954, 0.6278,  ..., 0.4403, 0.6708, 0.8543]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4723, 0.0239, 0.8312,  ..., 0.6749, 0.1846, 0.8368])
+tensor([0.7713, 0.8001, 0.0882,  ..., 0.6644, 0.4702, 0.2491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 20.652085304260254 seconds
+Time: 10.479424238204956 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      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]),
+tensor(crow_indices=tensor([      0,      43,     100,  ..., 2499912,
+                            2499964, 2500000]),
+       col_indices=tensor([  471,   539,  1515,  ..., 46324, 49367, 49678]),
+       values=tensor([0.0688, 0.1954, 0.6278,  ..., 0.4403, 0.6708, 0.8543]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4723, 0.0239, 0.8312,  ..., 0.6749, 0.1846, 0.8368])
+tensor([0.7713, 0.8001, 0.0882,  ..., 0.6644, 0.4702, 0.2491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 20.652085304260254 seconds
+Time: 10.479424238204956 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}
+[18.09, 17.68, 17.78, 17.6, 17.88, 17.97, 17.49, 17.67, 17.75, 17.8]
+[53.24]
+14.86968445777893
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1973, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.479424238204956, 'TIME_S_1KI': 5.311416238319795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.6620005321503, 'W': 53.24}
+[18.09, 17.68, 17.78, 17.6, 17.88, 17.97, 17.49, 17.67, 17.75, 17.8, 18.63, 17.66, 18.2, 17.66, 17.95, 17.62, 18.05, 17.65, 17.58, 18.42]
+320.65999999999997
+16.032999999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1973, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.479424238204956, 'TIME_S_1KI': 5.311416238319795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.6620005321503, 'W': 53.24, 'J_1KI': 401.2478461896352, 'W_1KI': 26.984287886467307, 'W_D': 37.20700000000001, 'J_D': 553.2563496205807, 'W_D_1KI': 18.858084135833757, 'J_D_1KI': 9.558076095202107}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..0990f32
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21464, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.162778615951538, "TIME_S_1KI": 0.47348018151097365, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 708.2274965262413, "W": 50.99, "J_1KI": 32.99606301370859, "W_1KI": 2.3756056653000375, "W_D": 25.345, "J_D": 352.0303176987171, "W_D_1KI": 1.1808143868803578, "J_D_1KI": 0.055013715378324536}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
similarity index 57%
rename from pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.output
rename to pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
index 0874c10..eca4709 100644
--- 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_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.49491095542907715}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.4891834259033203}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     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]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16409, 39665, 45486,  ..., 40216, 44015, 30698]),
+       values=tensor([0.3828, 0.2137, 0.3194,  ..., 0.5609, 0.6557, 0.9594]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5953, 0.1156, 0.8276,  ..., 0.3405, 0.8051, 0.4714])
+tensor([0.1367, 0.4150, 0.8251,  ..., 0.6451, 0.2178, 0.9645])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.49491095542907715 seconds
+Time: 0.4891834259033203 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21464', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.162778615951538}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 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]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24999, 25000]),
+       col_indices=tensor([27591, 28713, 10997,  ...,  3373, 26495, 43984]),
+       values=tensor([0.0595, 0.2219, 0.9508,  ..., 0.7420, 0.6896, 0.8252]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9110, 0.1233, 0.5259,  ..., 0.7671, 0.0751, 0.3217])
+tensor([0.4890, 0.2230, 0.0247,  ..., 0.5863, 0.9029, 0.3113])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,15 +34,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 20.11350655555725 seconds
+Time: 10.162778615951538 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 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]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24999, 25000]),
+       col_indices=tensor([27591, 28713, 10997,  ...,  3373, 26495, 43984]),
+       values=tensor([0.0595, 0.2219, 0.9508,  ..., 0.7420, 0.6896, 0.8252]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9110, 0.1233, 0.5259,  ..., 0.7671, 0.0751, 0.3217])
+tensor([0.4890, 0.2230, 0.0247,  ..., 0.5863, 0.9029, 0.3113])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -50,13 +50,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 20.11350655555725 seconds
+Time: 10.162778615951538 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}
+[22.1, 18.96, 18.21, 17.89, 17.76, 17.62, 17.96, 18.44, 17.92, 17.8]
+[50.99]
+13.88953709602356
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.162778615951538, 'TIME_S_1KI': 0.47348018151097365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.2274965262413, 'W': 50.99}
+[22.1, 18.96, 18.21, 17.89, 17.76, 17.62, 17.96, 18.44, 17.92, 17.8, 27.02, 48.47, 52.31, 52.11, 52.93, 45.58, 32.74, 23.06, 18.26, 18.44]
+512.9000000000001
+25.645000000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.162778615951538, 'TIME_S_1KI': 0.47348018151097365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.2274965262413, 'W': 50.99, 'J_1KI': 32.99606301370859, 'W_1KI': 2.3756056653000375, 'W_D': 25.345, 'J_D': 352.0303176987171, 'W_D_1KI': 1.1808143868803578, 'J_D_1KI': 0.055013715378324536}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..cdd4446
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220548, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.518115997314453, "TIME_S_1KI": 0.04769082466091034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 712.8332035136224, "W": 50.77000000000001, "J_1KI": 3.2321000576456025, "W_1KI": 0.23019932168960958, "W_D": 34.49475000000001, "J_D": 484.32151165848984, "W_D_1KI": 0.15640472822242782, "J_D_1KI": 0.0007091641194770654}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..0444e69
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.06373429298400879}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2496, 2500, 2500]),
+       col_indices=tensor([ 225,  423, 3600,  ..., 1030, 3468, 3660]),
+       values=tensor([0.7007, 0.4494, 0.9248,  ..., 0.2922, 0.0433, 0.9500]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8445, 0.6906, 0.6660,  ..., 0.8648, 0.6232, 0.6893])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.06373429298400879 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '164746', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.8433122634887695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3043, 3415, 2314,  ..., 4144,   83, 2442]),
+       values=tensor([0.9885, 0.5870, 0.9255,  ..., 0.0554, 0.8705, 0.0319]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7612, 0.3828, 0.3624,  ..., 0.7209, 0.0836, 0.1248])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 7.8433122634887695 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '220548', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.518115997314453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    4,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1110, 1648, 1178,  ..., 3403,  882, 3863]),
+       values=tensor([0.7053, 0.9818, 0.3657,  ..., 0.7070, 0.0906, 0.0064]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7696, 0.8663, 0.2054,  ..., 0.2110, 0.6343, 0.9754])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.518115997314453 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    4,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1110, 1648, 1178,  ..., 3403,  882, 3863]),
+       values=tensor([0.7053, 0.9818, 0.3657,  ..., 0.7070, 0.0906, 0.0064]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7696, 0.8663, 0.2054,  ..., 0.2110, 0.6343, 0.9754])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.518115997314453 seconds
+
+[18.38, 17.72, 18.11, 17.9, 17.72, 18.84, 18.26, 17.75, 18.1, 19.21]
+[50.77]
+14.040441274642944
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.518115997314453, 'TIME_S_1KI': 0.04769082466091034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.8332035136224, 'W': 50.77000000000001}
+[18.38, 17.72, 18.11, 17.9, 17.72, 18.84, 18.26, 17.75, 18.1, 19.21, 18.75, 19.27, 17.62, 17.61, 18.13, 17.64, 17.82, 17.78, 18.21, 17.71]
+325.505
+16.27525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.518115997314453, 'TIME_S_1KI': 0.04769082466091034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.8332035136224, 'W': 50.77000000000001, 'J_1KI': 3.2321000576456025, 'W_1KI': 0.23019932168960958, 'W_D': 34.49475000000001, 'J_D': 484.32151165848984, 'W_D_1KI': 0.15640472822242782, 'J_D_1KI': 0.0007091641194770654}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..8603573
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 110820, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.466707706451416, "TIME_S_1KI": 0.0944478226534147, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.0924870371819, "W": 51.05, "J_1KI": 6.606140471369625, "W_1KI": 0.46065692113336937, "W_D": 34.78399999999999, "J_D": 498.82673984527577, "W_D_1KI": 0.3138783613066233, "J_D_1KI": 0.0028323259457374416}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..d9f1f9f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.10903120040893555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    12,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([ 238, 1233, 1853,  ..., 2176, 2430, 4262]),
+       values=tensor([0.6643, 0.7436, 0.3106,  ..., 0.6873, 0.4400, 0.9022]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1554, 0.8998, 0.5501,  ..., 0.9645, 0.8024, 0.0587])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.10903120040893555 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '96302', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.124423503875732}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,     5,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([ 172,  514, 1428,  ..., 3067, 4065, 4821]),
+       values=tensor([0.3942, 0.3525, 0.9893,  ..., 0.1091, 0.2236, 0.5194]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3072, 0.9146, 0.5714,  ..., 0.0055, 0.2166, 0.7033])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.124423503875732 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '110820', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.466707706451416}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 254, 2428, 3765,  ..., 2763, 3021, 4452]),
+       values=tensor([0.4991, 0.2229, 0.1709,  ..., 0.9765, 0.1191, 0.1560]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0044, 0.7640, 0.5767,  ..., 0.5512, 0.1474, 0.2527])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.466707706451416 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 254, 2428, 3765,  ..., 2763, 3021, 4452]),
+       values=tensor([0.4991, 0.2229, 0.1709,  ..., 0.9765, 0.1191, 0.1560]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0044, 0.7640, 0.5767,  ..., 0.5512, 0.1474, 0.2527])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.466707706451416 seconds
+
+[18.71, 18.12, 17.8, 18.17, 17.79, 18.66, 18.24, 17.82, 17.96, 18.33]
+[51.05]
+14.340695142745972
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 110820, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.466707706451416, 'TIME_S_1KI': 0.0944478226534147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.0924870371819, 'W': 51.05}
+[18.71, 18.12, 17.8, 18.17, 17.79, 18.66, 18.24, 17.82, 17.96, 18.33, 18.24, 17.71, 17.88, 18.05, 17.89, 18.02, 18.2, 18.01, 17.96, 18.8]
+325.32000000000005
+16.266000000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 110820, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.466707706451416, 'TIME_S_1KI': 0.0944478226534147, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.0924870371819, 'W': 51.05, 'J_1KI': 6.606140471369625, 'W_1KI': 0.46065692113336937, 'W_D': 34.78399999999999, 'J_D': 498.82673984527577, 'W_D_1KI': 0.3138783613066233, 'J_D_1KI': 0.0028323259457374416}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..480b88c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20672, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.150692462921143, "TIME_S_1KI": 0.4910358196072534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 730.3927840781212, "W": 52.39, "J_1KI": 35.332468270032955, "W_1KI": 2.534345975232198, "W_D": 36.085, "J_D": 503.07737380146983, "W_D_1KI": 1.7455979102167183, "J_D_1KI": 0.08444262336574683}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..a5c980d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5079245567321777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     49,     95,  ..., 249890, 249948,
+                            250000]),
+       col_indices=tensor([  55,   65,  142,  ..., 4926, 4940, 4998]),
+       values=tensor([0.9119, 0.0018, 0.8572,  ..., 0.6690, 0.1772, 0.9395]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5150, 0.8940, 0.4191,  ..., 0.2946, 0.8617, 0.5629])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.5079245567321777 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '20672', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.150692462921143}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     43,     97,  ..., 249889, 249944,
+                            250000]),
+       col_indices=tensor([   6,   85,  316,  ..., 4939, 4964, 4997]),
+       values=tensor([0.9982, 0.1843, 0.4498,  ..., 0.0146, 0.5221, 0.3769]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3609, 0.3004, 0.4171,  ..., 0.6127, 0.3616, 0.7085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.150692462921143 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     43,     97,  ..., 249889, 249944,
+                            250000]),
+       col_indices=tensor([   6,   85,  316,  ..., 4939, 4964, 4997]),
+       values=tensor([0.9982, 0.1843, 0.4498,  ..., 0.0146, 0.5221, 0.3769]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3609, 0.3004, 0.4171,  ..., 0.6127, 0.3616, 0.7085])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.150692462921143 seconds
+
+[18.49, 17.73, 17.92, 17.82, 17.93, 17.75, 17.92, 17.66, 18.2, 17.78]
+[52.39]
+13.9414541721344
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20672, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.150692462921143, 'TIME_S_1KI': 0.4910358196072534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 730.3927840781212, 'W': 52.39}
+[18.49, 17.73, 17.92, 17.82, 17.93, 17.75, 17.92, 17.66, 18.2, 17.78, 18.3, 17.44, 17.89, 17.79, 17.89, 17.6, 18.39, 22.34, 17.77, 17.55]
+326.1
+16.305
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20672, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.150692462921143, 'TIME_S_1KI': 0.4910358196072534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 730.3927840781212, 'W': 52.39, 'J_1KI': 35.332468270032955, 'W_1KI': 2.534345975232198, 'W_D': 36.085, 'J_D': 503.07737380146983, 'W_D_1KI': 1.7455979102167183, 'J_D_1KI': 0.08444262336574683}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..e52e6e6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4507, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511178016662598, "TIME_S_1KI": 2.332189486723452, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.7805129575729, "W": 51.93, "J_1KI": 168.13412756990746, "W_1KI": 11.522076769469715, "W_D": 35.70625, "J_D": 521.0379441708326, "W_D_1KI": 7.922398491235855, "J_D_1KI": 1.7577986446052485}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..a885cd8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.3295679092407227}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     254,     504,  ..., 1249528,
+                            1249756, 1250000]),
+       col_indices=tensor([   6,   36,   59,  ..., 4952, 4989, 4991]),
+       values=tensor([0.0659, 0.7749, 0.0668,  ..., 0.7589, 0.1810, 0.5312]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3067, 0.0072, 0.7740,  ..., 0.2122, 0.3107, 0.3197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 2.3295679092407227 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4507', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511178016662598}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     219,     483,  ..., 1249530,
+                            1249766, 1250000]),
+       col_indices=tensor([  20,   32,  102,  ..., 4974, 4977, 4994]),
+       values=tensor([0.6920, 0.8171, 0.6223,  ..., 0.4625, 0.9983, 0.7249]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2561, 0.5591, 0.5400,  ..., 0.8818, 0.4529, 0.6860])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511178016662598 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     219,     483,  ..., 1249530,
+                            1249766, 1250000]),
+       col_indices=tensor([  20,   32,  102,  ..., 4974, 4977, 4994]),
+       values=tensor([0.6920, 0.8171, 0.6223,  ..., 0.4625, 0.9983, 0.7249]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2561, 0.5591, 0.5400,  ..., 0.8818, 0.4529, 0.6860])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511178016662598 seconds
+
+[18.39, 17.88, 18.12, 17.92, 17.75, 18.25, 17.78, 17.64, 17.79, 18.46]
+[51.93]
+14.592345714569092
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511178016662598, 'TIME_S_1KI': 2.332189486723452, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.7805129575729, 'W': 51.93}
+[18.39, 17.88, 18.12, 17.92, 17.75, 18.25, 17.78, 17.64, 17.79, 18.46, 18.69, 17.8, 17.97, 17.87, 17.86, 17.58, 17.99, 19.45, 18.2, 17.71]
+324.47499999999997
+16.22375
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511178016662598, 'TIME_S_1KI': 2.332189486723452, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 757.7805129575729, 'W': 51.93, 'J_1KI': 168.13412756990746, 'W_1KI': 11.522076769469715, 'W_D': 35.70625, 'J_D': 521.0379441708326, 'W_D_1KI': 7.922398491235855, 'J_D_1KI': 1.7577986446052485}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..635a552
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2058, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.45784878730774, "TIME_S_1KI": 5.08155917750619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 785.1421909189224, "W": 52.34, "J_1KI": 381.5073813988933, "W_1KI": 25.432458697764822, "W_D": 35.778000000000006, "J_D": 536.6988404030801, "W_D_1KI": 17.384839650145775, "J_D_1KI": 8.44744395050815}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..4a3b3d8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.10130500793457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     521,     995,  ..., 2499020,
+                            2499527, 2500000]),
+       col_indices=tensor([  19,   49,   51,  ..., 4986, 4987, 4995]),
+       values=tensor([0.7936, 0.5375, 0.7301,  ..., 0.7605, 0.2307, 0.9856]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5487, 0.7747, 0.8035,  ..., 0.5625, 0.3730, 0.5706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 5.10130500793457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2058', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.45784878730774}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     509,    1017,  ..., 2498984,
+                            2499497, 2500000]),
+       col_indices=tensor([   6,   22,   24,  ..., 4979, 4998, 4999]),
+       values=tensor([0.4917, 0.1142, 0.9293,  ..., 0.2344, 0.9124, 0.9917]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7101, 0.7759, 0.4138,  ..., 0.4795, 0.2601, 0.9117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.45784878730774 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     509,    1017,  ..., 2498984,
+                            2499497, 2500000]),
+       col_indices=tensor([   6,   22,   24,  ..., 4979, 4998, 4999]),
+       values=tensor([0.4917, 0.1142, 0.9293,  ..., 0.2344, 0.9124, 0.9917]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7101, 0.7759, 0.4138,  ..., 0.4795, 0.2601, 0.9117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.45784878730774 seconds
+
+[17.93, 17.71, 18.06, 17.99, 17.73, 17.68, 22.42, 18.22, 17.93, 17.72]
+[52.34]
+15.000806093215942
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2058, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.45784878730774, 'TIME_S_1KI': 5.08155917750619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 785.1421909189224, 'W': 52.34}
+[17.93, 17.71, 18.06, 17.99, 17.73, 17.68, 22.42, 18.22, 17.93, 17.72, 17.97, 22.23, 18.27, 18.03, 17.94, 17.65, 17.66, 17.82, 18.34, 17.5]
+331.24
+16.562
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2058, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.45784878730774, 'TIME_S_1KI': 5.08155917750619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 785.1421909189224, 'W': 52.34, 'J_1KI': 381.5073813988933, 'W_1KI': 25.432458697764822, 'W_D': 35.778000000000006, 'J_D': 536.6988404030801, 'W_D_1KI': 17.384839650145775, 'J_D_1KI': 8.44744395050815}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..6c5cd21
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 359075, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.526627540588379, "TIME_S_1KI": 0.029315957782046587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.3910086989404, "W": 50.77, "J_1KI": 2.0257355947892233, "W_1KI": 0.14139107428810138, "W_D": 34.40475000000001, "J_D": 492.92310038477194, "W_D_1KI": 0.09581494116827963, "J_D_1KI": 0.00026683824039066943}
diff --git a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..f806f78
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.11398792266845703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 241, 1973,  126, 4921, 4422, 2653, 3082, 2201,  909,
+                            773, 2476, 1101, 4124, 1149, 4932, 4150,  708, 3916,
+                           3901, 3756, 2285, 2145, 2412, 4449, 1421, 1959,  273,
+                            295,  438, 3557, 1406, 2159, 1555, 1352, 2308,  123,
+                            422,  816, 1668, 2887,  824, 2337,  308, 3497,  990,
+                            532, 4077,  543, 4572, 3537, 2814,  363, 2178,  459,
+                            194, 3590, 3027, 4470, 4045, 3521, 3600, 3448, 3378,
+                           3735, 2740, 4248, 3124, 1351, 4670,  655,   21, 1574,
+                           1992, 4925, 3906, 2630, 1378, 3476, 2249, 1157,  791,
+                           4242,  829, 3492,  751, 3125,  155, 1550, 3503, 3772,
+                           4314, 2771, 3009,  651,  454, 3292, 4403, 3040, 3507,
+                           2608, 4119, 1826, 4717, 3363,  464, 3190, 2566, 1334,
+                           3602, 3134, 4282, 4686, 3398,  415, 1914, 1278, 3697,
+                           3496,  579, 3955, 1068, 4099,  763, 3707, 3389, 1217,
+                           1044, 4869, 1375, 3824, 2384, 1580, 1119, 2286, 4182,
+                            194, 4854, 2427, 3130, 4857, 3962, 2164, 2297, 3429,
+                           4738, 1374, 1526, 1469,  698, 2341, 4993, 1945, 4526,
+                           2645, 2777, 3401,  889, 4389,  444, 1509, 4747, 2279,
+                           4668,   72,  129, 1221, 3493,  378,  595,   67, 1157,
+                           1657, 2497, 2001, 1078, 4882, 3030, 2378,  193, 2365,
+                           3970, 4956, 3547,  158, 4478, 3594, 3986, 4843, 4633,
+                           1401, 3655,  934, 1838, 4467, 1935, 2294,  329, 1885,
+                           2444, 2560, 3870, 4475,  843, 2939, 3686, 4333, 3066,
+                           1183,  367, 3706, 3954, 4842, 1757, 4835, 4167, 4982,
+                           1096, 3863, 1904, 2261, 4656, 4688, 3811, 4079, 2898,
+                            525, 3689,   59, 2698,  369, 2440, 1363, 4533, 2450,
+                           3223, 1033, 4049, 3368, 2542, 4831, 3226, 3742, 4496,
+                            434, 1015, 2564, 1295, 3848, 4039,  804]),
+       values=tensor([0.2184, 0.5485, 0.5631, 0.7186, 0.3971, 0.9050, 0.7143,
+                      0.7288, 0.3895, 0.9734, 0.7253, 0.3854, 0.7553, 0.4272,
+                      0.9870, 0.8470, 0.2594, 0.4864, 0.4236, 0.8391, 0.1976,
+                      0.0203, 0.1892, 0.3198, 0.2335, 0.4485, 0.4766, 0.2460,
+                      0.8756, 0.2717, 0.6013, 0.3920, 0.2318, 0.2314, 0.6325,
+                      0.7402, 0.4011, 0.6801, 0.0374, 0.5386, 0.8760, 0.4919,
+                      0.9099, 0.6426, 0.0752, 0.2458, 0.7495, 0.4949, 0.4717,
+                      0.8587, 0.9263, 0.5756, 0.1987, 0.1048, 0.8736, 0.4765,
+                      0.2414, 0.4379, 0.9381, 0.5720, 0.7831, 0.1225, 0.0871,
+                      0.1953, 0.0019, 0.7763, 0.7548, 0.3103, 0.4088, 0.9386,
+                      0.6409, 0.3915, 0.4398, 0.8886, 0.6326, 0.8708, 0.6836,
+                      0.2686, 0.0291, 0.4089, 0.8430, 0.7311, 0.2220, 0.0973,
+                      0.4335, 0.3659, 0.1254, 0.1858, 0.2947, 0.6441, 0.6573,
+                      0.8939, 0.8485, 0.7258, 0.8542, 0.3356, 0.6753, 0.2728,
+                      0.1795, 0.8246, 0.2224, 0.2674, 0.8957, 0.1897, 0.5785,
+                      0.0612, 0.0570, 0.6450, 0.0772, 0.5313, 0.3238, 0.7938,
+                      0.9961, 0.4101, 0.7007, 0.3996, 0.0865, 0.3609, 0.3202,
+                      0.4978, 0.4886, 0.2294, 0.1102, 0.5506, 0.2172, 0.1849,
+                      0.3574, 0.0197, 0.0592, 0.3653, 0.9739, 0.5626, 0.3629,
+                      0.5946, 0.5286, 0.9497, 0.4607, 0.1036, 0.7227, 0.1313,
+                      0.2695, 0.1429, 0.5049, 0.5045, 0.0131, 0.8291, 0.1488,
+                      0.2606, 0.8600, 0.2356, 0.5905, 0.8817, 0.3417, 0.2576,
+                      0.1052, 0.2996, 0.2243, 0.4829, 0.2637, 0.4923, 0.6774,
+                      0.3415, 0.2189, 0.4198, 0.9822, 0.0220, 0.9119, 0.7410,
+                      0.2466, 0.2072, 0.8839, 0.7516, 0.8153, 0.2575, 0.8303,
+                      0.9406, 0.0281, 0.0637, 0.8256, 0.0137, 0.8551, 0.6904,
+                      0.7955, 0.7126, 0.4854, 0.7077, 0.7877, 0.2703, 0.2627,
+                      0.1225, 0.6814, 0.1981, 0.0012, 0.1101, 0.2261, 0.0650,
+                      0.7540, 0.2474, 0.6597, 0.2387, 0.2473, 0.3505, 0.4892,
+                      0.1885, 0.9295, 0.0390, 0.0947, 0.3171, 0.4778, 0.2438,
+                      0.6996, 0.4455, 0.6953, 0.9830, 0.4988, 0.5386, 0.2650,
+                      0.2674, 0.7866, 0.9811, 0.0823, 0.0951, 0.2368, 0.8950,
+                      0.6075, 0.7359, 0.6430, 0.6470, 0.0664, 0.2765, 0.1109,
+                      0.1504, 0.4845, 0.0431, 0.3770, 0.2384, 0.0687, 0.8824,
+                      0.9446, 0.8249, 0.8327, 0.3623, 0.1484, 0.9592, 0.8566,
+                      0.3466, 0.6434, 0.1142, 0.1855, 0.2031]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1137, 0.5017, 0.2439,  ..., 0.6384, 0.0681, 0.9585])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.11398792266845703 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '92115', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.6936018466949463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 137, 3972, 2939, 2536, 3585, 3536, 4694, 4081, 1091,
+                           3547, 2158, 1560, 4654, 3916, 1298, 1826,  148, 3363,
+                           2515,  695, 1436, 2549, 3112, 1426, 4349, 4876, 3863,
+                           2266,   79, 3433, 3354, 3087, 4915, 1126, 3703, 2213,
+                           4969, 2103, 3978, 4220, 3833, 3752, 4926, 1827, 2953,
+                           4810,  372, 1434,  633, 4328, 3235, 2981, 1886, 1672,
+                           4865, 3611, 2035, 3841, 2469, 1487, 1861, 3293, 4642,
+                           1604, 4933, 4004, 2061, 3358, 3726, 2632,  960,  126,
+                           2232, 2877,  895,  621, 3810, 4400, 2844, 3004, 2625,
+                           1260, 1779,  776, 2146, 1667, 3230,  539, 2113, 1737,
+                           4402,  465, 2922, 3985,  142, 4315, 2921, 2750,  885,
+                            710, 4008, 1590, 1261, 4292, 3623, 3503, 1672, 3336,
+                           2572, 3267, 2993,   70, 1995,  836, 1449, 4056, 4774,
+                           1934, 3439, 2960, 4562, 3889, 2634, 1182, 2896, 3385,
+                            205,  905, 4516, 1281,  169, 4524,  563,  927, 1718,
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+                           2960, 2505, 3939, 4782, 2656, 1648, 2553,  588, 2612,
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+                      0.0848, 0.7623, 0.7257, 0.3674, 0.7093]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1760, 0.3447, 0.5672,  ..., 0.4540, 0.2179, 0.2738])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 2.6936018466949463 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '359075', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.526627540588379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3778, 4984, 4122, 2676, 3957, 4059, 4909, 4911, 2572,
+                           1267, 1150, 3364, 3576, 4257, 4803, 3469, 2315, 1996,
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+                            592, 1388, 3114, 4183, 4318, 1850, 4771,  843, 2522,
+                           2774, 2939, 3529, 1857, 2895, 2137, 4447]),
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7545, 0.7162, 0.2861,  ..., 0.9381, 0.3630, 0.3493])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.526627540588379 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3778, 4984, 4122, 2676, 3957, 4059, 4909, 4911, 2572,
+                           1267, 1150, 3364, 3576, 4257, 4803, 3469, 2315, 1996,
+                           1589, 4554, 3627,  222,  735, 2019, 1196, 3402,  918,
+                            508, 1833, 3932, 3749, 3244, 4451, 1193, 3387, 2934,
+                           4933, 2676, 1892, 1253, 2562, 3303,   93, 1367, 4037,
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+                           3071, 4886, 2701, 3617, 3981, 2453, 1634,  906, 2460,
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+                            592, 1388, 3114, 4183, 4318, 1850, 4771,  843, 2522,
+                           2774, 2939, 3529, 1857, 2895, 2137, 4447]),
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+                      0.3930, 0.1891, 0.0713, 0.9951, 0.8365, 0.9415, 0.9314,
+                      0.4274, 0.7485, 0.9571, 0.9768, 0.5673, 0.4241, 0.5508,
+                      0.4033, 0.2950, 0.2855, 0.8415, 0.9844, 0.7770, 0.3923,
+                      0.5787, 0.9241, 0.3429, 0.2388, 0.7432, 0.5287, 0.4894,
+                      0.3564, 0.1539, 0.3683, 0.3338, 0.2500, 0.3763, 0.4479,
+                      0.2028, 0.8079, 0.0187, 0.3962, 0.2530, 0.6932, 0.4307,
+                      0.2510, 0.2498, 0.5817, 0.8657, 0.8402]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7545, 0.7162, 0.2861,  ..., 0.9381, 0.3630, 0.3493])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.526627540588379 seconds
+
+[18.43, 17.7, 18.24, 17.84, 17.84, 17.8, 18.19, 17.69, 17.91, 17.97]
+[50.77]
+14.327181577682495
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 359075, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.526627540588379, 'TIME_S_1KI': 0.029315957782046587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.3910086989404, 'W': 50.77}
+[18.43, 17.7, 18.24, 17.84, 17.84, 17.8, 18.19, 17.69, 17.91, 17.97, 18.65, 17.88, 17.9, 17.81, 18.36, 17.89, 17.8, 17.9, 21.76, 18.54]
+327.305
+16.36525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 359075, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.526627540588379, 'TIME_S_1KI': 0.029315957782046587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.3910086989404, 'W': 50.77, 'J_1KI': 2.0257355947892233, 'W_1KI': 0.14139107428810138, 'W_D': 34.40475000000001, 'J_D': 492.92310038477194, 'W_D_1KI': 0.09581494116827963, 'J_D_1KI': 0.00026683824039066943}
diff --git a/pytorch/output_synthetic_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
deleted file mode 100644
index 72b0c08..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 88175d1..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_1000000_1e-05.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 7aceaa7..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 62dfba2..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 1517cae..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 03d76fb..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index b8a679c..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 3163e2f..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_0.005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 0a18e77..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_100000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e69de29..0000000
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
deleted file mode 100644
index 95dcd7c..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_3000000_1e-05.output
+++ /dev/null
@@ -1,10 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index b220cd4..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 48e92e3..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0001.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index efafaea..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index f20383f..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.0005.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index e69de29..0000000
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
deleted file mode 100644
index 30ff4c4..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_0.001.output
+++ /dev/null
@@ -1,10 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 49d4d09..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 46d6c02..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_300000_1e-05.output
+++ /dev/null
@@ -1,68 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index aa132a9..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 5969df1..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0001.output
+++ /dev/null
@@ -1,62 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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
deleted file mode 100644
index e7b3ed3..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 2f47860..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index a79d073..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 4ec6b31..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.001.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 4201fbb..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 98b9204..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 46b364e..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 5a27d6d..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.01.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index e69de29..0000000
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
deleted file mode 100644
index 1e221b3..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.05.output
+++ /dev/null
@@ -1,10 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index e69de29..0000000
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
deleted file mode 100644
index 3d666e4..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_0.1.output
+++ /dev/null
@@ -1,10 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 0a79d7f..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index b924b07..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_30000_1e-05.output
+++ /dev/null
@@ -1,81 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', '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
deleted file mode 100644
index bdc1501..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 53f20ab..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_0.0001.output
+++ /dev/null
@@ -1,47 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 4c5f481..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index fddfb78..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_500000_1e-05.output
+++ /dev/null
@@ -1,68 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index e4cd738..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index f35b37b..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e222138..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.0005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index b01cbb8..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.001.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index b7790eb..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index e6456ba..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.005.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index 17bdd0a..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
deleted file mode 100644
index 0c0f915..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_0.01.output
+++ /dev/null
@@ -1,45 +0,0 @@
-['apptainer', 'run', '--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
deleted file mode 100644
index cd77447..0000000
--- a/pytorch/output_synthetic_1core_old/xeon_4216_1_csr_20_10_10_synthetic_50000_1e-05.json
+++ /dev/null
@@ -1 +0,0 @@
-{"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
index 41d6bb0..b76a936 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 17.146100997924805, "TIME_S_1KI": 17.146100997924805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1342.6130161285403, "W": 65.43610945887401, "J_1KI": 1342.6130161285403, "W_1KI": 65.43610945887401, "W_D": 46.466109458874016, "J_D": 953.388028173447, "W_D_1KI": 46.466109458874016, "J_D_1KI": 46.466109458874016}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 690, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.003246307373047, "TIME_S_1KI": 21.74383522807688, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.2026811027528, "W": 51.180846035126734, "J_1KI": 1066.960407395294, "W_1KI": 74.1751391813431, "W_D": 35.27984603512673, "J_D": 507.4772937932015, "W_D_1KI": 51.1302116451112, "J_D_1KI": 74.10175600740754}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
index 451f542..175c45d 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 17.146100997924805}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.520033359527588}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      17,  ...,  999972,
-                             999990, 1000000]),
-       col_indices=tensor([13952, 31113, 48803,  ..., 72766, 82982, 86351]),
-       values=tensor([0.4430, 0.0507, 0.5237,  ..., 0.5341, 0.7602, 0.3481]),
+tensor(crow_indices=tensor([      0,       9,      21,  ...,  999972,
+                             999987, 1000000]),
+       col_indices=tensor([ 5448,  9227, 13530,  ..., 69235, 78462, 82074]),
+       values=tensor([0.3723, 0.4802, 0.5484,  ..., 0.8279, 0.7827, 0.0235]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1669, 0.1860, 0.1675,  ..., 0.5137, 0.0308, 0.0638])
+tensor([0.9343, 0.5585, 0.5996,  ..., 0.1233, 0.3217, 0.3044])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 17.146100997924805 seconds
+Time: 1.520033359527588 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 690 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.003246307373047}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      17,  ...,  999972,
-                             999990, 1000000]),
-       col_indices=tensor([13952, 31113, 48803,  ..., 72766, 82982, 86351]),
-       values=tensor([0.4430, 0.0507, 0.5237,  ..., 0.5341, 0.7602, 0.3481]),
+tensor(crow_indices=tensor([      0,      11,      20,  ...,  999982,
+                             999992, 1000000]),
+       col_indices=tensor([  122,  3109, 10697,  ..., 57820, 90383, 91253]),
+       values=tensor([0.9003, 0.9806, 0.3302,  ..., 0.9034, 0.0249, 0.7877]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1669, 0.1860, 0.1675,  ..., 0.5137, 0.0308, 0.0638])
+tensor([0.6863, 0.9695, 0.8316,  ..., 0.5738, 0.3295, 0.1413])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 17.146100997924805 seconds
+Time: 15.003246307373047 seconds
 
-[21.44, 21.28, 21.16, 21.16, 20.96, 20.96, 20.96, 21.0, 21.24, 21.4]
-[21.44, 21.28, 21.92, 23.96, 24.84, 38.64, 55.84, 70.04, 84.52, 91.04, 91.04, 91.56, 90.84, 89.12, 88.88, 89.24, 89.64, 88.8, 89.08, 90.0]
-20.517922401428223
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 17.146100997924805, 'TIME_S_1KI': 17.146100997924805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1342.6130161285403, 'W': 65.43610945887401}
-[21.44, 21.28, 21.16, 21.16, 20.96, 20.96, 20.96, 21.0, 21.24, 21.4, 21.16, 21.04, 21.08, 20.92, 20.88, 20.88, 21.04, 20.96, 21.2, 21.36]
-379.4
-18.97
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 17.146100997924805, 'TIME_S_1KI': 17.146100997924805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1342.6130161285403, 'W': 65.43610945887401, 'J_1KI': 1342.6130161285403, 'W_1KI': 65.43610945887401, 'W_D': 46.466109458874016, 'J_D': 953.388028173447, 'W_D_1KI': 46.466109458874016, 'J_D_1KI': 46.466109458874016}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      20,  ...,  999982,
+                             999992, 1000000]),
+       col_indices=tensor([  122,  3109, 10697,  ..., 57820, 90383, 91253]),
+       values=tensor([0.9003, 0.9806, 0.3302,  ..., 0.9034, 0.0249, 0.7877]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6863, 0.9695, 0.8316,  ..., 0.5738, 0.3295, 0.1413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 15.003246307373047 seconds
+
+[17.56, 17.56, 17.72, 17.72, 17.72, 17.92, 17.92, 17.64, 17.48, 17.56]
+[17.24, 17.28, 18.12, 18.12, 20.28, 29.36, 44.04, 63.04, 74.68, 91.04, 91.28, 91.4, 88.68, 90.76]
+14.384339809417725
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.003246307373047, 'TIME_S_1KI': 21.74383522807688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.2026811027528, 'W': 51.180846035126734}
+[17.56, 17.56, 17.72, 17.72, 17.72, 17.92, 17.92, 17.64, 17.48, 17.56, 17.84, 17.84, 17.6, 17.92, 17.8, 17.8, 17.68, 17.52, 17.16, 17.08]
+318.02000000000004
+15.901000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.003246307373047, 'TIME_S_1KI': 21.74383522807688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.2026811027528, 'W': 51.180846035126734, 'J_1KI': 1066.960407395294, 'W_1KI': 74.1751391813431, 'W_D': 35.27984603512673, 'J_D': 507.4772937932015, 'W_D_1KI': 51.1302116451112, 'J_D_1KI': 74.10175600740754}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
index c78bc8a..a46b44b 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 159.18061113357544, "TIME_S_1KI": 159.18061113357544, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 13305.551260681154, "W": 81.94491850885544, "J_1KI": 13305.551260681154, "W_1KI": 81.94491850885544, "W_D": 61.991918508855434, "J_D": 10065.7449476676, "W_D_1KI": 61.991918508855434, "J_D_1KI": 61.991918508855434}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.453001976013184, "TIME_S_1KI": 144.53001976013184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.84748960495, "W": 50.98554654857658, "J_1KI": 10498.4748960495, "W_1KI": 509.85546548576576, "W_D": 35.17954654857658, "J_D": 724.3848723731041, "W_D_1KI": 351.7954654857658, "J_D_1KI": 3517.954654857658}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
index a21bcbb..f3d22b6 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,15 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 159.18061113357544}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.453001976013184}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       95,      182,  ...,  9999785,
-                             9999891, 10000000]),
-       col_indices=tensor([ 2375,  2397,  2562,  ..., 95994, 97725, 99229]),
-       values=tensor([3.2988e-01, 7.8520e-04, 8.6482e-01,  ...,
-                      9.5198e-01, 4.5600e-01, 9.5863e-01]),
+tensor(crow_indices=tensor([       0,      101,      216,  ...,  9999796,
+                             9999906, 10000000]),
+       col_indices=tensor([ 2079,  2370,  2404,  ..., 91560, 92604, 94393]),
+       values=tensor([0.9041, 0.3243, 0.4250,  ..., 0.8376, 0.1046, 0.5896]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4135, 0.2091, 0.0976,  ..., 0.1293, 0.9759, 0.9614])
+tensor([0.3984, 0.6112, 0.9728,  ..., 0.2358, 0.4123, 0.2200])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -17,17 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 159.18061113357544 seconds
+Time: 14.453001976013184 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       95,      182,  ...,  9999785,
-                             9999891, 10000000]),
-       col_indices=tensor([ 2375,  2397,  2562,  ..., 95994, 97725, 99229]),
-       values=tensor([3.2988e-01, 7.8520e-04, 8.6482e-01,  ...,
-                      9.5198e-01, 4.5600e-01, 9.5863e-01]),
+tensor(crow_indices=tensor([       0,      101,      216,  ...,  9999796,
+                             9999906, 10000000]),
+       col_indices=tensor([ 2079,  2370,  2404,  ..., 91560, 92604, 94393]),
+       values=tensor([0.9041, 0.3243, 0.4250,  ..., 0.8376, 0.1046, 0.5896]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4135, 0.2091, 0.0976,  ..., 0.1293, 0.9759, 0.9614])
+tensor([0.3984, 0.6112, 0.9728,  ..., 0.2358, 0.4123, 0.2200])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -35,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 159.18061113357544 seconds
+Time: 14.453001976013184 seconds
 
-[22.04, 21.88, 21.96, 22.12, 21.96, 21.92, 22.08, 22.08, 22.24, 22.08]
-[22.12, 22.0, 22.4, 24.16, 25.0, 26.76, 35.48, 36.76, 45.64, 60.64, 68.76, 79.68, 90.72, 91.28, 91.28, 90.96, 89.36, 89.36, 87.68, 88.24, 87.52, 88.44, 91.68, 90.56, 91.8, 92.44, 92.04, 93.12, 92.52, 92.52, 92.88, 91.88, 91.24, 91.12, 90.44, 89.64, 89.48, 89.16, 87.68, 85.84, 85.0, 86.96, 88.04, 88.04, 88.0, 89.08, 87.48, 87.52, 86.2, 84.8, 84.64, 85.48, 85.76, 86.32, 87.44, 87.44, 88.64, 89.72, 89.72, 89.4, 87.64, 88.72, 89.0, 89.12, 89.76, 91.04, 88.36, 87.88, 86.68, 87.88, 86.48, 86.8, 86.68, 87.68, 87.68, 86.8, 87.56, 86.76, 84.72, 85.2, 85.08, 85.44, 86.48, 85.92, 86.4, 86.84, 84.56, 83.28, 84.6, 84.6, 85.76, 88.64, 88.68, 89.48, 90.88, 87.96, 88.04, 89.64, 89.6, 88.16, 88.6, 87.04, 86.96, 86.24, 86.24, 87.56, 87.32, 88.48, 89.36, 88.68, 89.56, 88.2, 85.8, 85.8, 86.36, 86.36, 88.2, 88.52, 91.48, 91.48, 91.08, 90.04, 89.24, 88.08, 88.36, 89.92, 89.76, 90.6, 89.4, 87.04, 84.72, 85.04, 83.32, 84.92, 84.92, 84.2, 85.16, 84.0, 84.2, 83.92, 84.84, 84.84, 87.04, 88.68, 91.04, 91.52, 89.88, 89.96, 89.44, 89.44, 88.36, 88.12, 90.24, 89.76, 88.8, 88.0, 86.72, 86.4]
-162.37188959121704
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 159.18061113357544, 'TIME_S_1KI': 159.18061113357544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13305.551260681154, 'W': 81.94491850885544}
-[22.04, 21.88, 21.96, 22.12, 21.96, 21.92, 22.08, 22.08, 22.24, 22.08, 21.88, 22.04, 22.48, 22.64, 22.36, 22.6, 22.6, 22.44, 21.76, 21.8]
-399.06000000000006
-19.953000000000003
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 159.18061113357544, 'TIME_S_1KI': 159.18061113357544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13305.551260681154, 'W': 81.94491850885544, 'J_1KI': 13305.551260681154, 'W_1KI': 81.94491850885544, 'W_D': 61.991918508855434, 'J_D': 10065.7449476676, 'W_D_1KI': 61.991918508855434, 'J_D_1KI': 61.991918508855434}
+[17.32, 17.28, 17.28, 17.36, 17.64, 17.72, 17.68, 17.92, 17.88, 17.72]
+[17.76, 17.96, 18.52, 20.36, 21.52, 25.36, 25.36, 31.2, 31.76, 45.88, 58.28, 65.88, 77.32, 84.48, 83.76, 84.4, 84.16, 84.96, 85.8, 86.16]
+20.5910804271698
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.453001976013184, 'TIME_S_1KI': 144.53001976013184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.84748960495, 'W': 50.98554654857658}
+[17.32, 17.28, 17.28, 17.36, 17.64, 17.72, 17.68, 17.92, 17.88, 17.72, 17.16, 17.24, 17.32, 17.32, 17.44, 17.8, 17.76, 17.76, 17.72, 17.8]
+316.12
+15.806000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.453001976013184, 'TIME_S_1KI': 144.53001976013184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.84748960495, 'W': 50.98554654857658, 'J_1KI': 10498.4748960495, 'W_1KI': 509.85546548576576, 'W_D': 35.17954654857658, 'J_D': 724.3848723731041, 'W_D_1KI': 351.7954654857658, 'J_D_1KI': 3517.954654857658}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
index dc43877..c8834ef 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3301, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.605360507965088, "TIME_S_1KI": 3.8186490481566455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 881.8972729587555, "W": 57.01281240597429, "J_1KI": 267.1606400965633, "W_1KI": 17.27137606966807, "W_D": 37.77781240597429, "J_D": 584.3625026237966, "W_D_1KI": 11.444353955157311, "J_D_1KI": 3.466935460514181}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4862, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.123133897781372, "TIME_S_1KI": 3.316152591069801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1103.8810626983643, "W": 59.41987873604025, "J_1KI": 227.04258796757802, "W_1KI": 12.221283162492854, "W_D": 43.51287873604025, "J_D": 808.3665574879647, "W_D_1KI": 8.949584273146904, "J_D_1KI": 1.8407207472535796}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
index 842e972..7c14ca6 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.180464267730713}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3613910675048828}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ...,  99997,  99997,
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([ 3926, 50379, 15277,  ..., 29136, 40772, 68436]),
-       values=tensor([0.5699, 0.5366, 0.1661,  ..., 0.2141, 0.3018, 0.3946]),
+       col_indices=tensor([27177, 91140, 35351,  ..., 36842, 63353, 40213]),
+       values=tensor([0.8085, 0.0220, 0.0238,  ..., 0.9528, 0.8072, 0.8356]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8865, 0.6102, 0.2945,  ..., 0.5701, 0.8700, 0.6634])
+tensor([0.5173, 0.6337, 0.4770,  ..., 0.8095, 0.3804, 0.7829])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 3.180464267730713 seconds
+Time: 0.3613910675048828 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3301 -ss 100000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.605360507965088}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2905 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.272582054138184}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99998,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99997,
                             100000]),
-       col_indices=tensor([33916, 32242, 16140,  ..., 45457, 58350, 84955]),
-       values=tensor([0.9718, 0.7827, 0.4187,  ..., 0.1750, 0.8602, 0.7313]),
+       col_indices=tensor([57428, 26674, 73957,  ..., 55311, 85675, 87326]),
+       values=tensor([0.1133, 0.1214, 0.0575,  ..., 0.3604, 0.8021, 0.3274]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7754, 0.6786, 0.3605,  ..., 0.9739, 0.1301, 0.4075])
+tensor([0.7862, 0.8717, 0.9240,  ..., 0.8758, 0.8236, 0.0748])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 12.605360507965088 seconds
+Time: 6.272582054138184 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4862 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.123133897781372}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99998,
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([33916, 32242, 16140,  ..., 45457, 58350, 84955]),
-       values=tensor([0.9718, 0.7827, 0.4187,  ..., 0.1750, 0.8602, 0.7313]),
+       col_indices=tensor([36017, 48507, 97216,  ..., 44545, 10809,  6488]),
+       values=tensor([0.2196, 0.1379, 0.1607,  ..., 0.1720, 0.9833, 0.1649]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7754, 0.6786, 0.3605,  ..., 0.9739, 0.1301, 0.4075])
+tensor([0.6606, 0.9945, 0.6366,  ..., 0.7352, 0.9827, 0.2989])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 12.605360507965088 seconds
+Time: 16.123133897781372 seconds
 
-[21.24, 21.24, 21.36, 21.4, 21.52, 21.72, 21.56, 21.64, 21.48, 21.48]
-[21.4, 21.4, 21.8, 22.88, 23.96, 35.92, 52.84, 66.08, 81.2, 91.72, 90.6, 91.36, 91.84, 91.24, 91.24]
-15.46840500831604
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.605360507965088, 'TIME_S_1KI': 3.8186490481566455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 881.8972729587555, 'W': 57.01281240597429}
-[21.24, 21.24, 21.36, 21.4, 21.52, 21.72, 21.56, 21.64, 21.48, 21.48, 21.28, 21.28, 21.32, 21.48, 21.6, 21.48, 21.28, 21.12, 20.84, 20.76]
-384.69999999999993
-19.234999999999996
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.605360507965088, 'TIME_S_1KI': 3.8186490481566455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 881.8972729587555, 'W': 57.01281240597429, 'J_1KI': 267.1606400965633, 'W_1KI': 17.27137606966807, 'W_D': 37.77781240597429, 'J_D': 584.3625026237966, 'W_D_1KI': 11.444353955157311, 'J_D_1KI': 3.466935460514181}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([36017, 48507, 97216,  ..., 44545, 10809,  6488]),
+       values=tensor([0.2196, 0.1379, 0.1607,  ..., 0.1720, 0.9833, 0.1649]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6606, 0.9945, 0.6366,  ..., 0.7352, 0.9827, 0.2989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 16.123133897781372 seconds
+
+[17.72, 17.76, 17.68, 17.88, 17.72, 17.52, 17.6, 17.48, 17.6, 17.6]
+[17.72, 17.72, 17.8, 21.08, 22.76, 35.32, 49.56, 66.44, 76.68, 89.56, 88.0, 87.44, 86.52, 85.84, 85.76, 86.6, 86.04, 85.28]
+18.57763910293579
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4862, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.123133897781372, 'TIME_S_1KI': 3.316152591069801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.8810626983643, 'W': 59.41987873604025}
+[17.72, 17.76, 17.68, 17.88, 17.72, 17.52, 17.6, 17.48, 17.6, 17.6, 17.8, 17.8, 17.76, 17.68, 17.76, 17.88, 17.72, 17.48, 17.52, 17.48]
+318.14
+15.907
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4862, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.123133897781372, 'TIME_S_1KI': 3.316152591069801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.8810626983643, 'W': 59.41987873604025, 'J_1KI': 227.04258796757802, 'W_1KI': 12.221283162492854, 'W_D': 43.51287873604025, 'J_D': 808.3665574879647, 'W_D_1KI': 8.949584273146904, 'J_D_1KI': 1.8407207472535796}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..0831cb8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1295, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.363306522369385, "TIME_S_1KI": 10.319155615729255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1148.1477067947387, "W": 58.62479671787461, "J_1KI": 886.600545787443, "W_1KI": 45.27011329565607, "W_D": 42.857796717874606, "J_D": 839.3561048357486, "W_D_1KI": 33.09482372036649, "J_D_1KI": 25.55584843271544}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..fe6c43d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.8107287883758545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 499988, 499996,
+                            500000]),
+       col_indices=tensor([ 2924,  5581, 32898,  ..., 25573, 35176, 44980]),
+       values=tensor([0.4338, 0.2090, 0.8667,  ..., 0.7968, 0.4161, 0.0285]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7311, 0.8750, 0.4611,  ..., 0.2473, 0.3600, 0.7684])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.8107287883758545 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1295 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.363306522369385}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499992, 499997,
+                            500000]),
+       col_indices=tensor([20312, 45798, 57469,  ...,  9915, 72511, 98823]),
+       values=tensor([0.2450, 0.9842, 0.2161,  ..., 0.8948, 0.9103, 0.6478]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.2448, 0.6389, 0.3272,  ..., 0.3843, 0.8480, 0.1017])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 13.363306522369385 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499992, 499997,
+                            500000]),
+       col_indices=tensor([20312, 45798, 57469,  ...,  9915, 72511, 98823]),
+       values=tensor([0.2450, 0.9842, 0.2161,  ..., 0.8948, 0.9103, 0.6478]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.2448, 0.6389, 0.3272,  ..., 0.3843, 0.8480, 0.1017])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 13.363306522369385 seconds
+
+[17.48, 17.6, 17.56, 17.72, 17.52, 17.48, 17.56, 17.6, 17.6, 17.8]
+[18.0, 17.84, 17.76, 18.88, 19.6, 35.4, 49.36, 64.12, 77.28, 86.8, 84.84, 85.28, 83.64, 83.64, 82.96, 82.4, 81.96, 80.44, 80.16]
+19.58467698097229
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.363306522369385, 'TIME_S_1KI': 10.319155615729255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1148.1477067947387, 'W': 58.62479671787461}
+[17.48, 17.6, 17.56, 17.72, 17.52, 17.48, 17.56, 17.6, 17.6, 17.8, 17.64, 17.44, 17.36, 17.52, 17.36, 17.2, 17.52, 17.44, 17.6, 17.6]
+315.34
+15.767
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.363306522369385, 'TIME_S_1KI': 10.319155615729255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1148.1477067947387, 'W': 58.62479671787461, 'J_1KI': 886.600545787443, 'W_1KI': 45.27011329565607, 'W_D': 42.857796717874606, 'J_D': 839.3561048357486, 'W_D_1KI': 33.09482372036649, 'J_D_1KI': 25.55584843271544}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
index b901590..30708d9 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 32089, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109246492385864, "TIME_S_1KI": 0.3150377541333748, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.60778367996215, "W": 22.396548812340328, "J_1KI": 9.92887854654125, "W_1KI": 0.6979509742385342, "W_D": 4.033548812340328, "J_D": 57.38027131915093, "W_D_1KI": 0.1256988005964763, "J_D_1KI": 0.003917192826092315}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 33188, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.848255395889282, "TIME_S_1KI": 0.3268728274041606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 283.2797647285461, "W": 19.966696619139725, "J_1KI": 8.535608193580394, "W_1KI": 0.6016239791231688, "W_D": 4.899696619139723, "J_D": 69.51499950075144, "W_D_1KI": 0.14763458536638915, "J_D_1KI": 0.0044484327276843785}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
index 8b8a1a7..ce5f5e4 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3272056579589844}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.039971351623535156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([ 654, 4587, 9013,  ..., 1787, 1854, 8773]),
-       values=tensor([0.1124, 0.2109, 0.1818,  ..., 0.9520, 0.5472, 0.0091]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([5709, 5957, 5382,  ..., 8260, 3428, 9778]),
+       values=tensor([0.1410, 0.8327, 0.5618,  ..., 0.6127, 0.7950, 0.7693]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1189, 0.4488, 0.9345,  ..., 0.0324, 0.3464, 0.4030])
+tensor([0.9182, 0.7141, 0.2679,  ..., 0.7671, 0.9231, 0.7252])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.3272056579589844 seconds
+Time: 0.039971351623535156 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32089 -ss 10000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109246492385864}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26268 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.310471773147583}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
-       col_indices=tensor([6261, 1350, 3983,  ..., 9586, 2579, 6781]),
-       values=tensor([0.3771, 0.7405, 0.3284,  ..., 0.1626, 0.7239, 0.9996]),
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([6011, 6260, 4075,  ..., 5576, 1824, 8975]),
+       values=tensor([0.9641, 0.0766, 0.9967,  ..., 0.9539, 0.3769, 0.8002]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1818, 0.1444, 0.2139,  ..., 0.0964, 0.7255, 0.0411])
+tensor([0.0022, 0.8912, 0.7670,  ..., 0.3905, 0.8453, 0.1961])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,15 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.109246492385864 seconds
+Time: 8.310471773147583 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 33188 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.848255395889282}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
-       col_indices=tensor([6261, 1350, 3983,  ..., 9586, 2579, 6781]),
-       values=tensor([0.3771, 0.7405, 0.3284,  ..., 0.1626, 0.7239, 0.9996]),
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9995,  9997, 10000]),
+       col_indices=tensor([1948, 7195, 8876,  ...,  111, 6612, 9607]),
+       values=tensor([0.9860, 0.8888, 0.2852,  ..., 0.2300, 0.3266, 0.6773]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1818, 0.1444, 0.2139,  ..., 0.0964, 0.7255, 0.0411])
+tensor([0.8303, 0.1186, 0.7718,  ..., 0.9103, 0.1807, 0.5186])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -50,13 +53,29 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.109246492385864 seconds
+Time: 10.848255395889282 seconds
 
-[20.52, 20.4, 20.52, 20.52, 20.52, 20.32, 20.32, 20.2, 20.36, 20.24]
-[20.32, 20.48, 20.88, 25.52, 26.52, 27.24, 27.6, 24.68, 23.88, 23.88, 23.36, 23.6, 23.68, 23.6]
-14.225753545761108
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32089, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109246492385864, 'TIME_S_1KI': 0.3150377541333748, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.60778367996215, 'W': 22.396548812340328}
-[20.52, 20.4, 20.52, 20.52, 20.52, 20.32, 20.32, 20.2, 20.36, 20.24, 20.72, 20.52, 20.6, 20.4, 20.32, 20.52, 20.44, 20.32, 20.16, 20.16]
-367.26
-18.363
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32089, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109246492385864, 'TIME_S_1KI': 0.3150377541333748, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.60778367996215, 'W': 22.396548812340328, 'J_1KI': 9.92887854654125, 'W_1KI': 0.6979509742385342, 'W_D': 4.033548812340328, 'J_D': 57.38027131915093, 'W_D_1KI': 0.1256988005964763, 'J_D_1KI': 0.003917192826092315}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9995,  9997, 10000]),
+       col_indices=tensor([1948, 7195, 8876,  ...,  111, 6612, 9607]),
+       values=tensor([0.9860, 0.8888, 0.2852,  ..., 0.2300, 0.3266, 0.6773]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8303, 0.1186, 0.7718,  ..., 0.9103, 0.1807, 0.5186])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.848255395889282 seconds
+
+[17.16, 17.24, 17.28, 17.28, 17.12, 16.8, 16.4, 16.48, 16.32, 16.6]
+[16.76, 16.84, 20.04, 22.12, 23.96, 24.76, 24.76, 25.32, 22.2, 20.84, 19.88, 20.16, 20.24, 20.04]
+14.187613010406494
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 33188, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.848255395889282, 'TIME_S_1KI': 0.3268728274041606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.2797647285461, 'W': 19.966696619139725}
+[17.16, 17.24, 17.28, 17.28, 17.12, 16.8, 16.4, 16.48, 16.32, 16.6, 16.72, 16.48, 16.8, 16.64, 16.64, 16.68, 16.68, 16.56, 16.44, 16.52]
+301.34000000000003
+15.067000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 33188, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.848255395889282, 'TIME_S_1KI': 0.3268728274041606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.2797647285461, 'W': 19.966696619139725, 'J_1KI': 8.535608193580394, 'W_1KI': 0.6016239791231688, 'W_D': 4.899696619139723, 'J_D': 69.51499950075144, 'W_D_1KI': 0.14763458536638915, 'J_D_1KI': 0.0044484327276843785}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
index 28a5b99..9b3c069 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.211250305175781, "TIME_S_1KI": 2.236366689701222, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.4135251998902, "W": 22.432139051903395, "J_1KI": 64.91754822599435, "W_1KI": 4.912864444131274, "W_D": 4.068139051903394, "J_D": 53.75552614879615, "W_D_1KI": 0.8909634366849307, "J_D_1KI": 0.19512996861255602}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.500732183456421, "TIME_S_1KI": 2.2427877367484883, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.49395805358887, "W": 21.02945948615313, "J_1KI": 63.753515175905356, "W_1KI": 4.491554781322753, "W_D": 5.974459486153128, "J_D": 84.80199219703674, "W_D_1KI": 1.2760485873885365, "J_D_1KI": 0.27254348299627007}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
index f081110..faf5980 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2992472648620605}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2965726852416992}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     16,  ...,  99980,  99989,
+tensor(crow_indices=tensor([     0,     10,     21,  ...,  99978,  99991,
                             100000]),
-       col_indices=tensor([ 655, 1592, 1705,  ..., 9238, 9783, 9811]),
-       values=tensor([0.0624, 0.8226, 0.1738,  ..., 0.6448, 0.8074, 0.7220]),
+       col_indices=tensor([ 670, 2215, 2340,  ..., 5626, 6766, 7426]),
+       values=tensor([0.8454, 0.9971, 0.8602,  ..., 0.7286, 0.6853, 0.9650]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5841, 0.1855, 0.2176,  ..., 0.5967, 0.9561, 0.0240])
+tensor([0.8368, 0.0517, 0.8880,  ..., 0.1583, 0.3761, 0.3465])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 2.2992472648620605 seconds
+Time: 0.2965726852416992 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4566 -ss 10000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.211250305175781}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3540 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.9380598068237305}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     14,     28,  ...,  99989,  99992,
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99981,  99990,
                             100000]),
-       col_indices=tensor([ 778, 1147, 3454,  ..., 4854, 5919, 8867]),
-       values=tensor([0.6002, 0.4939, 0.0259,  ..., 0.9282, 0.0584, 0.5342]),
+       col_indices=tensor([ 218, 1968, 1988,  ..., 6580, 8417, 9626]),
+       values=tensor([0.6495, 0.9774, 0.8878,  ..., 0.7618, 0.8151, 0.2290]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8806, 0.9663, 0.5124,  ..., 0.2617, 0.2277, 0.6355])
+tensor([0.1753, 0.6932, 0.8031,  ..., 0.2090, 0.4929, 0.1708])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.211250305175781 seconds
+Time: 7.9380598068237305 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4682 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.500732183456421}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     14,     28,  ...,  99989,  99992,
+tensor(crow_indices=tensor([     0,      6,     20,  ...,  99977,  99983,
                             100000]),
-       col_indices=tensor([ 778, 1147, 3454,  ..., 4854, 5919, 8867]),
-       values=tensor([0.6002, 0.4939, 0.0259,  ..., 0.9282, 0.0584, 0.5342]),
+       col_indices=tensor([  32,  224, 1507,  ..., 8865, 9626, 9660]),
+       values=tensor([0.3062, 0.4385, 0.1947,  ..., 0.8116, 0.9937, 0.3510]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8806, 0.9663, 0.5124,  ..., 0.2617, 0.2277, 0.6355])
+tensor([0.0637, 0.2734, 0.9054,  ..., 0.1369, 0.8787, 0.2539])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.211250305175781 seconds
+Time: 10.500732183456421 seconds
 
-[20.52, 20.28, 20.28, 20.24, 20.2, 20.2, 20.16, 20.44, 20.56, 20.64]
-[20.68, 20.52, 20.84, 21.8, 22.68, 26.2, 26.72, 26.88, 26.88, 26.92, 24.56, 24.6, 24.48]
-13.21378779411316
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.211250305175781, 'TIME_S_1KI': 2.236366689701222, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.4135251998902, 'W': 22.432139051903395}
-[20.52, 20.28, 20.28, 20.24, 20.2, 20.2, 20.16, 20.44, 20.56, 20.64, 20.56, 20.52, 20.32, 20.28, 20.24, 20.4, 20.6, 20.68, 20.68, 20.68]
-367.28000000000003
-18.364
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.211250305175781, 'TIME_S_1KI': 2.236366689701222, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.4135251998902, 'W': 22.432139051903395, 'J_1KI': 64.91754822599435, 'W_1KI': 4.912864444131274, 'W_D': 4.068139051903394, 'J_D': 53.75552614879615, 'W_D_1KI': 0.8909634366849307, 'J_D_1KI': 0.19512996861255602}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     20,  ...,  99977,  99983,
+                            100000]),
+       col_indices=tensor([  32,  224, 1507,  ..., 8865, 9626, 9660]),
+       values=tensor([0.3062, 0.4385, 0.1947,  ..., 0.8116, 0.9937, 0.3510]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0637, 0.2734, 0.9054,  ..., 0.1369, 0.8787, 0.2539])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.500732183456421 seconds
+
+[16.56, 16.28, 16.36, 16.32, 16.4, 16.32, 16.36, 16.44, 16.36, 16.28]
+[16.24, 15.96, 19.32, 19.32, 21.08, 28.0, 29.28, 30.04, 27.12, 25.96, 20.44, 20.04, 19.96, 19.96]
+14.194086074829102
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.500732183456421, 'TIME_S_1KI': 2.2427877367484883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.49395805358887, 'W': 21.02945948615313}
+[16.56, 16.28, 16.36, 16.32, 16.4, 16.32, 16.36, 16.44, 16.36, 16.28, 16.52, 16.8, 17.04, 17.04, 17.16, 17.48, 17.4, 17.4, 16.84, 16.84]
+301.1
+15.055000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.500732183456421, 'TIME_S_1KI': 2.2427877367484883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.49395805358887, 'W': 21.02945948615313, 'J_1KI': 63.753515175905356, 'W_1KI': 4.491554781322753, 'W_D': 5.974459486153128, 'J_D': 84.80199219703674, 'W_D_1KI': 1.2760485873885365, 'J_D_1KI': 0.27254348299627007}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
index 123a4ab..b135810 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.205244779586792, "TIME_S_1KI": 21.205244779586792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 620.3200452423096, "W": 24.48389957916532, "J_1KI": 620.3200452423096, "W_1KI": 24.48389957916532, "W_D": 6.114899579165321, "J_D": 154.92608811497686, "W_D_1KI": 6.114899579165321, "J_D_1KI": 6.114899579165321}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 481, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.463550806045532, "TIME_S_1KI": 21.75374387951254, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.4283616256714, "W": 23.410421194984455, "J_1KI": 691.1192549390258, "W_1KI": 48.67031433468701, "W_D": 6.792421194984453, "J_D": 96.45249141454698, "W_D_1KI": 14.121457785830463, "J_D_1KI": 29.35854009528163}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
index 9e8858d..bf59b23 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 21.205244779586792}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1813583374023438}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      87,     190,  ...,  999787,
-                             999893, 1000000]),
-       col_indices=tensor([  40,  232,  261,  ..., 9741, 9779, 9904]),
-       values=tensor([0.6083, 0.3635, 0.2569,  ..., 0.1971, 0.1171, 0.3174]),
+tensor(crow_indices=tensor([      0,     110,     205,  ...,  999812,
+                             999909, 1000000]),
+       col_indices=tensor([ 238,  361,  384,  ..., 9612, 9634, 9698]),
+       values=tensor([0.5750, 0.8151, 0.3516,  ..., 0.9302, 0.8544, 0.3311]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7546, 0.0325, 0.8716,  ..., 0.3834, 0.9539, 0.7452])
+tensor([0.6330, 0.0586, 0.5281,  ..., 0.1634, 0.9727, 0.9265])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.205244779586792 seconds
+Time: 2.1813583374023438 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 481 -ss 10000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.463550806045532}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      87,     190,  ...,  999787,
-                             999893, 1000000]),
-       col_indices=tensor([  40,  232,  261,  ..., 9741, 9779, 9904]),
-       values=tensor([0.6083, 0.3635, 0.2569,  ..., 0.1971, 0.1171, 0.3174]),
+tensor(crow_indices=tensor([      0,      92,     212,  ...,  999818,
+                             999919, 1000000]),
+       col_indices=tensor([ 159,  190,  205,  ..., 9628, 9649, 9961]),
+       values=tensor([0.6162, 0.4289, 0.4486,  ..., 0.9461, 0.6549, 0.6632]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7546, 0.0325, 0.8716,  ..., 0.3834, 0.9539, 0.7452])
+tensor([0.8230, 0.2220, 0.9348,  ..., 0.2779, 0.8915, 0.3439])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 21.205244779586792 seconds
+Time: 10.463550806045532 seconds
 
-[20.12, 20.4, 20.4, 20.36, 20.16, 20.24, 20.32, 20.32, 20.32, 20.52]
-[20.52, 20.76, 21.12, 22.84, 24.76, 33.76, 34.6, 34.52, 33.84, 26.88, 24.24, 24.24, 24.12, 24.12, 24.04, 24.0, 24.0, 24.04, 24.12, 23.96, 24.0, 24.12, 23.96, 23.96, 24.0]
-25.335835218429565
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.205244779586792, 'TIME_S_1KI': 21.205244779586792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 620.3200452423096, 'W': 24.48389957916532}
-[20.12, 20.4, 20.4, 20.36, 20.16, 20.24, 20.32, 20.32, 20.32, 20.52, 20.52, 20.56, 20.6, 20.44, 20.44, 20.32, 20.28, 20.64, 20.68, 20.64]
-367.38
-18.369
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.205244779586792, 'TIME_S_1KI': 21.205244779586792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 620.3200452423096, 'W': 24.48389957916532, 'J_1KI': 620.3200452423096, 'W_1KI': 24.48389957916532, 'W_D': 6.114899579165321, 'J_D': 154.92608811497686, 'W_D_1KI': 6.114899579165321, 'J_D_1KI': 6.114899579165321}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      92,     212,  ...,  999818,
+                             999919, 1000000]),
+       col_indices=tensor([ 159,  190,  205,  ..., 9628, 9649, 9961]),
+       values=tensor([0.6162, 0.4289, 0.4486,  ..., 0.9461, 0.6549, 0.6632]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8230, 0.2220, 0.9348,  ..., 0.2779, 0.8915, 0.3439])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.463550806045532 seconds
+
+[20.24, 19.08, 17.84, 18.08, 19.12, 19.88, 20.84, 22.4, 22.4, 22.88]
+[22.68, 22.68, 21.44, 22.2, 23.24, 31.92, 32.08, 31.76, 30.52, 23.92, 21.88, 21.88, 22.0, 22.24]
+14.200016260147095
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.463550806045532, 'TIME_S_1KI': 21.75374387951254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.4283616256714, 'W': 23.410421194984455}
+[20.24, 19.08, 17.84, 18.08, 19.12, 19.88, 20.84, 22.4, 22.4, 22.88, 16.72, 17.0, 17.08, 16.92, 17.04, 16.84, 16.64, 16.56, 16.56, 16.32]
+332.36
+16.618000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.463550806045532, 'TIME_S_1KI': 21.75374387951254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.4283616256714, 'W': 23.410421194984455, 'J_1KI': 691.1192549390258, 'W_1KI': 48.67031433468701, 'W_D': 6.792421194984453, 'J_D': 96.45249141454698, 'W_D_1KI': 14.121457785830463, 'J_D_1KI': 29.35854009528163}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
index 054792b..18a5242 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.56615614891052, "TIME_S_1KI": 106.56615614891052, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2781.6071025085453, "W": 24.542113905929394, "J_1KI": 2781.6071025085453, "W_1KI": 24.542113905929394, "W_D": 6.099113905929396, "J_D": 691.2745423955923, "W_D_1KI": 6.099113905929396, "J_D_1KI": 6.099113905929396}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 11.54654312133789, "TIME_S_1KI": 115.4654312133789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.263671875, "W": 21.71035974754772, "J_1KI": 3532.63671875, "W_1KI": 217.1035974754772, "W_D": 6.712359747547719, "J_D": 109.22126021575927, "W_D_1KI": 67.1235974754772, "J_D_1KI": 671.235974754772}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
index 1064fac..d49136b 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 106.56615614891052}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 11.54654312133789}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     509,    1020,  ..., 4998992,
-                            4999488, 5000000]),
-       col_indices=tensor([   3,   11,   31,  ..., 9971, 9976, 9990]),
-       values=tensor([0.8435, 0.0304, 0.5451,  ..., 0.3255, 0.3710, 0.6386]),
+tensor(crow_indices=tensor([      0,     487,     979,  ..., 4998949,
+                            4999474, 5000000]),
+       col_indices=tensor([   0,    2,    9,  ..., 9960, 9969, 9987]),
+       values=tensor([0.4699, 0.5377, 0.9444,  ..., 0.8781, 0.2092, 0.8180]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1386, 0.0671, 0.1165,  ..., 0.0400, 0.5375, 0.5366])
+tensor([0.7212, 0.1994, 0.8974,  ..., 0.2164, 0.6888, 0.0461])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 106.56615614891052 seconds
+Time: 11.54654312133789 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     509,    1020,  ..., 4998992,
-                            4999488, 5000000]),
-       col_indices=tensor([   3,   11,   31,  ..., 9971, 9976, 9990]),
-       values=tensor([0.8435, 0.0304, 0.5451,  ..., 0.3255, 0.3710, 0.6386]),
+tensor(crow_indices=tensor([      0,     487,     979,  ..., 4998949,
+                            4999474, 5000000]),
+       col_indices=tensor([   0,    2,    9,  ..., 9960, 9969, 9987]),
+       values=tensor([0.4699, 0.5377, 0.9444,  ..., 0.8781, 0.2092, 0.8180]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1386, 0.0671, 0.1165,  ..., 0.0400, 0.5375, 0.5366])
+tensor([0.7212, 0.1994, 0.8974,  ..., 0.2164, 0.6888, 0.0461])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 106.56615614891052 seconds
+Time: 11.54654312133789 seconds
 
-[20.4, 20.64, 20.52, 20.56, 20.6, 20.52, 20.56, 20.52, 20.2, 20.56]
-[20.56, 20.68, 20.68, 24.36, 26.16, 32.64, 39.96, 40.52, 36.88, 36.0, 27.96, 24.36, 24.12, 24.28, 24.28, 24.44, 24.64, 24.72, 24.6, 24.36, 24.2, 23.96, 24.12, 24.08, 24.12, 24.24, 24.24, 24.28, 24.68, 24.56, 24.72, 24.84, 24.88, 24.56, 24.48, 24.32, 24.44, 24.52, 24.52, 24.72, 24.68, 24.68, 24.28, 24.0, 23.92, 23.84, 24.0, 24.24, 24.44, 24.48, 24.44, 24.44, 24.64, 24.68, 24.76, 24.72, 24.68, 24.52, 24.56, 24.64, 24.52, 24.68, 24.44, 24.44, 24.4, 24.64, 24.8, 24.68, 24.76, 24.64, 24.4, 24.12, 24.48, 24.56, 24.72, 24.72, 24.8, 24.96, 24.52, 24.32, 24.36, 24.24, 24.08, 24.04, 23.96, 24.08, 24.4, 24.48, 24.48, 24.64, 24.76, 24.4, 24.36, 24.4, 24.56, 24.68, 24.68, 24.48, 24.36, 24.2, 24.2, 24.56, 24.48, 24.4, 24.36, 24.44, 24.36, 24.44, 24.36, 24.64, 24.52, 24.48]
-113.34015941619873
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56615614891052, 'TIME_S_1KI': 106.56615614891052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2781.6071025085453, 'W': 24.542113905929394}
-[20.4, 20.64, 20.52, 20.56, 20.6, 20.52, 20.56, 20.52, 20.2, 20.56, 20.52, 20.48, 20.48, 20.56, 20.48, 20.36, 20.48, 20.52, 20.44, 20.4]
-368.85999999999996
-18.442999999999998
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56615614891052, 'TIME_S_1KI': 106.56615614891052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2781.6071025085453, 'W': 24.542113905929394, 'J_1KI': 2781.6071025085453, 'W_1KI': 24.542113905929394, 'W_D': 6.099113905929396, 'J_D': 691.2745423955923, 'W_D_1KI': 6.099113905929396, 'J_D_1KI': 6.099113905929396}
+[16.56, 16.48, 16.72, 16.72, 16.8, 16.8, 16.64, 16.52, 16.52, 16.48]
+[16.44, 16.64, 17.04, 18.24, 19.64, 27.0, 30.2, 30.0, 30.0, 30.08, 26.08, 22.8, 20.4, 20.48, 20.64, 20.48]
+16.271663665771484
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 11.54654312133789, 'TIME_S_1KI': 115.4654312133789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 353.263671875, 'W': 21.71035974754772}
+[16.56, 16.48, 16.72, 16.72, 16.8, 16.8, 16.64, 16.52, 16.52, 16.48, 16.52, 16.36, 16.6, 16.84, 16.68, 16.68, 16.68, 16.96, 16.76, 16.84]
+299.96000000000004
+14.998000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 11.54654312133789, 'TIME_S_1KI': 115.4654312133789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 353.263671875, 'W': 21.71035974754772, 'J_1KI': 3532.63671875, 'W_1KI': 217.1035974754772, 'W_D': 6.712359747547719, 'J_D': 109.22126021575927, 'W_D_1KI': 67.1235974754772, 'J_D_1KI': 671.235974754772}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
index 175168f..94990e6 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.99842429161072, "TIME_S_1KI": 210.99842429161072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5320.685762977602, "W": 24.45864835325204, "J_1KI": 5320.685762977602, "W_1KI": 24.45864835325204, "W_D": 6.0876483532520425, "J_D": 1324.2949264960312, "W_D_1KI": 6.0876483532520425, "J_D_1KI": 6.0876483532520425}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.129345178604126, "TIME_S_1KI": 211.29345178604126, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 619.1563564968109, "W": 21.836067076186225, "J_1KI": 6191.563564968109, "W_1KI": 218.36067076186225, "W_D": 6.581067076186224, "J_D": 186.6045519340038, "W_D_1KI": 65.81067076186224, "J_D_1KI": 658.1067076186224}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
index 1928ba2..12caf81 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.99842429161072}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.129345178604126}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1065,     2071,  ...,  9998045,
-                             9999047, 10000000]),
-       col_indices=tensor([   6,   19,   22,  ..., 9974, 9992, 9993]),
-       values=tensor([0.1921, 0.6014, 0.9806,  ..., 0.7679, 0.7737, 0.6028]),
+tensor(crow_indices=tensor([       0,      944,     1951,  ...,  9997982,
+                             9998990, 10000000]),
+       col_indices=tensor([  12,   17,   18,  ..., 9973, 9979, 9988]),
+       values=tensor([0.0454, 0.8748, 0.1892,  ..., 0.1417, 0.8974, 0.6226]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1676, 0.2617, 0.2303,  ..., 0.3636, 0.4445, 0.4181])
+tensor([0.7518, 0.8666, 0.4972,  ..., 0.4338, 0.0856, 0.8852])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 210.99842429161072 seconds
+Time: 21.129345178604126 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1065,     2071,  ...,  9998045,
-                             9999047, 10000000]),
-       col_indices=tensor([   6,   19,   22,  ..., 9974, 9992, 9993]),
-       values=tensor([0.1921, 0.6014, 0.9806,  ..., 0.7679, 0.7737, 0.6028]),
+tensor(crow_indices=tensor([       0,      944,     1951,  ...,  9997982,
+                             9998990, 10000000]),
+       col_indices=tensor([  12,   17,   18,  ..., 9973, 9979, 9988]),
+       values=tensor([0.0454, 0.8748, 0.1892,  ..., 0.1417, 0.8974, 0.6226]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1676, 0.2617, 0.2303,  ..., 0.3636, 0.4445, 0.4181])
+tensor([0.7518, 0.8666, 0.4972,  ..., 0.4338, 0.0856, 0.8852])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 210.99842429161072 seconds
+Time: 21.129345178604126 seconds
 
-[20.48, 20.52, 20.52, 20.68, 20.64, 20.64, 20.6, 20.56, 20.36, 20.48]
-[20.52, 20.6, 21.44, 23.16, 24.32, 34.12, 34.12, 35.68, 38.2, 37.44, 37.12, 27.84, 27.04, 24.4, 24.44, 24.4, 24.32, 24.48, 24.48, 24.36, 24.44, 24.72, 25.0, 24.92, 24.96, 24.96, 24.52, 24.4, 24.32, 24.24, 24.36, 24.36, 24.64, 24.56, 24.44, 24.52, 24.4, 24.6, 24.68, 24.84, 24.88, 24.64, 24.52, 24.52, 24.6, 24.48, 24.4, 24.28, 24.28, 24.32, 24.44, 24.44, 24.56, 24.52, 24.2, 24.16, 24.16, 24.12, 24.32, 24.36, 24.28, 24.28, 24.0, 24.12, 24.24, 24.4, 24.56, 25.04, 25.04, 24.84, 24.8, 24.76, 24.48, 24.44, 24.56, 24.48, 24.48, 24.4, 24.48, 24.36, 24.48, 24.48, 24.6, 24.68, 25.0, 25.0, 24.84, 24.76, 24.68, 24.44, 24.52, 24.52, 24.6, 24.6, 24.76, 24.48, 24.52, 24.4, 24.16, 24.24, 24.0, 24.24, 24.12, 24.24, 24.44, 24.44, 24.8, 24.8, 24.72, 24.64, 24.52, 24.28, 24.24, 24.16, 24.2, 24.32, 24.48, 24.32, 24.32, 24.28, 24.28, 24.32, 24.52, 24.56, 24.56, 24.6, 24.48, 24.4, 24.28, 24.24, 24.24, 24.24, 24.32, 24.48, 24.4, 24.4, 24.2, 24.08, 24.24, 24.4, 24.64, 24.68, 24.64, 24.64, 24.8, 24.6, 24.72, 24.8, 24.76, 24.76, 24.92, 25.08, 24.92, 24.88, 24.68, 24.68, 24.48, 24.32, 24.64, 24.68, 24.92, 24.92, 24.8, 24.68, 24.64, 24.44, 24.6, 24.6, 24.68, 24.52, 24.4, 24.44, 24.36, 24.12, 24.32, 24.24, 24.16, 24.24, 24.0, 24.24, 24.24, 24.44, 24.44, 24.6, 24.64, 24.44, 24.36, 24.48, 24.4, 24.64, 24.44, 24.64, 24.64, 24.6, 24.44, 24.64, 24.64, 24.32, 24.36, 24.24, 24.08, 24.36, 24.4, 24.48, 24.56, 24.56, 24.44, 24.32, 24.2, 24.36, 24.56, 24.68, 24.76, 24.92, 24.88]
-217.5380129814148
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.99842429161072, 'TIME_S_1KI': 210.99842429161072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5320.685762977602, 'W': 24.45864835325204}
-[20.48, 20.52, 20.52, 20.68, 20.64, 20.64, 20.6, 20.56, 20.36, 20.48, 20.4, 20.36, 20.32, 20.32, 20.2, 20.2, 20.28, 20.16, 20.24, 20.28]
-367.41999999999996
-18.371
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.99842429161072, 'TIME_S_1KI': 210.99842429161072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5320.685762977602, 'W': 24.45864835325204, 'J_1KI': 5320.685762977602, 'W_1KI': 24.45864835325204, 'W_D': 6.0876483532520425, 'J_D': 1324.2949264960312, 'W_D_1KI': 6.0876483532520425, 'J_D_1KI': 6.0876483532520425}
+[16.8, 16.76, 16.76, 16.76, 16.88, 16.92, 16.92, 17.0, 17.04, 17.04]
+[16.88, 17.04, 20.52, 21.6, 23.68, 23.68, 29.08, 30.56, 30.32, 30.08, 28.48, 23.96, 23.32, 20.92, 20.92, 21.08, 21.44, 21.44, 20.92, 20.72, 20.64, 20.4, 20.4, 20.56, 20.44, 20.36, 20.48, 20.68]
+28.354756116867065
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.129345178604126, 'TIME_S_1KI': 211.29345178604126, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 619.1563564968109, 'W': 21.836067076186225}
+[16.8, 16.76, 16.76, 16.76, 16.88, 16.92, 16.92, 17.0, 17.04, 17.04, 17.12, 17.28, 17.24, 17.24, 16.84, 16.64, 16.96, 16.72, 17.12, 17.08]
+305.1
+15.255
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.129345178604126, 'TIME_S_1KI': 211.29345178604126, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 619.1563564968109, 'W': 21.836067076186225, 'J_1KI': 6191.563564968109, 'W_1KI': 218.36067076186225, 'W_D': 6.581067076186224, 'J_D': 186.6045519340038, 'W_D_1KI': 65.81067076186224, 'J_D_1KI': 658.1067076186224}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..364c8ea
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.021928787231445, "TIME_S_1KI": 420.21928787231445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1136.679587249756, "W": 21.603080734566593, "J_1KI": 11366.79587249756, "W_1KI": 216.03080734566592, "W_D": 6.324080734566593, "J_D": 332.751312992096, "W_D_1KI": 63.24080734566592, "J_D_1KI": 632.4080734566592}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..b23f94c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.021928787231445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1977,     4005,  ..., 19996092,
+                            19998061, 20000000]),
+       col_indices=tensor([   0,   21,   29,  ..., 9987, 9990, 9994]),
+       values=tensor([0.7851, 0.1514, 0.2063,  ..., 0.8497, 0.4761, 0.4899]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7703, 0.2799, 0.3874,  ..., 0.1897, 0.4899, 0.7472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.021928787231445 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1977,     4005,  ..., 19996092,
+                            19998061, 20000000]),
+       col_indices=tensor([   0,   21,   29,  ..., 9987, 9990, 9994]),
+       values=tensor([0.7851, 0.1514, 0.2063,  ..., 0.8497, 0.4761, 0.4899]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7703, 0.2799, 0.3874,  ..., 0.1897, 0.4899, 0.7472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.021928787231445 seconds
+
+[16.84, 16.68, 16.68, 16.88, 16.96, 17.08, 17.12, 17.24, 17.0, 17.0]
+[16.76, 16.8, 19.84, 20.68, 22.88, 22.88, 24.36, 33.76, 31.92, 32.48, 31.68, 31.64, 25.4, 24.92, 23.64, 20.2, 20.04, 20.04, 20.04, 19.84, 20.12, 20.36, 20.72, 20.76, 20.48, 20.32, 20.4, 20.28, 20.76, 20.84, 20.84, 20.92, 21.0, 20.96, 20.8, 20.8, 20.84, 20.6, 20.56, 20.56, 20.84, 20.76, 20.76, 20.8, 21.0, 21.08, 20.8, 20.96, 20.72, 20.72, 20.72, 20.76]
+52.61655044555664
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.021928787231445, 'TIME_S_1KI': 420.21928787231445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.679587249756, 'W': 21.603080734566593}
+[16.84, 16.68, 16.68, 16.88, 16.96, 17.08, 17.12, 17.24, 17.0, 17.0, 17.2, 17.16, 17.08, 16.84, 16.84, 16.8, 17.0, 17.04, 17.16, 17.0]
+305.58
+15.279
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.021928787231445, 'TIME_S_1KI': 420.21928787231445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.679587249756, 'W': 21.603080734566593, 'J_1KI': 11366.79587249756, 'W_1KI': 216.03080734566592, 'W_D': 6.324080734566593, 'J_D': 332.751312992096, 'W_D_1KI': 63.24080734566592, 'J_D_1KI': 632.4080734566592}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..f2ba0ed
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.62427043914795, "TIME_S_1KI": 636.2427043914795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1670.8393529319762, "W": 21.725499941161722, "J_1KI": 16708.393529319765, "W_1KI": 217.2549994116172, "W_D": 6.518499941161723, "J_D": 501.3171735184193, "W_D_1KI": 65.18499941161723, "J_D_1KI": 651.8499941161723}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..6258d91
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.62427043914795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2940,     5882,  ..., 29993962,
+                            29997042, 30000000]),
+       col_indices=tensor([   8,   14,   15,  ..., 9977, 9993, 9996]),
+       values=tensor([0.8256, 0.2654, 0.0882,  ..., 0.9659, 0.5243, 0.2720]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4780, 0.6736, 0.3887,  ..., 0.7294, 0.7408, 0.5543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 63.62427043914795 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2940,     5882,  ..., 29993962,
+                            29997042, 30000000]),
+       col_indices=tensor([   8,   14,   15,  ..., 9977, 9993, 9996]),
+       values=tensor([0.8256, 0.2654, 0.0882,  ..., 0.9659, 0.5243, 0.2720]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4780, 0.6736, 0.3887,  ..., 0.7294, 0.7408, 0.5543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 63.62427043914795 seconds
+
+[16.52, 16.44, 16.52, 16.76, 16.76, 16.68, 17.12, 16.6, 16.6, 16.64]
+[16.76, 16.68, 16.6, 20.16, 21.48, 23.48, 24.68, 31.48, 34.68, 34.64, 35.08, 35.08, 34.68, 24.92, 25.84, 25.44, 24.52, 23.52, 20.92, 20.76, 20.76, 20.88, 20.6, 20.8, 20.8, 20.96, 20.88, 20.6, 20.8, 20.56, 20.44, 20.52, 20.48, 20.8, 20.56, 20.6, 20.6, 20.68, 20.72, 20.68, 20.88, 20.92, 20.8, 20.8, 20.52, 20.56, 20.68, 20.96, 20.8, 20.8, 20.92, 20.8, 20.56, 20.68, 20.8, 20.72, 20.8, 21.12, 21.24, 21.16, 21.12, 21.12, 21.04, 20.6, 20.36, 20.2, 20.4, 20.48, 20.52, 20.88, 20.64, 20.32, 20.4, 20.4, 20.4, 20.4]
+76.90683102607727
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.62427043914795, 'TIME_S_1KI': 636.2427043914795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.8393529319762, 'W': 21.725499941161722}
+[16.52, 16.44, 16.52, 16.76, 16.76, 16.68, 17.12, 16.6, 16.6, 16.64, 16.32, 16.72, 16.92, 17.2, 17.32, 17.52, 17.16, 17.24, 17.28, 17.12]
+304.14
+15.206999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.62427043914795, 'TIME_S_1KI': 636.2427043914795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.8393529319762, 'W': 21.725499941161722, 'J_1KI': 16708.393529319765, 'W_1KI': 217.2549994116172, 'W_D': 6.518499941161723, 'J_D': 501.3171735184193, 'W_D_1KI': 65.18499941161723, 'J_D_1KI': 651.8499941161723}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
index 40d44d2..66821f5 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 142926, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.514646053314209, "TIME_S_1KI": 0.07356706304880994, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 341.9344939994813, "W": 24.04792007204229, "J_1KI": 2.3923883268228403, "W_1KI": 0.1682543419114947, "W_D": 4.089920072042293, "J_D": 58.15408343601235, "W_D_1KI": 0.02861564776207473, "J_D_1KI": 0.00020021303165326625}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 141552, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.798607587814331, "TIME_S_1KI": 0.07628721309352274, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.76709407806396, "W": 18.985490730093364, "J_1KI": 1.9057808725985077, "W_1KI": 0.13412379005661076, "W_D": 4.025490730093365, "J_D": 57.198676185607916, "W_D_1KI": 0.028438246934648505, "J_D_1KI": 0.0002009031799949736}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
index e3d4653..2de78b8 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,645 +1,266 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08297038078308105}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
-       col_indices=tensor([9541, 4744, 3707, 3759, 5602,  648, 5760, 9547, 5348,
-                           1622, 5522, 1425, 7886, 8949, 1399, 4987, 2045, 2195,
-                           1877, 8225, 7439, 3742, 1704, 9009, 2824, 6038, 7459,
-                           2392, 4934, 1115, 7057, 1326, 9427, 1487, 6052,  271,
-                           2404, 6442, 2401, 5281,  847, 9237, 5881, 2117, 9030,
-                           4040, 5058, 3876, 5704, 2576,  376, 9599, 6293,  631,
-                           3850, 2531, 5459, 4807, 1976, 4129, 6044, 7528,  128,
-                            620, 4258,  707, 3186, 4767, 6203, 7053, 3266, 3634,
-                           6287, 7059, 8930, 8238,  249, 9263, 6668, 2968, 8693,
-                           1448, 3963, 9472, 8236, 7658,   21,  522, 5082, 5332,
-                           4645, 2332, 6408, 7231, 8736, 9779, 4215, 3799, 2187,
-                           9640,  706, 3366,  478, 6738, 3826, 4896, 4399, 9716,
-                            617, 1830, 9549, 4294, 2027, 1533, 9629, 7815, 4667,
-                           4504, 5733, 7722, 3123, 8029, 3228, 5140, 2985, 4485,
-                            906, 8472,  993, 7143, 5174, 5432, 8332, 4083, 2173,
-                           2104, 5924, 4632, 1377,  420, 8402,  112, 8458, 5830,
-                           9221, 5106, 3118, 6351, 6259, 8778, 5907, 9815,  391,
-                           7254, 9485, 4166, 1814, 2095,  177, 6065, 4798, 3294,
-                           9118, 4002, 6468, 4676, 4594, 1033, 5568, 2567, 2946,
-                           7002, 6944, 4198, 2354, 5428, 7206,  480, 6548, 8449,
-                            121, 5762, 8140, 9120, 3426, 1673, 7470, 1305, 2311,
-                            979, 2867, 3482, 4187, 1379, 5373, 5104, 9058, 7249,
-                           5269, 8473, 4187, 4514, 9226, 1893, 4287,  720, 3491,
-                           9637,  605, 6889,   21, 1152, 4929, 3127, 8079,  678,
-                           4219, 9741,  975, 5212, 6243, 1307, 5836, 8196, 5327,
-                           8749, 6752, 6677, 4788, 6827, 8125, 2049, 1591, 7592,
-                            449, 4957, 6437, 2880, 1837, 2987, 3733, 5822, 2888,
-                           2962, 4631, 3914,  466, 7123, 9568, 7077,  239, 8789,
-                           8002, 7541, 1395, 9017, 1554, 8609, 1114,  359, 1644,
-                           9296, 2622, 6861, 1946, 6878, 3199, 6756, 6576, 8441,
-                           6782, 9208, 3800, 6755, 9307, 2546, 2751, 9936, 3072,
-                           5887, 1740, 5436, 8353,  856, 3061, 4683,  282, 2344,
-                           2737, 8185, 6739, 1483, 9825, 4566, 2572, 5286, 3306,
-                           6854, 1701, 8164, 6844, 5021, 3885, 1016, 8535, 7619,
-                            101, 9406,  391, 7997, 5109, 9604, 6641, 4700, 8743,
-                           1701,  220, 1307, 2989, 4960, 7899, 4634, 4188, 3055,
-                           6943,  153, 1825, 1183, 7470, 7761, 3324, 6926, 5802,
-                           3938, 9690, 4429, 4674, 8612, 5692, 7360,  969, 4901,
-                           9007, 2615, 7523, 6755,  185, 2934, 1782,  331, 1633,
-                           3371, 7658, 1018, 3353, 1634,  820, 3145,  941, 5177,
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-tensor([0.3956, 0.7164, 0.0973,  ..., 0.3827, 0.2591, 0.9120])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
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-NNZ: 1000
-Density: 1e-05
-Time: 0.08297038078308105 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126551 -ss 10000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.296976089477539}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015316009521484375}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.4603, 0.7024, 0.4804, 0.7634, 0.3067, 0.6368]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.6841, 0.8830, 0.8336,  ..., 0.0293, 0.2899, 0.6914])
+tensor([0.8762, 0.1751, 0.9607,  ..., 0.4579, 0.7370, 0.8447])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -647,378 +268,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 9.296976089477539 seconds
+Time: 0.015316009521484375 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 142926 -ss 10000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.514646053314209}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68555 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.085230827331543}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      1.5969e-01, 1.8190e-01, 7.3046e-01, 7.7660e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7781, 0.3926, 0.9305,  ..., 0.7246, 0.2621, 0.2068])
+tensor([0.4288, 0.6749, 0.2273,  ..., 0.2100, 0.8485, 0.9690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,375 +647,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.514646053314209 seconds
+Time: 5.085230827331543 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141552 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.798607587814331}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([4116, 7192, 1414,  602, 9261, 9755, 3418, 3677, 1346,
-                           4915, 1923, 5999, 8929, 3632,  514, 7579, 9728,  993,
-                           4226, 2729, 2969, 7063, 8946, 3199, 2641, 3551, 3369,
-                           5419, 1831, 1652, 6779, 7428, 3773, 5376,  162,  579,
-                           7703, 6315,  199, 8043, 3670, 9337, 2098, 2118, 8554,
-                           2706, 4081, 7007, 1627, 5281, 2169, 7536, 2244, 9570,
-                           3079, 5784, 1151, 8783, 1389, 8630, 6457, 6608, 4618,
-                           9063, 5053, 6181, 9948, 5748,  552, 4335, 6638, 3245,
-                           5740, 6165, 6638, 3389, 4075, 7308, 3538, 1808, 7667,
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+                      0.1147, 0.0390, 0.9320, 0.5858, 0.0975, 0.5982, 0.1467,
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+                      0.0512, 0.8850, 0.2142, 0.1547, 0.5876, 0.8678, 0.5430,
+                      0.4686, 0.4656, 0.5329, 0.4015, 0.3146, 0.2257, 0.1820,
+                      0.9287, 0.0585, 0.6678, 0.0868, 0.7648, 0.2970, 0.6893,
+                      0.7312, 0.6106, 0.1958, 0.8679, 0.9976, 0.5849, 0.7869,
+                      0.3363, 0.5231, 0.9619, 0.1567, 0.1143, 0.9307, 0.2825,
+                      0.3303, 0.5892, 0.7606, 0.7858, 0.0785, 0.3935, 0.0941,
+                      0.7542, 0.7552, 0.7909, 0.6337, 0.4503, 0.8151, 0.1544,
+                      0.0385, 0.1762, 0.7871, 0.9429, 0.7065, 0.2556, 0.7752,
+                      0.3810, 0.5819, 0.5096, 0.6816, 0.5826, 0.0960, 0.1244,
+                      0.3464, 0.1206, 0.8110, 0.0102, 0.2242, 0.3161]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7781, 0.3926, 0.9305,  ..., 0.7246, 0.2621, 0.2068])
+tensor([0.0219, 0.9207, 0.3669,  ..., 0.7955, 0.2670, 0.3543])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1402,13 +919,282 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.514646053314209 seconds
+Time: 10.798607587814331 seconds
 
-[21.04, 21.72, 22.28, 22.76, 23.44, 23.84, 24.4, 25.56, 26.08, 25.56]
-[25.56, 25.8, 25.36, 25.76, 26.12, 26.96, 27.2, 26.52, 26.28, 25.72, 25.56, 25.04, 24.92, 24.92]
-14.21888017654419
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 142926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.514646053314209, 'TIME_S_1KI': 0.07356706304880994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.9344939994813, 'W': 24.04792007204229}
-[21.04, 21.72, 22.28, 22.76, 23.44, 23.84, 24.4, 25.56, 26.08, 25.56, 20.64, 20.76, 20.84, 20.68, 20.84, 20.92, 20.48, 20.28, 20.36, 20.6]
-399.15999999999997
-19.958
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 142926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.514646053314209, 'TIME_S_1KI': 0.07356706304880994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.9344939994813, 'W': 24.04792007204229, 'J_1KI': 2.3923883268228403, 'W_1KI': 0.1682543419114947, 'W_D': 4.089920072042293, 'J_D': 58.15408343601235, 'W_D_1KI': 0.02861564776207473, 'J_D_1KI': 0.00020021303165326625}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/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([3726, 7095, 4684, 2020,  836, 1255, 7107, 2910, 7765,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0219, 0.9207, 0.3669,  ..., 0.7955, 0.2670, 0.3543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.798607587814331 seconds
+
+[16.32, 16.4, 16.24, 16.76, 16.72, 17.12, 17.44, 17.24, 17.36, 17.12]
+[16.8, 16.56, 16.56, 19.68, 21.72, 23.52, 24.24, 24.8, 20.88, 20.0, 19.92, 19.92, 19.64, 19.64]
+14.209118843078613
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.798607587814331, 'TIME_S_1KI': 0.07628721309352274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.76709407806396, 'W': 18.985490730093364}
+[16.32, 16.4, 16.24, 16.76, 16.72, 17.12, 17.44, 17.24, 17.36, 17.12, 16.28, 16.24, 16.24, 16.36, 16.6, 16.4, 16.24, 16.4, 16.44, 16.28]
+299.2
+14.959999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.798607587814331, 'TIME_S_1KI': 0.07628721309352274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.76709407806396, 'W': 18.985490730093364, 'J_1KI': 1.9057808725985077, 'W_1KI': 0.13412379005661076, 'W_D': 4.025490730093365, 'J_D': 57.198676185607916, 'W_D_1KI': 0.028438246934648505, 'J_D_1KI': 0.0002009031799949736}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..ccc8254
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 52454, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.6396803855896, "TIME_S_1KI": 0.20283830376309908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 254.19103302001952, "W": 17.900584259593533, "J_1KI": 4.845979963778158, "W_1KI": 0.3412625206770415, "W_D": 2.811584259593534, "J_D": 39.92492630434038, "W_D_1KI": 0.05360095053939707, "J_D_1KI": 0.0010218658355777839}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..6f0e024
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02812814712524414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([4541, 9541, 3383,  ..., 9920, 3344, 2731]),
+       values=tensor([0.3320, 0.1825, 0.5042,  ..., 0.6612, 0.1900, 0.5121]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0144, 0.2553, 0.6494,  ..., 0.0787, 0.9201, 0.6475])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.02812814712524414 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37329 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.472296953201294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([6333, 2190, 7526,  ..., 2226, 3084, 9881]),
+       values=tensor([0.4590, 0.1089, 0.5094,  ..., 0.8341, 0.9457, 0.0387]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3900, 0.6084, 0.4843,  ..., 0.7689, 0.5332, 0.9837])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.472296953201294 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52454 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.6396803855896}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([2165, 7276,  636,  ..., 1970, 2680, 2527]),
+       values=tensor([0.0210, 0.6731, 0.7347,  ..., 0.2518, 0.3264, 0.9787]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5715, 0.5141, 0.9224,  ..., 0.0062, 0.8236, 0.9187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.6396803855896 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([2165, 7276,  636,  ..., 1970, 2680, 2527]),
+       values=tensor([0.0210, 0.6731, 0.7347,  ..., 0.2518, 0.3264, 0.9787]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5715, 0.5141, 0.9224,  ..., 0.0062, 0.8236, 0.9187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.6396803855896 seconds
+
+[16.64, 16.48, 16.64, 16.84, 17.0, 16.8, 16.72, 16.4, 16.4, 16.2]
+[16.28, 16.44, 16.8, 17.88, 19.0, 19.88, 20.6, 20.92, 20.48, 20.0, 20.12, 20.12, 19.92, 19.84]
+14.20015287399292
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 52454, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.6396803855896, 'TIME_S_1KI': 0.20283830376309908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 254.19103302001952, 'W': 17.900584259593533}
+[16.64, 16.48, 16.64, 16.84, 17.0, 16.8, 16.72, 16.4, 16.4, 16.2, 16.72, 16.92, 16.8, 16.88, 17.04, 17.16, 16.96, 16.8, 16.84, 16.64]
+301.78
+15.088999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 52454, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.6396803855896, 'TIME_S_1KI': 0.20283830376309908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 254.19103302001952, 'W': 17.900584259593533, 'J_1KI': 4.845979963778158, 'W_1KI': 0.3412625206770415, 'W_D': 2.811584259593534, 'J_D': 39.92492630434038, 'W_D_1KI': 0.05360095053939707, 'J_D_1KI': 0.0010218658355777839}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..2f5c509
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.72194480895996, "TIME_S_1KI": 227.2194480895996, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2080.890781612396, "W": 61.453409935621025, "J_1KI": 20808.90781612396, "W_1KI": 614.5340993562103, "W_D": 45.112409935621024, "J_D": 1527.5636953213213, "W_D_1KI": 451.12409935621025, "J_D_1KI": 4511.240993562103}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..4a89b40
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.72194480895996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       56,      116,  ..., 24999900,
+                            24999953, 25000000]),
+       col_indices=tensor([   647,   4700,  33413,  ..., 445020, 463377,
+                           482076]),
+       values=tensor([0.2494, 0.9199, 0.9974,  ..., 0.2647, 0.3316, 0.8056]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1920, 0.0118, 0.3050,  ..., 0.0609, 0.1776, 0.7503])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 22.72194480895996 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       56,      116,  ..., 24999900,
+                            24999953, 25000000]),
+       col_indices=tensor([   647,   4700,  33413,  ..., 445020, 463377,
+                           482076]),
+       values=tensor([0.2494, 0.9199, 0.9974,  ..., 0.2647, 0.3316, 0.8056]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1920, 0.0118, 0.3050,  ..., 0.0609, 0.1776, 0.7503])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 22.72194480895996 seconds
+
+[17.96, 18.12, 17.96, 17.96, 17.88, 17.96, 17.88, 17.92, 17.76, 17.8]
+[17.8, 17.6, 18.48, 20.36, 21.4, 22.72, 22.72, 24.08, 25.12, 34.04, 34.44, 33.88, 34.28, 32.56, 45.4, 59.36, 77.8, 92.4, 98.2, 98.2, 95.48, 95.96, 96.12, 95.0, 93.72, 96.32, 96.24, 93.28, 93.88, 93.4, 91.28, 91.4, 93.2]
+33.8612744808197
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.72194480895996, 'TIME_S_1KI': 227.2194480895996, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2080.890781612396, 'W': 61.453409935621025}
+[17.96, 18.12, 17.96, 17.96, 17.88, 17.96, 17.88, 17.92, 17.76, 17.8, 17.72, 18.0, 18.44, 18.6, 18.6, 18.64, 18.76, 18.48, 18.16, 17.92]
+326.82000000000005
+16.341
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.72194480895996, 'TIME_S_1KI': 227.2194480895996, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2080.890781612396, 'W': 61.453409935621025, 'J_1KI': 20808.90781612396, 'W_1KI': 614.5340993562103, 'W_D': 45.112409935621024, 'J_D': 1527.5636953213213, 'W_D_1KI': 451.12409935621025, 'J_D_1KI': 4511.240993562103}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
index 510a9ba..208d33f 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 37.050822496414185, "TIME_S_1KI": 37.050822496414185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3160.1361892318723, "W": 76.88509213042873, "J_1KI": 3160.1361892318723, "W_1KI": 76.88509213042873, "W_D": 56.91509213042873, "J_D": 2339.3279161286355, "W_D_1KI": 56.91509213042873, "J_D_1KI": 56.91509213042873}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 279, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.429174900054932, "TIME_S_1KI": 44.549013978691505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 925.7997315406801, "W": 55.99377793769148, "J_1KI": 3318.2786076726884, "W_1KI": 200.69454457953935, "W_D": 40.002777937691484, "J_D": 661.4049353270533, "W_D_1KI": 143.37913239315944, "J_D_1KI": 513.9037003339048}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
index 259a25f..2fab080 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 37.050822496414185}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.175914764404297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499993,
-                            2499998, 2500000]),
-       col_indices=tensor([159663, 166958, 205263,  ..., 483859,  36662,
-                           138241]),
-       values=tensor([0.8479, 0.2779, 0.6227,  ..., 0.0012, 0.5209, 0.2466]),
+tensor(crow_indices=tensor([      0,       5,       9,  ..., 2499991,
+                            2499995, 2500000]),
+       col_indices=tensor([236491, 268930, 282894,  ..., 290854, 362096,
+                           428990]),
+       values=tensor([0.4942, 0.2006, 0.6461,  ..., 0.6339, 0.7923, 0.0061]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9479, 0.6643, 0.5077,  ..., 0.6908, 0.0479, 0.6658])
+tensor([0.6272, 0.4782, 0.6613,  ..., 0.5722, 0.7323, 0.6099])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 37.050822496414185 seconds
+Time: 4.175914764404297 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 251 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.986790895462036}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499993,
+tensor(crow_indices=tensor([      0,       3,       4,  ..., 2499995,
                             2499998, 2500000]),
-       col_indices=tensor([159663, 166958, 205263,  ..., 483859,  36662,
-                           138241]),
-       values=tensor([0.8479, 0.2779, 0.6227,  ..., 0.0012, 0.5209, 0.2466]),
+       col_indices=tensor([168226, 184311, 332682,  ..., 175948,  40749,
+                           152556]),
+       values=tensor([0.8367, 0.0584, 0.1423,  ..., 0.1509, 0.1566, 0.6036]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9479, 0.6643, 0.5077,  ..., 0.6908, 0.0479, 0.6658])
+tensor([0.8910, 0.6965, 0.0939,  ..., 0.1566, 0.5700, 0.8005])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,73 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 37.050822496414185 seconds
+Time: 9.986790895462036 seconds
 
-[22.44, 22.28, 22.0, 22.08, 22.44, 22.44, 22.24, 22.28, 22.28, 21.92]
-[21.84, 21.84, 22.08, 23.32, 24.04, 37.0, 51.0, 68.72, 84.04, 93.2, 93.2, 96.84, 97.16, 96.0, 93.56, 93.56, 93.4, 92.48, 94.2, 94.4, 93.76, 94.28, 92.52, 92.4, 93.48, 93.48, 95.4, 93.6, 93.04, 91.68, 87.68, 87.08, 87.96, 88.4, 87.72, 87.2, 88.56, 88.0, 89.28, 89.12]
-41.1020667552948
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 37.050822496414185, 'TIME_S_1KI': 37.050822496414185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3160.1361892318723, 'W': 76.88509213042873}
-[22.44, 22.28, 22.0, 22.08, 22.44, 22.44, 22.24, 22.28, 22.28, 21.92, 21.72, 21.72, 21.8, 22.04, 22.2, 22.32, 22.48, 22.44, 22.24, 22.16]
-399.4
-19.97
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 37.050822496414185, 'TIME_S_1KI': 37.050822496414185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3160.1361892318723, 'W': 76.88509213042873, 'J_1KI': 3160.1361892318723, 'W_1KI': 76.88509213042873, 'W_D': 56.91509213042873, 'J_D': 2339.3279161286355, 'W_D_1KI': 56.91509213042873, 'J_D_1KI': 56.91509213042873}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 263 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.86619520187378}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       1,       4,  ..., 2499987,
+                            2499997, 2500000]),
+       col_indices=tensor([120869, 339930, 358219,  ...,  35981,  71933,
+                           400518]),
+       values=tensor([0.0243, 0.7300, 0.4495,  ..., 0.8433, 0.9453, 0.9296]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0560, 0.8217, 0.0541,  ..., 0.5269, 0.7792, 0.2112])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.86619520187378 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 279 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.429174900054932}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499986,
+                            2499991, 2500000]),
+       col_indices=tensor([  4708,  62252, 239037,  ..., 346193, 443276,
+                           467019]),
+       values=tensor([0.7542, 0.0207, 0.5398,  ..., 0.0649, 0.4673, 0.8331]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8471, 0.4897, 0.4001,  ..., 0.3407, 0.6143, 0.4869])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 12.429174900054932 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499986,
+                            2499991, 2500000]),
+       col_indices=tensor([  4708,  62252, 239037,  ..., 346193, 443276,
+                           467019]),
+       values=tensor([0.7542, 0.0207, 0.5398,  ..., 0.0649, 0.4673, 0.8331]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8471, 0.4897, 0.4001,  ..., 0.3407, 0.6143, 0.4869])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 12.429174900054932 seconds
+
+[17.72, 17.72, 17.84, 17.8, 17.8, 18.04, 17.92, 17.8, 17.72, 17.32]
+[17.36, 17.64, 18.04, 20.2, 21.44, 35.64, 48.92, 66.32, 77.96, 77.96, 91.04, 90.76, 89.6, 87.56, 86.4, 84.76]
+16.53397512435913
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 279, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.429174900054932, 'TIME_S_1KI': 44.549013978691505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 925.7997315406801, 'W': 55.99377793769148}
+[17.72, 17.72, 17.84, 17.8, 17.8, 18.04, 17.92, 17.8, 17.72, 17.32, 17.84, 17.72, 17.8, 17.92, 17.8, 17.72, 17.6, 17.72, 17.72, 17.48]
+319.82
+15.991
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 279, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.429174900054932, 'TIME_S_1KI': 44.549013978691505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 925.7997315406801, 'W': 55.99377793769148, 'J_1KI': 3318.2786076726884, 'W_1KI': 200.69454457953935, 'W_D': 40.002777937691484, 'J_D': 661.4049353270533, 'W_D_1KI': 143.37913239315944, 'J_D_1KI': 513.9037003339048}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..fd07ea0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.55699610710144, "TIME_S_1KI": 165.5699610710144, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1413.1975269508362, "W": 57.485721407839506, "J_1KI": 14131.97526950836, "W_1KI": 574.857214078395, "W_D": 41.59072140783951, "J_D": 1022.44354246974, "W_D_1KI": 415.9072140783951, "J_D_1KI": 4159.072140783951}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..33e7f40
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.55699610710144}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       44,  ..., 12499945,
+                            12499971, 12500000]),
+       col_indices=tensor([  7086,  20899,  31000,  ..., 441979, 480995,
+                           482795]),
+       values=tensor([0.3494, 0.1791, 0.5321,  ..., 0.0256, 0.8127, 0.6614]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9181, 0.2128, 0.6316,  ..., 0.6360, 0.7946, 0.3835])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 16.55699610710144 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       44,  ..., 12499945,
+                            12499971, 12500000]),
+       col_indices=tensor([  7086,  20899,  31000,  ..., 441979, 480995,
+                           482795]),
+       values=tensor([0.3494, 0.1791, 0.5321,  ..., 0.0256, 0.8127, 0.6614]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9181, 0.2128, 0.6316,  ..., 0.6360, 0.7946, 0.3835])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 16.55699610710144 seconds
+
+[17.52, 17.52, 17.88, 17.84, 17.84, 17.64, 17.48, 17.72, 17.44, 17.28]
+[17.4, 17.16, 20.64, 21.36, 23.44, 25.32, 33.76, 33.76, 31.56, 35.12, 47.32, 60.6, 67.04, 80.92, 86.0, 85.72, 87.24, 88.0, 90.8, 92.64, 93.72, 92.0, 92.0, 90.8]
+24.583452939987183
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 16.55699610710144, 'TIME_S_1KI': 165.5699610710144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1413.1975269508362, 'W': 57.485721407839506}
+[17.52, 17.52, 17.88, 17.84, 17.84, 17.64, 17.48, 17.72, 17.44, 17.28, 18.0, 17.76, 17.6, 17.64, 17.44, 17.4, 17.8, 17.88, 17.76, 17.72]
+317.9
+15.895
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 16.55699610710144, 'TIME_S_1KI': 165.5699610710144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1413.1975269508362, 'W': 57.485721407839506, 'J_1KI': 14131.97526950836, 'W_1KI': 574.857214078395, 'W_D': 41.59072140783951, 'J_D': 1022.44354246974, 'W_D_1KI': 415.9072140783951, 'J_D_1KI': 4159.072140783951}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
index 372bd49..9346872 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.036840677261353, "TIME_S_1KI": 10.036840677261353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.6639336013795, "W": 51.991207713568286, "J_1KI": 692.6639336013795, "W_1KI": 51.991207713568286, "W_D": 33.03220771356828, "J_D": 440.0786197633745, "W_D_1KI": 33.03220771356828, "J_D_1KI": 33.03220771356828}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1658, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.466236591339111, "TIME_S_1KI": 8.725112540011526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1691.1909151077273, "W": 65.36355506616682, "J_1KI": 1020.0186460239609, "W_1KI": 39.423133333031856, "W_D": 49.293555066166824, "J_D": 1275.40205573082, "W_D_1KI": 29.730732850522816, "J_D_1KI": 17.93168446955538}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
index 338ad01..686a0dc 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.036840677261353}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8603906631469727}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     16,  ..., 249987, 249992,
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 249990, 249996,
                             250000]),
-       col_indices=tensor([ 4880, 10510, 11344,  ..., 23863, 34979, 45750]),
-       values=tensor([0.1743, 0.6413, 0.1893,  ..., 0.8376, 0.7119, 0.1905]),
+       col_indices=tensor([  116,  2220,  2597,  ..., 31423, 34504, 36695]),
+       values=tensor([0.0356, 0.7526, 0.0114,  ..., 0.2051, 0.2717, 0.4326]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5173, 0.4884, 0.4084,  ..., 0.9473, 0.2501, 0.4146])
+tensor([0.6822, 0.2973, 0.4245,  ..., 0.4266, 0.4462, 0.3842])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.036840677261353 seconds
+Time: 0.8603906631469727 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1220 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.725424766540527}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     16,  ..., 249987, 249992,
+tensor(crow_indices=tensor([     0,      3,     12,  ..., 249990, 249995,
                             250000]),
-       col_indices=tensor([ 4880, 10510, 11344,  ..., 23863, 34979, 45750]),
-       values=tensor([0.1743, 0.6413, 0.1893,  ..., 0.8376, 0.7119, 0.1905]),
+       col_indices=tensor([ 1339, 17035, 23748,  ..., 19329, 30492, 33219]),
+       values=tensor([0.1487, 0.4152, 0.3651,  ..., 0.6580, 0.7478, 0.7026]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5173, 0.4884, 0.4084,  ..., 0.9473, 0.2501, 0.4146])
+tensor([0.4336, 0.1383, 0.7901,  ..., 0.8031, 0.7630, 0.0295])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +36,50 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.036840677261353 seconds
+Time: 7.725424766540527 seconds
 
-[21.04, 21.04, 21.36, 21.48, 21.32, 21.52, 21.4, 21.16, 21.08, 21.12]
-[21.08, 21.08, 21.08, 22.4, 23.64, 33.72, 51.68, 65.48, 81.88, 95.88, 94.44, 94.2, 93.16]
-13.322712898254395
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.036840677261353, 'TIME_S_1KI': 10.036840677261353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6639336013795, 'W': 51.991207713568286}
-[21.04, 21.04, 21.36, 21.48, 21.32, 21.52, 21.4, 21.16, 21.08, 21.12, 21.08, 21.04, 20.96, 20.96, 20.76, 20.68, 20.64, 20.84, 20.92, 20.8]
-379.18
-18.959
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.036840677261353, 'TIME_S_1KI': 10.036840677261353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6639336013795, 'W': 51.991207713568286, 'J_1KI': 692.6639336013795, 'W_1KI': 51.991207713568286, 'W_D': 33.03220771356828, 'J_D': 440.0786197633745, 'W_D_1KI': 33.03220771356828, 'J_D_1KI': 33.03220771356828}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1658 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.466236591339111}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249992,
+                            250000]),
+       col_indices=tensor([14878, 23641, 26434,  ..., 39221, 43609, 44125]),
+       values=tensor([0.9348, 0.1734, 0.7472,  ..., 0.0129, 0.0523, 0.4218]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4659, 0.1302, 0.1589,  ..., 0.1214, 0.1279, 0.7413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 14.466236591339111 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249992,
+                            250000]),
+       col_indices=tensor([14878, 23641, 26434,  ..., 39221, 43609, 44125]),
+       values=tensor([0.9348, 0.1734, 0.7472,  ..., 0.0129, 0.0523, 0.4218]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4659, 0.1302, 0.1589,  ..., 0.1214, 0.1279, 0.7413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 14.466236591339111 seconds
+
+[18.12, 18.08, 18.12, 18.12, 18.08, 18.0, 17.76, 17.64, 17.48, 17.48]
+[17.28, 17.4, 17.28, 19.64, 20.44, 35.08, 49.0, 64.92, 77.28, 86.8, 86.8, 86.52, 85.52, 84.64, 84.32, 84.0, 84.16, 84.16, 84.24, 84.24, 84.04, 84.28, 82.92, 82.4, 81.88]
+25.87360668182373
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1658, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.466236591339111, 'TIME_S_1KI': 8.725112540011526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1691.1909151077273, 'W': 65.36355506616682}
+[18.12, 18.08, 18.12, 18.12, 18.08, 18.0, 17.76, 17.64, 17.48, 17.48, 18.04, 17.84, 17.8, 17.64, 17.76, 17.92, 17.8, 17.8, 17.72, 18.04]
+321.4
+16.07
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1658, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.466236591339111, 'TIME_S_1KI': 8.725112540011526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1691.1909151077273, 'W': 65.36355506616682, 'J_1KI': 1020.0186460239609, 'W_1KI': 39.423133333031856, 'W_D': 49.293555066166824, 'J_D': 1275.40205573082, 'W_D_1KI': 29.730732850522816, 'J_D_1KI': 17.93168446955538}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
index aeffedd..c314337 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 64.77457070350647, "TIME_S_1KI": 64.77457070350647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4994.562112493517, "W": 73.50821663676467, "J_1KI": 4994.562112493517, "W_1KI": 73.50821663676467, "W_D": 54.114216636764674, "J_D": 3676.8245582232494, "W_D_1KI": 54.114216636764674, "J_D_1KI": 54.114216636764674}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 135, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.963478088378906, "TIME_S_1KI": 81.21094880280671, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.8568821525573, "W": 43.83509814272925, "J_1KI": 4317.458386315239, "W_1KI": 324.7044306868833, "W_D": 28.06209814272925, "J_D": 373.12992837095254, "W_D_1KI": 207.8673936498463, "J_D_1KI": 1539.758471480343}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
index 56ca1f5..514e8a4 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 64.77457070350647}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.26869797706604}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499908,
-                            2499960, 2500000]),
-       col_indices=tensor([  291,  1039,  1041,  ..., 49096, 49434, 49928]),
-       values=tensor([0.5586, 0.2987, 0.2608,  ..., 0.4587, 0.5222, 0.8471]),
+tensor(crow_indices=tensor([      0,      44,     102,  ..., 2499900,
+                            2499955, 2500000]),
+       col_indices=tensor([  878,  3105,  3271,  ..., 44510, 45389, 45985]),
+       values=tensor([0.2421, 0.0051, 0.2486,  ..., 0.1294, 0.9249, 0.4412]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1693, 0.3191, 0.9556,  ..., 0.1736, 0.4599, 0.2505])
+tensor([0.3218, 0.5110, 0.5510,  ..., 0.0407, 0.3623, 0.3415])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 64.77457070350647 seconds
+Time: 8.26869797706604 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.729291677474976}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499908,
-                            2499960, 2500000]),
-       col_indices=tensor([  291,  1039,  1041,  ..., 49096, 49434, 49928]),
-       values=tensor([0.5586, 0.2987, 0.2608,  ..., 0.4587, 0.5222, 0.8471]),
+tensor(crow_indices=tensor([      0,      44,     101,  ..., 2499906,
+                            2499957, 2500000]),
+       col_indices=tensor([  430,  2871,  2934,  ..., 46471, 47392, 47877]),
+       values=tensor([0.4189, 0.2667, 0.2640,  ..., 0.7329, 0.4126, 0.0437]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1693, 0.3191, 0.9556,  ..., 0.1736, 0.4599, 0.2505])
+tensor([0.6895, 0.8779, 0.8508,  ..., 0.5330, 0.3990, 0.7739])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +36,50 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 64.77457070350647 seconds
+Time: 9.729291677474976 seconds
 
-[21.48, 21.56, 21.32, 21.36, 21.52, 21.32, 21.32, 21.4, 21.44, 21.56]
-[21.64, 21.6, 22.08, 23.76, 24.8, 35.36, 47.08, 60.56, 71.04, 80.24, 82.48, 82.48, 82.88, 83.68, 82.36, 82.16, 82.04, 80.88, 80.72, 80.88, 80.44, 80.6, 80.44, 80.8, 80.56, 79.4, 78.92, 78.92, 78.4, 78.64, 80.36, 81.28, 82.52, 82.68, 81.64, 81.52, 81.32, 81.2, 80.48, 80.32, 82.04, 81.64, 81.64, 82.44, 82.48, 81.2, 81.08, 81.04, 81.44, 81.2, 81.68, 81.32, 81.0, 81.28, 81.36, 81.24, 81.36, 81.36, 81.88, 82.48, 83.6, 83.4, 83.64, 82.8, 82.56, 82.36]
-67.94563031196594
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 64.77457070350647, 'TIME_S_1KI': 64.77457070350647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4994.562112493517, 'W': 73.50821663676467}
-[21.48, 21.56, 21.32, 21.36, 21.52, 21.32, 21.32, 21.4, 21.44, 21.56, 21.88, 21.92, 21.8, 21.52, 21.68, 21.52, 21.68, 21.68, 21.6, 21.56]
-387.88
-19.394
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 64.77457070350647, 'TIME_S_1KI': 64.77457070350647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4994.562112493517, 'W': 73.50821663676467, 'J_1KI': 4994.562112493517, 'W_1KI': 73.50821663676467, 'W_D': 54.114216636764674, 'J_D': 3676.8245582232494, 'W_D_1KI': 54.114216636764674, 'J_D_1KI': 54.114216636764674}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 135 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.963478088378906}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499884,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1316,  2608,  2921,  ..., 47281, 49169, 49691]),
+       values=tensor([0.1237, 0.8262, 0.6046,  ..., 0.7531, 0.6389, 0.8086]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9516, 0.0943, 0.1293,  ..., 0.9488, 0.5626, 0.5458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.963478088378906 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499884,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1316,  2608,  2921,  ..., 47281, 49169, 49691]),
+       values=tensor([0.1237, 0.8262, 0.6046,  ..., 0.7531, 0.6389, 0.8086]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9516, 0.0943, 0.1293,  ..., 0.9488, 0.5626, 0.5458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.963478088378906 seconds
+
+[17.96, 17.88, 17.56, 17.44, 17.36, 17.24, 17.44, 17.68, 17.68, 17.68]
+[17.88, 17.88, 17.88, 22.04, 23.04, 29.76, 42.2, 54.16, 64.96, 76.44, 81.04, 80.44, 80.44]
+13.296579837799072
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.963478088378906, 'TIME_S_1KI': 81.21094880280671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8568821525573, 'W': 43.83509814272925}
+[17.96, 17.88, 17.56, 17.44, 17.36, 17.24, 17.44, 17.68, 17.68, 17.68, 17.84, 17.6, 17.48, 17.48, 17.52, 17.32, 17.4, 17.36, 17.44, 17.68]
+315.46
+15.773
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.963478088378906, 'TIME_S_1KI': 81.21094880280671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8568821525573, 'W': 43.83509814272925, 'J_1KI': 4317.458386315239, 'W_1KI': 324.7044306868833, 'W_D': 28.06209814272925, 'J_D': 373.12992837095254, 'W_D_1KI': 207.8673936498463, 'J_D_1KI': 1539.758471480343}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..de9236a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.498249053955078, "TIME_S_1KI": 304.9824905395508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2007.0959778976435, "W": 47.944381683758614, "J_1KI": 20070.959778976434, "W_1KI": 479.44381683758616, "W_D": 32.291381683758615, "J_D": 1351.8143319842811, "W_D_1KI": 322.9138168375861, "J_D_1KI": 3229.138168375861}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..9e427ef
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.498249053955078}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      519,     1009,  ..., 24999042,
+                            24999488, 25000000]),
+       col_indices=tensor([  129,   342,   437,  ..., 49566, 49630, 49865]),
+       values=tensor([0.8700, 0.4704, 0.0527,  ..., 0.4978, 0.7115, 0.5319]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3352, 0.1102, 0.4216,  ..., 0.1668, 0.6074, 0.9924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 30.498249053955078 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      519,     1009,  ..., 24999042,
+                            24999488, 25000000]),
+       col_indices=tensor([  129,   342,   437,  ..., 49566, 49630, 49865]),
+       values=tensor([0.8700, 0.4704, 0.0527,  ..., 0.4978, 0.7115, 0.5319]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3352, 0.1102, 0.4216,  ..., 0.1668, 0.6074, 0.9924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 30.498249053955078 seconds
+
+[17.68, 17.6, 17.56, 17.16, 17.16, 17.0, 17.0, 17.08, 17.36, 17.4]
+[17.52, 17.72, 18.04, 18.88, 20.68, 21.64, 23.68, 24.04, 31.04, 33.36, 33.36, 32.8, 32.72, 32.52, 28.16, 34.64, 41.2, 49.36, 57.8, 58.4, 58.92, 62.72, 62.72, 61.04, 61.04, 59.8, 60.88, 61.56, 64.04, 67.12, 70.76, 68.32, 68.92, 65.8, 66.72, 65.88, 65.84, 68.88, 68.88, 67.08, 67.04]
+41.863006830215454
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 30.498249053955078, 'TIME_S_1KI': 304.9824905395508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.0959778976435, 'W': 47.944381683758614}
+[17.68, 17.6, 17.56, 17.16, 17.16, 17.0, 17.0, 17.08, 17.36, 17.4, 17.56, 17.4, 17.44, 17.44, 17.4, 17.6, 17.56, 17.52, 17.6, 17.72]
+313.05999999999995
+15.652999999999997
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 30.498249053955078, 'TIME_S_1KI': 304.9824905395508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.0959778976435, 'W': 47.944381683758614, 'J_1KI': 20070.959778976434, 'W_1KI': 479.44381683758616, 'W_D': 32.291381683758615, 'J_D': 1351.8143319842811, 'W_D_1KI': 322.9138168375861, 'J_D_1KI': 3229.138168375861}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
index 2428814..5f4cac5 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 6367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.819957971572876, "TIME_S_1KI": 2.3276202248426068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 977.0843494701387, "W": 58.720628093786544, "J_1KI": 153.46071139785437, "W_1KI": 9.222652441304625, "W_D": 39.857628093786545, "J_D": 663.2126712820532, "W_D_1KI": 6.260032683176778, "J_D_1KI": 0.9831997303560197}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8811, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.461963891983032, "TIME_S_1KI": 1.6413532961052129, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1601.2496216583254, "W": 63.889106877708535, "J_1KI": 181.7330180068466, "W_1KI": 7.251061954115144, "W_D": 47.822106877708535, "J_D": 1198.5631712055208, "W_D_1KI": 5.4275458946440285, "J_D_1KI": 0.6159965832078116}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
index c94062e..6d049d2 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.0054540634155273}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.2421858310699463}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([29300, 37118, 28917,  ..., 16725, 28059, 47397]),
-       values=tensor([0.1773, 0.7310, 0.0095,  ..., 0.4568, 0.7722, 0.2574]),
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([11801, 48673, 42443,  ..., 35599, 34008, 22453]),
+       values=tensor([0.3951, 0.6998, 0.6224,  ..., 0.4352, 0.5927, 0.1013]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0745, 0.0507, 0.1628,  ..., 0.0663, 0.8219, 0.2626])
+tensor([0.6391, 0.6971, 0.7049,  ..., 0.7658, 0.2053, 0.9702])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 2.0054540634155273 seconds
+Time: 0.2421858310699463 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5235 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.632020473480225}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4335 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.2435102462768555}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 6005,  4214, 13465,  ..., 35902,  7875,  2053]),
-       values=tensor([0.3591, 0.3792, 0.0771,  ..., 0.2893, 0.2529, 0.4673]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([48470, 32812, 24371,  ..., 21811,  5693, 27792]),
+       values=tensor([7.9337e-01, 8.6969e-01, 2.1228e-02,  ...,
+                      3.1628e-01, 5.2154e-01, 8.3659e-04]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1098, 0.6338, 0.4539,  ..., 0.7586, 0.0998, 0.7821])
+tensor([0.5123, 0.3871, 0.5639,  ..., 0.2434, 0.7885, 0.9337])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +35,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 8.632020473480225 seconds
+Time: 6.2435102462768555 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6367 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.819957971572876}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 7290 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.1812264919281}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([30839, 39998,  2326,  ..., 30652, 20576,  5061]),
-       values=tensor([0.3250, 0.6882, 0.6966,  ..., 0.0105, 0.7219, 0.0367]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 1561, 34915, 11685,  ...,  9985, 24943, 27218]),
+       values=tensor([0.3207, 0.5476, 0.7721,  ..., 0.5221, 0.2072, 0.7139]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0417, 0.5559, 0.6322,  ..., 0.5652, 0.2111, 0.1243])
+tensor([0.7737, 0.6011, 0.2764,  ..., 0.4575, 0.9058, 0.1946])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +54,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 14.819957971572876 seconds
+Time: 9.1812264919281 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8337 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.935004234313965}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([30839, 39998,  2326,  ..., 30652, 20576,  5061]),
-       values=tensor([0.3250, 0.6882, 0.6966,  ..., 0.0105, 0.7219, 0.0367]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 4660, 37796, 36819,  ..., 14791,   855,   165]),
+       values=tensor([0.6560, 0.1119, 0.0106,  ..., 0.5425, 0.3178, 0.8843]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0417, 0.5559, 0.6322,  ..., 0.5652, 0.2111, 0.1243])
+tensor([0.7672, 0.9593, 0.0238,  ..., 0.4054, 0.5730, 0.7422])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +73,48 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 14.819957971572876 seconds
+Time: 9.935004234313965 seconds
 
-[20.72, 20.6, 20.92, 21.12, 21.0, 21.04, 21.08, 20.8, 20.88, 21.08]
-[20.96, 21.0, 21.0, 24.32, 26.04, 35.64, 50.52, 67.6, 77.24, 93.48, 91.28, 89.6, 88.96, 88.4, 88.92, 90.04]
-16.63954186439514
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 6367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.819957971572876, 'TIME_S_1KI': 2.3276202248426068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 977.0843494701387, 'W': 58.720628093786544}
-[20.72, 20.6, 20.92, 21.12, 21.0, 21.04, 21.08, 20.8, 20.88, 21.08, 20.88, 20.92, 20.68, 20.84, 21.0, 21.0, 20.92, 21.32, 21.28, 21.04]
-377.26
-18.863
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 6367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.819957971572876, 'TIME_S_1KI': 2.3276202248426068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 977.0843494701387, 'W': 58.720628093786544, 'J_1KI': 153.46071139785437, 'W_1KI': 9.222652441304625, 'W_D': 39.857628093786545, 'J_D': 663.2126712820532, 'W_D_1KI': 6.260032683176778, 'J_D_1KI': 0.9831997303560197}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8811 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.461963891983032}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([ 8691, 13268, 31788,  ..., 20611,  3075,  9688]),
+       values=tensor([0.8778, 0.6640, 0.4350,  ..., 0.9614, 0.5782, 0.7592]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9817, 0.3893, 0.5308,  ..., 0.4402, 0.5461, 0.2337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 14.461963891983032 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([ 8691, 13268, 31788,  ..., 20611,  3075,  9688]),
+       values=tensor([0.8778, 0.6640, 0.4350,  ..., 0.9614, 0.5782, 0.7592]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9817, 0.3893, 0.5308,  ..., 0.4402, 0.5461, 0.2337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 14.461963891983032 seconds
+
+[18.04, 17.96, 17.84, 17.68, 17.72, 17.8, 17.84, 17.92, 17.8, 17.8]
+[17.8, 17.64, 17.64, 18.72, 19.96, 31.88, 49.36, 63.92, 79.84, 91.32, 91.4, 91.0, 90.8, 90.72, 90.68, 90.12, 89.48, 88.76, 88.76, 87.32, 86.72, 85.68, 84.4, 82.12]
+25.062952041625977
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.461963891983032, 'TIME_S_1KI': 1.6413532961052129, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.2496216583254, 'W': 63.889106877708535}
+[18.04, 17.96, 17.84, 17.68, 17.72, 17.8, 17.84, 17.92, 17.8, 17.8, 18.0, 17.84, 17.72, 17.92, 17.96, 17.88, 18.12, 17.92, 17.68, 17.64]
+321.34000000000003
+16.067
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.461963891983032, 'TIME_S_1KI': 1.6413532961052129, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.2496216583254, 'W': 63.889106877708535, 'J_1KI': 181.7330180068466, 'W_1KI': 7.251061954115144, 'W_D': 47.822106877708535, 'J_D': 1198.5631712055208, 'W_D_1KI': 5.4275458946440285, 'J_D_1KI': 0.6159965832078116}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..a856634
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3016, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.238471984863281, "TIME_S_1KI": 4.057848801347242, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1021.0734484481814, "W": 58.16446532539802, "J_1KI": 338.5522043926331, "W_1KI": 19.285300174203588, "W_D": 42.31446532539802, "J_D": 742.827717702389, "W_D_1KI": 14.02999513441579, "J_D_1KI": 4.651855150668366}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..0ab6a55
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.37006235122680664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124994, 124997,
+                            125000]),
+       col_indices=tensor([ 3192, 33329, 36206,  ..., 17521, 36763, 39198]),
+       values=tensor([0.7954, 0.1728, 0.6419,  ..., 0.6370, 0.0715, 0.4891]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.4965, 0.4773, 0.1313,  ..., 0.0503, 0.1495, 0.6552])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.37006235122680664 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2837 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.87511658668518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 124994, 124997,
+                            125000]),
+       col_indices=tensor([18930, 21812, 35293,  ...,   949,  2935, 28377]),
+       values=tensor([0.1354, 0.7141, 0.1182,  ..., 0.8833, 0.5348, 0.3796]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0699, 0.1291, 0.1295,  ..., 0.2384, 0.2084, 0.3934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 9.87511658668518 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3016 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.238471984863281}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      8,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([ 1947, 25944, 43942,  ..., 29833,   871, 28509]),
+       values=tensor([0.4807, 0.5814, 0.8403,  ..., 0.1650, 0.5150, 0.8001]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7842, 0.6693, 0.1792,  ..., 0.7791, 0.2472, 0.4723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 12.238471984863281 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      8,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([ 1947, 25944, 43942,  ..., 29833,   871, 28509]),
+       values=tensor([0.4807, 0.5814, 0.8403,  ..., 0.1650, 0.5150, 0.8001]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7842, 0.6693, 0.1792,  ..., 0.7791, 0.2472, 0.4723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 12.238471984863281 seconds
+
+[17.56, 17.48, 17.4, 17.56, 17.72, 17.76, 17.48, 17.56, 17.56, 17.52]
+[17.52, 17.68, 17.76, 20.16, 20.96, 34.48, 49.64, 65.8, 77.8, 88.2, 87.72, 86.68, 86.76, 87.64, 87.64, 89.04, 89.44]
+17.554935693740845
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3016, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.238471984863281, 'TIME_S_1KI': 4.057848801347242, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1021.0734484481814, 'W': 58.16446532539802}
+[17.56, 17.48, 17.4, 17.56, 17.72, 17.76, 17.48, 17.56, 17.56, 17.52, 17.72, 17.84, 17.8, 17.68, 17.68, 17.52, 17.56, 17.6, 17.48, 17.84]
+317.0
+15.85
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3016, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.238471984863281, 'TIME_S_1KI': 4.057848801347242, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1021.0734484481814, 'W': 58.16446532539802, 'J_1KI': 338.5522043926331, 'W_1KI': 19.285300174203588, 'W_D': 42.31446532539802, 'J_D': 742.827717702389, 'W_D_1KI': 14.02999513441579, 'J_D_1KI': 4.651855150668366}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
index fef2205..fa80299 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 97519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570974826812744, "TIME_S_1KI": 0.10839913070081465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.3015551567077, "W": 22.190891521159134, "J_1KI": 3.233232038440793, "W_1KI": 0.22755454343419368, "W_D": 3.682891521159135, "J_D": 52.32874141120907, "W_D_1KI": 0.037765886864704674, "J_D_1KI": 0.00038726696197361203}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 93664, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.030004501342773, "TIME_S_1KI": 0.107084947272621, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 253.85587940216067, "W": 17.876929919933424, "J_1KI": 2.7102822792338643, "W_1KI": 0.19086233686297213, "W_D": 2.859929919933423, "J_D": 40.61156071567538, "W_D_1KI": 0.03053392893676784, "J_D_1KI": 0.00032599428741851555}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
index c6cbfe2..c2bcc94 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.11602067947387695}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018899202346801758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([ 613, 2610, 3896,  ..., 2268, 1349, 1721]),
-       values=tensor([0.3594, 0.2050, 0.8766,  ..., 0.2511, 0.4340, 0.6606]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1031, 4368, 4092,  ...,  190, 4399, 1962]),
+       values=tensor([0.5248, 0.7492, 0.4749,  ..., 0.9820, 0.4892, 0.6710]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7862, 0.0116, 0.6512,  ..., 0.0192, 0.3599, 0.4463])
+tensor([0.7298, 0.5753, 0.5464,  ..., 0.2530, 0.5594, 0.0214])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.11602067947387695 seconds
+Time: 0.018899202346801758 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 90501 -ss 5000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.744299173355103}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 55557 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.228086948394775}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2497, 2498, 2500]),
-       col_indices=tensor([3869,  881, 2923,  ..., 3064, 1070, 3092]),
-       values=tensor([0.3867, 0.1123, 0.7736,  ..., 0.1665, 0.3688, 0.6121]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 532, 4399, 2173,  ..., 2637, 3554, 2146]),
+       values=tensor([0.0459, 0.9731, 0.3457,  ..., 0.8215, 0.1549, 0.6550]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7562, 0.4892, 0.9144,  ..., 0.6968, 0.8474, 0.7157])
+tensor([0.2796, 0.3928, 0.8109,  ..., 0.2089, 0.4148, 0.7694])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 9.744299173355103 seconds
+Time: 6.228086948394775 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97519 -ss 5000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570974826812744}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 93664 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.030004501342773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2497, 2499, 2500]),
-       col_indices=tensor([4211, 3231, 4340,  ..., 2446, 2540,  154]),
-       values=tensor([0.2167, 0.9555, 0.6550,  ..., 0.2361, 0.5850, 0.4084]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1824, 1160, 4169,  ..., 4733, 1262, 4559]),
+       values=tensor([0.9986, 0.6087, 0.2000,  ..., 0.7208, 0.5140, 0.1151]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3692, 0.1411, 0.4138,  ..., 0.1913, 0.1315, 0.0581])
+tensor([0.6500, 0.1776, 0.6063,  ..., 0.2915, 0.3213, 0.4804])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.570974826812744 seconds
+Time: 10.030004501342773 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2497, 2499, 2500]),
-       col_indices=tensor([4211, 3231, 4340,  ..., 2446, 2540,  154]),
-       values=tensor([0.2167, 0.9555, 0.6550,  ..., 0.2361, 0.5850, 0.4084]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1824, 1160, 4169,  ..., 4733, 1262, 4559]),
+       values=tensor([0.9986, 0.6087, 0.2000,  ..., 0.7208, 0.5140, 0.1151]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3692, 0.1411, 0.4138,  ..., 0.1913, 0.1315, 0.0581])
+tensor([0.6500, 0.1776, 0.6063,  ..., 0.2915, 0.3213, 0.4804])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.570974826812744 seconds
+Time: 10.030004501342773 seconds
 
-[20.44, 20.44, 20.48, 20.4, 20.48, 20.68, 20.76, 20.76, 20.72, 20.88]
-[20.64, 20.56, 21.92, 23.68, 23.68, 25.04, 25.48, 26.12, 24.52, 24.44, 23.92, 24.04, 24.32, 24.36]
-14.20860242843628
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570974826812744, 'TIME_S_1KI': 0.10839913070081465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.3015551567077, 'W': 22.190891521159134}
-[20.44, 20.44, 20.48, 20.4, 20.48, 20.68, 20.76, 20.76, 20.72, 20.88, 20.4, 20.52, 20.56, 20.56, 20.56, 20.6, 20.52, 20.4, 20.6, 20.52]
-370.15999999999997
-18.508
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570974826812744, 'TIME_S_1KI': 0.10839913070081465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.3015551567077, 'W': 22.190891521159134, 'J_1KI': 3.233232038440793, 'W_1KI': 0.22755454343419368, 'W_D': 3.682891521159135, 'J_D': 52.32874141120907, 'W_D_1KI': 0.037765886864704674, 'J_D_1KI': 0.00038726696197361203}
+[16.8, 16.8, 17.16, 17.24, 17.04, 16.96, 16.96, 17.0, 16.6, 17.0]
+[16.8, 16.84, 17.08, 18.24, 19.2, 19.72, 20.36, 20.2, 20.2, 20.04, 19.8, 19.8, 20.0, 19.96]
+14.200194358825684
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 93664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.030004501342773, 'TIME_S_1KI': 0.107084947272621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 253.85587940216067, 'W': 17.876929919933424}
+[16.8, 16.8, 17.16, 17.24, 17.04, 16.96, 16.96, 17.0, 16.6, 17.0, 16.72, 16.48, 16.44, 16.48, 16.32, 16.48, 16.4, 16.28, 16.28, 16.32]
+300.34000000000003
+15.017000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 93664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.030004501342773, 'TIME_S_1KI': 0.107084947272621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 253.85587940216067, 'W': 17.876929919933424, 'J_1KI': 2.7102822792338643, 'W_1KI': 0.19086233686297213, 'W_D': 2.859929919933423, 'J_D': 40.61156071567538, 'W_D_1KI': 0.03053392893676784, 'J_D_1KI': 0.00032599428741851555}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
index 6041c9a..37907ba 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17764, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.691047191619873, "TIME_S_1KI": 0.6018378288459735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.0331690597534, "W": 23.15708952749249, "J_1KI": 18.522470674383776, "W_1KI": 1.3035965732657337, "W_D": 4.568089527492493, "J_D": 64.90681706762312, "W_D_1KI": 0.2571543305276116, "J_D_1KI": 0.01447615010851225}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17878, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.553908824920654, "TIME_S_1KI": 0.5903293894686572, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 275.88499256134037, "W": 19.43945882245899, "J_1KI": 15.431535549912763, "W_1KI": 1.0873396813099334, "W_D": 4.574458822458993, "J_D": 64.92076501369485, "W_D_1KI": 0.2558708369201808, "J_D_1KI": 0.014312050392671485}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
index 626c252..e1fffa0 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6273210048675537}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06726479530334473}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     7,  ..., 24988, 24996, 25000]),
-       col_indices=tensor([1892, 2918, 4655,  ..., 2029, 2603, 3010]),
-       values=tensor([0.8283, 0.5273, 0.2909,  ..., 0.5828, 0.6477, 0.7502]),
+tensor(crow_indices=tensor([    0,    11,    16,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([ 219,  546,  972,  ..., 2610, 3216, 3318]),
+       values=tensor([0.2561, 0.1283, 0.3219,  ..., 0.1859, 0.9829, 0.7598]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8412, 0.7891, 0.2404,  ..., 0.8503, 0.9914, 0.6212])
+tensor([0.7133, 0.2668, 0.2369,  ..., 0.2811, 0.0980, 0.8981])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.6273210048675537 seconds
+Time: 0.06726479530334473 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 16737 -ss 5000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.892534494400024}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15609 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.167168855667114}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,    10,  ..., 24991, 24996, 25000]),
-       col_indices=tensor([4752,  479, 2068,  ..., 1338, 4478, 4539]),
-       values=tensor([0.3996, 0.8763, 0.4834,  ..., 0.3300, 0.4860, 0.9993]),
+tensor(crow_indices=tensor([    0,     9,    17,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 299, 1244, 1941,  ..., 4267,  280, 4025]),
+       values=tensor([0.2861, 0.6940, 0.5528,  ..., 0.3063, 0.0705, 0.3058]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9991, 0.1904, 0.1090,  ..., 0.8295, 0.4248, 0.2043])
+tensor([0.7288, 0.9023, 0.1050,  ..., 0.1276, 0.6415, 0.2460])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 9.892534494400024 seconds
+Time: 9.167168855667114 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17764 -ss 5000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.691047191619873}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17878 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.553908824920654}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    15,  ..., 24992, 24996, 25000]),
-       col_indices=tensor([1098, 1490, 1639,  ..., 3549, 3645, 4602]),
-       values=tensor([0.2763, 0.7775, 0.9451,  ..., 0.5590, 0.3508, 0.3085]),
+tensor(crow_indices=tensor([    0,     6,    13,  ..., 24995, 24998, 25000]),
+       col_indices=tensor([1009, 1198, 2341,  ..., 3808,  999, 4327]),
+       values=tensor([0.4127, 0.3151, 0.8058,  ..., 0.6562, 0.4614, 0.0831]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1047, 0.7118, 0.3308,  ..., 0.3344, 0.9893, 0.0200])
+tensor([0.8967, 0.5216, 0.2765,  ..., 0.9189, 0.4761, 0.3303])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.691047191619873 seconds
+Time: 10.553908824920654 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    15,  ..., 24992, 24996, 25000]),
-       col_indices=tensor([1098, 1490, 1639,  ..., 3549, 3645, 4602]),
-       values=tensor([0.2763, 0.7775, 0.9451,  ..., 0.5590, 0.3508, 0.3085]),
+tensor(crow_indices=tensor([    0,     6,    13,  ..., 24995, 24998, 25000]),
+       col_indices=tensor([1009, 1198, 2341,  ..., 3808,  999, 4327]),
+       values=tensor([0.4127, 0.3151, 0.8058,  ..., 0.6562, 0.4614, 0.0831]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1047, 0.7118, 0.3308,  ..., 0.3344, 0.9893, 0.0200])
+tensor([0.8967, 0.5216, 0.2765,  ..., 0.9189, 0.4761, 0.3303])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.691047191619873 seconds
+Time: 10.553908824920654 seconds
 
-[20.44, 20.2, 20.44, 20.64, 20.6, 20.72, 21.04, 21.04, 21.04, 20.88]
-[20.72, 20.68, 20.52, 21.28, 23.28, 29.52, 30.36, 30.6, 30.4, 23.8, 23.72, 23.72, 23.84, 23.92]
-14.208744525909424
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.691047191619873, 'TIME_S_1KI': 0.6018378288459735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.0331690597534, 'W': 23.15708952749249}
-[20.44, 20.2, 20.44, 20.64, 20.6, 20.72, 21.04, 21.04, 21.04, 20.88, 20.68, 20.8, 20.72, 20.4, 20.32, 20.52, 20.64, 20.6, 20.72, 20.68]
-371.78
-18.589
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.691047191619873, 'TIME_S_1KI': 0.6018378288459735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.0331690597534, 'W': 23.15708952749249, 'J_1KI': 18.522470674383776, 'W_1KI': 1.3035965732657337, 'W_D': 4.568089527492493, 'J_D': 64.90681706762312, 'W_D_1KI': 0.2571543305276116, 'J_D_1KI': 0.01447615010851225}
+[16.32, 16.36, 16.36, 16.56, 16.68, 16.68, 16.64, 16.56, 16.68, 16.52]
+[16.44, 16.88, 16.88, 17.92, 19.12, 25.32, 25.8, 25.92, 25.32, 22.16, 19.68, 19.6, 19.64, 19.4]
+14.192009925842285
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17878, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.553908824920654, 'TIME_S_1KI': 0.5903293894686572, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.88499256134037, 'W': 19.43945882245899}
+[16.32, 16.36, 16.36, 16.56, 16.68, 16.68, 16.64, 16.56, 16.68, 16.52, 16.52, 16.4, 16.4, 16.2, 16.28, 16.36, 16.32, 16.64, 17.04, 16.92]
+297.29999999999995
+14.864999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17878, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.553908824920654, 'TIME_S_1KI': 0.5903293894686572, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.88499256134037, 'W': 19.43945882245899, 'J_1KI': 15.431535549912763, 'W_1KI': 1.0873396813099334, 'W_D': 4.574458822458993, 'J_D': 64.92076501369485, 'W_D_1KI': 0.2558708369201808, 'J_D_1KI': 0.014312050392671485}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
index 5558ea5..b2ee0da 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1959, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.120546340942383, "TIME_S_1KI": 5.676644380266659, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.91848049163815, "W": 23.94849862540507, "J_1KI": 173.51632490640029, "W_1KI": 12.224858920574308, "W_D": 5.3214986254050665, "J_D": 75.53190515112868, "W_D_1KI": 2.716436255949498, "J_D_1KI": 1.3866443368808055}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1934, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.419332265853882, "TIME_S_1KI": 5.38745205059663, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.42124423980715, "W": 20.84298720811279, "J_1KI": 153.26848202678755, "W_1KI": 10.777139197576416, "W_D": 5.978987208112789, "J_D": 85.03094157409672, "W_D_1KI": 3.0915135512475644, "J_D_1KI": 1.598507523912908}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
index c4f4108..048fad0 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.658592224121094}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6361579895019531}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     40,     84,  ..., 249908, 249949,
+tensor(crow_indices=tensor([     0,     45,     88,  ..., 249912, 249956,
                             250000]),
-       col_indices=tensor([ 330,  398,  412,  ..., 4758, 4825, 4990]),
-       values=tensor([0.1241, 0.3411, 0.2552,  ..., 0.9324, 0.8443, 0.4144]),
+       col_indices=tensor([ 150,  155,  160,  ..., 4906, 4918, 4974]),
+       values=tensor([0.5565, 0.2611, 0.6011,  ..., 0.5545, 0.3341, 0.9118]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9270, 0.0262, 0.1807,  ..., 0.7250, 0.9803, 0.9114])
+tensor([0.3996, 0.8652, 0.4997,  ..., 0.5638, 0.5133, 0.5074])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 5.658592224121094 seconds
+Time: 0.6361579895019531 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1855 -ss 5000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.939346075057983}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1650 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.95723843574524}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     42,     99,  ..., 249900, 249944,
+tensor(crow_indices=tensor([     0,     48,    105,  ..., 249902, 249944,
                             250000]),
-       col_indices=tensor([  71,   83,  134,  ..., 4502, 4510, 4544]),
-       values=tensor([0.1222, 0.9313, 0.0593,  ..., 0.6337, 0.4012, 0.6808]),
+       col_indices=tensor([ 142,  192,  269,  ..., 4444, 4647, 4854]),
+       values=tensor([0.2391, 0.6427, 0.3721,  ..., 0.7376, 0.6381, 0.7309]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6318, 0.1040, 0.0347,  ..., 0.9714, 0.1743, 0.3337])
+tensor([0.0904, 0.1755, 0.9906,  ..., 0.2784, 0.3906, 0.1798])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 9.939346075057983 seconds
+Time: 8.95723843574524 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1959 -ss 5000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.120546340942383}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1934 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.419332265853882}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     52,     94,  ..., 249896, 249940,
+tensor(crow_indices=tensor([     0,     67,    123,  ..., 249901, 249950,
                             250000]),
-       col_indices=tensor([  62,  114,  171,  ..., 4675, 4821, 4860]),
-       values=tensor([0.5686, 0.1100, 0.2304,  ..., 0.6863, 0.4817, 0.3965]),
+       col_indices=tensor([  77,  297,  304,  ..., 4744, 4962, 4980]),
+       values=tensor([0.1458, 0.7428, 0.5307,  ..., 0.5713, 0.0836, 0.9823]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8036, 0.6564, 0.9943,  ..., 0.3026, 0.0525, 0.3398])
+tensor([0.8374, 0.5393, 0.4548,  ..., 0.1972, 0.5711, 0.3877])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 11.120546340942383 seconds
+Time: 10.419332265853882 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     52,     94,  ..., 249896, 249940,
+tensor(crow_indices=tensor([     0,     67,    123,  ..., 249901, 249950,
                             250000]),
-       col_indices=tensor([  62,  114,  171,  ..., 4675, 4821, 4860]),
-       values=tensor([0.5686, 0.1100, 0.2304,  ..., 0.6863, 0.4817, 0.3965]),
+       col_indices=tensor([  77,  297,  304,  ..., 4744, 4962, 4980]),
+       values=tensor([0.1458, 0.7428, 0.5307,  ..., 0.5713, 0.0836, 0.9823]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8036, 0.6564, 0.9943,  ..., 0.3026, 0.0525, 0.3398])
+tensor([0.8374, 0.5393, 0.4548,  ..., 0.1972, 0.5711, 0.3877])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 11.120546340942383 seconds
+Time: 10.419332265853882 seconds
 
-[20.4, 20.56, 20.84, 20.88, 21.08, 21.16, 21.16, 20.92, 20.72, 20.76]
-[20.2, 20.2, 20.96, 22.4, 23.64, 30.44, 31.56, 31.08, 30.2, 30.2, 24.44, 24.12, 24.04, 23.84]
-14.19372820854187
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.120546340942383, 'TIME_S_1KI': 5.676644380266659, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.91848049163815, 'W': 23.94849862540507}
-[20.4, 20.56, 20.84, 20.88, 21.08, 21.16, 21.16, 20.92, 20.72, 20.76, 20.32, 20.52, 20.64, 20.6, 20.44, 20.28, 20.44, 20.6, 20.64, 20.64]
-372.54
-18.627000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.120546340942383, 'TIME_S_1KI': 5.676644380266659, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.91848049163815, 'W': 23.94849862540507, 'J_1KI': 173.51632490640029, 'W_1KI': 12.224858920574308, 'W_D': 5.3214986254050665, 'J_D': 75.53190515112868, 'W_D_1KI': 2.716436255949498, 'J_D_1KI': 1.3866443368808055}
+[16.44, 16.56, 16.48, 16.6, 16.72, 16.56, 16.72, 16.6, 16.72, 16.64]
+[16.6, 16.56, 16.56, 20.2, 20.72, 28.04, 28.92, 29.0, 26.44, 26.36, 20.12, 20.28, 20.2, 20.4]
+14.221629619598389
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1934, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.419332265853882, 'TIME_S_1KI': 5.38745205059663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.42124423980715, 'W': 20.84298720811279}
+[16.44, 16.56, 16.48, 16.6, 16.72, 16.56, 16.72, 16.6, 16.72, 16.64, 16.68, 16.44, 16.44, 16.2, 16.24, 16.32, 16.56, 16.56, 16.52, 16.32]
+297.28
+14.863999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1934, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.419332265853882, 'TIME_S_1KI': 5.38745205059663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.42124423980715, 'W': 20.84298720811279, 'J_1KI': 153.26848202678755, 'W_1KI': 10.777139197576416, 'W_D': 5.978987208112789, 'J_D': 85.03094157409672, 'W_D_1KI': 3.0915135512475644, 'J_D_1KI': 1.598507523912908}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
index 88b3edd..e125259 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.64292335510254, "TIME_S_1KI": 26.64292335510254, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.6764411926268, "W": 24.280702381341985, "J_1KI": 738.6764411926268, "W_1KI": 24.280702381341985, "W_D": 5.882702381341982, "J_D": 178.96573136138895, "W_D_1KI": 5.882702381341982, "J_D_1KI": 5.882702381341982}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 394, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.532436847686768, "TIME_S_1KI": 26.732073217479105, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 305.06099658966065, "W": 21.37670924510136, "J_1KI": 774.2664888062453, "W_1KI": 54.25560722106944, "W_D": 6.54270924510136, "J_D": 93.36916079187395, "W_D_1KI": 16.60586102817604, "J_D_1KI": 42.146855401462034}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
index d050d33..49ea4fe 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.64292335510254}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.876847505569458}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     258,     500,  ..., 1249494,
-                            1249753, 1250000]),
-       col_indices=tensor([  51,   83,   92,  ..., 4940, 4981, 4997]),
-       values=tensor([0.9970, 0.1345, 0.9294,  ..., 0.5035, 0.6973, 0.4629]),
+tensor(crow_indices=tensor([      0,     270,     507,  ..., 1249492,
+                            1249752, 1250000]),
+       col_indices=tensor([  22,   47,   49,  ..., 4884, 4921, 4983]),
+       values=tensor([0.5298, 0.6030, 0.6480,  ..., 0.1911, 0.5303, 0.8187]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6657, 0.7944, 0.8404,  ..., 0.5502, 0.2324, 0.9138])
+tensor([0.2222, 0.3805, 0.6044,  ..., 0.3111, 0.2543, 0.4407])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 26.64292335510254 seconds
+Time: 2.876847505569458 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 364 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.686374425888062}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     258,     500,  ..., 1249494,
-                            1249753, 1250000]),
-       col_indices=tensor([  51,   83,   92,  ..., 4940, 4981, 4997]),
-       values=tensor([0.9970, 0.1345, 0.9294,  ..., 0.5035, 0.6973, 0.4629]),
+tensor(crow_indices=tensor([      0,     236,     499,  ..., 1249504,
+                            1249745, 1250000]),
+       col_indices=tensor([  18,   50,   76,  ..., 4926, 4932, 4975]),
+       values=tensor([0.1676, 0.6835, 0.4526,  ..., 0.3904, 0.9402, 0.3969]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6657, 0.7944, 0.8404,  ..., 0.5502, 0.2324, 0.9138])
+tensor([0.5247, 0.4082, 0.5074,  ..., 0.5246, 0.7808, 0.6822])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +36,50 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 26.64292335510254 seconds
+Time: 9.686374425888062 seconds
 
-[20.76, 20.56, 20.76, 20.24, 20.0, 20.0, 19.96, 19.88, 20.0, 20.0]
-[20.2, 20.56, 20.84, 23.48, 25.48, 31.96, 32.48, 32.96, 29.96, 29.16, 23.84, 23.8, 23.88, 23.88, 24.28, 24.2, 24.36, 24.32, 23.8, 24.12, 24.2, 24.36, 24.28, 24.24, 24.24, 24.24, 24.32, 24.24, 24.48, 24.52]
-30.422367095947266
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.64292335510254, 'TIME_S_1KI': 26.64292335510254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.6764411926268, 'W': 24.280702381341985}
-[20.76, 20.56, 20.76, 20.24, 20.0, 20.0, 19.96, 19.88, 20.0, 20.0, 20.52, 20.44, 20.56, 20.64, 20.52, 20.76, 20.64, 20.92, 20.96, 20.96]
-367.96000000000004
-18.398000000000003
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.64292335510254, 'TIME_S_1KI': 26.64292335510254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.6764411926268, 'W': 24.280702381341985, 'J_1KI': 738.6764411926268, 'W_1KI': 24.280702381341985, 'W_D': 5.882702381341982, 'J_D': 178.96573136138895, 'W_D_1KI': 5.882702381341982, 'J_D_1KI': 5.882702381341982}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 394 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.532436847686768}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     285,     546,  ..., 1249527,
+                            1249741, 1250000]),
+       col_indices=tensor([  14,   79,   87,  ..., 4936, 4956, 4998]),
+       values=tensor([0.7519, 0.7358, 0.0306,  ..., 0.8162, 0.7664, 0.2246]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9515, 0.5215, 0.2042,  ..., 0.7715, 0.5250, 0.3133])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.532436847686768 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     285,     546,  ..., 1249527,
+                            1249741, 1250000]),
+       col_indices=tensor([  14,   79,   87,  ..., 4936, 4956, 4998]),
+       values=tensor([0.7519, 0.7358, 0.0306,  ..., 0.8162, 0.7664, 0.2246]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9515, 0.5215, 0.2042,  ..., 0.7715, 0.5250, 0.3133])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.532436847686768 seconds
+
+[16.32, 16.28, 16.04, 16.2, 16.32, 16.36, 16.36, 16.36, 16.64, 16.6]
+[16.72, 16.84, 17.04, 21.76, 24.68, 30.6, 31.68, 29.24, 27.56, 20.44, 20.44, 20.32, 20.44, 20.32]
+14.270718336105347
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 394, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.532436847686768, 'TIME_S_1KI': 26.732073217479105, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 305.06099658966065, 'W': 21.37670924510136}
+[16.32, 16.28, 16.04, 16.2, 16.32, 16.36, 16.36, 16.36, 16.64, 16.6, 16.32, 16.36, 16.44, 16.6, 16.76, 16.8, 16.64, 16.76, 16.76, 16.76]
+296.68
+14.834
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 394, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.532436847686768, 'TIME_S_1KI': 26.732073217479105, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 305.06099658966065, 'W': 21.37670924510136, 'J_1KI': 774.2664888062453, 'W_1KI': 54.25560722106944, 'W_D': 6.54270924510136, 'J_D': 93.36916079187395, 'W_D_1KI': 16.60586102817604, 'J_D_1KI': 42.146855401462034}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
index 70fec2c..5879ae9 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.09800863265991, "TIME_S_1KI": 53.09800863265991, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1383.3292028236385, "W": 24.387974690726086, "J_1KI": 1383.3292028236385, "W_1KI": 24.387974690726086, "W_D": 5.857974690726085, "J_D": 332.2747198915477, "W_D_1KI": 5.857974690726085, "J_D_1KI": 5.857974690726085}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.453055620193481, "TIME_S_1KI": 53.06119604159128, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.8498130035401, "W": 21.582740338257725, "J_1KI": 1669.2883908809142, "W_1KI": 109.55705755460775, "W_D": 6.5647403382577245, "J_D": 100.02500140476232, "W_D_1KI": 33.32355501653667, "J_D_1KI": 169.15510160678514}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
index 20994df..45da6ca 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.09800863265991}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.324424982070923}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     484,     997,  ..., 2498998,
-                            2499500, 2500000]),
-       col_indices=tensor([   3,   13,   35,  ..., 4966, 4993, 4997]),
-       values=tensor([0.0178, 0.5574, 0.8921,  ..., 0.2131, 0.1882, 0.3495]),
+tensor(crow_indices=tensor([      0,     475,     961,  ..., 2499006,
+                            2499520, 2500000]),
+       col_indices=tensor([  15,   18,   19,  ..., 4987, 4990, 4996]),
+       values=tensor([0.8221, 0.3138, 0.3999,  ..., 0.4846, 0.5872, 0.2809]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5571, 0.9138, 0.4400,  ..., 0.1682, 0.6225, 0.2202])
+tensor([0.6625, 0.6086, 0.7821,  ..., 0.8108, 0.2752, 0.8534])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 53.09800863265991 seconds
+Time: 5.324424982070923 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 197 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.453055620193481}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     484,     997,  ..., 2498998,
-                            2499500, 2500000]),
-       col_indices=tensor([   3,   13,   35,  ..., 4966, 4993, 4997]),
-       values=tensor([0.0178, 0.5574, 0.8921,  ..., 0.2131, 0.1882, 0.3495]),
+tensor(crow_indices=tensor([      0,     504,     990,  ..., 2498981,
+                            2499511, 2500000]),
+       col_indices=tensor([   3,   15,   19,  ..., 4959, 4975, 4978]),
+       values=tensor([0.1275, 0.4769, 0.3626,  ..., 0.0765, 0.7881, 0.0735]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5571, 0.9138, 0.4400,  ..., 0.1682, 0.6225, 0.2202])
+tensor([0.2182, 0.1034, 0.8832,  ..., 0.0679, 0.0105, 0.3546])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +36,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 53.09800863265991 seconds
+Time: 10.453055620193481 seconds
 
-[20.6, 20.6, 20.72, 20.68, 20.72, 20.8, 20.84, 20.8, 20.92, 20.52]
-[20.6, 20.4, 23.56, 24.32, 24.32, 28.84, 34.48, 35.48, 33.04, 32.72, 26.72, 24.2, 24.24, 24.24, 24.0, 24.0, 23.96, 23.96, 23.88, 23.96, 23.96, 23.92, 23.92, 23.68, 23.64, 23.76, 24.08, 24.12, 24.08, 24.08, 24.32, 24.08, 23.92, 24.04, 24.0, 23.96, 24.12, 24.24, 24.28, 24.24, 24.2, 23.92, 23.92, 24.0, 24.2, 24.24, 24.4, 24.2, 24.16, 24.0, 24.16, 24.32, 24.36, 24.36, 24.36, 24.12]
-56.72177457809448
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.09800863265991, 'TIME_S_1KI': 53.09800863265991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.3292028236385, 'W': 24.387974690726086}
-[20.6, 20.6, 20.72, 20.68, 20.72, 20.8, 20.84, 20.8, 20.92, 20.52, 20.32, 20.36, 20.4, 20.48, 20.44, 20.64, 20.48, 20.36, 20.48, 20.32]
-370.6
-18.53
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.09800863265991, 'TIME_S_1KI': 53.09800863265991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.3292028236385, 'W': 24.387974690726086, 'J_1KI': 1383.3292028236385, 'W_1KI': 24.387974690726086, 'W_D': 5.857974690726085, 'J_D': 332.2747198915477, 'W_D_1KI': 5.857974690726085, 'J_D_1KI': 5.857974690726085}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     504,     990,  ..., 2498981,
+                            2499511, 2500000]),
+       col_indices=tensor([   3,   15,   19,  ..., 4959, 4975, 4978]),
+       values=tensor([0.1275, 0.4769, 0.3626,  ..., 0.0765, 0.7881, 0.0735]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2182, 0.1034, 0.8832,  ..., 0.0679, 0.0105, 0.3546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.453055620193481 seconds
+
+[16.48, 16.56, 16.52, 16.6, 16.92, 17.2, 17.24, 17.32, 17.32, 17.2]
+[17.2, 17.08, 16.8, 20.08, 22.0, 28.44, 31.8, 32.0, 28.2, 27.28, 20.16, 20.32, 20.56, 20.56, 20.32]
+15.236703395843506
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.453055620193481, 'TIME_S_1KI': 53.06119604159128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8498130035401, 'W': 21.582740338257725}
+[16.48, 16.56, 16.52, 16.6, 16.92, 17.2, 17.24, 17.32, 17.32, 17.2, 16.8, 16.52, 16.44, 16.56, 16.44, 16.36, 16.4, 16.48, 16.24, 16.0]
+300.36
+15.018
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.453055620193481, 'TIME_S_1KI': 53.06119604159128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8498130035401, 'W': 21.582740338257725, 'J_1KI': 1669.2883908809142, 'W_1KI': 109.55705755460775, 'W_D': 6.5647403382577245, 'J_D': 100.02500140476232, 'W_D_1KI': 33.32355501653667, 'J_D_1KI': 169.15510160678514}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..22c3547
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.520986080169678, "TIME_S_1KI": 105.20986080169678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.0604390716553, "W": 22.467293864380782, "J_1KI": 3430.604390716553, "W_1KI": 224.67293864380784, "W_D": 7.879293864380783, "J_D": 120.3115083198548, "W_D_1KI": 78.79293864380782, "J_D_1KI": 787.9293864380782}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..46cc854
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.520986080169678}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1010,    1992,  ..., 4998032,
+                            4999035, 5000000]),
+       col_indices=tensor([   7,   20,   27,  ..., 4987, 4995, 4999]),
+       values=tensor([0.6859, 0.1805, 0.1498,  ..., 0.8538, 0.5250, 0.8232]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6117, 0.2257, 0.9695,  ..., 0.2383, 0.1591, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.520986080169678 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1010,    1992,  ..., 4998032,
+                            4999035, 5000000]),
+       col_indices=tensor([   7,   20,   27,  ..., 4987, 4995, 4999]),
+       values=tensor([0.6859, 0.1805, 0.1498,  ..., 0.8538, 0.5250, 0.8232]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6117, 0.2257, 0.9695,  ..., 0.2383, 0.1591, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.520986080169678 seconds
+
+[16.36, 16.04, 16.16, 15.96, 15.88, 15.88, 16.04, 16.04, 16.28, 16.32]
+[16.4, 16.48, 16.8, 20.24, 21.88, 29.72, 32.92, 30.52, 30.52, 30.68, 27.2, 20.4, 20.72, 20.84, 20.72]
+15.269326210021973
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.520986080169678, 'TIME_S_1KI': 105.20986080169678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.0604390716553, 'W': 22.467293864380782}
+[16.36, 16.04, 16.16, 15.96, 15.88, 15.88, 16.04, 16.04, 16.28, 16.32, 16.84, 17.08, 16.68, 16.36, 16.32, 16.0, 16.08, 16.08, 16.04, 16.16]
+291.76
+14.588
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.520986080169678, 'TIME_S_1KI': 105.20986080169678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.0604390716553, 'W': 22.467293864380782, 'J_1KI': 3430.604390716553, 'W_1KI': 224.67293864380784, 'W_D': 7.879293864380783, 'J_D': 120.3115083198548, 'W_D_1KI': 78.79293864380782, 'J_D_1KI': 787.9293864380782}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..1e920c1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.019237279891968, "TIME_S_1KI": 160.19237279891968, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 445.7399830436707, "W": 20.90802632853554, "J_1KI": 4457.399830436707, "W_1KI": 209.08026328535541, "W_D": 5.958026328535542, "J_D": 127.0196676111222, "W_D_1KI": 59.58026328535542, "J_D_1KI": 595.8026328535542}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..039e7ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.019237279891968}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1526,    3031,  ..., 7497025,
+                            7498559, 7500000]),
+       col_indices=tensor([   0,    3,    7,  ..., 4995, 4996, 4999]),
+       values=tensor([0.4727, 0.1556, 0.1081,  ..., 0.9285, 0.0937, 0.3872]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9962, 0.8284, 0.0086,  ..., 0.9887, 0.5066, 0.4274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 16.019237279891968 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1526,    3031,  ..., 7497025,
+                            7498559, 7500000]),
+       col_indices=tensor([   0,    3,    7,  ..., 4995, 4996, 4999]),
+       values=tensor([0.4727, 0.1556, 0.1081,  ..., 0.9285, 0.0937, 0.3872]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9962, 0.8284, 0.0086,  ..., 0.9887, 0.5066, 0.4274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 16.019237279891968 seconds
+
+[16.44, 16.68, 16.72, 16.56, 16.6, 16.76, 16.84, 16.84, 16.68, 16.52]
+[16.44, 16.48, 16.8, 17.84, 18.92, 25.84, 27.76, 29.92, 29.96, 29.48, 23.32, 23.32, 20.24, 20.12, 20.24, 20.04, 20.04, 20.24, 20.28, 20.2, 20.12]
+21.319084644317627
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.019237279891968, 'TIME_S_1KI': 160.19237279891968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 445.7399830436707, 'W': 20.90802632853554}
+[16.44, 16.68, 16.72, 16.56, 16.6, 16.76, 16.84, 16.84, 16.68, 16.52, 16.44, 16.44, 16.56, 16.56, 16.72, 16.64, 16.56, 16.52, 16.4, 16.44]
+299.0
+14.95
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.019237279891968, 'TIME_S_1KI': 160.19237279891968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 445.7399830436707, 'W': 20.90802632853554, 'J_1KI': 4457.399830436707, 'W_1KI': 209.08026328535541, 'W_D': 5.958026328535542, 'J_D': 127.0196676111222, 'W_D_1KI': 59.58026328535542, 'J_D_1KI': 595.8026328535542}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d6dabaf
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.045433044433594, "TIME_S_1KI": 210.45433044433594, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 590.3603710937499, "W": 21.581829265499348, "J_1KI": 5903.603710937499, "W_1KI": 215.81829265499348, "W_D": 6.281829265499347, "J_D": 171.83636339187612, "W_D_1KI": 62.81829265499347, "J_D_1KI": 628.1829265499347}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..bcbbf0c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.045433044433594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2076,     4151,  ...,  9996002,
+                             9998013, 10000000]),
+       col_indices=tensor([   1,   14,   15,  ..., 4994, 4998, 4999]),
+       values=tensor([0.5831, 0.7619, 0.5912,  ..., 0.7349, 0.2932, 0.8119]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6981, 0.1886, 0.6279,  ..., 0.1836, 0.5536, 0.9370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.045433044433594 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2076,     4151,  ...,  9996002,
+                             9998013, 10000000]),
+       col_indices=tensor([   1,   14,   15,  ..., 4994, 4998, 4999]),
+       values=tensor([0.5831, 0.7619, 0.5912,  ..., 0.7349, 0.2932, 0.8119]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6981, 0.1886, 0.6279,  ..., 0.1836, 0.5536, 0.9370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.045433044433594 seconds
+
+[16.72, 16.72, 16.28, 16.36, 16.28, 17.72, 18.32, 19.12, 19.12, 19.28]
+[18.6, 17.76, 17.4, 20.72, 22.2, 26.08, 32.2, 30.12, 32.12, 31.16, 23.52, 23.52, 22.96, 20.28, 20.12, 20.24, 20.28, 20.28, 20.32, 20.32, 20.2, 20.12, 20.28, 20.28, 20.48, 20.6, 20.48]
+27.35451030731201
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.045433044433594, 'TIME_S_1KI': 210.45433044433594, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.3603710937499, 'W': 21.581829265499348}
+[16.72, 16.72, 16.28, 16.36, 16.28, 17.72, 18.32, 19.12, 19.12, 19.28, 16.44, 16.48, 16.36, 16.32, 16.32, 16.52, 16.72, 16.56, 16.4, 16.36]
+306.0
+15.3
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.045433044433594, 'TIME_S_1KI': 210.45433044433594, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.3603710937499, 'W': 21.581829265499348, 'J_1KI': 5903.603710937499, 'W_1KI': 215.81829265499348, 'W_D': 6.281829265499347, 'J_D': 171.83636339187612, 'W_D_1KI': 62.81829265499347, 'J_D_1KI': 628.1829265499347}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..edd5f52
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.53720736503601, "TIME_S_1KI": 265.3720736503601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 691.3309565734863, "W": 20.656596648884427, "J_1KI": 6913.309565734863, "W_1KI": 206.56596648884428, "W_D": 5.692596648884429, "J_D": 190.5187167835236, "W_D_1KI": 56.92596648884429, "J_D_1KI": 569.2596648884429}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..7bfb817
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.53720736503601}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2531,     5099,  ..., 12494952,
+                            12497469, 12500000]),
+       col_indices=tensor([   0,    7,    9,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6564, 0.0127, 0.9586,  ..., 0.9277, 0.7224, 0.6295]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9909, 0.2884, 0.1156,  ..., 0.4898, 0.4767, 0.4308])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.53720736503601 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2531,     5099,  ..., 12494952,
+                            12497469, 12500000]),
+       col_indices=tensor([   0,    7,    9,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6564, 0.0127, 0.9586,  ..., 0.9277, 0.7224, 0.6295]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9909, 0.2884, 0.1156,  ..., 0.4898, 0.4767, 0.4308])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.53720736503601 seconds
+
+[16.56, 16.48, 16.36, 16.32, 16.28, 16.28, 16.52, 16.48, 16.8, 16.84]
+[16.84, 16.88, 16.84, 18.36, 18.96, 19.92, 26.16, 27.2, 30.16, 30.2, 27.92, 24.32, 23.48, 20.0, 20.0, 20.32, 20.36, 20.32, 20.48, 20.28, 20.2, 20.28, 20.2, 20.12, 20.08, 19.92, 19.92, 19.92, 20.04, 20.04, 20.2, 20.32, 20.16]
+33.4678053855896
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.53720736503601, 'TIME_S_1KI': 265.3720736503601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 691.3309565734863, 'W': 20.656596648884427}
+[16.56, 16.48, 16.36, 16.32, 16.28, 16.28, 16.52, 16.48, 16.8, 16.84, 16.6, 16.96, 16.88, 17.08, 17.04, 16.88, 16.6, 16.6, 16.52, 16.4]
+299.28
+14.963999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.53720736503601, 'TIME_S_1KI': 265.3720736503601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 691.3309565734863, 'W': 20.656596648884427, 'J_1KI': 6913.309565734863, 'W_1KI': 206.56596648884428, 'W_D': 5.692596648884429, 'J_D': 190.5187167835236, 'W_D_1KI': 56.92596648884429, 'J_D_1KI': 569.2596648884429}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
index 7d0783d..50a61b1 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 275920, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.428882360458374, "TIME_S_1KI": 0.037796761236801875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.0905113220215, "W": 22.127187461407587, "J_1KI": 1.141963291251165, "W_1KI": 0.08019421376271234, "W_D": 3.664187461407586, "J_D": 52.177923778533895, "W_D_1KI": 0.013279890770540686, "J_D_1KI": 4.812949684887172e-05}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 277466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.306395292282104, "TIME_S_1KI": 0.037144714279522914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 274.4401516246796, "W": 19.314621204373413, "J_1KI": 0.9890947057465764, "W_1KI": 0.06961076746114267, "W_D": 4.503621204373411, "J_D": 63.99165032076835, "W_D_1KI": 0.016231254295565625, "J_D_1KI": 5.849817381432545e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
index a254447..19817c6 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,291 +1,75 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04542350769042969}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012943029403686523}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1381, 3398, 2478, 1052,  529,  491, 2775, 3229, 1279,
-                           3454,  296, 3084, 4650, 2467,  784,  568,  918,  741,
-                           4819, 1730,  837,  408, 1523,  948, 4825, 1342,  952,
-                           2524, 3378, 2774,  370, 2319, 3980, 4108,  276, 4067,
-                           3823, 3153, 3158,  540, 2360, 1999, 1044, 1298, 4540,
-                            533, 3507, 1489, 2361, 1008,  555, 3416,  305, 3290,
-                           1136, 3809, 4448, 2408, 3611, 2892, 2540, 3779, 2041,
-                           4793, 4839,  534, 3664, 2180, 4711, 4601, 1136, 3681,
-                            165, 2858, 2937, 1364, 4737, 4916, 4412, 4772, 4253,
-                            200, 1254, 2702, 3949, 1138, 3253, 4523, 3563, 4932,
-                            724, 1152, 3157, 1713, 4323, 2340,  951, 3022, 1343,
-                           2260, 3881, 2605,  161, 4434, 3331, 1742, 2563, 4238,
-                            127, 3937,  396, 2283, 1557, 1554, 3292, 4855, 4197,
-                           4720,  716,   85, 4379, 3823, 2263, 2186, 2869, 1787,
-                           1168, 2429, 3045, 2919, 2350, 3479, 2094, 1065,  340,
-                           1288, 1877, 3764, 3457,  509, 1055, 3089,  605, 1110,
-                           3765, 3334, 3358,  602, 1278, 2312, 2279, 3749, 3299,
-                           4530,  804, 4261,  418, 4624,  585, 3050, 3236,  596,
-                           2133,  933, 4209, 3895,  174,  765, 3980,  381, 2181,
-                           2969,   46, 3997, 2920, 1083, 1216, 4056,  126,  248,
-                           1696,  352, 2821,  625, 3058, 4954, 4557,  865, 2010,
-                           2268, 2460, 1542,  329, 4649, 4740, 2546, 1491, 1783,
-                           2436, 2269, 2383,  734, 4372, 4876, 3373,  210, 4004,
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-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 231157 -ss 5000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.796557664871216}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
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-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 275920 -ss 5000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.428882360458374}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
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-                      0.2829, 0.0867, 0.8730, 0.1781, 0.6966]),
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([1969, 3660, 2508, 3210, 4469, 2580, 1634, 4852, 2880,
+                           4171,  145, 1923, 1759,  292,  976, 2609, 4097, 2004,
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+                      0.6887, 0.7409, 0.5606, 0.9654, 0.1055]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7582, 0.3275, 0.7400,  ..., 0.8955, 0.3174, 0.3280])
+tensor([0.8735, 0.3833, 0.2694,  ..., 0.9142, 0.8243, 0.1368])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -293,77 +77,161 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.428882360458374 seconds
+Time: 0.012943029403686523 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 81124 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.069929599761963}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 250, 250]),
+       col_indices=tensor([4942, 4831, 3020, 4398,   15,  273, 1128,  112, 3723,
+                           3585,  170, 3865, 4631, 2789,  948, 1973, 2152, 1168,
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+                           1006, 2747, 1763, 4043, 3117, 1987, 1941,  903, 4871,
+                           2123, 2041, 2574,  168, 2922, 2931, 3435]),
+       values=tensor([0.2833, 0.5136, 0.0459, 0.1040, 0.7712, 0.7813, 0.1004,
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+                      0.8266, 0.0100, 0.6364, 0.4916, 0.8482, 0.7480, 0.7567,
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+                      0.6751, 0.0013, 0.9619, 0.9277, 0.1302, 0.1956, 0.7206,
+                      0.7741, 0.7104, 0.3550, 0.2532, 0.0939, 0.7434, 0.5649,
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+                      0.1493, 0.3104, 0.8119, 0.6904, 0.9596, 0.5459, 0.5380,
+                      0.4871, 0.4126, 0.3848, 0.8347, 0.6321]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3638, 0.9432, 0.7166,  ..., 0.3961, 0.0448, 0.0792])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.069929599761963 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 277466 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.306395292282104}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1877, 1947, 3773, 1993, 3968, 2885, 3290, 1001, 3173,
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-                           3986, 4768, 1832,  171,  533,  127, 3370, 4616, 3556,
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-                      0.0453, 0.2921, 0.4748, 0.9573, 0.6189, 0.2369, 0.4918,
-                      0.2829, 0.0867, 0.8730, 0.1781, 0.6966]),
+       col_indices=tensor([2530, 1671, 1710,  529, 4147, 2671,  774, 3521, 3142,
+                           4079, 2231, 3137,  198, 1214, 3263,  994, 2667, 2294,
+                            182, 3631, 1055, 2979,   71, 3078, 4821, 3439, 3949,
+                           2018, 1636, 1734, 4146, 1819,  670, 2737, 4839,  929,
+                            652, 4064, 1709, 1446,  637,  485, 3208, 2342, 4556,
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+                           1528, 3105, 3115, 2926,  448, 1508, 3766,  414,    0,
+                             99, 1356,  732, 2391, 4307,  374, 3096, 3847, 1168,
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+                           2685,  115, 2166, 1456, 3440, 4502, 1541,  136, 4160,
+                           2313, 2928, 4917, 3863, 3827, 2109, 4794]),
+       values=tensor([0.1255, 0.3198, 0.8133, 0.3742, 0.0163, 0.1439, 0.7607,
+                      0.6784, 0.8830, 0.0545, 0.8528, 0.7242, 0.8352, 0.4737,
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+                      0.8010, 0.5772, 0.0521, 0.5829, 0.4101, 0.3786, 0.0283,
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+                      0.6809, 0.4255, 0.1696, 0.7442, 0.9124, 0.8603, 0.9907,
+                      0.1133, 0.2677, 0.6551, 0.8223, 0.8137, 0.2411, 0.5924,
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+                      0.9253, 0.6087, 0.7948, 0.9058, 0.2527, 0.3922, 0.9638,
+                      0.1626, 0.4231, 0.5916, 0.0663, 0.3747, 0.8133, 0.1672,
+                      0.4958, 0.1234, 0.2670, 0.0752, 0.3763, 0.6411, 0.3294,
+                      0.4132, 0.2682, 0.3319, 0.1004, 0.6692, 0.2485, 0.0663,
+                      0.1318, 0.4180, 0.2011, 0.4748, 0.2487, 0.0200, 0.3002,
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+                      0.8055, 0.5114, 0.9547, 0.4277, 0.3391, 0.6653, 0.7441,
+                      0.9317, 0.2522, 0.9794, 0.9450, 0.7609, 0.7552, 0.3464,
+                      0.2683, 0.6131, 0.7507, 0.3858, 0.2947, 0.5291, 0.7914,
+                      0.4452, 0.6309, 0.6569, 0.3974, 0.5452, 0.9065, 0.8000,
+                      0.7314, 0.8661, 0.0826, 0.0659, 0.8406, 0.3397, 0.6235,
+                      0.6886, 0.4334, 0.9899, 0.6808, 0.0386, 0.9324, 0.6160,
+                      0.2724, 0.3632, 0.4386, 0.4733, 0.7494, 0.2806, 0.7238,
+                      0.0116, 0.8061, 0.3580, 0.8134, 0.7511, 0.4690, 0.9418,
+                      0.0495, 0.8282, 0.9024, 0.7411, 0.2424, 0.5263, 0.6983,
+                      0.9412, 0.6025, 0.1977, 0.9907, 0.4170, 0.2685, 0.9711,
+                      0.6755, 0.6817, 0.5130, 0.8481, 0.9901, 0.9980, 0.3527,
+                      0.5949, 0.5533, 0.2777, 0.4754, 0.0948, 0.6148, 0.7233,
+                      0.0545, 0.7637, 0.1155, 0.4005, 0.7155]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7582, 0.3275, 0.7400,  ..., 0.8955, 0.3174, 0.3280])
+tensor([0.1431, 0.4969, 0.0611,  ..., 0.8896, 0.3924, 0.7446])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -371,13 +239,91 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.428882360458374 seconds
+Time: 10.306395292282104 seconds
 
-[20.56, 20.68, 20.64, 20.44, 20.64, 20.48, 20.48, 20.64, 20.68, 20.72]
-[20.84, 20.84, 21.12, 24.24, 25.12, 25.84, 26.16, 24.96, 23.68, 23.68, 23.56, 23.8, 24.0, 23.88]
-14.239971160888672
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 275920, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.428882360458374, 'TIME_S_1KI': 0.037796761236801875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.0905113220215, 'W': 22.127187461407587}
-[20.56, 20.68, 20.64, 20.44, 20.64, 20.48, 20.48, 20.64, 20.68, 20.72, 20.56, 20.72, 20.56, 20.64, 20.56, 20.24, 20.2, 20.44, 20.2, 20.2]
-369.26
-18.463
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 275920, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.428882360458374, 'TIME_S_1KI': 0.037796761236801875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.0905113220215, 'W': 22.127187461407587, 'J_1KI': 1.141963291251165, 'W_1KI': 0.08019421376271234, 'W_D': 3.664187461407586, 'J_D': 52.177923778533895, 'W_D_1KI': 0.013279890770540686, 'J_D_1KI': 4.812949684887172e-05}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2530, 1671, 1710,  529, 4147, 2671,  774, 3521, 3142,
+                           4079, 2231, 3137,  198, 1214, 3263,  994, 2667, 2294,
+                            182, 3631, 1055, 2979,   71, 3078, 4821, 3439, 3949,
+                           2018, 1636, 1734, 4146, 1819,  670, 2737, 4839,  929,
+                            652, 4064, 1709, 1446,  637,  485, 3208, 2342, 4556,
+                           3470, 1760, 3837, 1164,  826, 2500,  338, 4213, 1539,
+                           2699,    7, 3593,  628, 2634, 1851, 2277, 2906, 1873,
+                           3675,  109,  933,   39, 1888, 3153, 1802, 2749, 4653,
+                             10, 1407, 3436, 4501, 1652, 4732, 4648, 3990, 3869,
+                           1528, 3105, 3115, 2926,  448, 1508, 3766,  414,    0,
+                             99, 1356,  732, 2391, 4307,  374, 3096, 3847, 1168,
+                           2149, 2270, 3071, 2538, 4038, 1887, 3751, 3671, 1345,
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+                           1351, 4437,   62, 3040, 3132, 4639, 4608, 3358, 1316,
+                           2346, 4344, 2385, 4204,  358, 3346, 4011,  297,  728,
+                           1635, 4143, 3858, 4661, 2365, 4156, 4923, 3921, 4212,
+                            419, 1025, 1912, 1997, 3589,  965, 4863,  581, 2400,
+                           2128, 2335, 3936, 4843, 1018, 2088, 3052, 1843, 3652,
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+                           2685,  115, 2166, 1456, 3440, 4502, 1541,  136, 4160,
+                           2313, 2928, 4917, 3863, 3827, 2109, 4794]),
+       values=tensor([0.1255, 0.3198, 0.8133, 0.3742, 0.0163, 0.1439, 0.7607,
+                      0.6784, 0.8830, 0.0545, 0.8528, 0.7242, 0.8352, 0.4737,
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+                      0.4330, 0.1067, 0.1112, 0.2683, 0.4340, 0.7229, 0.1649,
+                      0.0932, 0.0193, 0.5783, 0.2193, 0.3091, 0.4364, 0.5673,
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+                      0.4452, 0.6309, 0.6569, 0.3974, 0.5452, 0.9065, 0.8000,
+                      0.7314, 0.8661, 0.0826, 0.0659, 0.8406, 0.3397, 0.6235,
+                      0.6886, 0.4334, 0.9899, 0.6808, 0.0386, 0.9324, 0.6160,
+                      0.2724, 0.3632, 0.4386, 0.4733, 0.7494, 0.2806, 0.7238,
+                      0.0116, 0.8061, 0.3580, 0.8134, 0.7511, 0.4690, 0.9418,
+                      0.0495, 0.8282, 0.9024, 0.7411, 0.2424, 0.5263, 0.6983,
+                      0.9412, 0.6025, 0.1977, 0.9907, 0.4170, 0.2685, 0.9711,
+                      0.6755, 0.6817, 0.5130, 0.8481, 0.9901, 0.9980, 0.3527,
+                      0.5949, 0.5533, 0.2777, 0.4754, 0.0948, 0.6148, 0.7233,
+                      0.0545, 0.7637, 0.1155, 0.4005, 0.7155]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1431, 0.4969, 0.0611,  ..., 0.8896, 0.3924, 0.7446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.306395292282104 seconds
+
+[16.56, 16.4, 16.72, 16.72, 16.84, 16.92, 17.04, 16.64, 16.64, 16.64]
+[16.68, 16.8, 19.52, 20.28, 22.4, 23.28, 23.28, 24.04, 21.56, 21.2, 20.08, 19.92, 19.76, 19.72]
+14.208932638168335
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.306395292282104, 'TIME_S_1KI': 0.037144714279522914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 274.4401516246796, 'W': 19.314621204373413}
+[16.56, 16.4, 16.72, 16.72, 16.84, 16.92, 17.04, 16.64, 16.64, 16.64, 16.24, 16.2, 16.2, 16.48, 16.12, 16.2, 16.2, 16.16, 16.0, 16.04]
+296.22
+14.811000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.306395292282104, 'TIME_S_1KI': 0.037144714279522914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 274.4401516246796, 'W': 19.314621204373413, 'J_1KI': 0.9890947057465764, 'W_1KI': 0.06961076746114267, 'W_D': 4.503621204373411, 'J_D': 63.99165032076835, 'W_D_1KI': 0.016231254295565625, 'J_D_1KI': 5.849817381432545e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..ed92a63
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 147819, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.450809717178345, "TIME_S_1KI": 0.07070004341240534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 270.7087575721741, "W": 19.037186911173507, "J_1KI": 1.831352921966554, "W_1KI": 0.12878714448868891, "W_D": 4.033186911173505, "J_D": 57.35190933799741, "W_D_1KI": 0.027284631279967428, "J_D_1KI": 0.00018458135476472868}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..3732e5f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.015123844146728516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3446, 3211, 1459,  ..., 3404, 1400, 4328]),
+       values=tensor([0.9299, 0.4025, 0.7514,  ..., 0.4501, 0.7034, 0.4301]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5652, 0.8868, 0.6802,  ..., 0.3723, 0.2839, 0.7363])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.015123844146728516 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 69426 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.931497573852539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2025, 3924,  898,  ..., 1281, 3893, 4108]),
+       values=tensor([0.0304, 0.7639, 0.3864,  ..., 0.2285, 0.7727, 0.3264]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1740, 0.1105, 0.4664,  ..., 0.6286, 0.2183, 0.7582])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 4.931497573852539 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 147819 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.450809717178345}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4384, 3481, 3445,  ..., 2544, 3259, 4103]),
+       values=tensor([0.0351, 0.6486, 0.3996,  ..., 0.8336, 0.2691, 0.7388]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9301, 0.0450, 0.3581,  ..., 0.7152, 0.3980, 0.2103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.450809717178345 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4384, 3481, 3445,  ..., 2544, 3259, 4103]),
+       values=tensor([0.0351, 0.6486, 0.3996,  ..., 0.8336, 0.2691, 0.7388]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9301, 0.0450, 0.3581,  ..., 0.7152, 0.3980, 0.2103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.450809717178345 seconds
+
+[16.36, 16.36, 16.6, 16.8, 16.76, 16.88, 16.88, 16.64, 16.44, 16.48]
+[16.72, 16.76, 17.08, 20.92, 22.6, 23.2, 24.36, 21.56, 20.88, 20.88, 19.84, 19.92, 19.92, 20.04]
+14.219997882843018
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 147819, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.450809717178345, 'TIME_S_1KI': 0.07070004341240534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 270.7087575721741, 'W': 19.037186911173507}
+[16.36, 16.36, 16.6, 16.8, 16.76, 16.88, 16.88, 16.64, 16.44, 16.48, 16.52, 16.72, 16.68, 16.76, 16.76, 16.76, 16.6, 16.68, 16.72, 16.72]
+300.08000000000004
+15.004000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 147819, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.450809717178345, 'TIME_S_1KI': 0.07070004341240534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 270.7087575721741, 'W': 19.037186911173507, 'J_1KI': 1.831352921966554, 'W_1KI': 0.12878714448868891, 'W_D': 4.033186911173505, 'J_D': 57.35190933799741, 'W_D_1KI': 0.027284631279967428, 'J_D_1KI': 0.00018458135476472868}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
index 4a28d6c..4ca7cf3 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 70787, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.243863582611084, "TIME_S_1KI": 0.15884079820604183, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.8814449405672, "W": 142.52, "J_1KI": 29.184475185282146, "W_1KI": 2.01336403576928, "W_D": 106.74100000000001, "J_D": 1547.2512722032072, "W_D_1KI": 1.5079181205588599, "J_D_1KI": 0.02130218995802704}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 67064, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.610118389129639, "TIME_S_1KI": 0.15820885108448107, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1755.3175721168518, "W": 134.25, "J_1KI": 26.173767924920252, "W_1KI": 2.0018191578193965, "W_D": 98.104, "J_D": 1282.7089392547607, "W_D_1KI": 1.4628414648693786, "J_D_1KI": 0.021812618765200086}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
index 320d8b0..6ec2345 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,34 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.22568225860595703}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05684256553649902}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      21,  ...,  999976,
-                             999987, 1000000]),
-       col_indices=tensor([66167, 77335, 80388,  ..., 91843, 96961, 99110]),
-       values=tensor([0.4269, 0.3181, 0.3880,  ..., 0.8858, 0.0510, 0.2541]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0143, 0.7097, 0.7299,  ..., 0.1191, 0.1743, 0.7741])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 0.22568225860595703 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46525', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.901124477386475}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      22,  ...,  999980,
+tensor(crow_indices=tensor([      0,      13,      23,  ...,  999979,
                              999991, 1000000]),
-       col_indices=tensor([ 6899, 15825, 20330,  ..., 53773, 69034, 81991]),
-       values=tensor([0.2590, 0.4256, 0.8626,  ..., 0.0809, 0.7182, 0.1540]),
+       col_indices=tensor([ 4525, 11074, 13753,  ..., 80507, 85385, 86427]),
+       values=tensor([0.4106, 0.9983, 0.2404,  ..., 0.2427, 0.2624, 0.7034]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5717, 0.8218, 0.0250,  ..., 0.8733, 0.0737, 0.0088])
+tensor([0.4963, 0.1898, 0.5953,  ..., 0.4144, 0.1558, 0.0288])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 6.901124477386475 seconds
+Time: 0.05684256553649902 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70787', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.243863582611084}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18472', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8920857906341553}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      22,  ...,  999983,
-                             999990, 1000000]),
-       col_indices=tensor([  361,  1115,  8788,  ..., 71181, 76543, 91304]),
-       values=tensor([0.4904, 0.2440, 0.4094,  ..., 0.6184, 0.1804, 0.3924]),
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999981,
+                             999989, 1000000]),
+       col_indices=tensor([ 8653, 22699, 39303,  ..., 86578, 89246, 90775]),
+       values=tensor([0.9948, 0.4799, 0.6025,  ..., 0.7759, 0.3812, 0.6990]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2770, 0.4028, 0.6616,  ..., 0.6682, 0.3245, 0.3679])
+tensor([0.4256, 0.6072, 0.2987,  ..., 0.6512, 0.1573, 0.7068])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 11.243863582611084 seconds
+Time: 2.8920857906341553 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67064', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.610118389129639}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      22,  ...,  999983,
-                             999990, 1000000]),
-       col_indices=tensor([  361,  1115,  8788,  ..., 71181, 76543, 91304]),
-       values=tensor([0.4904, 0.2440, 0.4094,  ..., 0.6184, 0.1804, 0.3924]),
+tensor(crow_indices=tensor([      0,       9,      22,  ...,  999983,
+                             999993, 1000000]),
+       col_indices=tensor([27755, 29395, 33124,  ..., 74386, 97777, 99456]),
+       values=tensor([0.7148, 0.9361, 0.5875,  ..., 0.1256, 0.4168, 0.2712]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2770, 0.4028, 0.6616,  ..., 0.6682, 0.3245, 0.3679])
+tensor([0.0632, 0.1399, 0.4829,  ..., 0.0512, 0.0510, 0.5050])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 11.243863582611084 seconds
+Time: 10.610118389129639 seconds
 
-[40.71, 39.67, 39.62, 40.0, 39.39, 40.7, 39.22, 40.0, 39.3, 39.88]
-[142.52]
-14.495379209518433
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70787, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.243863582611084, 'TIME_S_1KI': 0.15884079820604183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.8814449405672, 'W': 142.52}
-[40.71, 39.67, 39.62, 40.0, 39.39, 40.7, 39.22, 40.0, 39.3, 39.88, 39.86, 40.15, 39.35, 40.1, 39.35, 40.22, 39.27, 39.7, 39.16, 40.31]
-715.5799999999999
-35.778999999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70787, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.243863582611084, 'TIME_S_1KI': 0.15884079820604183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.8814449405672, 'W': 142.52, 'J_1KI': 29.184475185282146, 'W_1KI': 2.01336403576928, 'W_D': 106.74100000000001, 'J_D': 1547.2512722032072, 'W_D_1KI': 1.5079181205588599, 'J_D_1KI': 0.02130218995802704}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      22,  ...,  999983,
+                             999993, 1000000]),
+       col_indices=tensor([27755, 29395, 33124,  ..., 74386, 97777, 99456]),
+       values=tensor([0.7148, 0.9361, 0.5875,  ..., 0.1256, 0.4168, 0.2712]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0632, 0.1399, 0.4829,  ..., 0.0512, 0.0510, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.610118389129639 seconds
+
+[42.15, 40.1, 39.72, 39.74, 39.84, 40.12, 39.64, 39.58, 39.89, 39.55]
+[134.25]
+13.074991226196289
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 67064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.610118389129639, 'TIME_S_1KI': 0.15820885108448107, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1755.3175721168518, 'W': 134.25}
+[42.15, 40.1, 39.72, 39.74, 39.84, 40.12, 39.64, 39.58, 39.89, 39.55, 42.7, 40.22, 40.9, 40.07, 39.72, 39.89, 39.69, 39.67, 39.56, 44.74]
+722.9200000000001
+36.146
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 67064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.610118389129639, 'TIME_S_1KI': 0.15820885108448107, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1755.3175721168518, 'W': 134.25, 'J_1KI': 26.173767924920252, 'W_1KI': 2.0018191578193965, 'W_D': 98.104, 'J_D': 1282.7089392547607, 'W_D_1KI': 1.4628414648693786, 'J_D_1KI': 0.021812618765200086}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
index 0f55c41..97a1aaa 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4257, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.433454513549805, "TIME_S_1KI": 2.685800919321072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1901.6043726658822, "W": 126.17, "J_1KI": 446.7005808470477, "W_1KI": 29.638242894056848, "W_D": 90.424, "J_D": 1362.8491225643158, "W_D_1KI": 21.241249706365988, "J_D_1KI": 4.989722740513504}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3865, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6199471950531, "TIME_S_1KI": 2.7477224308028725, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1736.0991844940186, "W": 125.54, "J_1KI": 449.1847825340281, "W_1KI": 32.48124191461837, "W_D": 89.51725000000002, "J_D": 1237.938702589989, "W_D_1KI": 23.160996119016822, "J_D_1KI": 5.992495761711985}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
index dc2dadc..ab63507 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.4663901329040527}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.29607152938842773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      118,      225,  ...,  9999805,
-                             9999897, 10000000]),
-       col_indices=tensor([ 1682,  1744,  2076,  ..., 96929, 97254, 99780]),
-       values=tensor([0.4019, 0.5057, 0.8739,  ..., 0.0479, 0.2913, 0.6813]),
+tensor(crow_indices=tensor([       0,      108,      205,  ...,  9999797,
+                             9999886, 10000000]),
+       col_indices=tensor([ 1353,  2200,  3779,  ..., 96854, 97028, 97339]),
+       values=tensor([0.4346, 0.2367, 0.4770,  ..., 0.1479, 0.4649, 0.9103]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2475, 0.7795, 0.3565,  ..., 0.8481, 0.6371, 0.4321])
+tensor([0.4412, 0.7177, 0.2059,  ..., 0.3280, 0.0589, 0.7180])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 2.4663901329040527 seconds
+Time: 0.29607152938842773 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4257', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.433454513549805}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3546', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.63291883468628}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       96,      213,  ...,  9999811,
-                             9999904, 10000000]),
-       col_indices=tensor([  561,   663,  1931,  ..., 97741, 99513, 99851]),
-       values=tensor([0.7974, 0.7905, 0.8203,  ..., 0.5966, 0.6231, 0.2009]),
+tensor(crow_indices=tensor([       0,       92,      201,  ...,  9999814,
+                             9999913, 10000000]),
+       col_indices=tensor([ 1097,  1389,  2328,  ..., 96293, 96542, 99036]),
+       values=tensor([0.4476, 0.1977, 0.6820,  ..., 0.8020, 0.1490, 0.2819]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4967, 0.7208, 0.9275,  ..., 0.8267, 0.3582, 0.8531])
+tensor([0.6506, 0.9195, 0.4022,  ..., 0.6497, 0.8706, 0.8621])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 11.433454513549805 seconds
+Time: 9.63291883468628 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3865', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6199471950531}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       96,      213,  ...,  9999811,
-                             9999904, 10000000]),
-       col_indices=tensor([  561,   663,  1931,  ..., 97741, 99513, 99851]),
-       values=tensor([0.7974, 0.7905, 0.8203,  ..., 0.5966, 0.6231, 0.2009]),
+tensor(crow_indices=tensor([       0,       98,      197,  ...,  9999799,
+                             9999902, 10000000]),
+       col_indices=tensor([ 2919,  3313,  3728,  ..., 97238, 97697, 98577]),
+       values=tensor([0.4198, 0.7828, 0.7567,  ..., 0.6995, 0.0988, 0.1528]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4967, 0.7208, 0.9275,  ..., 0.8267, 0.3582, 0.8531])
+tensor([0.5695, 0.4144, 0.5638,  ..., 0.3431, 0.2067, 0.0841])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 11.433454513549805 seconds
+Time: 10.6199471950531 seconds
 
-[41.19, 39.18, 40.43, 39.7, 39.84, 39.19, 40.16, 39.17, 40.24, 39.08]
-[126.17]
-15.071763277053833
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.433454513549805, 'TIME_S_1KI': 2.685800919321072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.6043726658822, 'W': 126.17}
-[41.19, 39.18, 40.43, 39.7, 39.84, 39.19, 40.16, 39.17, 40.24, 39.08, 41.18, 39.54, 39.33, 39.89, 39.75, 39.57, 39.78, 39.56, 39.24, 39.25]
-714.9200000000001
-35.746
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.433454513549805, 'TIME_S_1KI': 2.685800919321072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.6043726658822, 'W': 126.17, 'J_1KI': 446.7005808470477, 'W_1KI': 29.638242894056848, 'W_D': 90.424, 'J_D': 1362.8491225643158, 'W_D_1KI': 21.241249706365988, 'J_D_1KI': 4.989722740513504}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       98,      197,  ...,  9999799,
+                             9999902, 10000000]),
+       col_indices=tensor([ 2919,  3313,  3728,  ..., 97238, 97697, 98577]),
+       values=tensor([0.4198, 0.7828, 0.7567,  ..., 0.6995, 0.0988, 0.1528]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5695, 0.4144, 0.5638,  ..., 0.3431, 0.2067, 0.0841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.6199471950531 seconds
+
+[41.63, 40.06, 40.05, 39.72, 39.88, 39.71, 40.32, 40.32, 40.26, 39.8]
+[125.54]
+13.829051971435547
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3865, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6199471950531, 'TIME_S_1KI': 2.7477224308028725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1736.0991844940186, 'W': 125.54}
+[41.63, 40.06, 40.05, 39.72, 39.88, 39.71, 40.32, 40.32, 40.26, 39.8, 40.39, 39.7, 39.76, 39.75, 39.78, 40.5, 39.95, 39.84, 40.11, 39.67]
+720.4549999999999
+36.022749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3865, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6199471950531, 'TIME_S_1KI': 2.7477224308028725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1736.0991844940186, 'W': 125.54, 'J_1KI': 449.1847825340281, 'W_1KI': 32.48124191461837, 'W_D': 89.51725000000002, 'J_D': 1237.938702589989, 'W_D_1KI': 23.160996119016822, 'J_D_1KI': 5.992495761711985}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
index 382c4a4..9be5a17 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 103292, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.412234544754028, "TIME_S_1KI": 0.10080388166318813, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1494.9555576324462, "W": 114.72, "J_1KI": 14.473101088491328, "W_1KI": 1.1106378035085003, "W_D": 77.68374999999999, "J_D": 1012.3235163897275, "W_D_1KI": 0.7520790574294233, "J_D_1KI": 0.0072810968654825475}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102064, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.524274349212646, "TIME_S_1KI": 0.10311446101674092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1601.1415306377412, "W": 113.64000000000001, "J_1KI": 15.687622772355983, "W_1KI": 1.1134190311961123, "W_D": 77.54950000000002, "J_D": 1092.6410166331532, "W_D_1KI": 0.7598124706066784, "J_D_1KI": 0.007444470828173288}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
index a8441fc..a8ac003 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.13699960708618164}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.051177263259887695}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      1,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([ 8916, 68486, 49297,  ..., 83214, 51117, 46502]),
-       values=tensor([0.0565, 0.4187, 0.1663,  ..., 0.8089, 0.3832, 0.9501]),
+       col_indices=tensor([ 5338, 33433, 17911,  ..., 60039, 74427, 45774]),
+       values=tensor([0.9933, 0.3915, 0.2951,  ..., 0.3503, 0.7922, 0.2614]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6605, 0.5566, 0.3055,  ..., 0.1791, 0.1309, 0.6380])
+tensor([0.6274, 0.9288, 0.1155,  ..., 0.7548, 0.5951, 0.2372])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.13699960708618164 seconds
+Time: 0.051177263259887695 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '76642', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.790924310684204}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20516', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.11061429977417}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99998,
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([17249, 94297, 21433,  ..., 88389, 79911, 81112]),
-       values=tensor([0.0934, 0.2541, 0.4263,  ..., 0.3405, 0.2702, 0.1947]),
+       col_indices=tensor([35938, 84023, 26382,  ..., 80961, 25218, 78065]),
+       values=tensor([0.1771, 0.7263, 0.1955,  ..., 0.1569, 0.5183, 0.0872]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1521, 0.7703, 0.8999,  ..., 0.0235, 0.4756, 0.0049])
+tensor([0.4261, 0.9316, 0.9486,  ..., 0.4583, 0.3074, 0.5243])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 7.790924310684204 seconds
+Time: 2.11061429977417 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '103292', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.412234544754028}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102064', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.524274349212646}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99999,
+tensor(crow_indices=tensor([     0,      2,      3,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([40816, 84426, 84611,  ..., 44515, 10095, 58427]),
-       values=tensor([0.3036, 0.7331, 0.5691,  ..., 0.0050, 0.0920, 0.5982]),
+       col_indices=tensor([ 7923, 87583, 82060,  ..., 33729, 87076,   106]),
+       values=tensor([0.1731, 0.5965, 0.6757,  ..., 0.8844, 0.0621, 0.1000]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8398, 0.7355, 0.2034,  ..., 0.0172, 0.0859, 0.7739])
+tensor([0.3641, 0.2409, 0.5686,  ..., 0.5557, 0.7015, 0.9398])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.412234544754028 seconds
+Time: 10.524274349212646 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99999,
+tensor(crow_indices=tensor([     0,      2,      3,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([40816, 84426, 84611,  ..., 44515, 10095, 58427]),
-       values=tensor([0.3036, 0.7331, 0.5691,  ..., 0.0050, 0.0920, 0.5982]),
+       col_indices=tensor([ 7923, 87583, 82060,  ..., 33729, 87076,   106]),
+       values=tensor([0.1731, 0.5965, 0.6757,  ..., 0.8844, 0.0621, 0.1000]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8398, 0.7355, 0.2034,  ..., 0.0172, 0.0859, 0.7739])
+tensor([0.3641, 0.2409, 0.5686,  ..., 0.5557, 0.7015, 0.9398])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.412234544754028 seconds
+Time: 10.524274349212646 seconds
 
-[39.92, 39.29, 39.24, 40.21, 39.35, 45.54, 40.38, 40.14, 39.43, 39.27]
-[114.72]
-13.031342029571533
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103292, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.412234544754028, 'TIME_S_1KI': 0.10080388166318813, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.9555576324462, 'W': 114.72}
-[39.92, 39.29, 39.24, 40.21, 39.35, 45.54, 40.38, 40.14, 39.43, 39.27, 39.96, 39.14, 45.44, 43.38, 51.52, 39.13, 40.45, 39.57, 39.41, 39.06]
-740.7250000000001
-37.03625000000001
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103292, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.412234544754028, 'TIME_S_1KI': 0.10080388166318813, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.9555576324462, 'W': 114.72, 'J_1KI': 14.473101088491328, 'W_1KI': 1.1106378035085003, 'W_D': 77.68374999999999, 'J_D': 1012.3235163897275, 'W_D_1KI': 0.7520790574294233, 'J_D_1KI': 0.0072810968654825475}
+[40.73, 39.59, 39.74, 39.73, 39.63, 39.83, 40.08, 39.98, 40.09, 39.48]
+[113.64]
+14.089594602584839
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.524274349212646, 'TIME_S_1KI': 0.10311446101674092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.1415306377412, 'W': 113.64000000000001}
+[40.73, 39.59, 39.74, 39.73, 39.63, 39.83, 40.08, 39.98, 40.09, 39.48, 40.32, 39.82, 39.63, 39.81, 39.64, 39.48, 39.85, 39.99, 44.93, 39.45]
+721.81
+36.0905
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.524274349212646, 'TIME_S_1KI': 0.10311446101674092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.1415306377412, 'W': 113.64000000000001, 'J_1KI': 15.687622772355983, 'W_1KI': 1.1134190311961123, 'W_D': 77.54950000000002, 'J_D': 1092.6410166331532, 'W_D_1KI': 0.7598124706066784, 'J_D_1KI': 0.007444470828173288}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..24bf6da
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 83443, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331598043441772, "TIME_S_1KI": 0.12381623435688761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1753.1380189323427, "W": 133.96, "J_1KI": 21.010007057899916, "W_1KI": 1.6054072840142373, "W_D": 97.99700000000001, "J_D": 1282.489298606396, "W_D_1KI": 1.1744184652996656, "J_D_1KI": 0.014074499542198454}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..566b51a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04202604293823242}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,     10,  ..., 499989, 499993,
+                            500000]),
+       col_indices=tensor([44479,  4048, 15938,  ..., 81904, 89204, 96058]),
+       values=tensor([0.8024, 0.2371, 0.1804,  ..., 0.7304, 0.5867, 0.0881]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7637, 0.3596, 0.9771,  ..., 0.6123, 0.8042, 0.6339])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.04202604293823242 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24984', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.1438426971435547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 499992, 499996,
+                            500000]),
+       col_indices=tensor([25849, 26475, 42516,  ..., 54532, 74351, 87242]),
+       values=tensor([0.9779, 0.7287, 0.9943,  ..., 0.8976, 0.9175, 0.6342]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.3426, 0.5652, 0.2473,  ..., 0.4439, 0.3784, 0.0403])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 3.1438426971435547 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '83443', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331598043441772}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 499992, 499996,
+                            500000]),
+       col_indices=tensor([44464, 48782, 50602,  ..., 44812, 48851, 96308]),
+       values=tensor([0.0768, 0.4231, 0.3229,  ..., 0.7263, 0.8571, 0.9151]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5899, 0.0490, 0.9717,  ..., 0.2037, 0.9811, 0.9760])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.331598043441772 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 499992, 499996,
+                            500000]),
+       col_indices=tensor([44464, 48782, 50602,  ..., 44812, 48851, 96308]),
+       values=tensor([0.0768, 0.4231, 0.3229,  ..., 0.7263, 0.8571, 0.9151]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5899, 0.0490, 0.9717,  ..., 0.2037, 0.9811, 0.9760])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.331598043441772 seconds
+
+[41.17, 39.77, 40.36, 40.06, 41.66, 39.61, 39.74, 39.58, 39.55, 39.78]
+[133.96]
+13.087026119232178
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331598043441772, 'TIME_S_1KI': 0.12381623435688761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1753.1380189323427, 'W': 133.96}
+[41.17, 39.77, 40.36, 40.06, 41.66, 39.61, 39.74, 39.58, 39.55, 39.78, 41.11, 39.76, 39.73, 39.7, 39.67, 40.13, 39.73, 39.66, 39.62, 39.8]
+719.26
+35.963
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331598043441772, 'TIME_S_1KI': 0.12381623435688761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1753.1380189323427, 'W': 133.96, 'J_1KI': 21.010007057899916, 'W_1KI': 1.6054072840142373, 'W_D': 97.99700000000001, 'J_D': 1282.489298606396, 'W_D_1KI': 1.1744184652996656, 'J_D_1KI': 0.014074499542198454}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
index 2ba417a..846867c 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 289765, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.56649661064148, "TIME_S_1KI": 0.03646574503698334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1246.5325114130974, "W": 97.77, "J_1KI": 4.3018739717118955, "W_1KI": 0.33741135057719185, "W_D": 62.23799999999999, "J_D": 793.5122271180152, "W_D_1KI": 0.21478784532293407, "J_D_1KI": 0.0007412484093073148}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 288187, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643323421478271, "TIME_S_1KI": 0.03693200394701451, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.6877346038818, "W": 98.76, "J_1KI": 4.315558073764194, "W_1KI": 0.34269415344897586, "W_D": 63.04600000000001, "J_D": 793.9402279853822, "W_D_1KI": 0.21876767515536788, "J_D_1KI": 0.0007591170842382477}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
index 4b8f527..c5c4dec 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.053604841232299805}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02071547508239746}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([4181, 1858, 2276,  ..., 2485, 7240, 8510]),
-       values=tensor([0.9106, 0.2407, 0.2677,  ..., 0.1883, 0.5204, 0.9919]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([ 662, 2214, 9373,  ..., 9890, 1994, 4209]),
+       values=tensor([0.5116, 0.1051, 0.5373,  ..., 0.4151, 0.7725, 0.9175]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4673, 0.8867, 0.2183,  ..., 0.9392, 0.5032, 0.8250])
+tensor([0.1401, 0.0502, 0.3458,  ..., 0.2506, 0.9913, 0.9973])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.053604841232299805 seconds
+Time: 0.02071547508239746 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '195877', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.097846031188965}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50686', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8467259407043457}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9998, 10000]),
-       col_indices=tensor([6113, 1564,  232,  ..., 3255, 2043, 9640]),
-       values=tensor([0.7859, 0.4083, 0.3727,  ..., 0.9664, 0.2618, 0.1646]),
+tensor(crow_indices=tensor([    0,     3,     5,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([2329, 7525, 8810,  ..., 9177, 1519, 2359]),
+       values=tensor([0.1835, 0.9536, 0.7906,  ..., 0.4035, 0.0564, 0.3832]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1848, 0.2081, 0.2382,  ..., 0.7788, 0.6054, 0.6678])
+tensor([0.5918, 0.9817, 0.1058,  ..., 0.3816, 0.0120, 0.7112])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 7.097846031188965 seconds
+Time: 1.8467259407043457 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289765', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.56649661064148}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288187', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643323421478271}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([4848, 1770,   22,  ..., 2903,  374, 1123]),
-       values=tensor([0.4832, 0.4922, 0.1673,  ..., 0.2881, 0.3225, 0.2417]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([5118, 9103, 6912,  ..., 6081, 2494, 7728]),
+       values=tensor([0.1845, 0.4117, 0.0579,  ..., 0.8363, 0.9429, 0.5429]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.7933, 0.2380, 0.0639,  ..., 0.5554, 0.1913, 0.9685])
+tensor([0.5505, 0.3516, 0.4265,  ..., 0.6375, 0.7561, 0.2541])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.56649661064148 seconds
+Time: 10.643323421478271 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([4848, 1770,   22,  ..., 2903,  374, 1123]),
-       values=tensor([0.4832, 0.4922, 0.1673,  ..., 0.2881, 0.3225, 0.2417]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([5118, 9103, 6912,  ..., 6081, 2494, 7728]),
+       values=tensor([0.1845, 0.4117, 0.0579,  ..., 0.8363, 0.9429, 0.5429]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.7933, 0.2380, 0.0639,  ..., 0.5554, 0.1913, 0.9685])
+tensor([0.5505, 0.3516, 0.4265,  ..., 0.6375, 0.7561, 0.2541])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.56649661064148 seconds
+Time: 10.643323421478271 seconds
 
-[39.58, 39.81, 39.03, 39.86, 38.88, 39.9, 39.07, 38.97, 41.59, 39.84]
-[97.77]
-12.749642133712769
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289765, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.56649661064148, 'TIME_S_1KI': 0.03646574503698334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5325114130974, 'W': 97.77}
-[39.58, 39.81, 39.03, 39.86, 38.88, 39.9, 39.07, 38.97, 41.59, 39.84, 40.25, 39.66, 38.91, 39.84, 38.8, 39.81, 38.94, 39.0, 38.89, 39.69]
-710.6400000000001
-35.532000000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289765, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.56649661064148, 'TIME_S_1KI': 0.03646574503698334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5325114130974, 'W': 97.77, 'J_1KI': 4.3018739717118955, 'W_1KI': 0.33741135057719185, 'W_D': 62.23799999999999, 'J_D': 793.5122271180152, 'W_D_1KI': 0.21478784532293407, 'J_D_1KI': 0.0007412484093073148}
+[40.51, 40.09, 39.52, 39.33, 39.47, 39.48, 39.43, 39.77, 39.53, 39.82]
+[98.76]
+12.59303092956543
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 288187, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643323421478271, 'TIME_S_1KI': 0.03693200394701451, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.6877346038818, 'W': 98.76}
+[40.51, 40.09, 39.52, 39.33, 39.47, 39.48, 39.43, 39.77, 39.53, 39.82, 41.15, 40.14, 40.36, 39.64, 39.34, 39.28, 39.84, 39.18, 39.56, 39.16]
+714.28
+35.714
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 288187, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643323421478271, 'TIME_S_1KI': 0.03693200394701451, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.6877346038818, 'W': 98.76, 'J_1KI': 4.315558073764194, 'W_1KI': 0.34269415344897586, 'W_D': 63.04600000000001, 'J_D': 793.9402279853822, 'W_D_1KI': 0.21876767515536788, 'J_D_1KI': 0.0007591170842382477}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
index 463b3c7..d5b0fd0 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 132694, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.224570512771606, "TIME_S_1KI": 0.07705375158463537, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.35663690567, "W": 103.59999999999998, "J_1KI": 8.043744531822615, "W_1KI": 0.7807436658778842, "W_D": 68.22474999999997, "J_D": 702.8971014838812, "W_D_1KI": 0.5141509789440364, "J_D_1KI": 0.003874711584126158}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 192082, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.38179612159729, "TIME_S_1KI": 0.054048771470503694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.0875409460068, "W": 107.47, "J_1KI": 7.997040539696623, "W_1KI": 0.5595006299392968, "W_D": 71.52775, "J_D": 1022.3586638773679, "W_D_1KI": 0.3723813267250445, "J_D_1KI": 0.0019386581081259277}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
index f18d35d..41b527b 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07912898063659668}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02809286117553711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     15,  ...,  99979,  99988,
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99983,  99993,
                             100000]),
-       col_indices=tensor([ 430,  646,  878,  ..., 7983, 8028, 8773]),
-       values=tensor([0.1249, 0.1009, 0.6404,  ..., 0.8347, 0.6604, 0.7086]),
+       col_indices=tensor([2653, 3722, 5304,  ..., 7707, 8674, 8869]),
+       values=tensor([0.5856, 0.9425, 0.9349,  ..., 0.4089, 0.4268, 0.7151]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6668, 0.6238, 0.5068,  ..., 0.0173, 0.0134, 0.2844])
+tensor([0.5412, 0.5320, 0.4895,  ..., 0.9332, 0.4774, 0.7844])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.07912898063659668 seconds
+Time: 0.02809286117553711 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '132694', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.224570512771606}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37376', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.043123722076416}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     19,  ...,  99972,  99988,
+tensor(crow_indices=tensor([     0,     18,     27,  ...,  99987,  99996,
                             100000]),
-       col_indices=tensor([ 681, 2736, 3433,  ..., 9108, 9366, 9692]),
-       values=tensor([0.5511, 0.6516, 0.1231,  ..., 0.0939, 0.8699, 0.6381]),
+       col_indices=tensor([ 496, 1705, 2513,  ..., 6230, 8377, 9882]),
+       values=tensor([0.7106, 0.5928, 0.5041,  ..., 0.9691, 0.8218, 0.7424]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2594, 0.0089, 0.5427,  ..., 0.9106, 0.5838, 0.6290])
+tensor([0.6869, 0.6587, 0.3624,  ..., 0.7168, 0.6886, 0.1198])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.224570512771606 seconds
+Time: 2.043123722076416 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '192082', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.38179612159729}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     19,  ...,  99972,  99988,
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99972,  99987,
                             100000]),
-       col_indices=tensor([ 681, 2736, 3433,  ..., 9108, 9366, 9692]),
-       values=tensor([0.5511, 0.6516, 0.1231,  ..., 0.0939, 0.8699, 0.6381]),
+       col_indices=tensor([  27, 2567, 2642,  ..., 7209, 7267, 7735]),
+       values=tensor([0.8851, 0.6027, 0.9664,  ..., 0.7310, 0.7426, 0.3698]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2594, 0.0089, 0.5427,  ..., 0.9106, 0.5838, 0.6290])
+tensor([0.8025, 0.3022, 0.3457,  ..., 0.3811, 0.1140, 0.9144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.224570512771606 seconds
+Time: 10.38179612159729 seconds
 
-[39.52, 39.72, 39.2, 38.94, 38.87, 40.37, 39.0, 39.66, 38.95, 39.86]
-[103.6]
-10.302670240402222
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132694, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.224570512771606, 'TIME_S_1KI': 0.07705375158463537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.35663690567, 'W': 103.59999999999998}
-[39.52, 39.72, 39.2, 38.94, 38.87, 40.37, 39.0, 39.66, 38.95, 39.86, 39.41, 39.62, 39.01, 39.67, 38.86, 38.84, 38.84, 39.8, 38.92, 39.68]
-707.505
-35.37525
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132694, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.224570512771606, 'TIME_S_1KI': 0.07705375158463537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.35663690567, 'W': 103.59999999999998, 'J_1KI': 8.043744531822615, 'W_1KI': 0.7807436658778842, 'W_D': 68.22474999999997, 'J_D': 702.8971014838812, 'W_D_1KI': 0.5141509789440364, 'J_D_1KI': 0.003874711584126158}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99972,  99987,
+                            100000]),
+       col_indices=tensor([  27, 2567, 2642,  ..., 7209, 7267, 7735]),
+       values=tensor([0.8851, 0.6027, 0.9664,  ..., 0.7310, 0.7426, 0.3698]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8025, 0.3022, 0.3457,  ..., 0.3811, 0.1140, 0.9144])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.38179612159729 seconds
+
+[40.11, 39.55, 39.33, 44.82, 39.9, 39.64, 39.98, 39.29, 39.99, 39.21]
+[107.47]
+14.293175220489502
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 192082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.38179612159729, 'TIME_S_1KI': 0.054048771470503694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.0875409460068, 'W': 107.47}
+[40.11, 39.55, 39.33, 44.82, 39.9, 39.64, 39.98, 39.29, 39.99, 39.21, 40.63, 39.47, 39.85, 39.28, 39.66, 39.35, 39.46, 39.87, 39.78, 39.3]
+718.845
+35.94225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 192082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.38179612159729, 'TIME_S_1KI': 0.054048771470503694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.0875409460068, 'W': 107.47, 'J_1KI': 7.997040539696623, 'W_1KI': 0.5595006299392968, 'W_D': 71.52775, 'J_D': 1022.3586638773679, 'W_D_1KI': 0.3723813267250445, 'J_D_1KI': 0.0019386581081259277}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
index 995b9fb..c0a8393 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 107069, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.18355131149292, "TIME_S_1KI": 0.10445181435796468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1763.49504935503, "W": 132.69, "J_1KI": 16.470640889099833, "W_1KI": 1.2392942868617434, "W_D": 96.9815, "J_D": 1288.9169879344702, "W_D_1KI": 0.905785054497567, "J_D_1KI": 0.008459825481675993}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102052, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.32213807106018, "TIME_S_1KI": 0.10114586750931075, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1720.3125169992445, "W": 134.17, "J_1KI": 16.857215115815904, "W_1KI": 1.3147219064790499, "W_D": 98.29849999999999, "J_D": 1260.3722139990327, "W_D_1KI": 0.9632197311174694, "J_D_1KI": 0.0094385189032794}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
index afaa491..3b630d1 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,74 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.1338520050048828}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.04350447654724121}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      91,     190,  ...,  999794,
-                             999887, 1000000]),
-       col_indices=tensor([  40,  344,  548,  ..., 9830, 9841, 9960]),
-       values=tensor([0.4008, 0.1162, 0.8586,  ..., 0.0804, 0.9517, 0.8982]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6204, 0.8036, 0.5749,  ..., 0.0150, 0.4782, 0.5342])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 0.1338520050048828 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78444', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.085982084274292}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      93,     187,  ...,  999812,
-                             999901, 1000000]),
-       col_indices=tensor([ 276,  302,  470,  ..., 9539, 9540, 9930]),
-       values=tensor([0.4664, 0.1616, 0.7456,  ..., 0.5929, 0.0487, 0.3579]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7338, 0.4039, 0.6812,  ..., 0.4093, 0.7174, 0.1386])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 8.085982084274292 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '101862', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.98931097984314}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      97,     208,  ...,  999782,
-                             999887, 1000000]),
-       col_indices=tensor([ 113,  292,  413,  ..., 9756, 9814, 9863]),
-       values=tensor([0.7037, 0.4902, 0.2249,  ..., 0.1343, 0.1681, 0.3653]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0898, 0.3365, 0.9954,  ..., 0.9623, 0.9055, 0.9870])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 9.98931097984314 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '107069', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.18355131149292}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     101,     199,  ...,  999771,
+tensor(crow_indices=tensor([      0,      98,     200,  ...,  999802,
                              999895, 1000000]),
-       col_indices=tensor([  30,   45,   94,  ..., 9508, 9668, 9839]),
-       values=tensor([0.9351, 0.0667, 0.7279,  ..., 0.8651, 0.3266, 0.8240]),
+       col_indices=tensor([  47,   93,  107,  ..., 9931, 9947, 9964]),
+       values=tensor([0.2387, 0.2735, 0.7135,  ..., 0.1692, 0.6802, 0.4186]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5009, 0.1141, 0.1222,  ..., 0.6365, 0.9492, 0.1421])
+tensor([0.2660, 0.3479, 0.7430,  ..., 0.3350, 0.7379, 0.6869])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -76,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 11.18355131149292 seconds
+Time: 0.04350447654724121 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24135', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.483203411102295}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     101,     199,  ...,  999771,
-                             999895, 1000000]),
-       col_indices=tensor([  30,   45,   94,  ..., 9508, 9668, 9839]),
-       values=tensor([0.9351, 0.0667, 0.7279,  ..., 0.8651, 0.3266, 0.8240]),
+tensor(crow_indices=tensor([      0,     121,     211,  ...,  999813,
+                             999902, 1000000]),
+       col_indices=tensor([ 152,  193,  233,  ..., 9824, 9889, 9990]),
+       values=tensor([0.9787, 0.2142, 0.0572,  ..., 0.5889, 0.8836, 0.8390]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5009, 0.1141, 0.1222,  ..., 0.6365, 0.9492, 0.1421])
+tensor([0.2484, 0.0072, 0.5266,  ..., 0.8378, 0.3257, 0.7895])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -93,13 +36,50 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 11.18355131149292 seconds
+Time: 2.483203411102295 seconds
 
-[39.9, 40.21, 39.53, 40.02, 39.22, 40.03, 39.19, 40.06, 39.89, 39.6]
-[132.69]
-13.29033875465393
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107069, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.18355131149292, 'TIME_S_1KI': 0.10445181435796468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1763.49504935503, 'W': 132.69}
-[39.9, 40.21, 39.53, 40.02, 39.22, 40.03, 39.19, 40.06, 39.89, 39.6, 40.49, 39.14, 39.91, 39.61, 39.91, 39.69, 39.58, 39.1, 39.07, 40.03]
-714.1700000000001
-35.7085
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107069, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.18355131149292, 'TIME_S_1KI': 0.10445181435796468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1763.49504935503, 'W': 132.69, 'J_1KI': 16.470640889099833, 'W_1KI': 1.2392942868617434, 'W_D': 96.9815, 'J_D': 1288.9169879344702, 'W_D_1KI': 0.905785054497567, 'J_D_1KI': 0.008459825481675993}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102052', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.32213807106018}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     197,  ...,  999808,
+                             999909, 1000000]),
+       col_indices=tensor([  12,  158,  312,  ..., 9915, 9965, 9970]),
+       values=tensor([0.1097, 0.7996, 0.8802,  ..., 0.2965, 0.2793, 0.1775]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4138, 0.6115, 0.5428,  ..., 0.5829, 0.0748, 0.9104])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.32213807106018 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     197,  ...,  999808,
+                             999909, 1000000]),
+       col_indices=tensor([  12,  158,  312,  ..., 9915, 9965, 9970]),
+       values=tensor([0.1097, 0.7996, 0.8802,  ..., 0.2965, 0.2793, 0.1775]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4138, 0.6115, 0.5428,  ..., 0.5829, 0.0748, 0.9104])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.32213807106018 seconds
+
+[40.44, 39.51, 39.85, 40.81, 39.86, 41.16, 39.43, 39.73, 39.64, 39.53]
+[134.17]
+12.821886539459229
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102052, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.32213807106018, 'TIME_S_1KI': 0.10114586750931075, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.3125169992445, 'W': 134.17}
+[40.44, 39.51, 39.85, 40.81, 39.86, 41.16, 39.43, 39.73, 39.64, 39.53, 40.32, 40.34, 39.64, 39.51, 39.49, 39.33, 39.71, 39.95, 39.67, 39.31]
+717.43
+35.8715
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102052, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.32213807106018, 'TIME_S_1KI': 0.10114586750931075, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.3125169992445, 'W': 134.17, 'J_1KI': 16.857215115815904, 'W_1KI': 1.3147219064790499, 'W_D': 98.29849999999999, 'J_D': 1260.3722139990327, 'W_D_1KI': 0.9632197311174694, 'J_D_1KI': 0.0094385189032794}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
index 05bc7cf..0c8793e 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28163, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459697484970093, "TIME_S_1KI": 0.371398554307783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2122.055966448784, "W": 150.9, "J_1KI": 75.34907383619587, "W_1KI": 5.358093953058979, "W_D": 115.1085, "J_D": 1618.7321352814438, "W_D_1KI": 4.087224372403509, "J_D_1KI": 0.1451274499308848}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27901, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459260940551758, "TIME_S_1KI": 0.3748704684617669, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2101.511001520157, "W": 151.74, "J_1KI": 75.32027531343526, "W_1KI": 5.43851474857532, "W_D": 115.6735, "J_D": 1602.0108925421239, "W_D_1KI": 4.145854987276442, "J_D_1KI": 0.14859162708420637}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
index e823d57..d46dec8 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.4528634548187256}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07326960563659668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     492,     984,  ..., 4999007,
-                            4999498, 5000000]),
-       col_indices=tensor([  17,   26,   49,  ..., 9943, 9965, 9968]),
-       values=tensor([0.3785, 0.7951, 0.2972,  ..., 0.3720, 0.7853, 0.1204]),
+tensor(crow_indices=tensor([      0,     503,     989,  ..., 4998955,
+                            4999450, 5000000]),
+       col_indices=tensor([  38,   72,   81,  ..., 9956, 9978, 9983]),
+       values=tensor([0.2927, 0.3163, 0.4567,  ..., 0.1935, 0.9639, 0.3715]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5665, 0.1637, 0.5801,  ..., 0.5211, 0.8646, 0.6970])
+tensor([0.4283, 0.0472, 0.5653,  ..., 0.2916, 0.5894, 0.9993])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 0.4528634548187256 seconds
+Time: 0.07326960563659668 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23185', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.643981695175171}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14330', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.392660140991211}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     462,     943,  ..., 4999021,
-                            4999500, 5000000]),
-       col_indices=tensor([   4,   33,   72,  ..., 9956, 9968, 9998]),
-       values=tensor([0.9717, 0.2077, 0.4481,  ..., 0.1268, 0.5535, 0.1753]),
+tensor(crow_indices=tensor([      0,     524,    1025,  ..., 4998972,
+                            4999489, 5000000]),
+       col_indices=tensor([   6,   15,   16,  ..., 9973, 9985, 9996]),
+       values=tensor([0.1466, 0.4320, 0.8734,  ..., 0.2839, 0.7163, 0.2149]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9761, 0.2557, 0.3900,  ..., 0.3250, 0.2223, 0.7021])
+tensor([0.6711, 0.1737, 0.7087,  ..., 0.1819, 0.7746, 0.6924])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 8.643981695175171 seconds
+Time: 5.392660140991211 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28163', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459697484970093}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27901', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459260940551758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     510,    1022,  ..., 4999000,
-                            4999485, 5000000]),
-       col_indices=tensor([  31,   34,   40,  ..., 9926, 9941, 9984]),
-       values=tensor([0.9067, 0.8635, 0.5661,  ..., 0.0254, 0.7052, 0.7869]),
+tensor(crow_indices=tensor([      0,     478,     963,  ..., 4998976,
+                            4999469, 5000000]),
+       col_indices=tensor([   4,    6,    8,  ..., 9977, 9981, 9998]),
+       values=tensor([0.4938, 0.7817, 0.2868,  ..., 0.2355, 0.4075, 0.9137]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2490, 0.1590, 0.1294,  ..., 0.2235, 0.7822, 0.7952])
+tensor([0.7828, 0.6669, 0.8649,  ..., 0.0217, 0.0077, 0.7398])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.459697484970093 seconds
+Time: 10.459260940551758 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     510,    1022,  ..., 4999000,
-                            4999485, 5000000]),
-       col_indices=tensor([  31,   34,   40,  ..., 9926, 9941, 9984]),
-       values=tensor([0.9067, 0.8635, 0.5661,  ..., 0.0254, 0.7052, 0.7869]),
+tensor(crow_indices=tensor([      0,     478,     963,  ..., 4998976,
+                            4999469, 5000000]),
+       col_indices=tensor([   4,    6,    8,  ..., 9977, 9981, 9998]),
+       values=tensor([0.4938, 0.7817, 0.2868,  ..., 0.2355, 0.4075, 0.9137]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2490, 0.1590, 0.1294,  ..., 0.2235, 0.7822, 0.7952])
+tensor([0.7828, 0.6669, 0.8649,  ..., 0.0217, 0.0077, 0.7398])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.459697484970093 seconds
+Time: 10.459260940551758 seconds
 
-[40.87, 39.6, 40.38, 39.19, 40.47, 39.26, 39.48, 39.2, 40.2, 39.44]
-[150.9]
-14.062663793563843
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28163, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459697484970093, 'TIME_S_1KI': 0.371398554307783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2122.055966448784, 'W': 150.9}
-[40.87, 39.6, 40.38, 39.19, 40.47, 39.26, 39.48, 39.2, 40.2, 39.44, 41.45, 39.66, 39.99, 39.31, 39.56, 39.24, 40.15, 39.52, 39.84, 39.8]
-715.8299999999999
-35.7915
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28163, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459697484970093, 'TIME_S_1KI': 0.371398554307783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2122.055966448784, 'W': 150.9, 'J_1KI': 75.34907383619587, 'W_1KI': 5.358093953058979, 'W_D': 115.1085, 'J_D': 1618.7321352814438, 'W_D_1KI': 4.087224372403509, 'J_D_1KI': 0.1451274499308848}
+[40.28, 39.74, 40.09, 39.52, 39.81, 39.49, 40.09, 39.86, 39.96, 39.9]
+[151.74]
+13.849420070648193
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459260940551758, 'TIME_S_1KI': 0.3748704684617669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.511001520157, 'W': 151.74}
+[40.28, 39.74, 40.09, 39.52, 39.81, 39.49, 40.09, 39.86, 39.96, 39.9, 41.6, 42.09, 41.06, 39.67, 39.75, 39.6, 39.57, 40.11, 40.26, 39.54]
+721.33
+36.066500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459260940551758, 'TIME_S_1KI': 0.3748704684617669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.511001520157, 'W': 151.74, 'J_1KI': 75.32027531343526, 'W_1KI': 5.43851474857532, 'W_D': 115.6735, 'J_D': 1602.0108925421239, 'W_D_1KI': 4.145854987276442, 'J_D_1KI': 0.14859162708420637}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
index 8638631..2bdc411 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 5238, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.192334175109863, "TIME_S_1KI": 2.1367571926517495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2038.619791316986, "W": 124.02, "J_1KI": 389.1981273991955, "W_1KI": 23.676975945017183, "W_D": 88.21424999999999, "J_D": 1450.0509266746044, "W_D_1KI": 16.841208476517753, "J_D_1KI": 3.2151982582126295}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.465762615203857, "TIME_S_1KI": 2.3423416987137604, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1961.564714949131, "W": 125.13, "J_1KI": 400.72823594466416, "W_1KI": 25.562819203268642, "W_D": 88.61599999999999, "J_D": 1389.1634202823636, "W_D_1KI": 18.10337078651685, "J_D_1KI": 3.698339282230205}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
index 6292d32..c73c74f 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.209188461303711}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.26529860496520996}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      961,     2007,  ...,  9997952,
-                             9998968, 10000000]),
-       col_indices=tensor([  14,   18,   26,  ..., 9968, 9972, 9997]),
-       values=tensor([0.9669, 0.3653, 0.3089,  ..., 0.5289, 0.5202, 0.9028]),
+tensor(crow_indices=tensor([       0,      948,     1895,  ...,  9998021,
+                             9999002, 10000000]),
+       col_indices=tensor([   3,    4,   24,  ..., 9958, 9984, 9986]),
+       values=tensor([0.2249, 0.5337, 0.8362,  ..., 0.6636, 0.7975, 0.6242]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8016, 0.0222, 0.4456,  ..., 0.4115, 0.6943, 0.5313])
+tensor([0.1264, 0.7394, 0.5519,  ..., 0.0745, 0.0081, 0.2644])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 2.209188461303711 seconds
+Time: 0.26529860496520996 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4752', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.52530813217163}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3957', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.487387418746948}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      954,     1940,  ...,  9998038,
-                             9998994, 10000000]),
-       col_indices=tensor([   0,    3,    4,  ..., 9964, 9979, 9998]),
-       values=tensor([0.5875, 0.0019, 0.5119,  ..., 0.4152, 0.5002, 0.2921]),
+tensor(crow_indices=tensor([       0,      978,     1933,  ...,  9998004,
+                             9999014, 10000000]),
+       col_indices=tensor([   3,    5,    6,  ..., 9972, 9982, 9998]),
+       values=tensor([0.7080, 0.9187, 0.9413,  ..., 0.1315, 0.2244, 0.9797]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8144, 0.0248, 0.0526,  ..., 0.0067, 0.4287, 0.2758])
+tensor([0.5306, 0.8726, 0.4027,  ..., 0.7037, 0.0033, 0.8016])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 9.52530813217163 seconds
+Time: 8.487387418746948 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5238', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.192334175109863}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4895', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.465762615203857}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1001,     2002,  ...,  9997918,
-                             9998966, 10000000]),
-       col_indices=tensor([   9,   21,   97,  ..., 9973, 9981, 9990]),
-       values=tensor([0.6111, 0.6801, 0.6895,  ..., 0.1092, 0.3002, 0.2815]),
+tensor(crow_indices=tensor([       0,      986,     1965,  ...,  9997996,
+                             9999022, 10000000]),
+       col_indices=tensor([   5,   25,   37,  ..., 9984, 9993, 9998]),
+       values=tensor([0.8800, 0.4752, 0.0446,  ..., 0.6391, 0.5084, 0.8692]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0277, 0.0823, 0.3111,  ..., 0.6513, 0.2238, 0.0558])
+tensor([0.6122, 0.5951, 0.3953,  ..., 0.4999, 0.2315, 0.6538])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 11.192334175109863 seconds
+Time: 11.465762615203857 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1001,     2002,  ...,  9997918,
-                             9998966, 10000000]),
-       col_indices=tensor([   9,   21,   97,  ..., 9973, 9981, 9990]),
-       values=tensor([0.6111, 0.6801, 0.6895,  ..., 0.1092, 0.3002, 0.2815]),
+tensor(crow_indices=tensor([       0,      986,     1965,  ...,  9997996,
+                             9999022, 10000000]),
+       col_indices=tensor([   5,   25,   37,  ..., 9984, 9993, 9998]),
+       values=tensor([0.8800, 0.4752, 0.0446,  ..., 0.6391, 0.5084, 0.8692]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0277, 0.0823, 0.3111,  ..., 0.6513, 0.2238, 0.0558])
+tensor([0.6122, 0.5951, 0.3953,  ..., 0.4999, 0.2315, 0.6538])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 11.192334175109863 seconds
+Time: 11.465762615203857 seconds
 
-[41.1, 40.26, 39.38, 39.47, 39.45, 40.18, 39.58, 40.31, 39.46, 40.14]
-[124.02]
-16.437830924987793
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.192334175109863, 'TIME_S_1KI': 2.1367571926517495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2038.619791316986, 'W': 124.02}
-[41.1, 40.26, 39.38, 39.47, 39.45, 40.18, 39.58, 40.31, 39.46, 40.14, 40.09, 40.24, 39.54, 40.08, 39.32, 40.12, 39.35, 39.35, 39.19, 40.34]
-716.115
-35.80575
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.192334175109863, 'TIME_S_1KI': 2.1367571926517495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2038.619791316986, 'W': 124.02, 'J_1KI': 389.1981273991955, 'W_1KI': 23.676975945017183, 'W_D': 88.21424999999999, 'J_D': 1450.0509266746044, 'W_D_1KI': 16.841208476517753, 'J_D_1KI': 3.2151982582126295}
+[40.6, 45.25, 40.43, 39.72, 40.94, 40.35, 40.49, 39.74, 42.24, 39.59]
+[125.13]
+15.676214456558228
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.465762615203857, 'TIME_S_1KI': 2.3423416987137604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.564714949131, 'W': 125.13}
+[40.6, 45.25, 40.43, 39.72, 40.94, 40.35, 40.49, 39.74, 42.24, 39.59, 41.13, 40.62, 40.17, 40.53, 40.27, 40.01, 39.62, 39.57, 39.92, 39.5]
+730.2800000000001
+36.514
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.465762615203857, 'TIME_S_1KI': 2.3423416987137604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.564714949131, 'W': 125.13, 'J_1KI': 400.72823594466416, 'W_1KI': 25.562819203268642, 'W_D': 88.61599999999999, 'J_D': 1389.1634202823636, 'W_D_1KI': 18.10337078651685, 'J_D_1KI': 3.698339282230205}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..0c05e29
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2082, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.557694435119629, "TIME_S_1KI": 5.070938729644394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1859.5419220256806, "W": 120.04, "J_1KI": 893.1517396857256, "W_1KI": 57.656099903938525, "W_D": 83.7395, "J_D": 1297.2101864334345, "W_D_1KI": 40.22070124879924, "J_D_1KI": 19.31830031162307}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..68bbca5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5041141510009766}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2037,     4062,  ..., 19995956,
+                            19997954, 20000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 9996, 9997, 9998]),
+       values=tensor([0.3088, 0.0777, 0.1762,  ..., 0.6057, 0.6562, 0.8467]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.5661, 0.3739, 0.3594,  ..., 0.8068, 0.7143, 0.9609])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 0.5041141510009766 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2082', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.557694435119629}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1900,     3860,  ..., 19996021,
+                            19998015, 20000000]),
+       col_indices=tensor([   0,    3,   11,  ..., 9989, 9992, 9996]),
+       values=tensor([0.1071, 0.4523, 0.1080,  ..., 0.2881, 0.4034, 0.8495]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3748, 0.6654, 0.9133,  ..., 0.7126, 0.6760, 0.9288])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.557694435119629 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1900,     3860,  ..., 19996021,
+                            19998015, 20000000]),
+       col_indices=tensor([   0,    3,   11,  ..., 9989, 9992, 9996]),
+       values=tensor([0.1071, 0.4523, 0.1080,  ..., 0.2881, 0.4034, 0.8495]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3748, 0.6654, 0.9133,  ..., 0.7126, 0.6760, 0.9288])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.557694435119629 seconds
+
+[40.83, 40.15, 39.72, 39.76, 39.84, 39.84, 40.49, 39.96, 40.13, 39.73]
+[120.04]
+15.491019010543823
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.557694435119629, 'TIME_S_1KI': 5.070938729644394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.5419220256806, 'W': 120.04}
+[40.83, 40.15, 39.72, 39.76, 39.84, 39.84, 40.49, 39.96, 40.13, 39.73, 41.31, 40.05, 40.38, 40.21, 39.71, 39.65, 45.13, 39.78, 40.38, 39.79]
+726.01
+36.3005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.557694435119629, 'TIME_S_1KI': 5.070938729644394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.5419220256806, 'W': 120.04, 'J_1KI': 893.1517396857256, 'W_1KI': 57.656099903938525, 'W_D': 83.7395, 'J_D': 1297.2101864334345, 'W_D_1KI': 40.22070124879924, 'J_D_1KI': 19.31830031162307}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..b94bf6c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1470, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.845632314682007, "TIME_S_1KI": 7.377981166450344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2161.157882115841, "W": 117.03, "J_1KI": 1470.1754300107762, "W_1KI": 79.61224489795917, "W_D": 80.6345, "J_D": 1489.0531081386805, "W_D_1KI": 54.853401360544225, "J_D_1KI": 37.315239020778385}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..4e7bca2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7518825531005859}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2974,     5950,  ..., 29994087,
+                            29997108, 30000000]),
+       col_indices=tensor([   6,    8,   10,  ..., 9985, 9992, 9996]),
+       values=tensor([0.7151, 0.6737, 0.4043,  ..., 0.5812, 0.5679, 0.6733]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.6135, 0.6008, 0.5882,  ..., 0.6628, 0.8539, 0.9204])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 0.7518825531005859 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1396', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.968559503555298}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3001,     6027,  ..., 29994039,
+                            29997052, 30000000]),
+       col_indices=tensor([   7,   11,   16,  ..., 9989, 9996, 9999]),
+       values=tensor([0.2908, 0.3192, 0.9662,  ..., 0.5726, 0.8523, 0.1200]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.6009, 0.9845, 0.3791,  ..., 0.1987, 0.1714, 0.4278])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 9.968559503555298 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1470', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.845632314682007}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2929,     5851,  ..., 29993945,
+                            29997029, 30000000]),
+       col_indices=tensor([   6,    7,   11,  ..., 9986, 9997, 9998]),
+       values=tensor([0.6210, 0.5427, 0.8130,  ..., 0.4194, 0.0441, 0.7442]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.0141, 0.0033, 0.5199,  ..., 0.4699, 0.7276, 0.5761])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.845632314682007 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2929,     5851,  ..., 29993945,
+                            29997029, 30000000]),
+       col_indices=tensor([   6,    7,   11,  ..., 9986, 9997, 9998]),
+       values=tensor([0.6210, 0.5427, 0.8130,  ..., 0.4194, 0.0441, 0.7442]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.0141, 0.0033, 0.5199,  ..., 0.4699, 0.7276, 0.5761])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.845632314682007 seconds
+
+[46.28, 39.97, 40.2, 40.69, 40.27, 40.47, 40.33, 41.03, 39.84, 40.37]
+[117.03]
+18.466699838638306
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1470, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.845632314682007, 'TIME_S_1KI': 7.377981166450344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2161.157882115841, 'W': 117.03}
+[46.28, 39.97, 40.2, 40.69, 40.27, 40.47, 40.33, 41.03, 39.84, 40.37, 41.81, 40.41, 40.59, 39.82, 40.04, 39.71, 39.78, 40.7, 39.78, 40.1]
+727.9100000000001
+36.395500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1470, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.845632314682007, 'TIME_S_1KI': 7.377981166450344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2161.157882115841, 'W': 117.03, 'J_1KI': 1470.1754300107762, 'W_1KI': 79.61224489795917, 'W_D': 80.6345, 'J_D': 1489.0531081386805, 'W_D_1KI': 54.853401360544225, 'J_D_1KI': 37.315239020778385}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
index 4449f80..b4516b2 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 362169, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.681929588317871, "TIME_S_1KI": 0.029494323336116207, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.685609395504, "W": 96.13, "J_1KI": 3.4864541399056903, "W_1KI": 0.2654285706396737, "W_D": 60.50875, "J_D": 794.7937986841798, "W_D_1KI": 0.1670732448111241, "J_D_1KI": 0.0004613129362566208}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 366482, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.093939065933228, "TIME_S_1KI": 0.0302714432521467, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1264.2006668257713, "W": 98.79, "J_1KI": 3.4495573229402026, "W_1KI": 0.26956303447372587, "W_D": 62.66125, "J_D": 801.866525297463, "W_D_1KI": 0.17098043014390885, "J_D_1KI": 0.0004665452331735497}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
index f681b74..b43f8ba 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,373 +1,1024 @@
-['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": 0.044791460037231445}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019162416458129883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([2508, 9046,  543, 4312, 1967, 3077, 2314, 3402, 8716,
+                           3244, 7898, 5856, 5314, 5976, 2342, 2492, 8104, 2147,
+                           1979, 1751, 5860, 9783, 5705, 5222, 9824,   95, 1929,
+                           8803, 8548,  677, 1693, 5788, 5523, 9109, 5789, 1810,
+                           1126, 4820, 9808, 8419, 2599, 1229, 2963, 1212, 2144,
+                           3318, 3062, 2566, 7505, 6545, 1590, 1285,  717, 2005,
+                           5957, 3726, 9706,  857, 1431, 3655, 7849, 8981, 2848,
+                           7191, 9483, 8286, 6981,  629,  346, 7910, 8938, 1876,
+                           6475,  566, 6786, 4287, 8002, 5447, 9857, 7286, 3753,
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+                      2.2592e-02, 4.9539e-01, 7.1062e-02, 1.8299e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4331, 0.6450, 0.9910,  ..., 0.2595, 0.5081, 0.5389])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.019162416458129883 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54794', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.6940038204193115}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      2.9480e-01, 2.6967e-01, 8.8380e-03, 9.0770e-02]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8612, 0.4581, 0.7630,  ..., 0.0492, 0.2811, 0.3451])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 1.6940038204193115 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '339631', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.730679035186768}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      0.0043, 0.4775, 0.5628, 0.5494, 0.7407, 0.3406]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4545, 0.4841, 0.5462,  ..., 0.7193, 0.8873, 0.8657])
+tensor([0.4250, 0.1408, 0.2235,  ..., 0.5768, 0.6841, 0.8384])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,650 +1026,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.044791460037231445 seconds
+Time: 9.730679035186768 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '234419', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.79626727104187}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
-       col_indices=tensor([9785,  166, 4732, 1054, 2677,  737, 2692, 6702, 6696,
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-                            792,  320, 7566, 9599, 1370, 2235, 8718, 1363, 2332,
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8987, 0.7248, 0.0383,  ..., 0.6918, 0.0447, 0.2254])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 6.79626727104187 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '362169', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.681929588317871}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '366482', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.093939065933228}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      3.1057e-01, 3.6455e-01, 8.0890e-01, 2.7987e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4226, 0.0556, 0.1398,  ..., 0.5751, 0.9814, 0.4838])
+tensor([0.7740, 0.9703, 0.1840,  ..., 0.7477, 0.1526, 0.5369])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,375 +1405,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.681929588317871 seconds
+Time: 11.093939065933228 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      1.9274e-01, 3.4014e-01, 3.0741e-01, 3.4144e-01,
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+                      3.1057e-01, 3.6455e-01, 8.0890e-01, 2.7987e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4226, 0.0556, 0.1398,  ..., 0.5751, 0.9814, 0.4838])
+tensor([0.7740, 0.9703, 0.1840,  ..., 0.7477, 0.1526, 0.5369])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1402,13 +1781,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.681929588317871 seconds
+Time: 11.093939065933228 seconds
 
-[41.15, 38.98, 39.75, 38.85, 39.88, 39.03, 39.18, 38.82, 39.8, 38.85]
-[96.13]
-13.135187864303589
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 362169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.681929588317871, 'TIME_S_1KI': 0.029494323336116207, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.685609395504, 'W': 96.13}
-[41.15, 38.98, 39.75, 38.85, 39.88, 39.03, 39.18, 38.82, 39.8, 38.85, 40.18, 39.93, 38.85, 39.83, 38.86, 39.3, 38.99, 39.61, 39.19, 46.97]
-712.425
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-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 362169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.681929588317871, 'TIME_S_1KI': 0.029494323336116207, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.685609395504, 'W': 96.13, 'J_1KI': 3.4864541399056903, 'W_1KI': 0.2654285706396737, 'W_D': 60.50875, 'J_D': 794.7937986841798, 'W_D_1KI': 0.1670732448111241, 'J_D_1KI': 0.0004613129362566208}
+[40.33, 40.0, 39.88, 40.14, 39.62, 39.53, 39.86, 39.53, 39.55, 39.82]
+[98.79]
+12.79684853553772
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 366482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.093939065933228, 'TIME_S_1KI': 0.0302714432521467, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.2006668257713, 'W': 98.79}
+[40.33, 40.0, 39.88, 40.14, 39.62, 39.53, 39.86, 39.53, 39.55, 39.82, 40.52, 39.54, 40.01, 39.91, 40.14, 39.87, 40.07, 39.9, 44.97, 39.44]
+722.575
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..c87bd7a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 305580, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.080953121185303, "TIME_S_1KI": 0.032989571049104334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.7398206591606, "W": 98.25, "J_1KI": 4.027553572416914, "W_1KI": 0.32151973296681724, "W_D": 62.38325, "J_D": 781.450889741838, "W_D_1KI": 0.20414703187381372, "J_D_1KI": 0.0006680641137306555}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..9e7563d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018548250198364258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([1572, 8127, 3303,  ..., 3635, 8012, 8701]),
+       values=tensor([0.7029, 0.2681, 0.5472,  ..., 0.1372, 0.6564, 0.9870]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6228, 0.5154, 0.0077,  ..., 0.6369, 0.2601, 0.0192])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.018548250198364258 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '56609', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9451327323913574}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([3842,  387, 3686,  ..., 4115, 6419, 2917]),
+       values=tensor([0.9231, 0.0215, 0.0697,  ..., 0.2708, 0.2879, 0.7516]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.2403, 0.0214, 0.1380,  ..., 0.6094, 0.2095, 0.9923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 1.9451327323913574 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '305580', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.080953121185303}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([1481, 9557, 9045,  ...,  186, 1024,  519]),
+       values=tensor([0.0681, 0.8562, 0.9064,  ..., 0.7770, 0.2010, 0.9088]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5005, 0.6529, 0.0782,  ..., 0.6202, 0.1736, 0.9901])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.080953121185303 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([1481, 9557, 9045,  ...,  186, 1024,  519]),
+       values=tensor([0.0681, 0.8562, 0.9064,  ..., 0.7770, 0.2010, 0.9088]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5005, 0.6529, 0.0782,  ..., 0.6202, 0.1736, 0.9901])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.080953121185303 seconds
+
+[40.69, 39.86, 40.65, 39.74, 39.56, 39.52, 39.78, 39.47, 39.45, 39.45]
+[98.25]
+12.52661395072937
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 305580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.080953121185303, 'TIME_S_1KI': 0.032989571049104334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7398206591606, 'W': 98.25}
+[40.69, 39.86, 40.65, 39.74, 39.56, 39.52, 39.78, 39.47, 39.45, 39.45, 40.13, 40.88, 39.46, 41.14, 39.99, 39.46, 39.46, 39.44, 39.59, 39.5]
+717.335
+35.86675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 305580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.080953121185303, 'TIME_S_1KI': 0.032989571049104334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7398206591606, 'W': 98.25, 'J_1KI': 4.027553572416914, 'W_1KI': 0.32151973296681724, 'W_D': 62.38325, 'J_D': 781.450889741838, 'W_D_1KI': 0.20414703187381372, 'J_D_1KI': 0.0006680641137306555}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..0a007ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1273, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.290857076644897, "TIME_S_1KI": 8.083941144261507, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2248.191835269928, "W": 120.44000000000001, "J_1KI": 1766.0580009975868, "W_1KI": 94.61115475255303, "W_D": 84.17750000000001, "J_D": 1571.2983079826834, "W_D_1KI": 66.12529457973291, "J_D_1KI": 51.944457643152326}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..1b9241c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.824350118637085}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       55,      100,  ..., 24999899,
+                            24999953, 25000000]),
+       col_indices=tensor([  1283,  31647,  40047,  ..., 487577, 491974,
+                           492635]),
+       values=tensor([0.2687, 0.0076, 0.0743,  ..., 0.5051, 0.4444, 0.1527]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2422, 0.7782, 0.2817,  ..., 0.4809, 0.1219, 0.8722])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 0.824350118637085 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1273', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.290857076644897}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,      101,  ..., 24999883,
+                            24999939, 25000000]),
+       col_indices=tensor([  9313,  23523,  43031,  ..., 488537, 498363,
+                           498593]),
+       values=tensor([0.9134, 0.4019, 0.3601,  ..., 0.2723, 0.3306, 0.0527]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9993, 0.9766, 0.6194,  ..., 0.0672, 0.4807, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.290857076644897 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,      101,  ..., 24999883,
+                            24999939, 25000000]),
+       col_indices=tensor([  9313,  23523,  43031,  ..., 488537, 498363,
+                           498593]),
+       values=tensor([0.9134, 0.4019, 0.3601,  ..., 0.2723, 0.3306, 0.0527]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9993, 0.9766, 0.6194,  ..., 0.0672, 0.4807, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.290857076644897 seconds
+
+[40.99, 40.04, 40.78, 39.98, 40.28, 39.98, 39.98, 39.94, 40.19, 39.9]
+[120.44]
+18.66648817062378
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1273, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.290857076644897, 'TIME_S_1KI': 8.083941144261507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2248.191835269928, 'W': 120.44000000000001}
+[40.99, 40.04, 40.78, 39.98, 40.28, 39.98, 39.98, 39.94, 40.19, 39.9, 41.12, 40.02, 40.17, 39.62, 39.57, 39.49, 39.52, 45.4, 39.55, 39.47]
+725.25
+36.2625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1273, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.290857076644897, 'TIME_S_1KI': 8.083941144261507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2248.191835269928, 'W': 120.44000000000001, 'J_1KI': 1766.0580009975868, 'W_1KI': 94.61115475255303, 'W_D': 84.17750000000001, 'J_D': 1571.2983079826834, 'W_D_1KI': 66.12529457973291, 'J_D_1KI': 51.944457643152326}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
index c41b46b..22e8b8e 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21272, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.296250820159912, "TIME_S_1KI": 0.4840283386686683, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2004.567332353592, "W": 151.77, "J_1KI": 94.23501938480594, "W_1KI": 7.134731101918015, "W_D": 115.36950000000002, "J_D": 1523.7921252551082, "W_D_1KI": 5.423537984204589, "J_D_1KI": 0.2549613569107084}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21531, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.381713628768921, "TIME_S_1KI": 0.48217517202029264, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2048.753864378929, "W": 153.79, "J_1KI": 95.15367908499043, "W_1KI": 7.142724443825182, "W_D": 117.79275, "J_D": 1569.2070470012427, "W_D_1KI": 5.4708443639403646, "J_D_1KI": 0.25409151288562376}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
index ff821c9..da8bd5c 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5370402336120605}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08279204368591309}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499988,
+tensor(crow_indices=tensor([      0,       5,      10,  ..., 2499988,
                             2499995, 2500000]),
-       col_indices=tensor([   667,  84326, 231414,  ..., 445492, 452435,
-                           478533]),
-       values=tensor([0.3723, 0.9059, 0.5582,  ..., 0.5128, 0.0660, 0.1881]),
+       col_indices=tensor([111354, 133493, 148601,  ..., 214459, 291734,
+                           295580]),
+       values=tensor([0.7692, 0.5972, 0.6345,  ..., 0.2595, 0.9828, 0.2512]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0315, 0.2189, 0.8055,  ..., 0.9902, 0.0196, 0.5860])
+tensor([0.1544, 0.9362, 0.6152,  ..., 0.8648, 0.4518, 0.0330])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.5370402336120605 seconds
+Time: 0.08279204368591309 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19551', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.650388717651367}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12682', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.184589385986328}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499988,
-                            2499994, 2500000]),
-       col_indices=tensor([ 11262,  76750, 152870,  ..., 221537, 283064,
-                           452441]),
-       values=tensor([0.8111, 0.5495, 0.0260,  ..., 0.8118, 0.4893, 0.3789]),
+tensor(crow_indices=tensor([      0,       6,      12,  ..., 2499985,
+                            2499992, 2500000]),
+       col_indices=tensor([135614, 168986, 215859,  ..., 402290, 443216,
+                           486549]),
+       values=tensor([0.4455, 0.2288, 0.5445,  ..., 0.6029, 0.8332, 0.9959]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5436, 0.8281, 0.7063,  ..., 0.1699, 0.2640, 0.5110])
+tensor([0.5969, 0.6605, 0.1157,  ..., 0.5750, 0.9019, 0.4949])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 9.650388717651367 seconds
+Time: 6.184589385986328 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21272', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.296250820159912}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21531', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.381713628768921}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499993,
-                            2499998, 2500000]),
-       col_indices=tensor([ 13054, 157067, 258216,  ..., 445117, 194165,
-                           431781]),
-       values=tensor([0.8472, 0.4724, 0.5562,  ..., 0.8941, 0.8667, 0.3682]),
+tensor(crow_indices=tensor([      0,       4,       6,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([192037, 290494, 298239,  ..., 203209, 269872,
+                           299833]),
+       values=tensor([0.2087, 0.5501, 0.3490,  ..., 0.1907, 0.4204, 0.3032]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2043, 0.9144, 0.3718,  ..., 0.9024, 0.4544, 0.2083])
+tensor([0.3717, 0.6256, 0.6803,  ..., 0.1727, 0.9290, 0.7130])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,17 +59,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.296250820159912 seconds
+Time: 10.381713628768921 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499993,
-                            2499998, 2500000]),
-       col_indices=tensor([ 13054, 157067, 258216,  ..., 445117, 194165,
-                           431781]),
-       values=tensor([0.8472, 0.4724, 0.5562,  ..., 0.8941, 0.8667, 0.3682]),
+tensor(crow_indices=tensor([      0,       4,       6,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([192037, 290494, 298239,  ..., 203209, 269872,
+                           299833]),
+       values=tensor([0.2087, 0.5501, 0.3490,  ..., 0.1907, 0.4204, 0.3032]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2043, 0.9144, 0.3718,  ..., 0.9024, 0.4544, 0.2083])
+tensor([0.3717, 0.6256, 0.6803,  ..., 0.1727, 0.9290, 0.7130])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -77,13 +77,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.296250820159912 seconds
+Time: 10.381713628768921 seconds
 
-[40.04, 40.36, 39.54, 40.31, 47.07, 40.26, 39.56, 39.95, 39.57, 41.2]
-[151.77]
-13.207928657531738
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.296250820159912, 'TIME_S_1KI': 0.4840283386686683, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2004.567332353592, 'W': 151.77}
-[40.04, 40.36, 39.54, 40.31, 47.07, 40.26, 39.56, 39.95, 39.57, 41.2, 46.29, 39.48, 40.21, 39.42, 40.34, 39.68, 39.47, 39.18, 40.2, 39.29]
-728.01
-36.4005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.296250820159912, 'TIME_S_1KI': 0.4840283386686683, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2004.567332353592, 'W': 151.77, 'J_1KI': 94.23501938480594, 'W_1KI': 7.134731101918015, 'W_D': 115.36950000000002, 'J_D': 1523.7921252551082, 'W_D_1KI': 5.423537984204589, 'J_D_1KI': 0.2549613569107084}
+[40.84, 39.87, 40.71, 40.09, 39.75, 39.79, 39.67, 39.6, 39.76, 40.21]
+[153.79]
+13.321762561798096
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.381713628768921, 'TIME_S_1KI': 0.48217517202029264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.753864378929, 'W': 153.79}
+[40.84, 39.87, 40.71, 40.09, 39.75, 39.79, 39.67, 39.6, 39.76, 40.21, 41.58, 40.07, 40.12, 40.08, 39.68, 39.76, 39.59, 39.74, 40.65, 39.4]
+719.9449999999999
+35.997249999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.381713628768921, 'TIME_S_1KI': 0.48217517202029264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.753864378929, 'W': 153.79, 'J_1KI': 95.15367908499043, 'W_1KI': 7.142724443825182, 'W_D': 117.79275, 'J_D': 1569.2070470012427, 'W_D_1KI': 5.4708443639403646, 'J_D_1KI': 0.25409151288562376}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..2972d33
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.664037466049194, "TIME_S_1KI": 4.660855535860661, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2026.948439025879, "W": 124.48, "J_1KI": 885.9040380357864, "W_1KI": 54.40559440559441, "W_D": 87.53675000000001, "J_D": 1425.389450272322, "W_D_1KI": 38.25906905594406, "J_D_1KI": 16.721621090884643}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..16454d4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.45888853073120117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       46,  ..., 12499954,
+                            12499981, 12500000]),
+       col_indices=tensor([ 49072, 112972, 116125,  ..., 361100, 370525,
+                           412609]),
+       values=tensor([0.2354, 0.3643, 0.0075,  ..., 0.8603, 0.9033, 0.4787]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8534, 0.6179, 0.0838,  ..., 0.4832, 0.1451, 0.6650])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.45888853073120117 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2288', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.664037466049194}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       48,  ..., 12499952,
+                            12499972, 12500000]),
+       col_indices=tensor([  3842,   7633,   8971,  ..., 455163, 462741,
+                           476944]),
+       values=tensor([0.8075, 0.4724, 0.8976,  ..., 0.5541, 0.2969, 0.9431]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0930, 0.1654, 0.5776,  ..., 0.1397, 0.2168, 0.6873])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.664037466049194 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       48,  ..., 12499952,
+                            12499972, 12500000]),
+       col_indices=tensor([  3842,   7633,   8971,  ..., 455163, 462741,
+                           476944]),
+       values=tensor([0.8075, 0.4724, 0.8976,  ..., 0.5541, 0.2969, 0.9431]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0930, 0.1654, 0.5776,  ..., 0.1397, 0.2168, 0.6873])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.664037466049194 seconds
+
+[40.58, 39.64, 39.65, 39.68, 39.54, 40.08, 40.01, 55.98, 39.52, 39.5]
+[124.48]
+16.283326148986816
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.664037466049194, 'TIME_S_1KI': 4.660855535860661, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.948439025879, 'W': 124.48}
+[40.58, 39.64, 39.65, 39.68, 39.54, 40.08, 40.01, 55.98, 39.52, 39.5, 40.22, 40.13, 39.62, 39.51, 44.89, 40.37, 40.29, 40.46, 39.51, 39.67]
+738.865
+36.94325
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.664037466049194, 'TIME_S_1KI': 4.660855535860661, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.948439025879, 'W': 124.48, 'J_1KI': 885.9040380357864, 'W_1KI': 54.40559440559441, 'W_D': 87.53675000000001, 'J_D': 1425.389450272322, 'W_D_1KI': 38.25906905594406, 'J_D_1KI': 16.721621090884643}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
index 8fb8b3c..f1d4670 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91738, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.729677200317383, "TIME_S_1KI": 0.1169600078518976, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1421.8676947784425, "W": 116.68, "J_1KI": 15.49922272971334, "W_1KI": 1.2718829710697859, "W_D": 81.037, "J_D": 987.5205037860871, "W_D_1KI": 0.883352591074582, "J_D_1KI": 0.009629080545407377}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 94004, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.384105920791626, "TIME_S_1KI": 0.12110235650388947, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1783.8070621061327, "W": 114.34, "J_1KI": 18.975863389920992, "W_1KI": 1.2163312199480873, "W_D": 78.24925, "J_D": 1220.758831157148, "W_D_1KI": 0.8324034083656016, "J_D_1KI": 0.008854978600544674}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
index e8b4fe3..0b0a270 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.13608026504516602}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04173541069030762}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     12,  ..., 249989, 249998,
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249994, 249996,
                             250000]),
-       col_indices=tensor([17323, 35611, 42973,  ..., 47252,  2994, 12259]),
-       values=tensor([0.7287, 0.3464, 0.0193,  ..., 0.7636, 0.2298, 0.3699]),
+       col_indices=tensor([ 2875, 11250, 41033,  ..., 32140, 46339, 48534]),
+       values=tensor([0.9791, 0.8918, 0.3698,  ..., 0.3708, 0.0646, 0.7857]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4030, 0.5063, 0.1399,  ..., 0.2219, 0.6631, 0.1030])
+tensor([0.2100, 0.1946, 0.2511,  ..., 0.6374, 0.0985, 0.4430])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.13608026504516602 seconds
+Time: 0.04173541069030762 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77160', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.564647197723389}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25158', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8100624084472656}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     10,  ..., 249992, 249997,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249990, 249993,
                             250000]),
-       col_indices=tensor([ 7731,  9587, 38710,  ..., 32177, 32664, 36235]),
-       values=tensor([0.0671, 0.3654, 0.2011,  ..., 0.4377, 0.9797, 0.5456]),
+       col_indices=tensor([41615, 42906, 15488,  ..., 31340, 31947, 35417]),
+       values=tensor([0.0772, 0.0729, 0.2688,  ..., 0.4463, 0.5032, 0.2162]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4354, 0.6450, 0.5949,  ..., 0.4585, 0.1162, 0.0017])
+tensor([0.5388, 0.5198, 0.1392,  ..., 0.7254, 0.7688, 0.9922])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 9.564647197723389 seconds
+Time: 2.8100624084472656 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84705', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.694962739944458}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '94004', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.384105920791626}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     11,  ..., 249991, 249993,
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249990,
                             250000]),
-       col_indices=tensor([19445, 22750, 27321,  ..., 31731, 39710, 46259]),
-       values=tensor([0.4009, 0.2006, 0.6920,  ..., 0.2884, 0.6470, 0.2171]),
+       col_indices=tensor([ 2939, 14473, 20084,  ..., 45023, 47616, 49448]),
+       values=tensor([0.6894, 0.8051, 0.8240,  ..., 0.7425, 0.1769, 0.4023]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3109, 0.8999, 0.0558,  ..., 0.1822, 0.8563, 0.0744])
+tensor([0.4931, 0.5539, 0.3107,  ..., 0.8523, 0.9706, 0.6879])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,19 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 9.694962739944458 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91738', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.729677200317383}
+Time: 11.384105920791626 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249990,
                             250000]),
-       col_indices=tensor([20378, 29361, 44885,  ..., 25194, 39048, 45113]),
-       values=tensor([0.6839, 0.7204, 0.3118,  ..., 0.2854, 0.8671, 0.0496]),
+       col_indices=tensor([ 2939, 14473, 20084,  ..., 45023, 47616, 49448]),
+       values=tensor([0.6894, 0.8051, 0.8240,  ..., 0.7425, 0.1769, 0.4023]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2159, 0.7026, 0.3184,  ..., 0.1135, 0.4559, 0.6374])
+tensor([0.4931, 0.5539, 0.3107,  ..., 0.8523, 0.9706, 0.6879])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -76,30 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.729677200317383 seconds
+Time: 11.384105920791626 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 249990, 249995,
-                            250000]),
-       col_indices=tensor([20378, 29361, 44885,  ..., 25194, 39048, 45113]),
-       values=tensor([0.6839, 0.7204, 0.3118,  ..., 0.2854, 0.8671, 0.0496]),
-       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2159, 0.7026, 0.3184,  ..., 0.1135, 0.4559, 0.6374])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 250000
-Density: 0.0001
-Time: 10.729677200317383 seconds
-
-[40.36, 39.52, 40.11, 39.22, 40.19, 39.14, 40.18, 39.47, 39.42, 39.16]
-[116.68]
-12.186044692993164
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.729677200317383, 'TIME_S_1KI': 0.1169600078518976, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.8676947784425, 'W': 116.68}
-[40.36, 39.52, 40.11, 39.22, 40.19, 39.14, 40.18, 39.47, 39.42, 39.16, 39.82, 39.18, 40.07, 39.1, 40.14, 39.1, 40.11, 39.11, 39.63, 39.0]
-712.86
-35.643
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.729677200317383, 'TIME_S_1KI': 0.1169600078518976, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.8676947784425, 'W': 116.68, 'J_1KI': 15.49922272971334, 'W_1KI': 1.2718829710697859, 'W_D': 81.037, 'J_D': 987.5205037860871, 'W_D_1KI': 0.883352591074582, 'J_D_1KI': 0.009629080545407377}
+[40.36, 39.91, 39.67, 39.83, 39.58, 39.72, 40.16, 39.72, 39.91, 39.98]
+[114.34]
+15.600901365280151
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 94004, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.384105920791626, 'TIME_S_1KI': 0.12110235650388947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1783.8070621061327, 'W': 114.34}
+[40.36, 39.91, 39.67, 39.83, 39.58, 39.72, 40.16, 39.72, 39.91, 39.98, 40.89, 39.87, 39.99, 39.51, 39.5, 39.56, 39.75, 45.01, 39.69, 39.64]
+721.815
+36.09075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 94004, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.384105920791626, 'TIME_S_1KI': 0.12110235650388947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1783.8070621061327, 'W': 114.34, 'J_1KI': 18.975863389920992, 'W_1KI': 1.2163312199480873, 'W_D': 78.24925, 'J_D': 1220.758831157148, 'W_D_1KI': 0.8324034083656016, 'J_D_1KI': 0.008854978600544674}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
index 18b313c..ca129d7 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46932, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.4467294216156, "TIME_S_1KI": 0.22259288804260632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1943.0940554380418, "W": 146.55, "J_1KI": 41.40232795188873, "W_1KI": 3.122602914855536, "W_D": 110.75150000000002, "J_D": 1468.4447716195587, "W_D_1KI": 2.3598291144634795, "J_D_1KI": 0.05028187834448734}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.356630086898804, "TIME_S_1KI": 0.22311668074666732, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1954.5975055408476, "W": 147.82, "J_1KI": 42.10861100307742, "W_1KI": 3.1845404799862123, "W_D": 111.69725, "J_D": 1476.952822525859, "W_D_1KI": 2.4063348270067646, "J_D_1KI": 0.051840553815476}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
index c54c9fb..0dda590 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2965991497039795}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06997370719909668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      49,     105,  ..., 2499896,
-                            2499948, 2500000]),
-       col_indices=tensor([ 1888,  3456,  5299,  ..., 45108, 48153, 49689]),
-       values=tensor([0.2133, 0.4832, 0.5162,  ..., 0.1550, 0.2104, 0.0398]),
+tensor(crow_indices=tensor([      0,      52,     102,  ..., 2499883,
+                            2499945, 2500000]),
+       col_indices=tensor([  266,   347,  3014,  ..., 46062, 47055, 47354]),
+       values=tensor([0.8937, 0.7241, 0.1967,  ..., 0.6923, 0.3348, 0.4624]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8558, 0.3690, 0.3196,  ..., 0.7609, 0.2901, 0.1393])
+tensor([0.3743, 0.1265, 0.3703,  ..., 0.6234, 0.9781, 0.6963])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.2965991497039795 seconds
+Time: 0.06997370719909668 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35401', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.9200310707092285}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15005', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.3941891193389893}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      50,      98,  ..., 2499887,
-                            2499942, 2500000]),
-       col_indices=tensor([ 1341,  6881,  6901,  ..., 49243, 49539, 49603]),
-       values=tensor([0.6621, 0.7599, 0.1509,  ..., 0.9636, 0.0388, 0.7851]),
+tensor(crow_indices=tensor([      0,      54,      97,  ..., 2499911,
+                            2499955, 2500000]),
+       col_indices=tensor([ 2370,  4930,  5051,  ..., 41423, 44524, 44646]),
+       values=tensor([0.0412, 0.5807, 0.8088,  ..., 0.8046, 0.7553, 0.5801]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4875, 0.8207, 0.8190,  ..., 0.4243, 0.1238, 0.4257])
+tensor([0.6465, 0.4915, 0.1151,  ..., 0.6682, 0.4745, 0.9594])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 7.9200310707092285 seconds
+Time: 3.3941891193389893 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46932', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.4467294216156}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46418', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.356630086898804}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      37,      82,  ..., 2499888,
-                            2499942, 2500000]),
-       col_indices=tensor([ 2117,  2189,  2263,  ..., 47568, 48115, 49415]),
-       values=tensor([0.8006, 0.3321, 0.7026,  ..., 0.2322, 0.3552, 0.1894]),
+tensor(crow_indices=tensor([      0,      46,      86,  ..., 2499896,
+                            2499949, 2500000]),
+       col_indices=tensor([ 1254,  3268,  3363,  ..., 48004, 48805, 49373]),
+       values=tensor([0.4618, 0.8696, 0.7740,  ..., 0.9354, 0.3130, 0.0156]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9529, 0.8532, 0.0899,  ..., 0.0711, 0.7399, 0.8898])
+tensor([0.3463, 0.0749, 0.0037,  ..., 0.8223, 0.0446, 0.2738])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.4467294216156 seconds
+Time: 10.356630086898804 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      37,      82,  ..., 2499888,
-                            2499942, 2500000]),
-       col_indices=tensor([ 2117,  2189,  2263,  ..., 47568, 48115, 49415]),
-       values=tensor([0.8006, 0.3321, 0.7026,  ..., 0.2322, 0.3552, 0.1894]),
+tensor(crow_indices=tensor([      0,      46,      86,  ..., 2499896,
+                            2499949, 2500000]),
+       col_indices=tensor([ 1254,  3268,  3363,  ..., 48004, 48805, 49373]),
+       values=tensor([0.4618, 0.8696, 0.7740,  ..., 0.9354, 0.3130, 0.0156]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9529, 0.8532, 0.0899,  ..., 0.0711, 0.7399, 0.8898])
+tensor([0.3463, 0.0749, 0.0037,  ..., 0.8223, 0.0446, 0.2738])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.4467294216156 seconds
+Time: 10.356630086898804 seconds
 
-[40.61, 39.27, 40.33, 39.32, 40.41, 39.18, 40.21, 39.47, 39.59, 40.71]
-[146.55]
-13.258915424346924
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46932, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.4467294216156, 'TIME_S_1KI': 0.22259288804260632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.0940554380418, 'W': 146.55}
-[40.61, 39.27, 40.33, 39.32, 40.41, 39.18, 40.21, 39.47, 39.59, 40.71, 40.22, 40.14, 39.29, 40.17, 39.21, 40.37, 39.38, 39.54, 39.32, 40.0]
-715.9699999999999
-35.7985
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46932, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.4467294216156, 'TIME_S_1KI': 0.22259288804260632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.0940554380418, 'W': 146.55, 'J_1KI': 41.40232795188873, 'W_1KI': 3.122602914855536, 'W_D': 110.75150000000002, 'J_D': 1468.4447716195587, 'W_D_1KI': 2.3598291144634795, 'J_D_1KI': 0.05028187834448734}
+[40.8, 41.43, 39.77, 40.05, 40.2, 40.01, 40.25, 39.8, 39.91, 39.83]
+[147.82]
+13.222821712493896
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.356630086898804, 'TIME_S_1KI': 0.22311668074666732, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1954.5975055408476, 'W': 147.82}
+[40.8, 41.43, 39.77, 40.05, 40.2, 40.01, 40.25, 39.8, 39.91, 39.83, 40.83, 39.7, 40.25, 40.38, 40.19, 39.67, 41.03, 39.72, 39.6, 39.53]
+722.4549999999999
+36.122749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.356630086898804, 'TIME_S_1KI': 0.22311668074666732, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1954.5975055408476, 'W': 147.82, 'J_1KI': 42.10861100307742, 'W_1KI': 3.1845404799862123, 'W_D': 111.69725, 'J_D': 1476.952822525859, 'W_D_1KI': 2.4063348270067646, 'J_D_1KI': 0.051840553815476}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..ce3aa00
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1681, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.72816252708435, "TIME_S_1KI": 6.382012211233998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2210.374014186859, "W": 116.29999999999998, "J_1KI": 1314.9161297958708, "W_1KI": 69.18500892325996, "W_D": 80.01049999999998, "J_D": 1520.6631991581912, "W_D_1KI": 47.596966091612124, "J_D_1KI": 28.314673463183894}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..fde54b7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6244547367095947}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      524,     1064,  ..., 24999000,
+                            24999502, 25000000]),
+       col_indices=tensor([   60,    76,   165,  ..., 49872, 49944, 49977]),
+       values=tensor([0.3464, 0.5127, 0.2524,  ..., 0.4585, 0.6152, 0.8409]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3169, 0.2467, 0.7317,  ..., 0.4966, 0.9013, 0.2021])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 0.6244547367095947 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1681', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.72816252708435}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      503,      984,  ..., 24998995,
+                            24999487, 25000000]),
+       col_indices=tensor([   80,   111,   167,  ..., 49695, 49904, 49943]),
+       values=tensor([0.9741, 0.4832, 0.1000,  ..., 0.9253, 0.4991, 0.7681]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5734, 0.0323, 0.0030,  ..., 0.5787, 0.7337, 0.7260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.72816252708435 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      503,      984,  ..., 24998995,
+                            24999487, 25000000]),
+       col_indices=tensor([   80,   111,   167,  ..., 49695, 49904, 49943]),
+       values=tensor([0.9741, 0.4832, 0.1000,  ..., 0.9253, 0.4991, 0.7681]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5734, 0.0323, 0.0030,  ..., 0.5787, 0.7337, 0.7260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.72816252708435 seconds
+
+[40.52, 40.33, 46.14, 39.67, 40.04, 40.05, 39.67, 39.99, 39.82, 39.93]
+[116.3]
+19.0057954788208
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.72816252708435, 'TIME_S_1KI': 6.382012211233998, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2210.374014186859, 'W': 116.29999999999998}
+[40.52, 40.33, 46.14, 39.67, 40.04, 40.05, 39.67, 39.99, 39.82, 39.93, 42.61, 39.87, 39.87, 39.75, 39.67, 39.77, 39.75, 39.54, 40.39, 39.88]
+725.7900000000001
+36.289500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.72816252708435, 'TIME_S_1KI': 6.382012211233998, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2210.374014186859, 'W': 116.29999999999998, 'J_1KI': 1314.9161297958708, 'W_1KI': 69.18500892325996, 'W_D': 80.01049999999998, 'J_D': 1520.6631991581912, 'W_D_1KI': 47.596966091612124, 'J_D_1KI': 28.314673463183894}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
index a53c562..73ac2d2 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 132622, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.695917844772339, "TIME_S_1KI": 0.08064964971703291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1381.936604347229, "W": 102.52, "J_1KI": 10.420115850667528, "W_1KI": 0.7730240834853945, "W_D": 66.90350000000001, "J_D": 901.8376473755837, "W_D_1KI": 0.5044675845636472, "J_D_1KI": 0.0038038001580706607}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 128261, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.553161382675171, "TIME_S_1KI": 0.08227880168309283, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1360.8756747579575, "W": 104.26, "J_1KI": 10.610206335191194, "W_1KI": 0.8128737496199158, "W_D": 68.19500000000001, "J_D": 890.1296435844899, "W_D_1KI": 0.5316892898075019, "J_D_1KI": 0.004145369908292481}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
index 89e9f79..b70442e 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
-['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": 0.13474559783935547}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.0555570125579834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([43476,  3093, 41733,  ..., 42921, 16006, 37299]),
-       values=tensor([0.8834, 0.6775, 0.5620,  ..., 0.7889, 0.3307, 0.4663]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([42051, 34515, 37611,  ..., 41473, 46289, 26191]),
+       values=tensor([0.0144, 0.4378, 0.1715,  ..., 0.0832, 0.5030, 0.6687]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1655, 0.9515, 0.3152,  ..., 0.5133, 0.8067, 0.9282])
+tensor([0.1124, 0.4102, 0.7912,  ..., 0.3553, 0.2259, 0.3847])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,37 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.13474559783935547 seconds
+Time: 0.0555570125579834 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77924', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.767163991928101}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([35071, 44060, 31911,  ..., 37021, 35082, 17458]),
-       values=tensor([0.6370, 0.7388, 0.5924,  ..., 0.3636, 0.5677, 0.2522]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8033, 0.0482, 0.8958,  ..., 0.4016, 0.2560, 0.2344])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 6.767163991928101 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '120907', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.572461605072021}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18899', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.547147274017334}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([ 3082, 46101, 46713,  ..., 40768, 36655, 17054]),
-       values=tensor([0.2693, 0.1416, 0.6603,  ..., 0.5561, 0.2474, 0.5454]),
+       col_indices=tensor([ 9684, 40954, 42907,  ..., 26506, 37971, 35337]),
+       values=tensor([0.0766, 0.5354, 0.2778,  ..., 0.4912, 0.6494, 0.7856]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5277, 0.5906, 0.6144,  ..., 0.6636, 0.4334, 0.5688])
+tensor([0.2325, 0.6288, 0.8060,  ..., 0.4059, 0.0257, 0.5351])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.572461605072021 seconds
+Time: 1.547147274017334 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '132622', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.695917844772339}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '128261', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.553161382675171}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24998, 25000]),
-       col_indices=tensor([ 1978, 29423,  7022,  ..., 46456, 14629, 46564]),
-       values=tensor([0.3729, 0.4306, 0.6677,  ..., 0.7805, 0.6392, 0.2909]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([43490, 45422, 41208,  ..., 48729, 34812, 29106]),
+       values=tensor([0.6729, 0.4582, 0.1719,  ..., 0.9792, 0.1938, 0.4197]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9195, 0.7845, 0.1112,  ..., 0.9886, 0.0043, 0.8706])
+tensor([0.3715, 0.8721, 0.7070,  ..., 0.0207, 0.1985, 0.3006])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -72,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.695917844772339 seconds
+Time: 10.553161382675171 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24998, 25000]),
-       col_indices=tensor([ 1978, 29423,  7022,  ..., 46456, 14629, 46564]),
-       values=tensor([0.3729, 0.4306, 0.6677,  ..., 0.7805, 0.6392, 0.2909]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([43490, 45422, 41208,  ..., 48729, 34812, 29106]),
+       values=tensor([0.6729, 0.4582, 0.1719,  ..., 0.9792, 0.1938, 0.4197]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9195, 0.7845, 0.1112,  ..., 0.9886, 0.0043, 0.8706])
+tensor([0.3715, 0.8721, 0.7070,  ..., 0.0207, 0.1985, 0.3006])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -88,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.695917844772339 seconds
+Time: 10.553161382675171 seconds
 
-[40.91, 39.21, 40.28, 39.06, 40.18, 39.24, 39.39, 39.11, 40.12, 39.03]
-[102.52]
-13.4796781539917
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.695917844772339, 'TIME_S_1KI': 0.08064964971703291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.936604347229, 'W': 102.52}
-[40.91, 39.21, 40.28, 39.06, 40.18, 39.24, 39.39, 39.11, 40.12, 39.03, 40.67, 39.4, 40.19, 38.91, 39.61, 38.93, 39.87, 39.08, 40.01, 38.87]
-712.3299999999999
-35.616499999999995
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.695917844772339, 'TIME_S_1KI': 0.08064964971703291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.936604347229, 'W': 102.52, 'J_1KI': 10.420115850667528, 'W_1KI': 0.7730240834853945, 'W_D': 66.90350000000001, 'J_D': 901.8376473755837, 'W_D_1KI': 0.5044675845636472, 'J_D_1KI': 0.0038038001580706607}
+[42.38, 39.84, 39.75, 39.86, 39.86, 40.05, 40.23, 39.88, 39.93, 39.73]
+[104.26]
+13.052711248397827
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.553161382675171, 'TIME_S_1KI': 0.08227880168309283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8756747579575, 'W': 104.26}
+[42.38, 39.84, 39.75, 39.86, 39.86, 40.05, 40.23, 39.88, 39.93, 39.73, 40.22, 39.71, 39.72, 39.44, 39.86, 40.03, 39.57, 39.83, 40.02, 45.11]
+721.3
+36.065
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.553161382675171, 'TIME_S_1KI': 0.08227880168309283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8756747579575, 'W': 104.26, 'J_1KI': 10.610206335191194, 'W_1KI': 0.8128737496199158, 'W_D': 68.19500000000001, 'J_D': 890.1296435844899, 'W_D_1KI': 0.5316892898075019, 'J_D_1KI': 0.004145369908292481}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..b465c9c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 110115, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.965436697006226, "TIME_S_1KI": 0.1086630949190049, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1446.9478559374809, "W": 111.69, "J_1KI": 13.14033379591773, "W_1KI": 1.0143032284429914, "W_D": 75.71975, "J_D": 980.9520092633368, "W_D_1KI": 0.6876424646959997, "J_D_1KI": 0.006244766514062568}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..b1f3c1f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.05198168754577637}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124998, 124999,
+                            125000]),
+       col_indices=tensor([  927,  8914,  5646,  ..., 41839,  2622, 37662]),
+       values=tensor([0.2093, 0.3505, 0.4434,  ..., 0.7585, 0.2953, 0.8139]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7158, 0.3261, 0.8838,  ..., 0.1644, 0.9864, 0.1779])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.05198168754577637 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20199', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9260566234588623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 124993, 124995,
+                            125000]),
+       col_indices=tensor([ 5280, 12669, 18309,  ..., 32915, 33761, 44585]),
+       values=tensor([0.1104, 0.6442, 0.1166,  ..., 0.0611, 0.5204, 0.6774]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3981, 0.9235, 0.5295,  ..., 0.9056, 0.3690, 0.2596])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 1.9260566234588623 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '110115', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.965436697006226}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 124990, 124994,
+                            125000]),
+       col_indices=tensor([  332, 15911, 38702,  ..., 27905, 36936, 47310]),
+       values=tensor([0.9967, 0.4995, 0.1475,  ..., 0.0565, 0.7404, 0.0608]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3103, 0.3240, 0.6987,  ..., 0.1758, 0.7445, 0.7079])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 11.965436697006226 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 124990, 124994,
+                            125000]),
+       col_indices=tensor([  332, 15911, 38702,  ..., 27905, 36936, 47310]),
+       values=tensor([0.9967, 0.4995, 0.1475,  ..., 0.0565, 0.7404, 0.0608]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3103, 0.3240, 0.6987,  ..., 0.1758, 0.7445, 0.7079])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 11.965436697006226 seconds
+
+[40.59, 40.33, 40.2, 40.19, 39.75, 39.57, 39.56, 39.52, 39.58, 39.6]
+[111.69]
+12.955034971237183
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110115, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.965436697006226, 'TIME_S_1KI': 0.1086630949190049, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.9478559374809, 'W': 111.69}
+[40.59, 40.33, 40.2, 40.19, 39.75, 39.57, 39.56, 39.52, 39.58, 39.6, 40.22, 41.59, 39.67, 40.02, 39.96, 39.85, 39.52, 40.16, 39.94, 39.58]
+719.405
+35.97025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110115, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.965436697006226, 'TIME_S_1KI': 0.1086630949190049, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.9478559374809, 'W': 111.69, 'J_1KI': 13.14033379591773, 'W_1KI': 1.0143032284429914, 'W_D': 75.71975, 'J_D': 980.9520092633368, 'W_D_1KI': 0.6876424646959997, 'J_D_1KI': 0.006244766514062568}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
index c740a6d..2c10823 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 450692, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661210536956787, "TIME_S_1KI": 0.023655202526241394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.7642656326295, "W": 94.48, "J_1KI": 2.82845993634817, "W_1KI": 0.2096331863001784, "W_D": 59.36250000000001, "J_D": 800.9440486729146, "W_D_1KI": 0.13171411962049473, "J_D_1KI": 0.00029224863015206556}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 447788, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.585598468780518, "TIME_S_1KI": 0.023639754680296294, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1210.6447257900238, "W": 96.76, "J_1KI": 2.7036113647306848, "W_1KI": 0.21608439708076144, "W_D": 60.779250000000005, "J_D": 760.4596780691744, "W_D_1KI": 0.13573219916567664, "J_D_1KI": 0.00030311709819306603}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
index eed9694..38edffa 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05054283142089844}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01688361167907715}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    2,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([ 483, 2169,  757,  ...,  173, 4439, 4656]),
-       values=tensor([0.9876, 0.6258, 0.5982,  ..., 0.3562, 0.6626, 0.2988]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2619, 4724, 4043,  ...,  721, 4005, 3452]),
+       values=tensor([0.3560, 0.4737, 0.9490,  ..., 0.6650, 0.5511, 0.5102]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.5486, 0.1022, 0.5660,  ..., 0.0025, 0.4692, 0.8005])
+tensor([0.9035, 0.6347, 0.7264,  ..., 0.8885, 0.4271, 0.9746])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.05054283142089844 seconds
+Time: 0.01688361167907715 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '207744', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.839913845062256}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '62190', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.4582650661468506}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([1064,  259,  704,  ..., 2037, 4830,  899]),
-       values=tensor([0.7873, 0.2357, 0.4656,  ..., 0.3402, 0.5396, 0.7236]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 785, 2078,  964,  ..., 3093, 2409, 4914]),
+       values=tensor([0.2674, 0.7127, 0.0446,  ..., 0.5887, 0.3242, 0.2984]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3390, 0.6218, 0.4185,  ..., 0.9245, 0.2892, 0.5586])
+tensor([0.2053, 0.2435, 0.6452,  ..., 0.2463, 0.9693, 0.2980])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 4.839913845062256 seconds
+Time: 1.4582650661468506 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '450692', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661210536956787}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '447788', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.585598468780518}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([2769, 4978, 3269,  ..., 2907, 4470, 1850]),
-       values=tensor([0.1814, 0.5969, 0.2629,  ..., 0.3883, 0.1478, 0.5451]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3170,   56,  953,  ..., 2101, 4088, 4138]),
+       values=tensor([0.7441, 0.4324, 0.6982,  ..., 0.2565, 0.3946, 0.1156]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9019, 0.2172, 0.0888,  ..., 0.3698, 0.8940, 0.4050])
+tensor([0.7808, 0.5836, 0.6876,  ..., 0.2450, 0.1275, 0.2911])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.661210536956787 seconds
+Time: 10.585598468780518 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([2769, 4978, 3269,  ..., 2907, 4470, 1850]),
-       values=tensor([0.1814, 0.5969, 0.2629,  ..., 0.3883, 0.1478, 0.5451]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3170,   56,  953,  ..., 2101, 4088, 4138]),
+       values=tensor([0.7441, 0.4324, 0.6982,  ..., 0.2565, 0.3946, 0.1156]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9019, 0.2172, 0.0888,  ..., 0.3698, 0.8940, 0.4050])
+tensor([0.7808, 0.5836, 0.6876,  ..., 0.2450, 0.1275, 0.2911])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.661210536956787 seconds
+Time: 10.585598468780518 seconds
 
-[39.19, 39.19, 38.63, 39.33, 38.89, 39.39, 38.57, 39.43, 38.51, 39.19]
-[94.48]
-13.492424488067627
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 450692, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661210536956787, 'TIME_S_1KI': 0.023655202526241394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.7642656326295, 'W': 94.48}
-[39.19, 39.19, 38.63, 39.33, 38.89, 39.39, 38.57, 39.43, 38.51, 39.19, 39.32, 38.88, 39.47, 38.52, 39.48, 38.52, 39.58, 38.58, 39.31, 38.44]
-702.3499999999999
-35.11749999999999
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 450692, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661210536956787, 'TIME_S_1KI': 0.023655202526241394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.7642656326295, 'W': 94.48, 'J_1KI': 2.82845993634817, 'W_1KI': 0.2096331863001784, 'W_D': 59.36250000000001, 'J_D': 800.9440486729146, 'W_D_1KI': 0.13171411962049473, 'J_D_1KI': 0.00029224863015206556}
+[40.68, 39.36, 44.27, 39.07, 39.18, 39.33, 39.3, 39.56, 39.47, 39.52]
+[96.76]
+12.511830568313599
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 447788, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.585598468780518, 'TIME_S_1KI': 0.023639754680296294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1210.6447257900238, 'W': 96.76}
+[40.68, 39.36, 44.27, 39.07, 39.18, 39.33, 39.3, 39.56, 39.47, 39.52, 45.98, 39.49, 39.82, 39.41, 39.22, 39.42, 41.09, 39.62, 39.26, 39.31]
+719.615
+35.98075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 447788, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.585598468780518, 'TIME_S_1KI': 0.023639754680296294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1210.6447257900238, 'W': 96.76, 'J_1KI': 2.7036113647306848, 'W_1KI': 0.21608439708076144, 'W_D': 60.779250000000005, 'J_D': 760.4596780691744, 'W_D_1KI': 0.13573219916567664, 'J_D_1KI': 0.00030311709819306603}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
index 5a45f3d..f972143 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 249519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.236942052841187, "TIME_S_1KI": 0.04102670358907012, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1222.1109838342666, "W": 97.13999999999999, "J_1KI": 4.897867432276767, "W_1KI": 0.3893090305748259, "W_D": 61.76424999999999, "J_D": 777.0513520000576, "W_D_1KI": 0.24753325398065876, "J_D_1KI": 0.0009920417041614415}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 251242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.495816707611084, "TIME_S_1KI": 0.041775725028502735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.650104198456, "W": 98.84, "J_1KI": 5.073395786526361, "W_1KI": 0.393405561172097, "W_D": 63.200750000000006, "J_D": 815.0429236434699, "W_D_1KI": 0.25155328328862214, "J_D_1KI": 0.0010012389779122206}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
index bf3e9ad..489fd07 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05724024772644043}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.020148515701293945}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    15,  ..., 24983, 24992, 25000]),
-       col_indices=tensor([ 471, 1370, 1845,  ..., 3191, 3518, 3659]),
-       values=tensor([0.0299, 0.9557, 0.6054,  ..., 0.0635, 0.2604, 0.4528]),
+tensor(crow_indices=tensor([    0,     7,    10,  ..., 24991, 24995, 25000]),
+       col_indices=tensor([ 119,  931, 3406,  ..., 3461, 3840, 3846]),
+       values=tensor([0.6773, 0.1678, 0.7190,  ..., 0.1084, 0.6735, 0.7339]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0205, 0.7752, 0.1498,  ..., 0.2089, 0.1619, 0.7193])
+tensor([0.0928, 0.4745, 0.9490,  ..., 0.8279, 0.2614, 0.8062])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.05724024772644043 seconds
+Time: 0.020148515701293945 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '183437', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.719191074371338}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52113', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.177922248840332}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,     8,  ..., 24996, 24997, 25000]),
-       col_indices=tensor([1493, 2121, 2213,  ...,  623, 2347, 4713]),
-       values=tensor([0.6456, 0.4495, 0.4360,  ..., 0.5144, 0.5794, 0.1984]),
+tensor(crow_indices=tensor([    0,     6,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([1164, 1818, 2007,  ..., 3806, 4515, 4674]),
+       values=tensor([0.7293, 0.4677, 0.0557,  ..., 0.9608, 0.8022, 0.8772]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2703, 0.0672, 0.3072,  ..., 0.2566, 0.5122, 0.5785])
+tensor([0.5594, 0.0674, 0.3236,  ..., 0.9684, 0.1982, 0.3579])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 7.719191074371338 seconds
+Time: 2.177922248840332 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '249519', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.236942052841187}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '251242', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.495816707611084}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    11,  ..., 24991, 24997, 25000]),
-       col_indices=tensor([ 752,  886,  972,  ...,  802, 1974, 3630]),
-       values=tensor([0.4437, 0.0647, 0.4607,  ..., 0.1209, 0.0125, 0.5794]),
+tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
+       col_indices=tensor([1486, 3242, 3522,  ..., 1754, 2627, 4146]),
+       values=tensor([0.1836, 0.4006, 0.2197,  ..., 0.0536, 0.9699, 0.8761]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9393, 0.0560, 0.5479,  ..., 0.4533, 0.0776, 0.5900])
+tensor([0.8727, 0.2072, 0.9866,  ..., 0.7187, 0.7974, 0.6886])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.236942052841187 seconds
+Time: 10.495816707611084 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    11,  ..., 24991, 24997, 25000]),
-       col_indices=tensor([ 752,  886,  972,  ...,  802, 1974, 3630]),
-       values=tensor([0.4437, 0.0647, 0.4607,  ..., 0.1209, 0.0125, 0.5794]),
+tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
+       col_indices=tensor([1486, 3242, 3522,  ..., 1754, 2627, 4146]),
+       values=tensor([0.1836, 0.4006, 0.2197,  ..., 0.0536, 0.9699, 0.8761]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9393, 0.0560, 0.5479,  ..., 0.4533, 0.0776, 0.5900])
+tensor([0.8727, 0.2072, 0.9866,  ..., 0.7187, 0.7974, 0.6886])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.236942052841187 seconds
+Time: 10.495816707611084 seconds
 
-[40.22, 39.73, 39.42, 38.78, 39.93, 38.71, 40.68, 38.8, 39.45, 38.55]
-[97.14]
-12.580924272537231
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 249519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.236942052841187, 'TIME_S_1KI': 0.04102670358907012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1222.1109838342666, 'W': 97.13999999999999}
-[40.22, 39.73, 39.42, 38.78, 39.93, 38.71, 40.68, 38.8, 39.45, 38.55, 40.16, 39.21, 38.74, 39.25, 39.41, 39.27, 38.96, 39.33, 38.96, 38.84]
-707.515
-35.37575
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 249519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.236942052841187, 'TIME_S_1KI': 0.04102670358907012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1222.1109838342666, 'W': 97.13999999999999, 'J_1KI': 4.897867432276767, 'W_1KI': 0.3893090305748259, 'W_D': 61.76424999999999, 'J_D': 777.0513520000576, 'W_D_1KI': 0.24753325398065876, 'J_D_1KI': 0.0009920417041614415}
+[40.75, 39.26, 39.42, 39.22, 39.47, 40.61, 39.23, 39.53, 39.63, 39.38]
+[98.84]
+12.896095752716064
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.495816707611084, 'TIME_S_1KI': 0.041775725028502735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.650104198456, 'W': 98.84}
+[40.75, 39.26, 39.42, 39.22, 39.47, 40.61, 39.23, 39.53, 39.63, 39.38, 40.47, 39.12, 40.14, 40.63, 39.59, 39.14, 39.11, 39.22, 39.39, 39.55]
+712.785
+35.63925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.495816707611084, 'TIME_S_1KI': 0.041775725028502735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.650104198456, 'W': 98.84, 'J_1KI': 5.073395786526361, 'W_1KI': 0.393405561172097, 'W_D': 63.200750000000006, 'J_D': 815.0429236434699, 'W_D_1KI': 0.25155328328862214, 'J_D_1KI': 0.0010012389779122206}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
index e6c8fdb..7edf7f8 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 146173, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.55378007888794, "TIME_S_1KI": 0.07220061214374707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1340.7953616142272, "W": 116.1, "J_1KI": 9.17266089916898, "W_1KI": 0.7942643306219342, "W_D": 80.21499999999999, "J_D": 926.372953763008, "W_D_1KI": 0.5487675562518385, "J_D_1KI": 0.0037542333827166336}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 151851, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.677409887313843, "TIME_S_1KI": 0.07031504492768466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.7484180927277, "W": 117.47, "J_1KI": 9.507664869462351, "W_1KI": 0.7735872664651534, "W_D": 81.37549999999999, "J_D": 1000.1340716481208, "W_D_1KI": 0.5358904452390829, "J_D_1KI": 0.0035290544365139706}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
index 021f115..f6f6dab 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.09166121482849121}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.027439117431640625}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     47,     94,  ..., 249894, 249942,
+tensor(crow_indices=tensor([     0,     55,    107,  ..., 249902, 249955,
                             250000]),
-       col_indices=tensor([ 119,  293,  345,  ..., 4744, 4847, 4998]),
-       values=tensor([0.2600, 0.0492, 0.0782,  ..., 0.6942, 0.7814, 0.7527]),
+       col_indices=tensor([  26,  155,  397,  ..., 4652, 4756, 4760]),
+       values=tensor([0.9134, 0.8993, 0.8423,  ..., 0.2444, 0.0288, 0.7023]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8315, 0.0983, 0.7447,  ..., 0.4668, 0.9945, 0.1855])
+tensor([0.3636, 0.7288, 0.4529,  ..., 0.6147, 0.2907, 0.3015])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.09166121482849121 seconds
+Time: 0.027439117431640625 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '114552', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.2285475730896}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '38266', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.645953893661499}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     57,    113,  ..., 249897, 249950,
+tensor(crow_indices=tensor([     0,     49,     92,  ..., 249897, 249949,
                             250000]),
-       col_indices=tensor([  60,   61,   88,  ..., 4754, 4809, 4933]),
-       values=tensor([0.8655, 0.3309, 0.5749,  ..., 0.8443, 0.2705, 0.0665]),
+       col_indices=tensor([ 507,  568,  655,  ..., 4839, 4844, 4959]),
+       values=tensor([0.4543, 0.3787, 0.6932,  ..., 0.4487, 0.3087, 0.1431]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6570, 0.9775, 0.7976,  ..., 0.2365, 0.6987, 0.3821])
+tensor([0.4167, 0.3931, 0.0326,  ..., 0.8288, 0.4472, 0.1506])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 8.2285475730896 seconds
+Time: 2.645953893661499 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '146173', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.55378007888794}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '151851', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.677409887313843}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     53,    109,  ..., 249914, 249951,
+tensor(crow_indices=tensor([     0,     62,    123,  ..., 249890, 249948,
                             250000]),
-       col_indices=tensor([  51,   99,  229,  ..., 4435, 4821, 4904]),
-       values=tensor([0.7585, 0.4725, 0.0422,  ..., 0.0029, 0.1086, 0.7072]),
+       col_indices=tensor([  53,   60,  101,  ..., 4781, 4787, 4941]),
+       values=tensor([0.8546, 0.9316, 0.6470,  ..., 0.1212, 0.6179, 0.4318]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3786, 0.1898, 0.9439,  ..., 0.4562, 0.4771, 0.6918])
+tensor([0.7208, 0.6556, 0.2590,  ..., 0.8294, 0.6979, 0.2347])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.55378007888794 seconds
+Time: 10.677409887313843 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     53,    109,  ..., 249914, 249951,
+tensor(crow_indices=tensor([     0,     62,    123,  ..., 249890, 249948,
                             250000]),
-       col_indices=tensor([  51,   99,  229,  ..., 4435, 4821, 4904]),
-       values=tensor([0.7585, 0.4725, 0.0422,  ..., 0.0029, 0.1086, 0.7072]),
+       col_indices=tensor([  53,   60,  101,  ..., 4781, 4787, 4941]),
+       values=tensor([0.8546, 0.9316, 0.6470,  ..., 0.1212, 0.6179, 0.4318]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3786, 0.1898, 0.9439,  ..., 0.4562, 0.4771, 0.6918])
+tensor([0.7208, 0.6556, 0.2590,  ..., 0.8294, 0.6979, 0.2347])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.55378007888794 seconds
+Time: 10.677409887313843 seconds
 
-[39.38, 39.69, 39.36, 39.25, 38.8, 39.79, 38.95, 39.94, 38.86, 45.03]
-[116.1]
-11.548624992370605
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 146173, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.55378007888794, 'TIME_S_1KI': 0.07220061214374707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.7953616142272, 'W': 116.1}
-[39.38, 39.69, 39.36, 39.25, 38.8, 39.79, 38.95, 39.94, 38.86, 45.03, 39.59, 39.72, 39.05, 38.89, 38.89, 39.88, 38.77, 40.8, 45.19, 39.74]
-717.7
-35.885000000000005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 146173, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.55378007888794, 'TIME_S_1KI': 0.07220061214374707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.7953616142272, 'W': 116.1, 'J_1KI': 9.17266089916898, 'W_1KI': 0.7942643306219342, 'W_D': 80.21499999999999, 'J_D': 926.372953763008, 'W_D_1KI': 0.5487675562518385, 'J_D_1KI': 0.0037542333827166336}
+[39.83, 39.62, 39.22, 39.2, 39.58, 39.98, 44.8, 39.52, 39.58, 39.44]
+[117.47]
+12.290358543395996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 151851, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.677409887313843, 'TIME_S_1KI': 0.07031504492768466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.7484180927277, 'W': 117.47}
+[39.83, 39.62, 39.22, 39.2, 39.58, 39.98, 44.8, 39.52, 39.58, 39.44, 40.11, 39.32, 39.47, 44.63, 39.38, 39.24, 39.42, 39.88, 39.66, 39.4]
+721.8900000000001
+36.094500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 151851, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.677409887313843, 'TIME_S_1KI': 0.07031504492768466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.7484180927277, 'W': 117.47, 'J_1KI': 9.507664869462351, 'W_1KI': 0.7735872664651534, 'W_D': 81.37549999999999, 'J_D': 1000.1340716481208, 'W_D_1KI': 0.5358904452390829, 'J_D_1KI': 0.0035290544365139706}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
index 4f9e011..0dcc054 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 92778, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.476318120956421, "TIME_S_1KI": 0.11291812844592922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1732.4749909281732, "W": 131.59, "J_1KI": 18.6733384091937, "W_1KI": 1.4183319321390848, "W_D": 96.0545, "J_D": 1264.6251160126926, "W_D_1KI": 1.0353154842742893, "J_D_1KI": 0.011159062323765217}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91742, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460400104522705, "TIME_S_1KI": 0.11401975218027409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1797.7089305996894, "W": 131.45, "J_1KI": 19.595266405786766, "W_1KI": 1.4328224804342613, "W_D": 95.54724999999999, "J_D": 1306.7032683091759, "W_D_1KI": 1.0414777310283185, "J_D_1KI": 0.011352245765607012}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
index 85eae8d..ee62fae 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.1557161808013916}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03591585159301758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     231,     495,  ..., 1249487,
-                            1249744, 1250000]),
-       col_indices=tensor([   9,   30,   58,  ..., 4828, 4865, 4971]),
-       values=tensor([0.7438, 0.5258, 0.4698,  ..., 0.4344, 0.2594, 0.0033]),
+tensor(crow_indices=tensor([      0,     282,     523,  ..., 1249534,
+                            1249768, 1250000]),
+       col_indices=tensor([  14,   20,   65,  ..., 4981, 4988, 4994]),
+       values=tensor([0.5427, 0.7626, 0.3688,  ..., 0.1462, 0.4395, 0.5084]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.1880, 0.8169, 0.5226,  ..., 0.2752, 0.9006, 0.0611])
+tensor([0.3604, 0.6234, 0.1526,  ..., 0.4035, 0.4868, 0.5530])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.1557161808013916 seconds
+Time: 0.03591585159301758 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67430', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 7.631251096725464}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '29235', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.345984697341919}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     248,     518,  ..., 1249509,
-                            1249753, 1250000]),
-       col_indices=tensor([  31,   45,  102,  ..., 4944, 4977, 4981]),
-       values=tensor([0.8150, 0.4433, 0.0676,  ..., 0.5361, 0.0056, 0.9882]),
+tensor(crow_indices=tensor([      0,     247,     522,  ..., 1249509,
+                            1249766, 1250000]),
+       col_indices=tensor([  11,   41,   47,  ..., 4983, 4993, 4996]),
+       values=tensor([0.4860, 0.1371, 0.2214,  ..., 0.6634, 0.1469, 0.9637]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.0156, 0.0219, 0.6064,  ..., 0.7934, 0.6259, 0.0204])
+tensor([0.5776, 0.4725, 0.0368,  ..., 0.6036, 0.3775, 0.0011])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 7.631251096725464 seconds
+Time: 3.345984697341919 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '92778', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.476318120956421}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91742', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460400104522705}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     269,     520,  ..., 1249470,
-                            1249738, 1250000]),
-       col_indices=tensor([  32,   37,   46,  ..., 4950, 4963, 4989]),
-       values=tensor([0.4206, 0.9091, 0.7478,  ..., 0.6711, 0.2779, 0.9141]),
+tensor(crow_indices=tensor([      0,     245,     499,  ..., 1249503,
+                            1249746, 1250000]),
+       col_indices=tensor([  11,   13,   56,  ..., 4961, 4967, 4994]),
+       values=tensor([0.1643, 0.9353, 0.3976,  ..., 0.1683, 0.6963, 0.8462]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6953, 0.1111, 0.6307,  ..., 0.1029, 0.6511, 0.8226])
+tensor([0.6649, 0.4073, 0.4211,  ..., 0.1078, 0.2188, 0.0388])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.476318120956421 seconds
+Time: 10.460400104522705 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     269,     520,  ..., 1249470,
-                            1249738, 1250000]),
-       col_indices=tensor([  32,   37,   46,  ..., 4950, 4963, 4989]),
-       values=tensor([0.4206, 0.9091, 0.7478,  ..., 0.6711, 0.2779, 0.9141]),
+tensor(crow_indices=tensor([      0,     245,     499,  ..., 1249503,
+                            1249746, 1250000]),
+       col_indices=tensor([  11,   13,   56,  ..., 4961, 4967, 4994]),
+       values=tensor([0.1643, 0.9353, 0.3976,  ..., 0.1683, 0.6963, 0.8462]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6953, 0.1111, 0.6307,  ..., 0.1029, 0.6511, 0.8226])
+tensor([0.6649, 0.4073, 0.4211,  ..., 0.1078, 0.2188, 0.0388])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.476318120956421 seconds
+Time: 10.460400104522705 seconds
 
-[40.71, 39.95, 38.97, 39.83, 39.79, 39.14, 38.93, 39.78, 39.42, 39.71]
-[131.59]
-13.165704011917114
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92778, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.476318120956421, 'TIME_S_1KI': 0.11291812844592922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1732.4749909281732, 'W': 131.59}
-[40.71, 39.95, 38.97, 39.83, 39.79, 39.14, 38.93, 39.78, 39.42, 39.71, 40.83, 39.04, 39.95, 38.93, 39.14, 39.24, 39.86, 38.92, 39.75, 38.89]
-710.71
-35.5355
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92778, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.476318120956421, 'TIME_S_1KI': 0.11291812844592922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1732.4749909281732, 'W': 131.59, 'J_1KI': 18.6733384091937, 'W_1KI': 1.4183319321390848, 'W_D': 96.0545, 'J_D': 1264.6251160126926, 'W_D_1KI': 1.0353154842742893, 'J_D_1KI': 0.011159062323765217}
+[40.58, 39.92, 39.93, 39.37, 39.59, 39.9, 39.71, 40.07, 40.76, 39.67]
+[131.45]
+13.675990343093872
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460400104522705, 'TIME_S_1KI': 0.11401975218027409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1797.7089305996894, 'W': 131.45}
+[40.58, 39.92, 39.93, 39.37, 39.59, 39.9, 39.71, 40.07, 40.76, 39.67, 40.34, 39.45, 39.48, 40.31, 39.86, 39.84, 39.9, 39.88, 39.9, 39.78]
+718.055
+35.90275
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460400104522705, 'TIME_S_1KI': 0.11401975218027409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1797.7089305996894, 'W': 131.45, 'J_1KI': 19.595266405786766, 'W_1KI': 1.4328224804342613, 'W_D': 95.54724999999999, 'J_D': 1306.7032683091759, 'W_D_1KI': 1.0414777310283185, 'J_D_1KI': 0.011352245765607012}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
index d3bf1fe..44fa72d 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52513, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.26560354232788, "TIME_S_1KI": 0.19548689928832635, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1794.3579230117798, "W": 136.24, "J_1KI": 34.16978506297069, "W_1KI": 2.594405194904119, "W_D": 100.32050000000001, "J_D": 1321.2777746293546, "W_D_1KI": 1.9103936168186928, "J_D_1KI": 0.036379441601483306}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.236442565917969, "TIME_S_1KI": 0.195736706998833, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1815.1719595336915, "W": 138.62, "J_1KI": 34.70891178334688, "W_1KI": 2.650630055261296, "W_D": 102.202, "J_D": 1338.293208831787, "W_D_1KI": 1.9542612386943803, "J_D_1KI": 0.0373685151862321}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
index ca278ce..4fe4648 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.2491617202758789}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.053244590759277344}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     513,    1030,  ..., 2499018,
-                            2499503, 2500000]),
-       col_indices=tensor([   5,    7,    9,  ..., 4974, 4988, 4992]),
-       values=tensor([0.9314, 0.8722, 0.2786,  ..., 0.3461, 0.5001, 0.4531]),
+tensor(crow_indices=tensor([      0,     487,     976,  ..., 2498986,
+                            2499481, 2500000]),
+       col_indices=tensor([  13,   19,   40,  ..., 4975, 4977, 4981]),
+       values=tensor([0.0276, 0.4992, 0.8339,  ..., 0.1235, 0.3053, 0.8819]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5860, 0.7303, 0.0322,  ..., 0.3067, 0.0639, 0.6907])
+tensor([0.6982, 0.6943, 0.5654,  ..., 0.0343, 0.1924, 0.4615])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.2491617202758789 seconds
+Time: 0.053244590759277344 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42141', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.425995349884033}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19720', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.959235429763794}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     480,     969,  ..., 2498991,
-                            2499495, 2500000]),
-       col_indices=tensor([   1,    8,   15,  ..., 4990, 4995, 4997]),
-       values=tensor([0.6450, 0.7913, 0.7669,  ..., 0.2675, 0.7315, 0.7922]),
+tensor(crow_indices=tensor([      0,     500,     997,  ..., 2499001,
+                            2499525, 2500000]),
+       col_indices=tensor([  22,   31,   36,  ..., 4976, 4979, 4990]),
+       values=tensor([0.9139, 0.4529, 0.5623,  ..., 0.7413, 0.5022, 0.1210]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8872, 0.3458, 0.7222,  ..., 0.3185, 0.9459, 0.1327])
+tensor([0.1909, 0.5057, 0.7269,  ..., 0.6307, 0.9165, 0.6325])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 8.425995349884033 seconds
+Time: 3.959235429763794 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52513', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.26560354232788}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52297', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.236442565917969}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     496,    1000,  ..., 2499050,
-                            2499547, 2500000]),
-       col_indices=tensor([   1,    8,   12,  ..., 4944, 4951, 4977]),
-       values=tensor([0.2566, 0.4868, 0.9344,  ..., 0.5912, 0.8684, 0.6618]),
+tensor(crow_indices=tensor([      0,     495,     967,  ..., 2499009,
+                            2499501, 2500000]),
+       col_indices=tensor([   2,    3,   29,  ..., 4974, 4984, 4998]),
+       values=tensor([0.7947, 0.6825, 0.2906,  ..., 0.1208, 0.9049, 0.2265]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5960, 0.0213, 0.1088,  ..., 0.8621, 0.3601, 0.4544])
+tensor([0.7067, 0.8426, 0.8818,  ..., 0.1022, 0.5608, 0.1343])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.26560354232788 seconds
+Time: 10.236442565917969 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     496,    1000,  ..., 2499050,
-                            2499547, 2500000]),
-       col_indices=tensor([   1,    8,   12,  ..., 4944, 4951, 4977]),
-       values=tensor([0.2566, 0.4868, 0.9344,  ..., 0.5912, 0.8684, 0.6618]),
+tensor(crow_indices=tensor([      0,     495,     967,  ..., 2499009,
+                            2499501, 2500000]),
+       col_indices=tensor([   2,    3,   29,  ..., 4974, 4984, 4998]),
+       values=tensor([0.7947, 0.6825, 0.2906,  ..., 0.1208, 0.9049, 0.2265]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5960, 0.0213, 0.1088,  ..., 0.8621, 0.3601, 0.4544])
+tensor([0.7067, 0.8426, 0.8818,  ..., 0.1022, 0.5608, 0.1343])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.26560354232788 seconds
+Time: 10.236442565917969 seconds
 
-[47.36, 40.59, 40.08, 39.29, 39.8, 40.2, 40.01, 39.07, 40.08, 38.96]
-[136.24]
-13.170566082000732
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.26560354232788, 'TIME_S_1KI': 0.19548689928832635, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1794.3579230117798, 'W': 136.24}
-[47.36, 40.59, 40.08, 39.29, 39.8, 40.2, 40.01, 39.07, 40.08, 38.96, 40.34, 40.06, 39.51, 39.55, 39.19, 39.83, 39.16, 39.75, 38.95, 39.88]
-718.3900000000001
-35.919500000000006
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.26560354232788, 'TIME_S_1KI': 0.19548689928832635, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1794.3579230117798, 'W': 136.24, 'J_1KI': 34.16978506297069, 'W_1KI': 2.594405194904119, 'W_D': 100.32050000000001, 'J_D': 1321.2777746293546, 'W_D_1KI': 1.9103936168186928, 'J_D_1KI': 0.036379441601483306}
+[40.97, 39.52, 39.62, 39.4, 39.87, 45.09, 39.68, 39.56, 39.77, 39.67]
+[138.62]
+13.094589233398438
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.236442565917969, 'TIME_S_1KI': 0.195736706998833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1815.1719595336915, 'W': 138.62}
+[40.97, 39.52, 39.62, 39.4, 39.87, 45.09, 39.68, 39.56, 39.77, 39.67, 41.69, 43.46, 41.5, 39.76, 39.97, 39.88, 39.62, 39.85, 40.84, 39.61]
+728.3600000000001
+36.418000000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.236442565917969, 'TIME_S_1KI': 0.195736706998833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1815.1719595336915, 'W': 138.62, 'J_1KI': 34.70891178334688, 'W_1KI': 2.650630055261296, 'W_D': 102.202, 'J_D': 1338.293208831787, 'W_D_1KI': 1.9542612386943803, 'J_D_1KI': 0.0373685151862321}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..c26577c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28289, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.465468645095825, "TIME_S_1KI": 0.36994834193841514, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1925.348158209324, "W": 138.77, "J_1KI": 68.05995822437428, "W_1KI": 4.905440277139524, "W_D": 102.84225, "J_D": 1426.8727867954374, "W_D_1KI": 3.6354148255505674, "J_D_1KI": 0.1285098386493184}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..4cf3fef
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.07305312156677246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1021,    1969,  ..., 4997949,
+                            4999001, 5000000]),
+       col_indices=tensor([   1,    7,   18,  ..., 4990, 4995, 4998]),
+       values=tensor([0.6231, 0.9951, 0.8252,  ..., 0.9756, 0.2344, 0.2983]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0355, 0.1933, 0.9788,  ..., 0.6197, 0.2365, 0.0393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.07305312156677246 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14373', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.334789037704468}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1024,    2040,  ..., 4998012,
+                            4999011, 5000000]),
+       col_indices=tensor([   1,    7,   11,  ..., 4993, 4994, 4998]),
+       values=tensor([0.0452, 0.7491, 0.1728,  ..., 0.4616, 0.1426, 0.7347]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8794, 0.6343, 0.4463,  ..., 0.6355, 0.8597, 0.5087])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 5.334789037704468 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28289', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.465468645095825}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     999,    2016,  ..., 4997966,
+                            4998976, 5000000]),
+       col_indices=tensor([   2,    9,   14,  ..., 4993, 4994, 4995]),
+       values=tensor([0.1135, 0.5000, 0.4923,  ..., 0.1880, 0.5290, 0.9229]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0610, 0.8156, 0.2755,  ..., 0.7165, 0.9008, 0.9624])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.465468645095825 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     999,    2016,  ..., 4997966,
+                            4998976, 5000000]),
+       col_indices=tensor([   2,    9,   14,  ..., 4993, 4994, 4995]),
+       values=tensor([0.1135, 0.5000, 0.4923,  ..., 0.1880, 0.5290, 0.9229]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0610, 0.8156, 0.2755,  ..., 0.7165, 0.9008, 0.9624])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.465468645095825 seconds
+
+[40.8, 40.7, 39.71, 40.23, 39.76, 39.64, 39.79, 39.68, 39.73, 39.72]
+[138.77]
+13.874383211135864
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28289, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.465468645095825, 'TIME_S_1KI': 0.36994834193841514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1925.348158209324, 'W': 138.77}
+[40.8, 40.7, 39.71, 40.23, 39.76, 39.64, 39.79, 39.68, 39.73, 39.72, 40.37, 39.65, 40.3, 39.61, 40.3, 40.15, 39.64, 39.78, 39.65, 39.58]
+718.555
+35.927749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28289, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.465468645095825, 'TIME_S_1KI': 0.36994834193841514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1925.348158209324, 'W': 138.77, 'J_1KI': 68.05995822437428, 'W_1KI': 4.905440277139524, 'W_D': 102.84225, 'J_D': 1426.8727867954374, 'W_D_1KI': 3.6354148255505674, 'J_D_1KI': 0.1285098386493184}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..edb21c0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.708929538726807, "TIME_S_1KI": 0.5530043655423086, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1994.3257465744018, "W": 137.56, "J_1KI": 102.98609587267761, "W_1KI": 7.103537309579138, "W_D": 101.59625, "J_D": 1472.9283013260365, "W_D_1KI": 5.246385231087013, "J_D_1KI": 0.270921003412704}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..4cf90e5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.0932457447052002}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1472,    2953,  ..., 7496947,
+                            7498464, 7500000]),
+       col_indices=tensor([   7,    9,   10,  ..., 4989, 4991, 4994]),
+       values=tensor([0.9868, 0.9731, 0.3711,  ..., 0.9277, 0.6596, 0.4560]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.4150, 0.1407, 0.7534,  ..., 0.5098, 0.0887, 0.6433])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.0932457447052002 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11260', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.105090618133545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1440,    2968,  ..., 7497040,
+                            7498552, 7500000]),
+       col_indices=tensor([   4,   15,   16,  ..., 4993, 4997, 4998]),
+       values=tensor([0.0079, 0.6033, 0.5837,  ..., 0.4070, 0.1537, 0.2862]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9942, 0.8351, 0.2634,  ..., 0.1203, 0.3761, 0.2393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 6.105090618133545 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19365', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.708929538726807}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1483,    3029,  ..., 7497007,
+                            7498484, 7500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4991, 4992, 4999]),
+       values=tensor([0.2037, 0.3378, 0.5245,  ..., 0.5597, 0.6700, 0.6684]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7739, 0.8705, 0.9400,  ..., 0.4166, 0.9328, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.708929538726807 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1483,    3029,  ..., 7497007,
+                            7498484, 7500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4991, 4992, 4999]),
+       values=tensor([0.2037, 0.3378, 0.5245,  ..., 0.5597, 0.6700, 0.6684]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7739, 0.8705, 0.9400,  ..., 0.4166, 0.9328, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.708929538726807 seconds
+
+[42.29, 39.85, 39.72, 39.53, 39.58, 40.0, 40.36, 39.5, 39.8, 40.06]
+[137.56]
+14.4978609085083
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.708929538726807, 'TIME_S_1KI': 0.5530043655423086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1994.3257465744018, 'W': 137.56}
+[42.29, 39.85, 39.72, 39.53, 39.58, 40.0, 40.36, 39.5, 39.8, 40.06, 40.39, 40.11, 40.04, 39.88, 41.07, 39.92, 39.57, 39.46, 39.73, 39.57]
+719.275
+35.96375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.708929538726807, 'TIME_S_1KI': 0.5530043655423086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1994.3257465744018, 'W': 137.56, 'J_1KI': 102.98609587267761, 'W_1KI': 7.103537309579138, 'W_D': 101.59625, 'J_D': 1472.9283013260365, 'W_D_1KI': 5.246385231087013, 'J_D_1KI': 0.270921003412704}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d0ddde8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4054, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.937983989715576, "TIME_S_1KI": 2.6980720250901764, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1733.5753089761736, "W": 118.18, "J_1KI": 427.62094449338275, "W_1KI": 29.15145535273804, "W_D": 74.48075000000001, "J_D": 1092.5536401593092, "W_D_1KI": 18.37216329551061, "J_D_1KI": 4.531860704368675}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..fbc9c8c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.25896668434143066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1967,     3942,  ...,  9996007,
+                             9997987, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4989, 4995, 4996]),
+       values=tensor([0.9037, 0.0824, 0.8127,  ..., 0.2074, 0.6033, 0.5497]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6227, 0.6614, 0.9902,  ..., 0.2660, 0.9614, 0.3260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.25896668434143066 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4054', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.937983989715576}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2032,     3978,  ...,  9995983,
+                             9998000, 10000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4988, 4993, 4994]),
+       values=tensor([0.2457, 0.5907, 0.9941,  ..., 0.3357, 0.2301, 0.2269]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2230, 0.0807, 0.5998,  ..., 0.1430, 0.1498, 0.1360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.937983989715576 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2032,     3978,  ...,  9995983,
+                             9998000, 10000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4988, 4993, 4994]),
+       values=tensor([0.2457, 0.5907, 0.9941,  ..., 0.3357, 0.2301, 0.2269]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2230, 0.0807, 0.5998,  ..., 0.1430, 0.1498, 0.1360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.937983989715576 seconds
+
+[40.89, 40.0, 39.72, 39.6, 39.91, 39.64, 39.68, 39.81, 39.88, 45.41]
+[118.18]
+14.66893982887268
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.937983989715576, 'TIME_S_1KI': 2.6980720250901764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1733.5753089761736, 'W': 118.18}
+[40.89, 40.0, 39.72, 39.6, 39.91, 39.64, 39.68, 39.81, 39.88, 45.41, 40.78, 40.13, 44.65, 73.6, 81.22, 73.52, 67.52, 42.65, 48.49, 40.85]
+873.9849999999999
+43.69924999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.937983989715576, 'TIME_S_1KI': 2.6980720250901764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1733.5753089761736, 'W': 118.18, 'J_1KI': 427.62094449338275, 'W_1KI': 29.15145535273804, 'W_D': 74.48075000000001, 'J_D': 1092.5536401593092, 'W_D_1KI': 18.37216329551061, 'J_D_1KI': 4.531860704368675}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..a25c6eb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.1781005859375, "TIME_S_1KI": 2.708382274065327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1956.303444838524, "W": 122.9, "J_1KI": 520.570368504131, "W_1KI": 32.70356572645024, "W_D": 86.40525000000001, "J_D": 1375.3855836219193, "W_D_1KI": 22.992349654071315, "J_D_1KI": 6.118240993632601}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..fd072be
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.29501914978027344}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2490,     5019,  ..., 12495032,
+                            12497514, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4992, 4993, 4994]),
+       values=tensor([0.4521, 0.6419, 0.1807,  ..., 0.6429, 0.2936, 0.1963]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.6822, 0.2314, 0.5095,  ..., 0.2635, 0.2792, 0.8048])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.29501914978027344 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3559', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.942713260650635}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2478,     4936,  ..., 12495093,
+                            12497610, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4992, 4995, 4998]),
+       values=tensor([0.6608, 0.6509, 0.8650,  ..., 0.2551, 0.6130, 0.3679]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9258, 0.3878, 0.0027,  ..., 0.4707, 0.4169, 0.1792])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.942713260650635 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3758', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.1781005859375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2527,     4930,  ..., 12495017,
+                            12497540, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4997, 4999]),
+       values=tensor([0.2576, 0.5438, 0.7818,  ..., 0.1593, 0.4265, 0.7530]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0335, 0.1835, 0.7330,  ..., 0.5684, 0.8047, 0.4810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.1781005859375 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2527,     4930,  ..., 12495017,
+                            12497540, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4997, 4999]),
+       values=tensor([0.2576, 0.5438, 0.7818,  ..., 0.1593, 0.4265, 0.7530]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0335, 0.1835, 0.7330,  ..., 0.5684, 0.8047, 0.4810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.1781005859375 seconds
+
+[41.47, 40.36, 39.99, 40.33, 39.95, 40.28, 39.98, 39.88, 39.78, 45.28]
+[122.9]
+15.917847394943237
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.1781005859375, 'TIME_S_1KI': 2.708382274065327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.303444838524, 'W': 122.9}
+[41.47, 40.36, 39.99, 40.33, 39.95, 40.28, 39.98, 39.88, 39.78, 45.28, 40.38, 39.63, 39.74, 44.87, 40.1, 40.15, 40.75, 40.1, 40.4, 40.08]
+729.895
+36.494749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.1781005859375, 'TIME_S_1KI': 2.708382274065327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.303444838524, 'W': 122.9, 'J_1KI': 520.570368504131, 'W_1KI': 32.70356572645024, 'W_D': 86.40525000000001, 'J_D': 1375.3855836219193, 'W_D_1KI': 22.992349654071315, 'J_D_1KI': 6.118240993632601}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
index b172c0c..c2948bd 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 470922, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.104466915130615, "TIME_S_1KI": 0.021456773977708867, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1145.4606072449685, "W": 92.79, "J_1KI": 2.4323786258551703, "W_1KI": 0.19703900008918676, "W_D": 57.138000000000005, "J_D": 705.3489403681756, "W_D_1KI": 0.12133219514059655, "J_D_1KI": 0.0002576481777037313}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 505155, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3298921585083, "TIME_S_1KI": 0.020448955584935914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1240.863141503334, "W": 95.58, "J_1KI": 2.4564007908529737, "W_1KI": 0.1892092526056359, "W_D": 59.942499999999995, "J_D": 778.2008669131993, "W_D_1KI": 0.11866159891518444, "J_D_1KI": 0.0002349013647596964}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
index 263466b..107ac32 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06183266639709473}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.013948440551757812}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([2604,  880,   70, 3579, 4688, 1415, 4052, 2136, 2789,
-                           1920, 1039, 1558, 2117, 2959,  828,  201, 2786, 2764,
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-                           3135,   21, 1955,  802, 3984, 2259, 2773, 1786, 4464,
-                           4164, 2686, 4882, 4392, 2240, 1975, 2258]),
-       values=tensor([0.5027, 0.7084, 0.3487, 0.0753, 0.4164, 0.9980, 0.6580,
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-                      0.2972, 0.8380, 0.5688, 0.8007, 0.1354]),
+       col_indices=tensor([3548, 3508, 4386, 3528, 2702, 3004, 3629, 4756, 1243,
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+       values=tensor([0.6994, 0.2438, 0.4802, 0.0829, 0.0677, 0.0178, 0.7638,
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+                      0.8997, 0.2507, 0.1855, 0.6951, 0.2863, 0.1627, 0.3079,
+                      0.5000, 0.3625, 0.8186, 0.3705, 0.2957, 0.1551, 0.0216,
+                      0.3714, 0.8284, 0.9522, 0.8937, 0.5141, 0.0703, 0.2182,
+                      0.9274, 0.7097, 0.4349, 0.6001, 0.7581, 0.1855, 0.1138,
+                      0.0069, 0.0143, 0.6779, 0.4223, 0.2934, 0.1234, 0.5974,
+                      0.7303, 0.9182, 0.4432, 0.6166, 0.0534, 0.9601, 0.1664,
+                      0.7453, 0.2693, 0.7496, 0.1561, 0.1695, 0.4247, 0.5083,
+                      0.7464, 0.9108, 0.9708, 0.4346, 0.1849, 0.3357, 0.6306,
+                      0.3234, 0.0643, 0.0684, 0.2529, 0.3070, 0.5381, 0.4691,
+                      0.3912, 0.0111, 0.6019, 0.4700, 0.8282, 0.9967, 0.0138,
+                      0.0331, 0.4050, 0.1544, 0.2207, 0.6016, 0.9303, 0.9139,
+                      0.9840, 0.7431, 0.3482, 0.1124, 0.6413]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8800, 0.9246, 0.8175,  ..., 0.7580, 0.5437, 0.3847])
+tensor([0.3949, 0.9428, 0.0102,  ..., 0.0310, 0.9492, 0.7070])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,107 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.06183266639709473 seconds
+Time: 0.013948440551757812 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '169813', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.7862648963928223}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75277', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5646839141845703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4929, 3000, 2082, 1973, 3068,  607, 2961,   29,  351,
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-                           2961, 4938, 4706, 2892,  918, 4326, 4938, 1016, 1946,
-                           3193, 4622, 2689, 1925, 1828, 3491, 4755]),
-       values=tensor([6.5732e-01, 5.5709e-01, 9.0255e-01, 3.7373e-01,
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+       col_indices=tensor([3746, 1654, 2453, 1836, 1687, 4126,  577,  989, 1161,
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+                      0.2050, 0.6970, 0.0429, 0.1896, 0.1443]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0748, 0.8455, 0.5581,  ..., 0.9449, 0.9600, 0.8816])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -185,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 3.7862648963928223 seconds
+Time: 1.5646839141845703 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '470922', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.104466915130615}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '505155', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3298921585083}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.0961, 0.8665, 0.2552, 0.6175, 0.4850, 0.4310, 0.1165,
+                      0.3274, 0.7923, 0.1515, 0.5293, 0.8418, 0.1450, 0.8268,
+                      0.9665, 0.7626, 0.7605, 0.9986, 0.9489, 0.8011, 0.9290,
+                      0.5451, 0.8590, 0.5389, 0.0080, 0.8363, 0.8570, 0.5734,
+                      0.7613, 0.9018, 0.0697, 0.9293, 0.2543, 0.2531, 0.2854,
+                      0.3722, 0.6889, 0.4487, 0.3475, 0.2897]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4539, 0.8865, 0.6514,  ..., 0.0864, 0.1789, 0.3670])
+tensor([0.8985, 0.0813, 0.9894,  ..., 0.1805, 0.5543, 0.3501])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -266,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.104466915130615 seconds
+Time: 10.3298921585083 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
-       col_indices=tensor([ 286, 2098, 2957,   31, 4770, 3649, 4063, 3564, 3211,
-                            519,  372, 1653, 2583, 2464, 2987, 1744, 4556, 3480,
-                           4025,  965, 3221, 3480, 1237,  638, 2731, 2204, 3903,
-                            337, 4640, 3708, 4928,  846, 4069, 3241, 2342, 3155,
-                           3904, 4645, 1110, 3262,  206, 2707,  291, 1906, 3653,
-                           4410, 4931, 1727, 4173, 1383, 3385, 3838, 1305, 3168,
-                           1375, 4057, 2761, 2787, 2307,    6, 2503, 1872, 3680,
-                           2234, 1597, 3084, 1758,  491, 3779, 4890, 3184,  831,
-                            331, 2968, 3525, 1971,  454,  168, 2971, 2622, 1099,
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-                            958, 4865, 2336,  403, 4990,  684,  801, 4446,  671,
-                           4256, 4579, 3616,  522, 3560, 4436, 4875, 4839, 4252,
-                           2678, 3408,  277, 1706, 3353, 4272,  200, 4495, 1971,
-                           1057, 2080, 4776, 2636, 1840, 1457, 1455, 3267,  879,
-                           4146, 2502, 4940, 2313,   21, 1504,  535, 3781,  367,
-                           2250,  357, 4188,  146, 2230, 1761, 1304, 1785,  442,
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-                           2873, 2237, 1134, 2086, 3420, 2896, 4246, 2080, 1618,
-                            978, 1465, 2116, 4506, 3634, 1205, 3062,  601, 2140,
-                            765, 3494, 3345,  738, 3535, 3354, 3147, 4390,  602,
-                           4817, 1923, 2074,   44, 1678, 4913, 1057, 4051, 3685,
-                           2781, 3899, 4448, 4692, 1277,  259, 2144, 2798, 4087,
-                           2596, 4771, 4479,  733, 3005, 1161, 3811, 3147, 4464,
-                           4683,  773, 3834, 3088, 1039, 3766, 2820, 3923, 3718,
-                           3049, 1976,  990, 3587, 2696, 4263, 2139, 3191, 1101,
-                           4701, 4465,  551, 3012, 2514, 2260, 1927, 3611, 4115,
-                           4664,  772, 3814, 2744, 2328,  560, 3629, 3666, 4110,
-                           1272,  515, 3230, 2775, 3191, 4516, 1702]),
-       values=tensor([0.4950, 0.4387, 0.7062, 0.8184, 0.9685, 0.9491, 0.6387,
-                      0.3930, 0.4627, 0.2264, 0.4673, 0.2803, 0.8352, 0.7116,
-                      0.3144, 0.9721, 0.1277, 0.9601, 0.0123, 0.3968, 0.9183,
-                      0.0517, 0.5676, 0.9009, 0.4901, 0.3378, 0.4750, 0.6307,
-                      0.7160, 0.7754, 0.8317, 0.5508, 0.6443, 0.1719, 0.1190,
-                      0.2292, 0.9505, 0.2302, 0.5965, 0.4343, 0.9706, 0.9472,
-                      0.7071, 0.4120, 0.5080, 0.6133, 0.5804, 0.7848, 0.1131,
-                      0.7398, 0.2113, 0.5136, 0.9362, 0.4868, 0.7307, 0.9542,
-                      0.1907, 0.7842, 0.0075, 0.1654, 0.1604, 0.5554, 0.9265,
-                      0.9594, 0.1847, 0.0412, 0.1458, 0.3185, 0.9474, 0.7262,
-                      0.9867, 0.9175, 0.8563, 0.0555, 0.5865, 0.1402, 0.0777,
-                      0.1693, 0.3284, 0.8041, 0.3119, 0.6054, 0.1208, 0.1474,
-                      0.6411, 0.6397, 0.9233, 0.0205, 0.1838, 0.9985, 0.4716,
-                      0.4977, 0.8331, 0.9916, 0.5989, 0.7640, 0.9210, 0.4278,
-                      0.0911, 0.8508, 0.2547, 0.5851, 0.9233, 0.2665, 0.1213,
-                      0.8754, 0.6206, 0.7311, 0.2194, 0.9834, 0.8122, 0.4946,
-                      0.7260, 0.9509, 0.7893, 0.0815, 0.9968, 0.5027, 0.3558,
-                      0.7001, 0.1542, 0.3964, 0.0402, 0.9298, 0.1070, 0.4902,
-                      0.8333, 0.6213, 0.7680, 0.5975, 0.2149, 0.9396, 0.8765,
-                      0.8836, 0.3422, 0.3496, 0.7499, 0.8855, 0.3598, 0.7125,
-                      0.1563, 0.2571, 0.2028, 0.2313, 0.3287, 0.3989, 0.4172,
-                      0.9776, 0.9673, 0.6099, 0.3489, 0.5171, 0.3263, 0.3550,
-                      0.8206, 0.1824, 0.1805, 0.0479, 0.6241, 0.3393, 0.7730,
-                      0.0623, 0.4418, 0.3306, 0.0692, 0.1691, 0.9139, 0.9289,
-                      0.1653, 0.5991, 0.0793, 0.6308, 0.8611, 0.1878, 0.5735,
-                      0.8923, 0.1845, 0.1387, 0.3446, 0.0333, 0.5909, 0.0051,
-                      0.6730, 0.2001, 0.7864, 0.3596, 0.6702, 0.7444, 0.5210,
-                      0.7057, 0.5369, 0.0193, 0.2647, 0.1729, 0.2634, 0.6010,
-                      0.4976, 0.7177, 0.7966, 0.8166, 0.9702, 0.2066, 0.9091,
-                      0.4739, 0.8346, 0.6718, 0.2794, 0.6249, 0.0434, 0.4190,
-                      0.9938, 0.9770, 0.8053, 0.5102, 0.4949, 0.5149, 0.3290,
-                      0.8346, 0.3511, 0.4625, 0.1176, 0.9732, 0.6568, 0.0814,
-                      0.1466, 0.9735, 0.9996, 0.5023, 0.0806, 0.6393, 0.9851,
-                      0.9968, 0.7168, 0.8555, 0.4797, 0.5400, 0.6489, 0.3087,
-                      0.4955, 0.2041, 0.9406, 0.8471, 0.5173, 0.1622, 0.0921,
-                      0.5950, 0.5479, 0.1406, 0.5404, 0.7323]),
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1247, 4227, 2384, 1324, 2688,  777, 3611, 2096, 3777,
+                           4017, 3060, 3853,  275, 2455, 1734, 3565,   37, 2244,
+                           1733, 4350, 1234, 2896, 3213, 1561, 1980, 4472, 4663,
+                            506, 4951, 3982, 1179,  751, 4549, 4447, 2215,  404,
+                            243, 3834, 4527, 3698, 4091, 3198, 2812, 3614, 4523,
+                           1643, 1713, 1084, 2707,    4, 4057, 1938, 3151, 2591,
+                            736, 3345,  437, 2659,  175, 3499, 4582, 1472,  654,
+                           3080, 4209,  728, 2500, 4977, 4359, 4315, 4550, 1180,
+                             65, 1612, 4551, 4050, 2941, 4296, 4434, 1223, 1238,
+                           1086, 3880, 4530, 1316, 2102, 4761, 3908, 1050, 3705,
+                           3194, 4362, 3882,  122,  751, 3713, 3799, 1795, 2520,
+                           1510, 2238, 4244, 1885, 2083, 3076, 2704, 4833, 3679,
+                           4672, 3718, 2996, 4020, 4241, 4395,  853, 4475, 4071,
+                           1868, 2791,   21, 4190, 1530, 3067, 1932, 3665, 3853,
+                           1426, 1044, 3601, 1259, 2412,  133, 3883, 2099, 3331,
+                           2256, 2974, 3456, 1750, 1889, 4841, 2200, 4977, 1425,
+                           1536,  158, 2655, 3709, 3160, 4144, 3380, 1076, 1185,
+                           3818, 1983, 1852, 4777, 1300,  431, 3274, 3575, 4837,
+                            325, 4137, 4592,  671,  998, 3275, 4613,  992, 3846,
+                           2916, 2447, 2605, 2672, 2276, 4850, 4965, 1297,  694,
+                            259, 1199, 4640, 4880, 3240, 1438,   11, 2797, 2545,
+                           3917, 4254,  214, 1182, 3713, 4287, 4785, 2360, 1203,
+                           4398, 3290, 2436, 3411, 3739,  928, 1526,  663, 2059,
+                           3931,  404, 1876, 3217, 4800, 4377, 4206,  877, 1100,
+                           1979, 2229, 3016,  180,  945, 4382, 2217, 3064,  609,
+                           1772, 4045, 3022, 1213, 4864, 3162, 1697, 2645,  550,
+                           2485, 4784, 4550,  726, 4920, 1331,  357, 3709, 4153,
+                           3528, 2484, 3864, 1866, 4322,  727, 4703]),
+       values=tensor([0.4663, 0.4340, 0.1210, 0.0765, 0.6336, 0.1902, 0.3091,
+                      0.4203, 0.8220, 0.0850, 0.1025, 0.1361, 0.5908, 0.4685,
+                      0.3710, 0.6463, 0.1664, 0.6973, 0.9489, 0.6122, 0.1354,
+                      0.4027, 0.3554, 0.9045, 0.3464, 0.4691, 0.3320, 0.2792,
+                      0.0515, 0.5607, 0.4585, 0.0483, 0.1589, 0.9453, 0.6795,
+                      0.0575, 0.3527, 0.3951, 0.3470, 0.3333, 0.5355, 0.7562,
+                      0.1077, 0.3035, 0.4048, 0.6937, 0.2967, 0.6561, 0.1737,
+                      0.9035, 0.4547, 0.3603, 0.1408, 0.4830, 0.8065, 0.3582,
+                      0.5455, 0.5723, 0.4523, 0.5057, 0.6324, 0.9028, 0.0577,
+                      0.4956, 0.6657, 0.9848, 0.6391, 0.6395, 0.4769, 0.0056,
+                      0.1044, 0.2976, 0.6221, 0.9725, 0.0205, 0.3420, 0.9773,
+                      0.7515, 0.8006, 0.2336, 0.4394, 0.9756, 0.3057, 0.6186,
+                      0.7004, 0.6806, 0.0982, 0.4285, 0.8466, 0.0638, 0.7804,
+                      0.2686, 0.1423, 0.1658, 0.0440, 0.8558, 0.2543, 0.9629,
+                      0.0443, 0.2812, 0.7112, 0.2679, 0.0325, 0.1914, 0.4697,
+                      0.5212, 0.5036, 0.8278, 0.2212, 0.7637, 0.6411, 0.8324,
+                      0.1085, 0.7903, 0.3041, 0.7685, 0.7610, 0.1247, 0.0153,
+                      0.0898, 0.0347, 0.7987, 0.9119, 0.5255, 0.4670, 0.2413,
+                      0.8008, 0.0124, 0.0719, 0.3268, 0.5991, 0.1237, 0.5734,
+                      0.5860, 0.8964, 0.6851, 0.7980, 0.9940, 0.8001, 0.3866,
+                      0.4921, 0.3129, 0.6180, 0.1949, 0.6009, 0.4868, 0.5484,
+                      0.3765, 0.9472, 0.7499, 0.8386, 0.8281, 0.0445, 0.8521,
+                      0.1827, 0.1424, 0.9964, 0.2930, 0.6405, 0.8880, 0.8455,
+                      0.8730, 0.0256, 0.3303, 0.4997, 0.7068, 0.9986, 0.4515,
+                      0.8492, 0.9495, 0.7252, 0.1393, 0.3125, 0.0955, 0.0411,
+                      0.0855, 0.4594, 0.0571, 0.4760, 0.9753, 0.9680, 0.6765,
+                      0.6632, 0.7732, 0.2205, 0.7219, 0.1231, 0.5507, 0.9386,
+                      0.9853, 0.1484, 0.5471, 0.2415, 0.3412, 0.3970, 0.9721,
+                      0.4075, 0.7397, 0.6041, 0.4919, 0.1150, 0.1028, 0.3707,
+                      0.5907, 0.4305, 0.9162, 0.9956, 0.3282, 0.6112, 0.6540,
+                      0.0961, 0.8665, 0.2552, 0.6175, 0.4850, 0.4310, 0.1165,
+                      0.3274, 0.7923, 0.1515, 0.5293, 0.8418, 0.1450, 0.8268,
+                      0.9665, 0.7626, 0.7605, 0.9986, 0.9489, 0.8011, 0.9290,
+                      0.5451, 0.8590, 0.5389, 0.0080, 0.8363, 0.8570, 0.5734,
+                      0.7613, 0.9018, 0.0697, 0.9293, 0.2543, 0.2531, 0.2854,
+                      0.3722, 0.6889, 0.4487, 0.3475, 0.2897]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4539, 0.8865, 0.6514,  ..., 0.0864, 0.1789, 0.3670])
+tensor([0.8985, 0.0813, 0.9894,  ..., 0.1805, 0.5543, 0.3501])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -344,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.104466915130615 seconds
+Time: 10.3298921585083 seconds
 
-[39.77, 39.06, 39.0, 43.22, 38.95, 38.87, 39.0, 38.96, 40.09, 38.55]
-[92.79]
-12.344655752182007
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 470922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.104466915130615, 'TIME_S_1KI': 0.021456773977708867, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1145.4606072449685, 'W': 92.79}
-[39.77, 39.06, 39.0, 43.22, 38.95, 38.87, 39.0, 38.96, 40.09, 38.55, 44.05, 41.96, 39.49, 38.5, 38.79, 39.12, 39.88, 38.31, 39.38, 38.55]
-713.04
-35.652
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 470922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.104466915130615, 'TIME_S_1KI': 0.021456773977708867, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1145.4606072449685, 'W': 92.79, 'J_1KI': 2.4323786258551703, 'W_1KI': 0.19703900008918676, 'W_D': 57.138000000000005, 'J_D': 705.3489403681756, 'W_D_1KI': 0.12133219514059655, 'J_D_1KI': 0.0002576481777037313}
+[40.32, 39.94, 39.79, 39.67, 40.95, 39.71, 39.34, 39.43, 39.56, 39.61]
+[95.58]
+12.982455968856812
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 505155, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3298921585083, 'TIME_S_1KI': 0.020448955584935914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.863141503334, 'W': 95.58}
+[40.32, 39.94, 39.79, 39.67, 40.95, 39.71, 39.34, 39.43, 39.56, 39.61, 39.84, 39.42, 39.39, 39.45, 39.34, 39.21, 39.4, 39.18, 39.13, 39.91]
+712.75
+35.6375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 505155, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3298921585083, 'TIME_S_1KI': 0.020448955584935914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.863141503334, 'W': 95.58, 'J_1KI': 2.4564007908529737, 'W_1KI': 0.1892092526056359, 'W_D': 59.942499999999995, 'J_D': 778.2008669131993, 'W_D_1KI': 0.11866159891518444, 'J_D_1KI': 0.0002349013647596964}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..1cf76cc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 461197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.877047538757324, "TIME_S_1KI": 0.023584384848030937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.180625166893, "W": 95.74, "J_1KI": 2.6478503224585004, "W_1KI": 0.20759024885244265, "W_D": 60.15774999999999, "J_D": 767.3227360939383, "W_D_1KI": 0.13043829426470682, "J_D_1KI": 0.0002828255480081328}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..09b3a13
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0320582389831543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2956,  558, 3504,  ..., 1528, 4784, 1878]),
+       values=tensor([0.5224, 0.1438, 0.5941,  ..., 0.0368, 0.6760, 0.3012]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8976, 0.5094, 0.6995,  ..., 0.0327, 0.1649, 0.7937])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.0320582389831543 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '32752', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.745659589767456}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1743, 4461,  346,  ..., 1137, 3893, 4349]),
+       values=tensor([0.7861, 0.9854, 0.5411,  ..., 0.5282, 0.2898, 0.9587]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5708, 0.3567, 0.0850,  ..., 0.6472, 0.1624, 0.7150])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.745659589767456 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '461197', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.877047538757324}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4244, 4483, 4692,  ..., 3607, 4429,  290]),
+       values=tensor([0.6080, 0.0136, 0.3918,  ..., 0.5066, 0.3391, 0.6977]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1101, 0.0872, 0.5048,  ..., 0.5059, 0.1642, 0.4124])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.877047538757324 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4244, 4483, 4692,  ..., 3607, 4429,  290]),
+       values=tensor([0.6080, 0.0136, 0.3918,  ..., 0.5066, 0.3391, 0.6977]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1101, 0.0872, 0.5048,  ..., 0.5059, 0.1642, 0.4124])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.877047538757324 seconds
+
+[42.19, 40.35, 39.27, 40.36, 39.17, 39.06, 39.17, 39.21, 39.04, 39.45]
+[95.74]
+12.755176782608032
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 461197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.877047538757324, 'TIME_S_1KI': 0.023584384848030937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.180625166893, 'W': 95.74}
+[42.19, 40.35, 39.27, 40.36, 39.17, 39.06, 39.17, 39.21, 39.04, 39.45, 39.84, 39.7, 39.64, 39.58, 39.75, 39.24, 39.17, 39.36, 39.12, 39.43]
+711.645
+35.58225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 461197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.877047538757324, 'TIME_S_1KI': 0.023584384848030937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.180625166893, 'W': 95.74, 'J_1KI': 2.6478503224585004, 'W_1KI': 0.20759024885244265, 'W_D': 60.15774999999999, 'J_D': 767.3227360939383, 'W_D_1KI': 0.13043829426470682, 'J_D_1KI': 0.0002828255480081328}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
index 648e77d..b0c2a19 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33926, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.632460117340088, "TIME_S_1KI": 0.31340152441608465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1251.6098043680192, "W": 88.42000000000002, "J_1KI": 36.892348180393185, "W_1KI": 2.606260685020339, "W_D": 71.92675000000001, "J_D": 1018.1432424375416, "W_D_1KI": 2.120106997582975, "J_D_1KI": 0.062492100382685115}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33525, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643972158432007, "TIME_S_1KI": 0.3174935766870099, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.4049115991593, "W": 88.39, "J_1KI": 36.43265955553048, "W_1KI": 2.6365398956002983, "W_D": 72.1155, "J_D": 996.5179986698627, "W_D_1KI": 2.151096196868009, "J_D_1KI": 0.06416394323245365}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
index aff18b0..dc5d053 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.309490442276001}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04636812210083008}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      15,  ...,  999979,
-                             999989, 1000000]),
-       col_indices=tensor([ 8594, 29009, 41843,  ..., 77886, 78317, 95347]),
-       values=tensor([0.9328, 0.5746, 0.1196,  ..., 0.5058, 0.9583, 0.4434]),
+tensor(crow_indices=tensor([      0,       5,      13,  ...,  999983,
+                             999994, 1000000]),
+       col_indices=tensor([ 8176, 34026, 54478,  ..., 84998, 92494, 98961]),
+       values=tensor([0.4351, 0.9999, 0.3437,  ..., 0.3684, 0.7357, 0.5729]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8206, 0.6612, 0.6620,  ..., 0.9270, 0.4872, 0.3406])
+tensor([0.8699, 0.2767, 0.3378,  ..., 0.5349, 0.7243, 0.7857])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.309490442276001 seconds
+Time: 0.04636812210083008 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33926', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.632460117340088}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22644', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.091935634613037}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      18,  ...,  999986,
+tensor(crow_indices=tensor([      0,       6,      23,  ...,  999978,
                              999991, 1000000]),
-       col_indices=tensor([ 9555, 32072, 52846,  ..., 78086, 80072, 96075]),
-       values=tensor([0.9751, 0.3269, 0.5720,  ..., 0.0320, 0.6071, 0.6982]),
+       col_indices=tensor([26374, 42582, 44652,  ..., 65952, 74293, 78884]),
+       values=tensor([0.4256, 0.1611, 0.6127,  ..., 0.5242, 0.3400, 0.0348]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5445, 0.0121, 0.5604,  ..., 0.3280, 0.5430, 0.6322])
+tensor([0.0369, 0.6183, 0.8933,  ..., 0.8293, 0.4628, 0.9829])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.632460117340088 seconds
+Time: 7.091935634613037 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33525', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643972158432007}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      18,  ...,  999986,
-                             999991, 1000000]),
-       col_indices=tensor([ 9555, 32072, 52846,  ..., 78086, 80072, 96075]),
-       values=tensor([0.9751, 0.3269, 0.5720,  ..., 0.0320, 0.6071, 0.6982]),
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999978,
+                             999994, 1000000]),
+       col_indices=tensor([ 9594, 11946, 25379,  ..., 52892, 57506, 73818]),
+       values=tensor([0.4978, 0.8076, 0.6002,  ..., 0.2925, 0.4675, 0.5122]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5445, 0.0121, 0.5604,  ..., 0.3280, 0.5430, 0.6322])
+tensor([0.8765, 0.8954, 0.8874,  ..., 0.8137, 0.3245, 0.8007])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.632460117340088 seconds
+Time: 10.643972158432007 seconds
 
-[18.53, 17.97, 18.07, 18.07, 17.99, 18.09, 21.33, 17.98, 18.39, 17.84]
-[88.42]
-14.155279397964478
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.632460117340088, 'TIME_S_1KI': 0.31340152441608465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.6098043680192, 'W': 88.42000000000002}
-[18.53, 17.97, 18.07, 18.07, 17.99, 18.09, 21.33, 17.98, 18.39, 17.84, 18.71, 17.91, 17.99, 18.02, 18.68, 18.08, 17.92, 18.59, 18.24, 18.01]
-329.865
-16.49325
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.632460117340088, 'TIME_S_1KI': 0.31340152441608465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.6098043680192, 'W': 88.42000000000002, 'J_1KI': 36.892348180393185, 'W_1KI': 2.606260685020339, 'W_D': 71.92675000000001, 'J_D': 1018.1432424375416, 'W_D_1KI': 2.120106997582975, 'J_D_1KI': 0.062492100382685115}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999978,
+                             999994, 1000000]),
+       col_indices=tensor([ 9594, 11946, 25379,  ..., 52892, 57506, 73818]),
+       values=tensor([0.4978, 0.8076, 0.6002,  ..., 0.2925, 0.4675, 0.5122]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8765, 0.8954, 0.8874,  ..., 0.8137, 0.3245, 0.8007])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.643972158432007 seconds
+
+[18.37, 17.76, 17.76, 17.79, 18.13, 17.85, 18.03, 17.72, 18.33, 18.48]
+[88.39]
+13.818360805511475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643972158432007, 'TIME_S_1KI': 0.3174935766870099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4049115991593, 'W': 88.39}
+[18.37, 17.76, 17.76, 17.79, 18.13, 17.85, 18.03, 17.72, 18.33, 18.48, 18.41, 18.08, 18.64, 18.55, 18.21, 18.02, 18.07, 18.01, 17.96, 17.9]
+325.49
+16.2745
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643972158432007, 'TIME_S_1KI': 0.3174935766870099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4049115991593, 'W': 88.39, 'J_1KI': 36.43265955553048, 'W_1KI': 2.6365398956002983, 'W_D': 72.1155, 'J_D': 996.5179986698627, 'W_D_1KI': 2.151096196868009, 'J_D_1KI': 0.06416394323245365}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
index b1b6585..7fe967e 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2890, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.78333592414856, "TIME_S_1KI": 3.731258105241716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1463.5743112421035, "W": 81.86, "J_1KI": 506.4270973156068, "W_1KI": 28.325259515570934, "W_D": 65.62225000000001, "J_D": 1173.259703712523, "W_D_1KI": 22.706660899653983, "J_D_1KI": 7.856976089845669}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2660, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.18576693534851, "TIME_S_1KI": 3.829235689980643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1328.5220910072328, "W": 81.2, "J_1KI": 499.44439511550104, "W_1KI": 30.526315789473685, "W_D": 64.453, "J_D": 1054.5225902917387, "W_D_1KI": 24.23045112781955, "J_D_1KI": 9.10919215331562}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
index 1352df1..8cde86c 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.6327288150787354}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.39462947845458984}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       95,      182,  ...,  9999803,
-                             9999900, 10000000]),
-       col_indices=tensor([ 1164,  1511,  2606,  ..., 97059, 99366, 99637]),
-       values=tensor([0.1789, 0.4314, 0.0466,  ..., 0.4339, 0.7049, 0.9540]),
+tensor(crow_indices=tensor([       0,      104,      208,  ...,  9999814,
+                             9999906, 10000000]),
+       col_indices=tensor([ 1924,  2222,  4663,  ..., 98435, 98556, 99127]),
+       values=tensor([0.9193, 0.2961, 0.8826,  ..., 0.2999, 0.4100, 0.0457]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.5756, 0.3189, 0.9065,  ..., 0.6359, 0.4482, 0.1651])
+tensor([0.8186, 0.3714, 0.9798,  ..., 0.9009, 0.9275, 0.2252])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 3.6327288150787354 seconds
+Time: 0.39462947845458984 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2890', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.78333592414856}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2660', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.18576693534851}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      118,      207,  ...,  9999808,
-                             9999910, 10000000]),
-       col_indices=tensor([  712,   968,  1059,  ..., 96997, 98856, 99104]),
-       values=tensor([0.5177, 0.6712, 0.5343,  ..., 0.8226, 0.3425, 0.6939]),
+tensor(crow_indices=tensor([       0,      111,      209,  ...,  9999785,
+                             9999908, 10000000]),
+       col_indices=tensor([ 4849,  5332,  5597,  ..., 99100, 99293, 99777]),
+       values=tensor([0.3984, 0.3126, 0.3684,  ..., 0.2469, 0.5703, 0.8605]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8858, 0.8376, 0.8837,  ..., 0.6861, 0.2657, 0.8920])
+tensor([0.6012, 0.7247, 0.1820,  ..., 0.8515, 0.6518, 0.6577])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.78333592414856 seconds
+Time: 10.18576693534851 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      118,      207,  ...,  9999808,
-                             9999910, 10000000]),
-       col_indices=tensor([  712,   968,  1059,  ..., 96997, 98856, 99104]),
-       values=tensor([0.5177, 0.6712, 0.5343,  ..., 0.8226, 0.3425, 0.6939]),
+tensor(crow_indices=tensor([       0,      111,      209,  ...,  9999785,
+                             9999908, 10000000]),
+       col_indices=tensor([ 4849,  5332,  5597,  ..., 99100, 99293, 99777]),
+       values=tensor([0.3984, 0.3126, 0.3684,  ..., 0.2469, 0.5703, 0.8605]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8858, 0.8376, 0.8837,  ..., 0.6861, 0.2657, 0.8920])
+tensor([0.6012, 0.7247, 0.1820,  ..., 0.8515, 0.6518, 0.6577])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.78333592414856 seconds
+Time: 10.18576693534851 seconds
 
-[18.4, 18.1, 18.05, 18.1, 18.29, 18.11, 18.1, 17.9, 17.96, 17.98]
-[81.86]
-17.878992319107056
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2890, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.78333592414856, 'TIME_S_1KI': 3.731258105241716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1463.5743112421035, 'W': 81.86}
-[18.4, 18.1, 18.05, 18.1, 18.29, 18.11, 18.1, 17.9, 17.96, 17.98, 18.26, 17.88, 17.92, 17.96, 18.1, 17.9, 17.98, 18.14, 18.06, 17.77]
-324.755
-16.23775
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2890, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.78333592414856, 'TIME_S_1KI': 3.731258105241716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1463.5743112421035, 'W': 81.86, 'J_1KI': 506.4270973156068, 'W_1KI': 28.325259515570934, 'W_D': 65.62225000000001, 'J_D': 1173.259703712523, 'W_D_1KI': 22.706660899653983, 'J_D_1KI': 7.856976089845669}
+[18.33, 18.01, 18.45, 18.12, 17.88, 17.91, 18.33, 22.42, 18.21, 17.9]
+[81.2]
+16.361109495162964
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.18576693534851, 'TIME_S_1KI': 3.829235689980643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1328.5220910072328, 'W': 81.2}
+[18.33, 18.01, 18.45, 18.12, 17.88, 17.91, 18.33, 22.42, 18.21, 17.9, 22.63, 20.65, 18.09, 18.03, 18.02, 18.26, 18.0, 18.17, 17.93, 18.06]
+334.94000000000005
+16.747000000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.18576693534851, 'TIME_S_1KI': 3.829235689980643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1328.5220910072328, 'W': 81.2, 'J_1KI': 499.44439511550104, 'W_1KI': 30.526315789473685, 'W_D': 64.453, 'J_D': 1054.5225902917387, 'W_D_1KI': 24.23045112781955, 'J_D_1KI': 9.10919215331562}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
index c182694..fdd737d 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64311, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.418502807617188, "TIME_S_1KI": 0.16200187849072767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1158.3267517518998, "W": 82.98, "J_1KI": 18.011331681234932, "W_1KI": 1.2902924849559174, "W_D": 66.6565, "J_D": 930.4652582327127, "W_D_1KI": 1.036471210212872, "J_D_1KI": 0.01611654631731542}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64522, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3600492477417, "TIME_S_1KI": 0.1605661518201807, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1143.5515343093873, "W": 83.36000000000001, "J_1KI": 17.723435949124134, "W_1KI": 1.291962431418741, "W_D": 66.67275000000001, "J_D": 914.6320244616867, "W_D_1KI": 1.0333335916431607, "J_D_1KI": 0.01601521328605996}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
index 055aab1..8218685 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.17745423316955566}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03250718116760254}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      3,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([42546, 58983, 86183,  ..., 98460, 14991, 73616]),
-       values=tensor([0.4174, 0.2060, 0.0899,  ..., 0.6212, 0.4971, 0.7481]),
+       col_indices=tensor([12882, 21465, 63858,  ..., 96153,  4715, 69382]),
+       values=tensor([0.1495, 0.9028, 0.7353,  ..., 0.9651, 0.0553, 0.0388]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8074, 0.4851, 0.0283,  ..., 0.2070, 0.7576, 0.4733])
+tensor([0.6296, 0.2324, 0.7696,  ..., 0.0819, 0.5051, 0.6795])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.17745423316955566 seconds
+Time: 0.03250718116760254 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '59170', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.660528182983398}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '32300', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.256327390670776}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([96712,  9860, 17593,  ..., 59712, 70511, 99970]),
-       values=tensor([0.7958, 0.9740, 0.0109,  ..., 0.7243, 0.7214, 0.8821]),
+       col_indices=tensor([24100, 22524, 41698,  ..., 71518, 54296, 46275]),
+       values=tensor([0.6729, 0.0195, 0.1396,  ..., 0.6516, 0.4177, 0.7883]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4741, 0.0741, 0.4151,  ..., 0.2722, 0.2577, 0.9729])
+tensor([0.6330, 0.0132, 0.4522,  ..., 0.1249, 0.8426, 0.7168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 9.660528182983398 seconds
+Time: 5.256327390670776 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64311', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.418502807617188}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64522', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3600492477417}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99999,
                             100000]),
-       col_indices=tensor([60832, 83948,   658,  ..., 83631, 80017, 34658]),
-       values=tensor([0.5224, 0.7895, 0.2144,  ..., 0.4897, 0.2214, 0.9534]),
+       col_indices=tensor([63372, 90175, 43637,  ..., 48404, 84175, 41742]),
+       values=tensor([0.0143, 0.7083, 0.4138,  ..., 0.7171, 0.1589, 0.0907]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6371, 0.8407, 0.9472,  ..., 0.9476, 0.5347, 0.4303])
+tensor([0.2672, 0.1313, 0.6231,  ..., 0.4829, 0.5251, 0.7815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.418502807617188 seconds
+Time: 10.3600492477417 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99999,
                             100000]),
-       col_indices=tensor([60832, 83948,   658,  ..., 83631, 80017, 34658]),
-       values=tensor([0.5224, 0.7895, 0.2144,  ..., 0.4897, 0.2214, 0.9534]),
+       col_indices=tensor([63372, 90175, 43637,  ..., 48404, 84175, 41742]),
+       values=tensor([0.0143, 0.7083, 0.4138,  ..., 0.7171, 0.1589, 0.0907]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6371, 0.8407, 0.9472,  ..., 0.9476, 0.5347, 0.4303])
+tensor([0.2672, 0.1313, 0.6231,  ..., 0.4829, 0.5251, 0.7815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.418502807617188 seconds
+Time: 10.3600492477417 seconds
 
-[18.9, 18.01, 18.86, 18.3, 17.96, 18.02, 18.19, 17.91, 18.92, 17.86]
-[82.98]
-13.959107637405396
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64311, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.418502807617188, 'TIME_S_1KI': 0.16200187849072767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.3267517518998, 'W': 82.98}
-[18.9, 18.01, 18.86, 18.3, 17.96, 18.02, 18.19, 17.91, 18.92, 17.86, 18.32, 17.96, 18.01, 17.83, 18.19, 17.85, 17.88, 18.01, 18.1, 17.86]
-326.47
-16.323500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64311, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.418502807617188, 'TIME_S_1KI': 0.16200187849072767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.3267517518998, 'W': 82.98, 'J_1KI': 18.011331681234932, 'W_1KI': 1.2902924849559174, 'W_D': 66.6565, 'J_D': 930.4652582327127, 'W_D_1KI': 1.036471210212872, 'J_D_1KI': 0.01611654631731542}
+[18.53, 17.8, 18.06, 18.37, 18.25, 17.96, 18.14, 21.36, 18.43, 17.88]
+[83.36]
+13.718228578567505
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64522, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3600492477417, 'TIME_S_1KI': 0.1605661518201807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1143.5515343093873, 'W': 83.36000000000001}
+[18.53, 17.8, 18.06, 18.37, 18.25, 17.96, 18.14, 21.36, 18.43, 17.88, 18.55, 17.91, 18.58, 19.12, 20.8, 18.14, 18.33, 17.85, 18.25, 17.83]
+333.745
+16.68725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64522, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3600492477417, 'TIME_S_1KI': 0.1605661518201807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1143.5515343093873, 'W': 83.36000000000001, 'J_1KI': 17.723435949124134, 'W_1KI': 1.291962431418741, 'W_D': 66.67275000000001, 'J_D': 914.6320244616867, 'W_D_1KI': 1.0333335916431607, 'J_D_1KI': 0.01601521328605996}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..07c28cc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.460205316543579, "TIME_S_1KI": 0.22407363258951157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1228.6304648017883, "W": 87.08, "J_1KI": 26.319147954281917, "W_1KI": 1.865387087099953, "W_D": 70.695, "J_D": 997.4509727740286, "W_D_1KI": 1.51439527012553, "J_D_1KI": 0.03244066814030097}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..0398e28
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.03882646560668945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 499993, 499995,
+                            500000]),
+       col_indices=tensor([20130, 29829, 49027,  ..., 32515, 51857, 99803]),
+       values=tensor([0.4194, 0.2208, 0.8236,  ..., 0.3620, 0.7637, 0.5129]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7254, 0.4636, 0.7256,  ..., 0.4819, 0.1264, 0.5273])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.03882646560668945 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27043', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.0826029777526855}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 499987, 499992,
+                            500000]),
+       col_indices=tensor([19007, 19428, 24486,  ..., 87536, 92504, 96559]),
+       values=tensor([0.8398, 0.0370, 0.5128,  ..., 0.3625, 0.4907, 0.6853]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6134, 0.6519, 0.5356,  ..., 0.0589, 0.6530, 0.3358])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 6.0826029777526855 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46682', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.460205316543579}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ..., 499992, 499995,
+                            500000]),
+       col_indices=tensor([ 9218, 31473, 33160,  ..., 57052, 72094, 94375]),
+       values=tensor([0.1819, 0.5310, 0.0116,  ..., 0.3541, 0.3048, 0.3110]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6007, 0.7506, 0.6846,  ..., 0.1657, 0.4869, 0.7821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.460205316543579 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ..., 499992, 499995,
+                            500000]),
+       col_indices=tensor([ 9218, 31473, 33160,  ..., 57052, 72094, 94375]),
+       values=tensor([0.1819, 0.5310, 0.0116,  ..., 0.3541, 0.3048, 0.3110]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6007, 0.7506, 0.6846,  ..., 0.1657, 0.4869, 0.7821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.460205316543579 seconds
+
+[18.32, 17.95, 18.46, 18.2, 18.15, 17.94, 18.28, 18.06, 18.31, 18.04]
+[87.08]
+14.109215259552002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.460205316543579, 'TIME_S_1KI': 0.22407363258951157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1228.6304648017883, 'W': 87.08}
+[18.32, 17.95, 18.46, 18.2, 18.15, 17.94, 18.28, 18.06, 18.31, 18.04, 18.26, 18.28, 17.89, 18.48, 18.48, 18.03, 17.99, 18.35, 18.44, 18.2]
+327.69999999999993
+16.384999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.460205316543579, 'TIME_S_1KI': 0.22407363258951157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1228.6304648017883, 'W': 87.08, 'J_1KI': 26.319147954281917, 'W_1KI': 1.865387087099953, 'W_D': 70.695, 'J_D': 997.4509727740286, 'W_D_1KI': 1.51439527012553, 'J_D_1KI': 0.03244066814030097}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
index c677488..1b64231 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253635, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.510948419570923, "TIME_S_1KI": 0.04144123807664921, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.5495847654342, "W": 74.66, "J_1KI": 4.1380313630430905, "W_1KI": 0.29436000551974295, "W_D": 58.32449999999999, "J_D": 819.9096538528203, "W_D_1KI": 0.22995446212076406, "J_D_1KI": 0.0009066353702003433}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253108, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.677898168563843, "TIME_S_1KI": 0.0421871223689644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1070.6826658773423, "W": 74.61, "J_1KI": 4.230141543836395, "W_1KI": 0.2947753528138186, "W_D": 58.27775, "J_D": 836.3084939194918, "W_D_1KI": 0.23024855002607583, "J_D_1KI": 0.0009096849962311576}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
index d7cd9dc..1ec4855 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.057019948959350586}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02065730094909668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     5,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([5511, 5632, 9392,  ..., 1424, 5807, 9708]),
-       values=tensor([0.8862, 0.8794, 0.5579,  ..., 0.8535, 0.8536, 0.3017]),
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([6728, 7614, 8179,  ..., 1004, 2058, 8025]),
+       values=tensor([0.0279, 0.0803, 0.4096,  ..., 0.0871, 0.5549, 0.2943]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8843, 0.1620, 0.1106,  ..., 0.3314, 0.8529, 0.5084])
+tensor([0.5482, 0.4991, 0.5547,  ..., 0.6547, 0.2547, 0.5094])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.057019948959350586 seconds
+Time: 0.02065730094909668 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '184146', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.623284816741943}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50829', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.10860276222229}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     3,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([5228, 7612, 8334,  ..., 8947, 2750, 8241]),
-       values=tensor([0.5331, 0.8440, 0.9594,  ..., 0.6439, 0.5967, 0.7449]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([5153, 2587, 4463,  ..., 5061, 9520, 1424]),
+       values=tensor([0.2204, 0.6183, 0.5613,  ..., 0.3086, 0.3306, 0.5938]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9017, 0.2905, 0.1618,  ..., 0.3745, 0.4560, 0.4176])
+tensor([0.9842, 0.7142, 0.2121,  ..., 0.3434, 0.1561, 0.6145])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 7.623284816741943 seconds
+Time: 2.10860276222229 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253635', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.510948419570923}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253108', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.677898168563843}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([ 773, 7277, 5799,  ..., 6666, 7394, 1954]),
-       values=tensor([0.1024, 0.0437, 0.8987,  ..., 0.7237, 0.2930, 0.3597]),
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([7291,  527, 4481,  ..., 6785, 7922, 1484]),
+       values=tensor([0.3434, 0.3822, 0.2401,  ..., 0.8298, 0.4309, 0.4668]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1275, 0.0118, 0.1480,  ..., 0.4560, 0.1036, 0.8618])
+tensor([0.2882, 0.0253, 0.9805,  ..., 0.1323, 0.6315, 0.0794])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.510948419570923 seconds
+Time: 10.677898168563843 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([ 773, 7277, 5799,  ..., 6666, 7394, 1954]),
-       values=tensor([0.1024, 0.0437, 0.8987,  ..., 0.7237, 0.2930, 0.3597]),
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([7291,  527, 4481,  ..., 6785, 7922, 1484]),
+       values=tensor([0.3434, 0.3822, 0.2401,  ..., 0.8298, 0.4309, 0.4668]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1275, 0.0118, 0.1480,  ..., 0.4560, 0.1036, 0.8618])
+tensor([0.2882, 0.0253, 0.9805,  ..., 0.1323, 0.6315, 0.0794])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.510948419570923 seconds
+Time: 10.677898168563843 seconds
 
-[18.3, 17.87, 18.03, 17.87, 18.05, 17.86, 19.1, 17.97, 18.04, 17.74]
-[74.66]
-14.057722806930542
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.510948419570923, 'TIME_S_1KI': 0.04144123807664921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.5495847654342, 'W': 74.66}
-[18.3, 17.87, 18.03, 17.87, 18.05, 17.86, 19.1, 17.97, 18.04, 17.74, 18.05, 17.87, 18.1, 17.95, 17.96, 18.0, 19.85, 17.84, 18.3, 18.01]
-326.71000000000004
-16.335500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.510948419570923, 'TIME_S_1KI': 0.04144123807664921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.5495847654342, 'W': 74.66, 'J_1KI': 4.1380313630430905, 'W_1KI': 0.29436000551974295, 'W_D': 58.32449999999999, 'J_D': 819.9096538528203, 'W_D_1KI': 0.22995446212076406, 'J_D_1KI': 0.0009066353702003433}
+[18.38, 17.89, 18.34, 17.77, 18.06, 17.77, 18.13, 17.86, 17.98, 18.23]
+[74.61]
+14.350390911102295
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.677898168563843, 'TIME_S_1KI': 0.0421871223689644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1070.6826658773423, 'W': 74.61}
+[18.38, 17.89, 18.34, 17.77, 18.06, 17.77, 18.13, 17.86, 17.98, 18.23, 18.53, 18.0, 18.04, 18.11, 18.16, 18.08, 18.2, 18.27, 19.29, 18.25]
+326.645
+16.33225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.677898168563843, 'TIME_S_1KI': 0.0421871223689644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1070.6826658773423, 'W': 74.61, 'J_1KI': 4.230141543836395, 'W_1KI': 0.2947753528138186, 'W_D': 58.27775, 'J_D': 836.3084939194918, 'W_D_1KI': 0.23024855002607583, 'J_D_1KI': 0.0009096849962311576}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
index 8f37619..090450b 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 197679, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.670233726501465, "TIME_S_1KI": 0.053977578430189674, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1114.4275546693802, "W": 79.73, "J_1KI": 5.637561676603889, "W_1KI": 0.40333065221900155, "W_D": 63.12950000000001, "J_D": 882.3937578389646, "W_D_1KI": 0.31935359851071693, "J_D_1KI": 0.001615516056387967}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 194593, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.473386764526367, "TIME_S_1KI": 0.053822011914747024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1083.1964892935753, "W": 79.61, "J_1KI": 5.566472017459905, "W_1KI": 0.40911029687604383, "W_D": 62.8515, "J_D": 855.175532556653, "W_D_1KI": 0.3229895217196919, "J_D_1KI": 0.001659820865702733}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
index 4a4c171..8858416 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06928658485412598}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.020806312561035156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     25,  ...,  99976,  99987,
+tensor(crow_indices=tensor([     0,      7,     14,  ...,  99985,  99993,
                             100000]),
-       col_indices=tensor([ 333,  360, 7030,  ..., 7825, 8274, 9549]),
-       values=tensor([0.8393, 0.7372, 0.2908,  ..., 0.1152, 0.3448, 0.5520]),
+       col_indices=tensor([5438, 7119, 8479,  ..., 6797, 6979, 8109]),
+       values=tensor([0.2056, 0.5255, 0.6332,  ..., 0.1682, 0.9365, 0.4633]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6596, 0.1551, 0.2351,  ..., 0.2147, 0.9669, 0.0099])
+tensor([0.0316, 0.8438, 0.1562,  ..., 0.6730, 0.8332, 0.9126])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.06928658485412598 seconds
+Time: 0.020806312561035156 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '151544', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.049443006515503}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50465', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.723015785217285}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     20,  ...,  99982,  99989,
+tensor(crow_indices=tensor([     0,     18,     34,  ...,  99978,  99989,
                             100000]),
-       col_indices=tensor([ 534,  848, 1028,  ..., 7528, 7587, 7919]),
-       values=tensor([0.8744, 0.7231, 0.5055,  ..., 0.6485, 0.2326, 0.7897]),
+       col_indices=tensor([ 818, 1321, 1616,  ..., 5603, 7366, 9704]),
+       values=tensor([0.8713, 0.7316, 0.2331,  ..., 0.6687, 0.3725, 0.7818]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6730, 0.3279, 0.8164,  ..., 0.2443, 0.5036, 0.1429])
+tensor([0.5742, 0.6233, 0.0987,  ..., 0.1452, 0.2067, 0.1195])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 8.049443006515503 seconds
+Time: 2.723015785217285 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '197679', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.670233726501465}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '194593', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.473386764526367}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     18,  ...,  99979,  99990,
+tensor(crow_indices=tensor([     0,     11,     16,  ...,  99982,  99989,
                             100000]),
-       col_indices=tensor([ 654,  920, 2120,  ..., 5173, 5860, 7868]),
-       values=tensor([0.9786, 0.8942, 0.8907,  ..., 0.0590, 0.7963, 0.5333]),
+       col_indices=tensor([1034, 3380, 4243,  ..., 7428, 9116, 9600]),
+       values=tensor([0.4227, 0.1092, 0.7794,  ..., 0.2113, 0.3090, 0.9237]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8342, 0.1347, 0.2067,  ..., 0.1241, 0.4408, 0.8118])
+tensor([0.7442, 0.3504, 0.6358,  ..., 0.6138, 0.7536, 0.9226])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.670233726501465 seconds
+Time: 10.473386764526367 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     18,  ...,  99979,  99990,
+tensor(crow_indices=tensor([     0,     11,     16,  ...,  99982,  99989,
                             100000]),
-       col_indices=tensor([ 654,  920, 2120,  ..., 5173, 5860, 7868]),
-       values=tensor([0.9786, 0.8942, 0.8907,  ..., 0.0590, 0.7963, 0.5333]),
+       col_indices=tensor([1034, 3380, 4243,  ..., 7428, 9116, 9600]),
+       values=tensor([0.4227, 0.1092, 0.7794,  ..., 0.2113, 0.3090, 0.9237]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8342, 0.1347, 0.2067,  ..., 0.1241, 0.4408, 0.8118])
+tensor([0.7442, 0.3504, 0.6358,  ..., 0.6138, 0.7536, 0.9226])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.670233726501465 seconds
+Time: 10.473386764526367 seconds
 
-[18.34, 17.98, 18.08, 17.94, 18.13, 18.09, 21.2, 18.15, 17.85, 18.53]
-[79.73]
-13.977518558502197
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 197679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.670233726501465, 'TIME_S_1KI': 0.053977578430189674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.4275546693802, 'W': 79.73}
-[18.34, 17.98, 18.08, 17.94, 18.13, 18.09, 21.2, 18.15, 17.85, 18.53, 18.16, 18.1, 18.61, 18.87, 18.17, 17.77, 20.46, 17.84, 18.3, 17.91]
-332.01
-16.6005
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 197679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.670233726501465, 'TIME_S_1KI': 0.053977578430189674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.4275546693802, 'W': 79.73, 'J_1KI': 5.637561676603889, 'W_1KI': 0.40333065221900155, 'W_D': 63.12950000000001, 'J_D': 882.3937578389646, 'W_D_1KI': 0.31935359851071693, 'J_D_1KI': 0.001615516056387967}
+[19.08, 18.28, 17.95, 17.9, 19.17, 18.22, 19.03, 18.14, 18.71, 20.45]
+[79.61]
+13.606286764144897
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 194593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.473386764526367, 'TIME_S_1KI': 0.053822011914747024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1964892935753, 'W': 79.61}
+[19.08, 18.28, 17.95, 17.9, 19.17, 18.22, 19.03, 18.14, 18.71, 20.45, 18.56, 18.36, 18.2, 17.96, 18.09, 18.17, 22.32, 18.37, 18.26, 17.99]
+335.17
+16.7585
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 194593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.473386764526367, 'TIME_S_1KI': 0.053822011914747024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1964892935753, 'W': 79.61, 'J_1KI': 5.566472017459905, 'W_1KI': 0.40911029687604383, 'W_D': 62.8515, 'J_D': 855.175532556653, 'W_D_1KI': 0.3229895217196919, 'J_D_1KI': 0.001659820865702733}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
index 23a3327..8c6811d 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58160, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.467525959014893, "TIME_S_1KI": 0.17997809420589567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.377723479271, "W": 87.15, "J_1KI": 21.10346842295858, "W_1KI": 1.4984525447042643, "W_D": 70.98275000000001, "J_D": 999.6861285289527, "W_D_1KI": 1.2204736932599725, "J_D_1KI": 0.020984760888238866}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57740, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.343472957611084, "TIME_S_1KI": 0.17913877654331634, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.6624715328217, "W": 87.4, "J_1KI": 21.123354200429883, "W_1KI": 1.5136820228611017, "W_D": 71.01700000000001, "J_D": 991.0385553872587, "W_D_1KI": 1.2299445791479044, "J_D_1KI": 0.021301430189606934}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
index 35e5d90..ff97c3a 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.19839882850646973}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03377032279968262}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      97,     186,  ...,  999796,
-                             999897, 1000000]),
-       col_indices=tensor([ 169,  359,  528,  ..., 9765, 9789, 9792]),
-       values=tensor([0.6521, 0.9085, 0.4727,  ..., 0.8814, 0.1698, 0.8627]),
+tensor(crow_indices=tensor([      0,     110,     219,  ...,  999820,
+                             999911, 1000000]),
+       col_indices=tensor([   0,   38,  265,  ..., 9703, 9904, 9960]),
+       values=tensor([0.3683, 0.9828, 0.4174,  ..., 0.4331, 0.2376, 0.7467]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7127, 0.9881, 0.6892,  ..., 0.7113, 0.3734, 0.9813])
+tensor([0.8519, 0.1445, 0.2230,  ..., 0.9278, 0.7204, 0.3614])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,39 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.19839882850646973 seconds
+Time: 0.03377032279968262 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52923', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.554424524307251}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31092', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.654046535491943}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     112,     189,  ...,  999798,
+tensor(crow_indices=tensor([      0,     102,     199,  ...,  999795,
+                             999900, 1000000]),
+       col_indices=tensor([ 113,  165,  189,  ..., 9912, 9940, 9996]),
+       values=tensor([0.2048, 0.9236, 0.8269,  ..., 0.2195, 0.4387, 0.6731]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8364, 0.2950, 0.2365,  ..., 0.2102, 0.7661, 0.3156])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 5.654046535491943 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '57740', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.343472957611084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     216,  ...,  999804,
                              999899, 1000000]),
-       col_indices=tensor([ 113,  156,  184,  ..., 9769, 9838, 9941]),
-       values=tensor([0.0187, 0.7839, 0.6319,  ..., 0.9818, 0.7594, 0.0765]),
+       col_indices=tensor([  61,   88,  117,  ..., 9666, 9676, 9799]),
+       values=tensor([0.7050, 0.8533, 0.9508,  ..., 0.3667, 0.6991, 0.8071]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4252, 0.8416, 0.9146,  ..., 0.0970, 0.6595, 0.8304])
+tensor([0.9939, 0.1893, 0.9694,  ..., 0.0779, 0.3428, 0.9229])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 9.554424524307251 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58160', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.467525959014893}
+Time: 10.343472957611084 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      93,     191,  ...,  999802,
+tensor(crow_indices=tensor([      0,     106,     216,  ...,  999804,
                              999899, 1000000]),
-       col_indices=tensor([  46,   78,  103,  ..., 9585, 9899, 9954]),
-       values=tensor([0.1947, 0.9409, 0.0413,  ..., 0.0261, 0.0318, 0.5135]),
+       col_indices=tensor([  61,   88,  117,  ..., 9666, 9676, 9799]),
+       values=tensor([0.7050, 0.8533, 0.9508,  ..., 0.3667, 0.6991, 0.8071]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1045, 0.5937, 0.6366,  ..., 0.8712, 0.6092, 0.3132])
+tensor([0.9939, 0.1893, 0.9694,  ..., 0.0779, 0.3428, 0.9229])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.467525959014893 seconds
+Time: 10.343472957611084 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      93,     191,  ...,  999802,
-                             999899, 1000000]),
-       col_indices=tensor([  46,   78,  103,  ..., 9585, 9899, 9954]),
-       values=tensor([0.1947, 0.9409, 0.0413,  ..., 0.0261, 0.0318, 0.5135]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1045, 0.5937, 0.6366,  ..., 0.8712, 0.6092, 0.3132])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 10.467525959014893 seconds
-
-[18.42, 18.1, 18.09, 17.94, 17.96, 18.13, 17.89, 17.87, 18.11, 18.12]
-[87.15]
-14.083508014678955
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.467525959014893, 'TIME_S_1KI': 0.17997809420589567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.377723479271, 'W': 87.15}
-[18.42, 18.1, 18.09, 17.94, 17.96, 18.13, 17.89, 17.87, 18.11, 18.12, 18.39, 17.75, 17.9, 17.92, 17.94, 17.89, 17.89, 17.89, 17.75, 17.72]
-323.34499999999997
-16.16725
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.467525959014893, 'TIME_S_1KI': 0.17997809420589567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.377723479271, 'W': 87.15, 'J_1KI': 21.10346842295858, 'W_1KI': 1.4984525447042643, 'W_D': 70.98275000000001, 'J_D': 999.6861285289527, 'W_D_1KI': 1.2204736932599725, 'J_D_1KI': 0.020984760888238866}
+[18.73, 18.07, 18.16, 18.31, 18.98, 17.81, 18.18, 18.17, 18.06, 18.83]
+[87.4]
+13.95494818687439
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.343472957611084, 'TIME_S_1KI': 0.17913877654331634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.6624715328217, 'W': 87.4}
+[18.73, 18.07, 18.16, 18.31, 18.98, 17.81, 18.18, 18.17, 18.06, 18.83, 18.31, 18.07, 18.19, 17.94, 18.17, 18.13, 18.04, 18.09, 18.22, 18.27]
+327.65999999999997
+16.383
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.343472957611084, 'TIME_S_1KI': 0.17913877654331634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.6624715328217, 'W': 87.4, 'J_1KI': 21.123354200429883, 'W_1KI': 1.5136820228611017, 'W_D': 71.01700000000001, 'J_D': 991.0385553872587, 'W_D_1KI': 1.2299445791479044, 'J_D_1KI': 0.021301430189606934}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
index 4e517f7..9882866 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8810, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.41358470916748, "TIME_S_1KI": 1.18201869570573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1329.5588334417344, "W": 83.19, "J_1KI": 150.91473705354534, "W_1KI": 9.442678774120317, "W_D": 66.8505, "J_D": 1068.4177520735263, "W_D_1KI": 7.588024971623155, "J_D_1KI": 0.8612968185724353}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8902, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.860300064086914, "TIME_S_1KI": 1.2199842803961933, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1340.0520877552033, "W": 84.52, "J_1KI": 150.53382248429602, "W_1KI": 9.49449561896203, "W_D": 68.1745, "J_D": 1080.896605024457, "W_D_1KI": 7.658335205571781, "J_D_1KI": 0.8602937773053}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
index 8074157..266a604 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1917307376861572}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.13720321655273438}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     493,     986,  ..., 4999011,
-                            4999486, 5000000]),
-       col_indices=tensor([   9,   19,   72,  ..., 9981, 9987, 9993]),
-       values=tensor([0.5847, 0.5648, 0.9368,  ..., 0.4963, 0.0551, 0.2254]),
+tensor(crow_indices=tensor([      0,     497,     987,  ..., 4998984,
+                            4999490, 5000000]),
+       col_indices=tensor([  31,   32,   48,  ..., 9945, 9978, 9990]),
+       values=tensor([0.2379, 0.1839, 0.8156,  ..., 0.3545, 0.1897, 0.0490]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1357, 0.6996, 0.1280,  ..., 0.8014, 0.9186, 0.9128])
+tensor([0.7954, 0.2457, 0.2337,  ..., 0.1008, 0.2602, 0.7172])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 1.1917307376861572 seconds
+Time: 0.13720321655273438 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8810', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.41358470916748}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7652', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.025035858154297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     520,    1021,  ..., 4999015,
-                            4999518, 5000000]),
-       col_indices=tensor([   2,   21,   23,  ..., 9856, 9947, 9960]),
-       values=tensor([0.9436, 0.1483, 0.1830,  ..., 0.0068, 0.4770, 0.7006]),
+tensor(crow_indices=tensor([      0,     488,     990,  ..., 4999027,
+                            4999510, 5000000]),
+       col_indices=tensor([  13,   24,   30,  ..., 9939, 9983, 9997]),
+       values=tensor([0.8521, 0.2131, 0.0790,  ..., 0.0763, 0.7991, 0.5452]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0991, 0.7135, 0.2277,  ..., 0.9430, 0.0011, 0.3680])
+tensor([0.7324, 0.5581, 0.6877,  ..., 0.3893, 0.7172, 0.4223])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.41358470916748 seconds
+Time: 9.025035858154297 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8902', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.860300064086914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     520,    1021,  ..., 4999015,
-                            4999518, 5000000]),
-       col_indices=tensor([   2,   21,   23,  ..., 9856, 9947, 9960]),
-       values=tensor([0.9436, 0.1483, 0.1830,  ..., 0.0068, 0.4770, 0.7006]),
+tensor(crow_indices=tensor([      0,     512,     999,  ..., 4999039,
+                            4999553, 5000000]),
+       col_indices=tensor([  40,   52,   78,  ..., 9943, 9982, 9986]),
+       values=tensor([0.6271, 0.3251, 0.6536,  ..., 0.8006, 0.2414, 0.7322]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0991, 0.7135, 0.2277,  ..., 0.9430, 0.0011, 0.3680])
+tensor([0.7511, 0.5744, 0.2092,  ..., 0.0810, 0.1870, 0.5605])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.41358470916748 seconds
+Time: 10.860300064086914 seconds
 
-[18.42, 18.1, 18.34, 18.2, 17.91, 17.99, 18.73, 17.98, 18.05, 17.95]
-[83.19]
-15.982195377349854
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8810, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.41358470916748, 'TIME_S_1KI': 1.18201869570573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1329.5588334417344, 'W': 83.19}
-[18.42, 18.1, 18.34, 18.2, 17.91, 17.99, 18.73, 17.98, 18.05, 17.95, 18.53, 18.25, 18.01, 18.36, 18.13, 18.03, 18.01, 18.04, 18.22, 17.98]
-326.79
-16.3395
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8810, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.41358470916748, 'TIME_S_1KI': 1.18201869570573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1329.5588334417344, 'W': 83.19, 'J_1KI': 150.91473705354534, 'W_1KI': 9.442678774120317, 'W_D': 66.8505, 'J_D': 1068.4177520735263, 'W_D_1KI': 7.588024971623155, 'J_D_1KI': 0.8612968185724353}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     512,     999,  ..., 4999039,
+                            4999553, 5000000]),
+       col_indices=tensor([  40,   52,   78,  ..., 9943, 9982, 9986]),
+       values=tensor([0.6271, 0.3251, 0.6536,  ..., 0.8006, 0.2414, 0.7322]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7511, 0.5744, 0.2092,  ..., 0.0810, 0.1870, 0.5605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.860300064086914 seconds
+
+[18.66, 18.42, 18.15, 17.87, 18.05, 18.01, 18.31, 17.87, 17.97, 18.0]
+[84.52]
+15.854851961135864
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8902, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.860300064086914, 'TIME_S_1KI': 1.2199842803961933, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.0520877552033, 'W': 84.52}
+[18.66, 18.42, 18.15, 17.87, 18.05, 18.01, 18.31, 17.87, 17.97, 18.0, 18.28, 17.95, 18.41, 17.97, 19.13, 18.12, 18.3, 17.86, 18.09, 17.92]
+326.91
+16.3455
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8902, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.860300064086914, 'TIME_S_1KI': 1.2199842803961933, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.0520877552033, 'W': 84.52, 'J_1KI': 150.53382248429602, 'W_1KI': 9.49449561896203, 'W_D': 68.1745, 'J_D': 1080.896605024457, 'W_D_1KI': 7.658335205571781, 'J_D_1KI': 0.8602937773053}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
index 3f0bb83..4142b7d 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2918, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.42000699043274, "TIME_S_1KI": 3.5709413949392523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1397.7840516352655, "W": 79.43, "J_1KI": 479.0212651251767, "W_1KI": 27.220699108978753, "W_D": 63.01475000000001, "J_D": 1108.913666974485, "W_D_1KI": 21.595185058259084, "J_D_1KI": 7.400680280417781}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2952, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.514222383499146, "TIME_S_1KI": 3.5617284496948325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1409.0968302583694, "W": 80.82, "J_1KI": 477.3363246132688, "W_1KI": 27.378048780487802, "W_D": 64.16999999999999, "J_D": 1118.8040534234044, "W_D_1KI": 21.737804878048774, "J_D_1KI": 7.363755039989422}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
index 099bdad..f33aa65 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.597771644592285}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.3909003734588623}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      977,     1956,  ...,  9997922,
-                             9998976, 10000000]),
-       col_indices=tensor([   2,    3,    9,  ..., 9970, 9977, 9979]),
-       values=tensor([0.1332, 0.2138, 0.7669,  ..., 0.0474, 0.1604, 0.1097]),
+tensor(crow_indices=tensor([       0,      946,     1929,  ...,  9998013,
+                             9999030, 10000000]),
+       col_indices=tensor([   1,   29,   66,  ..., 9951, 9961, 9963]),
+       values=tensor([0.1920, 0.8019, 0.0618,  ..., 0.8349, 0.9652, 0.3956]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2601, 0.5133, 0.4344,  ..., 0.1772, 0.3859, 0.7315])
+tensor([0.5191, 0.4835, 0.4753,  ..., 0.1633, 0.4541, 0.9422])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 3.597771644592285 seconds
+Time: 0.3909003734588623 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2918', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.42000699043274}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2686', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.552263259887695}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1029,     2018,  ...,  9998096,
-                             9999045, 10000000]),
-       col_indices=tensor([   7,   14,   18,  ..., 9941, 9949, 9980]),
-       values=tensor([0.9805, 0.4931, 0.0315,  ..., 0.9071, 0.5605, 0.7269]),
+tensor(crow_indices=tensor([       0,     1009,     2066,  ...,  9998034,
+                             9999022, 10000000]),
+       col_indices=tensor([   1,    6,   11,  ..., 9982, 9996, 9999]),
+       values=tensor([0.5825, 0.5025, 0.2695,  ..., 0.8369, 0.3596, 0.5616]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0123, 0.2996, 0.0215,  ..., 0.5909, 0.6219, 0.0073])
+tensor([0.8229, 0.3714, 0.9138,  ..., 0.5827, 0.9903, 0.1706])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.42000699043274 seconds
+Time: 9.552263259887695 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2952', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.514222383499146}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1029,     2018,  ...,  9998096,
-                             9999045, 10000000]),
-       col_indices=tensor([   7,   14,   18,  ..., 9941, 9949, 9980]),
-       values=tensor([0.9805, 0.4931, 0.0315,  ..., 0.9071, 0.5605, 0.7269]),
+tensor(crow_indices=tensor([       0,     1012,     2027,  ...,  9998023,
+                             9999029, 10000000]),
+       col_indices=tensor([  20,   21,   65,  ..., 9939, 9966, 9982]),
+       values=tensor([0.6149, 0.2165, 0.5741,  ..., 0.9222, 0.5603, 0.9724]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0123, 0.2996, 0.0215,  ..., 0.5909, 0.6219, 0.0073])
+tensor([0.1720, 0.3458, 0.5186,  ..., 0.7493, 0.6588, 0.1643])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.42000699043274 seconds
+Time: 10.514222383499146 seconds
 
-[21.55, 18.44, 17.92, 18.49, 18.01, 17.89, 18.03, 17.94, 18.06, 18.03]
-[79.43]
-17.597684144973755
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2918, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.42000699043274, 'TIME_S_1KI': 3.5709413949392523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.7840516352655, 'W': 79.43}
-[21.55, 18.44, 17.92, 18.49, 18.01, 17.89, 18.03, 17.94, 18.06, 18.03, 19.32, 18.84, 17.98, 18.09, 18.0, 17.91, 18.09, 18.07, 18.13, 17.93]
-328.30499999999995
-16.415249999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2918, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.42000699043274, 'TIME_S_1KI': 3.5709413949392523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.7840516352655, 'W': 79.43, 'J_1KI': 479.0212651251767, 'W_1KI': 27.220699108978753, 'W_D': 63.01475000000001, 'J_D': 1108.913666974485, 'W_D_1KI': 21.595185058259084, 'J_D_1KI': 7.400680280417781}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1012,     2027,  ...,  9998023,
+                             9999029, 10000000]),
+       col_indices=tensor([  20,   21,   65,  ..., 9939, 9966, 9982]),
+       values=tensor([0.6149, 0.2165, 0.5741,  ..., 0.9222, 0.5603, 0.9724]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1720, 0.3458, 0.5186,  ..., 0.7493, 0.6588, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.514222383499146 seconds
+
+[18.22, 18.02, 18.22, 18.83, 18.28, 18.32, 18.36, 18.07, 18.03, 17.97]
+[80.82]
+17.435001611709595
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2952, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.514222383499146, 'TIME_S_1KI': 3.5617284496948325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.0968302583694, 'W': 80.82}
+[18.22, 18.02, 18.22, 18.83, 18.28, 18.32, 18.36, 18.07, 18.03, 17.97, 19.01, 19.01, 17.78, 18.5, 20.32, 20.23, 18.29, 18.21, 17.96, 17.94]
+333.0
+16.65
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2952, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.514222383499146, 'TIME_S_1KI': 3.5617284496948325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.0968302583694, 'W': 80.82, 'J_1KI': 477.3363246132688, 'W_1KI': 27.378048780487802, 'W_D': 64.16999999999999, 'J_D': 1118.8040534234044, 'W_D_1KI': 21.737804878048774, 'J_D_1KI': 7.363755039989422}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..047807c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1497, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375812530517578, "TIME_S_1KI": 6.931070494667721, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2016.6524360942842, "W": 63.56, "J_1KI": 1347.1292158278452, "W_1KI": 42.458249832999336, "W_D": 46.965500000000006, "J_D": 1490.1367210098506, "W_D_1KI": 31.373079492317977, "J_D_1KI": 20.95730093007213}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..e1c6993
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7011280059814453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1997,     4011,  ..., 19995978,
+                            19998029, 20000000]),
+       col_indices=tensor([   0,    6,   10,  ..., 9977, 9981, 9997]),
+       values=tensor([0.1409, 0.7742, 0.3684,  ..., 0.6455, 0.2528, 0.1779]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4126, 0.0545, 0.9867,  ..., 0.1672, 0.9147, 0.2153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 0.7011280059814453 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1497', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375812530517578}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2004,     3927,  ..., 19995948,
+                            19997998, 20000000]),
+       col_indices=tensor([   0,    7,    8,  ..., 9981, 9983, 9993]),
+       values=tensor([0.6769, 0.3987, 0.0257,  ..., 0.1977, 0.2040, 0.8027]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3084, 0.6863, 0.1716,  ..., 0.2861, 0.5214, 0.4353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.375812530517578 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2004,     3927,  ..., 19995948,
+                            19997998, 20000000]),
+       col_indices=tensor([   0,    7,    8,  ..., 9981, 9983, 9993]),
+       values=tensor([0.6769, 0.3987, 0.0257,  ..., 0.1977, 0.2040, 0.8027]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3084, 0.6863, 0.1716,  ..., 0.2861, 0.5214, 0.4353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.375812530517578 seconds
+
+[18.42, 17.88, 18.24, 17.8, 17.91, 17.84, 18.25, 18.2, 17.93, 17.84]
+[63.56]
+31.728326559066772
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375812530517578, 'TIME_S_1KI': 6.931070494667721, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2016.6524360942842, 'W': 63.56}
+[18.42, 17.88, 18.24, 17.8, 17.91, 17.84, 18.25, 18.2, 17.93, 17.84, 18.76, 17.93, 18.32, 21.74, 19.38, 18.29, 18.56, 18.06, 18.67, 18.76]
+331.89
+16.5945
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375812530517578, 'TIME_S_1KI': 6.931070494667721, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2016.6524360942842, 'W': 63.56, 'J_1KI': 1347.1292158278452, 'W_1KI': 42.458249832999336, 'W_D': 46.965500000000006, 'J_D': 1490.1367210098506, 'W_D_1KI': 31.373079492317977, 'J_D_1KI': 20.95730093007213}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..b205393
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 949, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.941918134689331, "TIME_S_1KI": 11.529945347407093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3564.162018098831, "W": 52.14, "J_1KI": 3755.7028641715815, "W_1KI": 54.94204425711275, "W_D": 35.83725, "J_D": 2449.7461696032283, "W_D_1KI": 37.763171759747095, "J_D_1KI": 39.79259405663551}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..d986f3d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9010562896728516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2954,     5989,  ..., 29993992,
+                            29996937, 30000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 9991, 9993, 9999]),
+       values=tensor([0.8641, 0.5764, 0.3491,  ..., 0.5822, 0.6256, 0.4859]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.5324, 0.9227, 0.4901,  ..., 0.4747, 0.1770, 0.2536])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 1.9010562896728516 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '552', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.5163209438323975}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3039,     6048,  ..., 29994088,
+                            29997031, 30000000]),
+       col_indices=tensor([   5,   17,   19,  ..., 9983, 9988, 9994]),
+       values=tensor([0.5897, 0.1355, 0.4586,  ..., 0.2138, 0.9666, 0.8141]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3973, 0.5491, 0.6398,  ..., 0.0595, 0.1069, 0.4910])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 6.5163209438323975 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '889', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.831693172454834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3022,     6006,  ..., 29994154,
+                            29996992, 30000000]),
+       col_indices=tensor([   2,    5,   11,  ..., 9993, 9994, 9997]),
+       values=tensor([0.4549, 0.7646, 0.3501,  ..., 0.7301, 0.5346, 0.2783]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.0238, 0.3406, 0.5500,  ..., 0.0227, 0.0108, 0.7785])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 9.831693172454834 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '949', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.941918134689331}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3007,     5955,  ..., 29993893,
+                            29996918, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9996, 9997, 9998]),
+       values=tensor([0.2348, 0.5341, 0.6896,  ..., 0.7208, 0.8300, 0.7790]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7258, 0.9160, 0.6029,  ..., 0.0530, 0.7513, 0.2296])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.941918134689331 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3007,     5955,  ..., 29993893,
+                            29996918, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9996, 9997, 9998]),
+       values=tensor([0.2348, 0.5341, 0.6896,  ..., 0.7208, 0.8300, 0.7790]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7258, 0.9160, 0.6029,  ..., 0.0530, 0.7513, 0.2296])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.941918134689331 seconds
+
+[18.62, 18.03, 18.0, 17.99, 18.26, 17.85, 18.27, 17.94, 18.3, 18.09]
+[52.14]
+68.35753774642944
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.941918134689331, 'TIME_S_1KI': 11.529945347407093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3564.162018098831, 'W': 52.14}
+[18.62, 18.03, 18.0, 17.99, 18.26, 17.85, 18.27, 17.94, 18.3, 18.09, 18.58, 17.83, 17.98, 17.87, 18.18, 18.09, 18.02, 17.83, 18.92, 18.1]
+326.055
+16.30275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.941918134689331, 'TIME_S_1KI': 11.529945347407093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3564.162018098831, 'W': 52.14, 'J_1KI': 3755.7028641715815, 'W_1KI': 54.94204425711275, 'W_D': 35.83725, 'J_D': 2449.7461696032283, 'W_D_1KI': 37.763171759747095, 'J_D_1KI': 39.79259405663551}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
index 114ef88..00c8b98 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 286411, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.634347915649414, "TIME_S_1KI": 0.03712967698743908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1001.8400530457496, "W": 73.11, "J_1KI": 3.4979105308306933, "W_1KI": 0.25526254229062434, "W_D": 56.78875, "J_D": 778.186900730431, "W_D_1KI": 0.19827712622769378, "J_D_1KI": 0.0006922818125969107}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 284305, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.350545883178711, "TIME_S_1KI": 0.03640648558125503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1004.1447986841201, "W": 73.05, "J_1KI": 3.5319280304043903, "W_1KI": 0.2569423682313009, "W_D": 56.574, "J_D": 777.6658157529831, "W_D_1KI": 0.19899052074356766, "J_D_1KI": 0.0006999191739278861}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
index b127831..cbcf9b7 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,373 +1,266 @@
-['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": 0.05389976501464844}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019997835159301758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
-       col_indices=tensor([5877, 4250, 4686, 7890, 7967, 6049, 7086, 7350, 7600,
-                           9415, 8494, 9960, 8463, 9769, 3650, 5660, 4765, 7065,
-                           9825, 3646, 4508, 4529,  606, 6653, 6914, 7898, 5776,
-                           6107, 2833, 6469, 9561, 9553, 1012, 7648, 3125, 4659,
-                           2350, 5605, 2978, 2552, 4772, 6484, 5546, 1359, 9003,
-                           7295, 3487, 3251, 5797, 4927,  389, 1539, 8241, 4519,
-                           1811, 2945, 8623, 2872,  552, 7492, 9923, 2010, 2604,
-                           1552, 2774, 1416, 5396, 4510, 3786, 3444, 9329, 2259,
-                           6656,  438, 9323, 9111, 8972,  134, 8976, 8888, 3908,
-                           3185, 8018, 3369, 5475, 1596, 1990, 7816, 5574, 9542,
-                           3040, 2756, 2500, 9055, 3476, 5796, 5461, 8969, 5649,
-                           7151, 2742, 7881, 4000, 8377, 5895,  253, 8238, 9426,
-                           8478, 3876, 7351, 2306,  878, 6391, 2133, 6974, 4104,
-                           1627, 9784, 8459, 8184, 8357, 1868, 3969, 2547, 7150,
-                           8567, 8961, 7840, 7042, 8040, 9932, 3202, 7093, 1327,
-                            135, 6831, 9394, 9093, 3306, 9671, 1032, 7617, 8998,
-                           2163, 4741, 8281, 7308, 2355, 3050, 9803,  475,  964,
-                           4964, 1287, 1268,  688, 5896, 7603, 4108, 1012, 5952,
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-                           4146, 7486, 1116, 7420, 7107, 4437, 8910, 4651,  246,
-                           2259, 7653, 1266, 8805, 4772, 8104, 9150, 1259, 1349,
-                            109,  204, 8540, 8676, 2304, 2566, 5439, 2010,  293,
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-                            720, 8203, 1204, 9773, 8158, 7049, 4816, 2638, 6704,
-                            487, 7745, 2464, 8290, 5518, 1983, 6972, 6806, 9672,
-                           8173,  709, 2961, 8609, 4687, 1057, 6278, 4688, 7327,
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-                           1351, 5236, 1433, 2355, 7050, 4629, 7395, 4891, 8440,
-                           1280, 9026, 9611, 4272,   63, 7053, 9273, 2845,  319,
-                           3849, 5109, 5397, 1333, 5226, 3167,  498, 8358, 6471,
-                           8441, 2470, 2584, 8523, 3869, 9952, 7331, 1491, 6616,
-                           4251, 7390, 2569, 8114, 4273, 6594, 7890, 3061, 7707,
-                           7020, 2254, 6838, 1734, 3358, 9413,  842, 6263, 4309,
-                           9410, 5850, 3843, 5235,  316, 1680, 4468, 2050,  864,
-                           4563, 2568, 8117, 5035, 1575, 2174, 5811, 4374, 7269,
-                           8559,  180, 3758, 3450, 6187, 7641, 2641, 5158, 1693,
-                           8702, 3953, 8641,  538, 3719, 8146,  831, 9181, 8252,
-                           4927, 1983, 7946, 8289, 1723, 7454,  571, 2844, 7290,
-                            976, 3469, 3835, 4443, 9997, 6253, 1783, 4479, 5621,
-                           5888, 6952, 5207, 6046, 5250, 6277, 1706,  492,  994,
-                           5337, 2917,  299, 5416, 4823, 2281, 9216, 9811,  536,
-                           7167, 9365, 8010, 8105, 7428, 9708, 9521, 1759, 7316,
-                           8408, 6581, 6990, 8370, 4815, 2491, 4443, 2226, 6874,
-                           4105, 2711, 6768, 3934, 2527, 4953,   24, 5356, 5377,
-                           4831, 6455, 8164, 4262, 6529, 7609, 5098, 9953,   73,
-                           5708, 4484, 9730, 7364, 2426, 2680, 3694, 2949, 3657,
-                           8014,  666, 1786, 4090, 8559,  238, 2595, 2466, 5713,
-                           7523, 1659, 8441, 1656,  970, 4970, 3471, 7507, 6622,
-                           7069,   76, 7125, 5124, 6896, 4874, 6040, 3626, 4009,
-                           5978, 7731, 9276, 8499, 2718, 5361, 3855, 8538, 8822,
-                           1102, 3105, 7443, 7481, 4376, 5676, 6866, 1405, 7485,
-                           2438, 4046, 5882, 1928, 5673, 4439,  607,  941, 5323,
-                           1977, 7526, 7365, 6688, 7725, 1906, 1609, 2579, 5018,
-                           3043, 3267, 6183, 7049, 4814, 9534, 4551, 7887, 5645,
-                           1813, 6873, 7950, 4035, 2409,  731, 2725, 7868, 9752,
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+                      0.4986, 0.5179, 0.7221, 0.2831, 0.8118, 0.0090]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7118, 0.5445, 0.7766,  ..., 0.3375, 0.1794, 0.0442])
+tensor([0.8851, 0.6493, 0.1793,  ..., 0.2200, 0.7121, 0.0570])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,378 +268,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.05389976501464844 seconds
+Time: 0.019997835159301758 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '194806', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.141697645187378}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52505', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9391217231750488}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.7942, 0.4563, 0.7095, 0.7390, 0.5213, 0.8669, 0.1933,
+                      0.8827, 0.3576, 0.3715, 0.3966, 0.7670, 0.8625, 0.0249,
+                      0.4165, 0.2028, 0.9277, 0.8840, 0.7235, 0.4226, 0.0014,
+                      0.6919, 0.7665, 0.1665, 0.5380, 0.1084, 0.7142]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1721, 0.8059, 0.1299,  ..., 0.7732, 0.0077, 0.2449])
+tensor([0.5397, 0.8345, 0.2583,  ..., 0.2923, 0.3741, 0.4815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,378 +540,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 7.141697645187378 seconds
+Time: 1.9391217231750488 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '286411', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.634347915649414}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '284305', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.350545883178711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7123, 0.9016, 0.3604,  ..., 0.7264, 0.3786, 0.3585])
+tensor([0.4825, 0.6787, 0.3927,  ..., 0.9418, 0.0965, 0.0788])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +919,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.634347915649414 seconds
+Time: 10.350545883178711 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      5.2047e-01, 2.0881e-01, 9.4192e-01, 4.9260e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7123, 0.9016, 0.3604,  ..., 0.7264, 0.3786, 0.3585])
+tensor([0.4825, 0.6787, 0.3927,  ..., 0.9418, 0.0965, 0.0788])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.634347915649414 seconds
+Time: 10.350545883178711 seconds
 
-[18.41, 17.8, 19.45, 17.82, 18.17, 17.94, 18.34, 18.13, 18.06, 17.93]
-[73.11]
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-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.634347915649414, 'TIME_S_1KI': 0.03712967698743908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1001.8400530457496, 'W': 73.11}
-[18.41, 17.8, 19.45, 17.82, 18.17, 17.94, 18.34, 18.13, 18.06, 17.93, 18.35, 18.13, 18.05, 17.73, 17.88, 18.02, 18.72, 17.91, 17.93, 18.0]
-326.42499999999995
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-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.634347915649414, 'TIME_S_1KI': 0.03712967698743908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1001.8400530457496, 'W': 73.11, 'J_1KI': 3.4979105308306933, 'W_1KI': 0.25526254229062434, 'W_D': 56.78875, 'J_D': 778.186900730431, 'W_D_1KI': 0.19827712622769378, 'J_D_1KI': 0.0006922818125969107}
+[18.41, 22.21, 18.1, 18.25, 17.88, 18.06, 18.18, 18.5, 18.02, 17.84]
+[73.05]
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+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 284305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.350545883178711, 'TIME_S_1KI': 0.03640648558125503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1004.1447986841201, 'W': 73.05}
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..3fe7967
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 264429, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.455932378768921, "TIME_S_1KI": 0.03954154944718212, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1008.905144047737, "W": 73.49, "J_1KI": 3.8154103522977323, "W_1KI": 0.2779195927829398, "W_D": 57.09475, "J_D": 783.8234722155332, "W_D_1KI": 0.21591712709271677, "J_D_1KI": 0.0008165410264861901}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..39a6146
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01993107795715332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4997, 4998, 5000]),
+       col_indices=tensor([9328, 8573, 6400,  ..., 9443, 3853, 6322]),
+       values=tensor([0.9995, 0.8210, 0.4187,  ..., 0.1342, 0.0596, 0.9033]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6106, 0.0696, 0.9188,  ..., 0.7595, 0.3313, 0.0671])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.01993107795715332 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52681', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.0918641090393066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4998, 5000]),
+       col_indices=tensor([2599, 3812, 3885,  ...,  206, 4492, 5501]),
+       values=tensor([0.4008, 0.6783, 0.6051,  ..., 0.8606, 0.8114, 0.5557]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4634, 0.0164, 0.5073,  ..., 0.3776, 0.2676, 0.0715])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 2.0918641090393066 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '264429', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.455932378768921}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([3612, 1318, 1874,  ..., 9308, 8111, 3978]),
+       values=tensor([0.0263, 0.8701, 0.1587,  ..., 0.1521, 0.0294, 0.0367]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4791, 0.9785, 0.1725,  ..., 0.5898, 0.0968, 0.4149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.455932378768921 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([3612, 1318, 1874,  ..., 9308, 8111, 3978]),
+       values=tensor([0.0263, 0.8701, 0.1587,  ..., 0.1521, 0.0294, 0.0367]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4791, 0.9785, 0.1725,  ..., 0.5898, 0.0968, 0.4149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.455932378768921 seconds
+
+[19.02, 17.97, 18.1, 18.25, 17.93, 18.7, 18.16, 18.15, 18.22, 17.98]
+[73.49]
+13.728468418121338
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 264429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.455932378768921, 'TIME_S_1KI': 0.03954154944718212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1008.905144047737, 'W': 73.49}
+[19.02, 17.97, 18.1, 18.25, 17.93, 18.7, 18.16, 18.15, 18.22, 17.98, 18.41, 18.7, 18.23, 17.8, 17.94, 18.94, 18.01, 18.04, 18.02, 18.08]
+327.905
+16.395249999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 264429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.455932378768921, 'TIME_S_1KI': 0.03954154944718212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1008.905144047737, 'W': 73.49, 'J_1KI': 3.8154103522977323, 'W_1KI': 0.2779195927829398, 'W_D': 57.09475, 'J_D': 783.8234722155332, 'W_D_1KI': 0.21591712709271677, 'J_D_1KI': 0.0008165410264861901}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..35c6334
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433570146560669, "TIME_S_1KI": 15.055656777143822, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2886.4447816610336, "W": 54.5, "J_1KI": 4165.1439850808565, "W_1KI": 78.64357864357864, "W_D": 38.16675, "J_D": 2021.3984655130505, "W_D_1KI": 55.074675324675326, "J_D_1KI": 79.47283596634246}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..f2efbf0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8274271488189697}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       57,       98,  ..., 24999897,
+                            24999949, 25000000]),
+       col_indices=tensor([  2496,   8518,  12544,  ..., 449306, 467869,
+                           486714]),
+       values=tensor([0.2667, 0.8213, 0.8309,  ..., 0.8074, 0.6926, 0.7796]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0179, 0.7693, 0.5874,  ..., 0.4128, 0.7472, 0.6195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 1.8274271488189697 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '574', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.696394205093384}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,       88,  ..., 24999889,
+                            24999948, 25000000]),
+       col_indices=tensor([ 21942,  37292,  56785,  ..., 479111, 486535,
+                           489318]),
+       values=tensor([0.4753, 0.7614, 0.3285,  ..., 0.3162, 0.0061, 0.9591]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2582, 0.0329, 0.5597,  ..., 0.0495, 0.5298, 0.4237])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 8.696394205093384 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '693', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433570146560669}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       38,       84,  ..., 24999899,
+                            24999948, 25000000]),
+       col_indices=tensor([ 30660,  43953,  94811,  ..., 484319, 487924,
+                           499108]),
+       values=tensor([0.4696, 0.5982, 0.2681,  ..., 0.1240, 0.7008, 0.8579]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3176, 0.2592, 0.6200,  ..., 0.0886, 0.2852, 0.1534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.433570146560669 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       38,       84,  ..., 24999899,
+                            24999948, 25000000]),
+       col_indices=tensor([ 30660,  43953,  94811,  ..., 484319, 487924,
+                           499108]),
+       values=tensor([0.4696, 0.5982, 0.2681,  ..., 0.1240, 0.7008, 0.8579]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3176, 0.2592, 0.6200,  ..., 0.0886, 0.2852, 0.1534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.433570146560669 seconds
+
+[18.16, 18.09, 17.95, 18.13, 17.83, 18.01, 18.2, 17.99, 18.23, 17.93]
+[54.5]
+52.962289571762085
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433570146560669, 'TIME_S_1KI': 15.055656777143822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2886.4447816610336, 'W': 54.5}
+[18.16, 18.09, 17.95, 18.13, 17.83, 18.01, 18.2, 17.99, 18.23, 17.93, 18.48, 17.91, 18.03, 18.08, 18.03, 18.09, 18.34, 19.26, 18.16, 18.1]
+326.66499999999996
+16.33325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433570146560669, 'TIME_S_1KI': 15.055656777143822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2886.4447816610336, 'W': 54.5, 'J_1KI': 4165.1439850808565, 'W_1KI': 78.64357864357864, 'W_D': 38.16675, 'J_D': 2021.3984655130505, 'W_D_1KI': 55.074675324675326, 'J_D_1KI': 79.47283596634246}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
index df20cd2..c977413 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8417, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.80616807937622, "TIME_S_1KI": 1.2838503123887632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1304.2690537071228, "W": 87.74, "J_1KI": 154.95652295439263, "W_1KI": 10.424141618153737, "W_D": 71.23675, "J_D": 1058.9456178672315, "W_D_1KI": 8.463437091600332, "J_D_1KI": 1.005517059712526}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8054, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420526504516602, "TIME_S_1KI": 1.293832444067122, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1257.315396785736, "W": 87.34, "J_1KI": 156.11067752492377, "W_1KI": 10.844300968462875, "W_D": 70.58375000000001, "J_D": 1016.0984158217908, "W_D_1KI": 8.763813012167867, "J_D_1KI": 1.0881317372942474}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
index 2e9f271..2324509 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '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": 1.2474722862243652}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1425309181213379}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      15,  ..., 2499985,
-                            2499994, 2500000]),
-       col_indices=tensor([131168, 178693, 230148,  ..., 341937, 350836,
-                           404119]),
-       values=tensor([0.5017, 0.1065, 0.8260,  ..., 0.9970, 0.9497, 0.3007]),
+tensor(crow_indices=tensor([      0,       5,       8,  ..., 2499994,
+                            2499996, 2500000]),
+       col_indices=tensor([ 55676, 267462, 335220,  ...,  59414, 387658,
+                           467981]),
+       values=tensor([0.3669, 0.3244, 0.1572,  ..., 0.0615, 0.1330, 0.1317]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0502, 0.1581, 0.5974,  ..., 0.5502, 0.6695, 0.7013])
+tensor([0.2232, 0.7011, 0.4607,  ..., 0.0263, 0.5751, 0.5574])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 1.2474722862243652 seconds
+Time: 0.1425309181213379 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8417', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.80616807937622}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7366', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.601978540420532}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      12,  ..., 2499995,
-                            2499999, 2500000]),
-       col_indices=tensor([108465, 113027, 118372,  ..., 354925, 391668,
-                            96483]),
-       values=tensor([0.8038, 0.4194, 0.3623,  ..., 0.9532, 0.5964, 0.0297]),
+tensor(crow_indices=tensor([      0,       5,      17,  ..., 2499992,
+                            2499998, 2500000]),
+       col_indices=tensor([ 78904, 197792, 264056,  ..., 387862, 102468,
+                           277870]),
+       values=tensor([0.6219, 0.1671, 0.9871,  ..., 0.4185, 0.9305, 0.2778]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4181, 0.0420, 0.6704,  ..., 0.4969, 0.1289, 0.9173])
+tensor([0.0210, 0.3595, 0.6743,  ..., 0.0036, 0.8741, 0.0347])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.80616807937622 seconds
+Time: 9.601978540420532 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8054', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420526504516602}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      12,  ..., 2499995,
-                            2499999, 2500000]),
-       col_indices=tensor([108465, 113027, 118372,  ..., 354925, 391668,
-                            96483]),
-       values=tensor([0.8038, 0.4194, 0.3623,  ..., 0.9532, 0.5964, 0.0297]),
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499986,
+                            2499993, 2500000]),
+       col_indices=tensor([136514, 185390, 204506,  ..., 365577, 371722,
+                           449843]),
+       values=tensor([0.2143, 0.0864, 0.9943,  ..., 0.6371, 0.6570, 0.8441]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4181, 0.0420, 0.6704,  ..., 0.4969, 0.1289, 0.9173])
+tensor([0.0733, 0.2994, 0.4999,  ..., 0.8006, 0.1699, 0.8850])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +59,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.80616807937622 seconds
+Time: 10.420526504516602 seconds
 
-[18.27, 18.7, 18.19, 18.06, 20.48, 17.92, 18.42, 18.1, 18.5, 17.88]
-[87.74]
-14.865159034729004
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8417, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.80616807937622, 'TIME_S_1KI': 1.2838503123887632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1304.2690537071228, 'W': 87.74}
-[18.27, 18.7, 18.19, 18.06, 20.48, 17.92, 18.42, 18.1, 18.5, 17.88, 18.52, 18.21, 18.12, 18.29, 18.48, 18.28, 17.96, 17.98, 18.11, 17.86]
-330.06500000000005
-16.50325
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8417, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.80616807937622, 'TIME_S_1KI': 1.2838503123887632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1304.2690537071228, 'W': 87.74, 'J_1KI': 154.95652295439263, 'W_1KI': 10.424141618153737, 'W_D': 71.23675, 'J_D': 1058.9456178672315, 'W_D_1KI': 8.463437091600332, 'J_D_1KI': 1.005517059712526}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499986,
+                            2499993, 2500000]),
+       col_indices=tensor([136514, 185390, 204506,  ..., 365577, 371722,
+                           449843]),
+       values=tensor([0.2143, 0.0864, 0.9943,  ..., 0.6371, 0.6570, 0.8441]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0733, 0.2994, 0.4999,  ..., 0.8006, 0.1699, 0.8850])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.420526504516602 seconds
+
+[18.47, 17.91, 18.18, 18.64, 18.09, 18.11, 18.17, 18.18, 22.06, 17.96]
+[87.34]
+14.395642280578613
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420526504516602, 'TIME_S_1KI': 1.293832444067122, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.315396785736, 'W': 87.34}
+[18.47, 17.91, 18.18, 18.64, 18.09, 18.11, 18.17, 18.18, 22.06, 17.96, 18.46, 18.6, 18.06, 19.85, 20.28, 18.18, 18.02, 17.82, 18.61, 17.84]
+335.125
+16.75625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420526504516602, 'TIME_S_1KI': 1.293832444067122, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.315396785736, 'W': 87.34, 'J_1KI': 156.11067752492377, 'W_1KI': 10.844300968462875, 'W_D': 70.58375000000001, 'J_D': 1016.0984158217908, 'W_D_1KI': 8.763813012167867, 'J_D_1KI': 1.0881317372942474}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..72c367a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.215147256851196, "TIME_S_1KI": 7.494605470910636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1461.7694299888612, "W": 77.68, "J_1KI": 1072.4647322001917, "W_1KI": 56.99192956713133, "W_D": 61.057, "J_D": 1148.960557245493, "W_D_1KI": 44.796038151137196, "J_D_1KI": 32.865765334656786}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..097beff
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.7700090408325195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       55,  ..., 12499948,
+                            12499975, 12500000]),
+       col_indices=tensor([  4417,  27723,  55822,  ..., 442008, 448310,
+                           496598]),
+       values=tensor([0.6543, 0.3065, 0.8363,  ..., 0.0104, 0.9892, 0.0257]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5546, 0.9568, 0.6697,  ..., 0.4050, 0.9738, 0.6596])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.7700090408325195 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1363', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.215147256851196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       32,       58,  ..., 12499956,
+                            12499981, 12500000]),
+       col_indices=tensor([  7736,  12237,  33305,  ..., 443222, 470958,
+                           475326]),
+       values=tensor([0.9583, 0.2636, 0.0225,  ..., 0.4084, 0.8296, 0.8114]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4848, 0.9833, 0.6868,  ..., 0.4430, 0.1817, 0.0586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.215147256851196 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       32,       58,  ..., 12499956,
+                            12499981, 12500000]),
+       col_indices=tensor([  7736,  12237,  33305,  ..., 443222, 470958,
+                           475326]),
+       values=tensor([0.9583, 0.2636, 0.0225,  ..., 0.4084, 0.8296, 0.8114]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4848, 0.9833, 0.6868,  ..., 0.4430, 0.1817, 0.0586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.215147256851196 seconds
+
+[18.64, 18.68, 18.32, 18.11, 18.55, 17.92, 17.92, 19.0, 18.5, 17.99]
+[77.68]
+18.817835092544556
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.215147256851196, 'TIME_S_1KI': 7.494605470910636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1461.7694299888612, 'W': 77.68}
+[18.64, 18.68, 18.32, 18.11, 18.55, 17.92, 17.92, 19.0, 18.5, 17.99, 18.42, 18.14, 18.35, 18.41, 18.1, 18.07, 18.11, 21.75, 17.97, 18.07]
+332.46000000000004
+16.623
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.215147256851196, 'TIME_S_1KI': 7.494605470910636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1461.7694299888612, 'W': 77.68, 'J_1KI': 1072.4647322001917, 'W_1KI': 56.99192956713133, 'W_D': 61.057, 'J_D': 1148.960557245493, 'W_D_1KI': 44.796038151137196, 'J_D_1KI': 32.865765334656786}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
index 3576579..393ceed 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 79200, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.557747602462769, "TIME_S_1KI": 0.1333048939704895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1194.9571797966958, "W": 76.09, "J_1KI": 15.087843179251209, "W_1KI": 0.9607323232323233, "W_D": 59.703, "J_D": 937.6071560704709, "W_D_1KI": 0.7538257575757576, "J_D_1KI": 0.009518001989592899}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 80365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.785846710205078, "TIME_S_1KI": 0.1342107473428119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1142.0150223541261, "W": 83.68, "J_1KI": 14.210353043664854, "W_1KI": 1.041249300068438, "W_D": 67.33675000000001, "J_D": 918.9720370041133, "W_D_1KI": 0.8378865177627077, "J_D_1KI": 0.01042601278868547}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
index 9da61ce..ace565c 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.14916634559631348}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.031140804290771484}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      7,  ..., 249992, 249995,
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 249988, 249996,
                             250000]),
-       col_indices=tensor([14210, 18192, 24309,  ..., 18863, 37423, 45495]),
-       values=tensor([0.9647, 0.6185, 0.9345,  ..., 0.6478, 0.4104, 0.2751]),
+       col_indices=tensor([ 3938, 11827, 18410,  ..., 25331, 39292, 43613]),
+       values=tensor([0.0696, 0.4480, 0.5528,  ..., 0.5051, 0.2445, 0.3952]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7636, 0.2305, 0.9236,  ..., 0.5850, 0.9097, 0.3088])
+tensor([0.1610, 0.2872, 0.6159,  ..., 0.3941, 0.7048, 0.2210])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.14916634559631348 seconds
+Time: 0.031140804290771484 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70391', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.332123041152954}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33717', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.405243873596191}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     13,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      8,     16,  ..., 249992, 249993,
                             250000]),
-       col_indices=tensor([ 8823, 10157, 22008,  ..., 15217, 25723, 27383]),
-       values=tensor([0.1165, 0.9082, 0.4420,  ..., 0.1019, 0.9218, 0.7818]),
+       col_indices=tensor([ 4798,  5191,  8748,  ..., 31389, 41207, 45142]),
+       values=tensor([0.4595, 0.5274, 0.3770,  ..., 0.1787, 0.1620, 0.3761]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6996, 0.2341, 0.0689,  ..., 0.7606, 0.0770, 0.0289])
+tensor([0.7848, 0.4939, 0.9259,  ..., 0.5597, 0.9743, 0.2454])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 9.332123041152954 seconds
+Time: 4.405243873596191 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '79200', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.557747602462769}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '80365', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.785846710205078}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      9,  ..., 249995, 249997,
+tensor(crow_indices=tensor([     0,      5,      6,  ..., 249987, 249993,
                             250000]),
-       col_indices=tensor([ 4540,  7121,  8304,  ...,  4489, 19051, 41158]),
-       values=tensor([0.2192, 0.6581, 0.9045,  ..., 0.0804, 0.2632, 0.1591]),
+       col_indices=tensor([ 3036, 18920, 36950,  ..., 33008, 35825, 42083]),
+       values=tensor([0.1216, 0.1218, 0.6996,  ..., 0.2614, 0.2596, 0.0475]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8844, 0.7148, 0.4526,  ..., 0.9882, 0.2475, 0.5582])
+tensor([0.0022, 0.5368, 0.2941,  ..., 0.2951, 0.8515, 0.2587])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.557747602462769 seconds
+Time: 10.785846710205078 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      9,  ..., 249995, 249997,
+tensor(crow_indices=tensor([     0,      5,      6,  ..., 249987, 249993,
                             250000]),
-       col_indices=tensor([ 4540,  7121,  8304,  ...,  4489, 19051, 41158]),
-       values=tensor([0.2192, 0.6581, 0.9045,  ..., 0.0804, 0.2632, 0.1591]),
+       col_indices=tensor([ 3036, 18920, 36950,  ..., 33008, 35825, 42083]),
+       values=tensor([0.1216, 0.1218, 0.6996,  ..., 0.2614, 0.2596, 0.0475]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8844, 0.7148, 0.4526,  ..., 0.9882, 0.2475, 0.5582])
+tensor([0.0022, 0.5368, 0.2941,  ..., 0.2951, 0.8515, 0.2587])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.557747602462769 seconds
+Time: 10.785846710205078 seconds
 
-[18.33, 17.85, 17.91, 18.15, 17.99, 17.93, 17.79, 17.99, 18.26, 17.87]
-[76.09]
-15.70452332496643
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 79200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.557747602462769, 'TIME_S_1KI': 0.1333048939704895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1194.9571797966958, 'W': 76.09}
-[18.33, 17.85, 17.91, 18.15, 17.99, 17.93, 17.79, 17.99, 18.26, 17.87, 18.24, 18.68, 17.78, 17.91, 20.54, 18.31, 17.97, 18.31, 18.26, 17.78]
-327.74
-16.387
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 79200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.557747602462769, 'TIME_S_1KI': 0.1333048939704895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1194.9571797966958, 'W': 76.09, 'J_1KI': 15.087843179251209, 'W_1KI': 0.9607323232323233, 'W_D': 59.703, 'J_D': 937.6071560704709, 'W_D_1KI': 0.7538257575757576, 'J_D_1KI': 0.009518001989592899}
+[18.57, 17.87, 18.3, 17.99, 18.1, 18.03, 18.33, 18.16, 18.09, 17.89]
+[83.68]
+13.647407054901123
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.785846710205078, 'TIME_S_1KI': 0.1342107473428119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1142.0150223541261, 'W': 83.68}
+[18.57, 17.87, 18.3, 17.99, 18.1, 18.03, 18.33, 18.16, 18.09, 17.89, 18.23, 17.91, 18.25, 19.1, 18.2, 17.88, 18.14, 18.11, 18.11, 17.9]
+326.865
+16.34325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.785846710205078, 'TIME_S_1KI': 0.1342107473428119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1142.0150223541261, 'W': 83.68, 'J_1KI': 14.210353043664854, 'W_1KI': 1.041249300068438, 'W_D': 67.33675000000001, 'J_D': 918.9720370041133, 'W_D_1KI': 0.8378865177627077, 'J_D_1KI': 0.01042601278868547}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
index fcc4d9e..3d88fb8 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17543, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.699259996414185, "TIME_S_1KI": 0.6098877042931189, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.7915307188034, "W": 87.71, "J_1KI": 74.14875053974825, "W_1KI": 4.9997149860343155, "W_D": 71.40625, "J_D": 1058.997209444642, "W_D_1KI": 4.070355697429174, "J_D_1KI": 0.2320216438140098}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17258, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.52219271659851, "TIME_S_1KI": 0.6096994273147822, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1290.1128985500336, "W": 87.73, "J_1KI": 74.75448479256191, "W_1KI": 5.083439564260054, "W_D": 71.3875, "J_D": 1049.7883796334268, "W_D_1KI": 4.136487426121219, "J_D_1KI": 0.23968521416857222}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
index 4d40449..c067392 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,34 @@
-['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": 0.5985264778137207}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07522106170654297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      56,     105,  ..., 2499904,
+tensor(crow_indices=tensor([      0,      54,      95,  ..., 2499899,
+                            2499951, 2500000]),
+       col_indices=tensor([  245,   316,   650,  ..., 46425, 47933, 49262]),
+       values=tensor([0.3598, 0.8732, 0.2112,  ..., 0.2076, 0.2855, 0.8514]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6227, 0.9680, 0.6855,  ..., 0.8971, 0.9917, 0.2060])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.07522106170654297 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '13958', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.492224931716919}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      52,      97,  ..., 2499905,
                             2499950, 2500000]),
-       col_indices=tensor([  106,  3863,  5117,  ..., 48831, 49457, 49843]),
-       values=tensor([0.6065, 0.7453, 0.1054,  ..., 0.0788, 0.7875, 0.5947]),
+       col_indices=tensor([ 3061,  5035,  6476,  ..., 48999, 49661, 49813]),
+       values=tensor([0.5243, 0.0379, 0.3507,  ..., 0.2954, 0.9764, 0.8519]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1569, 0.4932, 0.6676,  ..., 0.2477, 0.5860, 0.5432])
+tensor([0.8088, 0.8530, 0.3688,  ..., 0.5327, 0.1148, 0.7333])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.5985264778137207 seconds
+Time: 8.492224931716919 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17543', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.699259996414185}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17258', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.52219271659851}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      54,      99,  ..., 2499881,
-                            2499945, 2500000]),
-       col_indices=tensor([ 1025,  3202,  3517,  ..., 49482, 49487, 49789]),
-       values=tensor([0.3859, 0.1414, 0.1100,  ..., 0.9363, 0.6699, 0.1002]),
+tensor(crow_indices=tensor([      0,      60,      98,  ..., 2499898,
+                            2499955, 2500000]),
+       col_indices=tensor([  336,   813,  2467,  ..., 44805, 45101, 46338]),
+       values=tensor([0.1989, 0.3364, 0.1097,  ..., 0.3897, 0.4637, 0.0665]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4003, 0.0598, 0.2302,  ..., 0.6994, 0.7206, 0.2744])
+tensor([0.6060, 0.7426, 0.0681,  ..., 0.1484, 0.6293, 0.7345])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.699259996414185 seconds
+Time: 10.52219271659851 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      54,      99,  ..., 2499881,
-                            2499945, 2500000]),
-       col_indices=tensor([ 1025,  3202,  3517,  ..., 49482, 49487, 49789]),
-       values=tensor([0.3859, 0.1414, 0.1100,  ..., 0.9363, 0.6699, 0.1002]),
+tensor(crow_indices=tensor([      0,      60,      98,  ..., 2499898,
+                            2499955, 2500000]),
+       col_indices=tensor([  336,   813,  2467,  ..., 44805, 45101, 46338]),
+       values=tensor([0.1989, 0.3364, 0.1097,  ..., 0.3897, 0.4637, 0.0665]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4003, 0.0598, 0.2302,  ..., 0.6994, 0.7206, 0.2744])
+tensor([0.6060, 0.7426, 0.0681,  ..., 0.1484, 0.6293, 0.7345])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.699259996414185 seconds
+Time: 10.52219271659851 seconds
 
-[18.49, 17.83, 18.02, 18.17, 18.01, 17.87, 18.35, 18.02, 18.01, 17.87]
-[87.71]
-14.83059549331665
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17543, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.699259996414185, 'TIME_S_1KI': 0.6098877042931189, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.7915307188034, 'W': 87.71}
-[18.49, 17.83, 18.02, 18.17, 18.01, 17.87, 18.35, 18.02, 18.01, 17.87, 18.45, 18.41, 18.09, 17.88, 18.15, 18.34, 17.89, 18.68, 17.87, 18.16]
-326.075
-16.30375
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17543, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.699259996414185, 'TIME_S_1KI': 0.6098877042931189, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.7915307188034, 'W': 87.71, 'J_1KI': 74.14875053974825, 'W_1KI': 4.9997149860343155, 'W_D': 71.40625, 'J_D': 1058.997209444642, 'W_D_1KI': 4.070355697429174, 'J_D_1KI': 0.2320216438140098}
+[18.24, 17.95, 18.03, 17.9, 18.31, 18.2, 18.96, 17.99, 17.74, 18.24]
+[87.73]
+14.705492973327637
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17258, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.52219271659851, 'TIME_S_1KI': 0.6096994273147822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.1128985500336, 'W': 87.73}
+[18.24, 17.95, 18.03, 17.9, 18.31, 18.2, 18.96, 17.99, 17.74, 18.24, 18.79, 18.06, 18.38, 18.13, 18.12, 18.05, 18.08, 18.09, 18.27, 17.91]
+326.85
+16.3425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17258, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.52219271659851, 'TIME_S_1KI': 0.6096994273147822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.1128985500336, 'W': 87.73, 'J_1KI': 74.75448479256191, 'W_1KI': 5.083439564260054, 'W_D': 71.3875, 'J_D': 1049.7883796334268, 'W_D_1KI': 4.136487426121219, 'J_D_1KI': 0.23968521416857222}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..00dd221
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1116, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.347081184387207, "TIME_S_1KI": 9.27157812221076, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2825.247347984314, "W": 53.89, "J_1KI": 2531.5836451472346, "W_1KI": 48.288530465949826, "W_D": 37.37625, "J_D": 1959.4943624067305, "W_D_1KI": 33.491263440860216, "J_D_1KI": 30.010092688942844}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..1b20dcd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,86 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.2946875095367432}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      488,      979,  ..., 24999008,
+                            24999504, 25000000]),
+       col_indices=tensor([  111,   301,   401,  ..., 49766, 49891, 49958]),
+       values=tensor([2.3418e-01, 9.8131e-01, 8.1298e-03,  ...,
+                      6.0106e-01, 4.2789e-04, 8.6966e-01]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1063, 0.9255, 0.6653,  ..., 0.3278, 0.7920, 0.4701])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 1.2946875095367432 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '811', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.6258299350738525}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      521,     1028,  ..., 24999005,
+                            24999496, 25000000]),
+       col_indices=tensor([  169,   333,   382,  ..., 49620, 49646, 49746]),
+       values=tensor([0.1336, 0.2367, 0.6093,  ..., 0.7411, 0.2218, 0.9154]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7321, 0.9416, 0.5259,  ..., 0.9099, 0.7583, 0.2580])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 7.6258299350738525 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1116', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.347081184387207}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      467,      956,  ..., 24998994,
+                            24999500, 25000000]),
+       col_indices=tensor([   21,   163,   165,  ..., 49855, 49860, 49938]),
+       values=tensor([0.3050, 0.9077, 0.0930,  ..., 0.0680, 0.0415, 0.3010]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5141, 0.6637, 0.6626,  ..., 0.2332, 0.8993, 0.6899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.347081184387207 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      467,      956,  ..., 24998994,
+                            24999500, 25000000]),
+       col_indices=tensor([   21,   163,   165,  ..., 49855, 49860, 49938]),
+       values=tensor([0.3050, 0.9077, 0.0930,  ..., 0.0680, 0.0415, 0.3010]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5141, 0.6637, 0.6626,  ..., 0.2332, 0.8993, 0.6899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.347081184387207 seconds
+
+[18.41, 17.99, 18.33, 18.02, 17.96, 17.91, 21.56, 18.23, 18.08, 17.76]
+[53.89]
+52.42618942260742
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.347081184387207, 'TIME_S_1KI': 9.27157812221076, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2825.247347984314, 'W': 53.89}
+[18.41, 17.99, 18.33, 18.02, 17.96, 17.91, 21.56, 18.23, 18.08, 17.76, 18.54, 18.29, 18.11, 17.98, 18.09, 18.71, 18.38, 18.01, 18.26, 18.02]
+330.27500000000003
+16.51375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.347081184387207, 'TIME_S_1KI': 9.27157812221076, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2825.247347984314, 'W': 53.89, 'J_1KI': 2531.5836451472346, 'W_1KI': 48.288530465949826, 'W_D': 37.37625, 'J_D': 1959.4943624067305, 'W_D_1KI': 33.491263440860216, 'J_D_1KI': 30.010092688942844}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
index 8166250..3827350 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 109532, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.03889274597168, "TIME_S_1KI": 0.09165260148606508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1026.1644780921936, "W": 75.74, "J_1KI": 9.368627233066078, "W_1KI": 0.6914874192016944, "W_D": 59.13049999999999, "J_D": 801.1304287276266, "W_D_1KI": 0.539846802760837, "J_D_1KI": 0.004928667446598592}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 114513, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.690773725509644, "TIME_S_1KI": 0.09335860317614283, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1104.0986349916457, "W": 76.13, "J_1KI": 9.641688148870832, "W_1KI": 0.6648153484757189, "W_D": 59.82149999999999, "J_D": 867.5796202962398, "W_D_1KI": 0.5223992035838725, "J_D_1KI": 0.004561920511940762}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
index 709a214..1524e8c 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.12936139106750488}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025936126708984375}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24998, 25000]),
-       col_indices=tensor([44477, 18295, 41758,  ..., 46506, 28720, 46164]),
-       values=tensor([0.4132, 0.4608, 0.2599,  ..., 0.0448, 0.1303, 0.6544]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([20193,  9953, 42880,  ..., 12429,  7497, 42914]),
+       values=tensor([0.2197, 0.8269, 0.1857,  ..., 0.3505, 0.1509, 0.9771]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0039, 0.4422, 0.0639,  ..., 0.1130, 0.9521, 0.1334])
+tensor([0.1918, 0.1249, 0.1152,  ..., 0.8034, 0.5794, 0.7689])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,37 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.12936139106750488 seconds
+Time: 0.025936126708984375 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '81167', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.780844211578369}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40484', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.938389301300049}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([10460, 10153,  3528,  ..., 24271,  7757, 10191]),
+       values=tensor([0.9506, 0.4415, 0.5851,  ..., 0.4538, 0.6997, 0.3353]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8879, 0.8298, 0.7702,  ..., 0.5204, 0.0041, 0.9281])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 3.938389301300049 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '107932', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.896521091461182}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([38361, 15493, 29627,  ..., 27733, 22368, 35508]),
-       values=tensor([0.9149, 0.3524, 0.3637,  ..., 0.0393, 0.5821, 0.3741]),
+       col_indices=tensor([28383, 18616, 44948,  ..., 18982, 36427, 31817]),
+       values=tensor([0.7119, 0.6692, 0.1695,  ..., 0.9276, 0.2360, 0.8672]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5874, 0.0444, 0.7896,  ..., 0.3503, 0.3177, 0.2388])
+tensor([0.3408, 0.5698, 0.4224,  ..., 0.7704, 0.6104, 0.7559])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +53,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 7.780844211578369 seconds
+Time: 9.896521091461182 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '109532', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.03889274597168}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '114513', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.690773725509644}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24997, 24997, 25000]),
-       col_indices=tensor([17005,  6306, 21289,  ...,  8288,  8622, 19411]),
-       values=tensor([0.2779, 0.9469, 0.2610,  ..., 0.9922, 0.2668, 0.6005]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 6936, 24119, 42629,  ..., 33437,  2193,  4338]),
+       values=tensor([0.2031, 0.4334, 0.3517,  ..., 0.1037, 0.1982, 0.4022]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1194, 0.4917, 0.3228,  ..., 0.2258, 0.0044, 0.3600])
+tensor([0.0121, 0.3083, 0.6972,  ..., 0.1499, 0.8195, 0.8871])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +72,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.03889274597168 seconds
+Time: 10.690773725509644 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24997, 24997, 25000]),
-       col_indices=tensor([17005,  6306, 21289,  ...,  8288,  8622, 19411]),
-       values=tensor([0.2779, 0.9469, 0.2610,  ..., 0.9922, 0.2668, 0.6005]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 6936, 24119, 42629,  ..., 33437,  2193,  4338]),
+       values=tensor([0.2031, 0.4334, 0.3517,  ..., 0.1037, 0.1982, 0.4022]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1194, 0.4917, 0.3228,  ..., 0.2258, 0.0044, 0.3600])
+tensor([0.0121, 0.3083, 0.6972,  ..., 0.1499, 0.8195, 0.8871])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +88,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.03889274597168 seconds
+Time: 10.690773725509644 seconds
 
-[19.15, 18.96, 18.11, 18.03, 19.81, 17.96, 18.06, 18.12, 18.35, 17.94]
-[75.74]
-13.548514366149902
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109532, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.03889274597168, 'TIME_S_1KI': 0.09165260148606508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1026.1644780921936, 'W': 75.74}
-[19.15, 18.96, 18.11, 18.03, 19.81, 17.96, 18.06, 18.12, 18.35, 17.94, 18.12, 21.35, 17.92, 18.62, 18.1, 18.19, 17.98, 17.91, 18.07, 18.09]
-332.19000000000005
-16.609500000000004
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109532, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.03889274597168, 'TIME_S_1KI': 0.09165260148606508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1026.1644780921936, 'W': 75.74, 'J_1KI': 9.368627233066078, 'W_1KI': 0.6914874192016944, 'W_D': 59.13049999999999, 'J_D': 801.1304287276266, 'W_D_1KI': 0.539846802760837, 'J_D_1KI': 0.004928667446598592}
+[18.43, 18.05, 18.18, 17.94, 17.98, 18.17, 18.18, 17.96, 18.08, 18.28]
+[76.13]
+14.502806186676025
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.690773725509644, 'TIME_S_1KI': 0.09335860317614283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1104.0986349916457, 'W': 76.13}
+[18.43, 18.05, 18.18, 17.94, 17.98, 18.17, 18.18, 17.96, 18.08, 18.28, 18.27, 17.98, 18.37, 18.47, 18.2, 17.9, 18.1, 18.12, 18.02, 17.96]
+326.17
+16.308500000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.690773725509644, 'TIME_S_1KI': 0.09335860317614283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1104.0986349916457, 'W': 76.13, 'J_1KI': 9.641688148870832, 'W_1KI': 0.6648153484757189, 'W_D': 59.82149999999999, 'J_D': 867.5796202962398, 'W_D_1KI': 0.5223992035838725, 'J_D_1KI': 0.004561920511940762}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..7635079
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87123, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.505861759185791, "TIME_S_1KI": 0.12058654728585783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1083.1826461410521, "W": 80.24, "J_1KI": 12.43279783915903, "W_1KI": 0.9209967517188342, "W_D": 63.674249999999994, "J_D": 859.5568619896172, "W_D_1KI": 0.7308546537653662, "J_D_1KI": 0.00838876822154157}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..089ad96
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.029273509979248047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([  551, 18742, 22548,  ..., 32794, 16422, 37041]),
+       values=tensor([0.7421, 0.1633, 0.4685,  ..., 0.4955, 0.1690, 0.9373]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5671, 0.1718, 0.7006,  ..., 0.0603, 0.4651, 0.4190])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.029273509979248047 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '35868', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.322763681411743}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 124993, 124996,
+                            125000]),
+       col_indices=tensor([10292, 17103, 19384,  ..., 21480, 22459, 30474]),
+       values=tensor([0.3992, 0.1013, 0.2691,  ..., 0.1460, 0.2288, 0.5075]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.9479, 0.9531, 0.4260,  ..., 0.3198, 0.2541, 0.8697])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 4.322763681411743 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87123', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.505861759185791}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124993, 124998,
+                            125000]),
+       col_indices=tensor([ 2935, 11122, 10966,  ..., 49613,  4331, 15007]),
+       values=tensor([0.3574, 0.4392, 0.6710,  ..., 0.1033, 0.1015, 0.5102]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0826, 0.0461, 0.6873,  ..., 0.1063, 0.7746, 0.5118])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.505861759185791 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124993, 124998,
+                            125000]),
+       col_indices=tensor([ 2935, 11122, 10966,  ..., 49613,  4331, 15007]),
+       values=tensor([0.3574, 0.4392, 0.6710,  ..., 0.1033, 0.1015, 0.5102]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0826, 0.0461, 0.6873,  ..., 0.1063, 0.7746, 0.5118])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.505861759185791 seconds
+
+[18.58, 18.04, 18.0, 17.91, 17.9, 20.59, 18.05, 18.07, 17.88, 18.18]
+[80.24]
+13.499285221099854
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87123, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.505861759185791, 'TIME_S_1KI': 0.12058654728585783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1826461410521, 'W': 80.24}
+[18.58, 18.04, 18.0, 17.91, 17.9, 20.59, 18.05, 18.07, 17.88, 18.18, 18.74, 21.37, 18.26, 18.13, 18.12, 17.84, 18.06, 17.91, 18.35, 18.17]
+331.315
+16.56575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87123, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.505861759185791, 'TIME_S_1KI': 0.12058654728585783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1826461410521, 'W': 80.24, 'J_1KI': 12.43279783915903, 'W_1KI': 0.9209967517188342, 'W_D': 63.674249999999994, 'J_D': 859.5568619896172, 'W_D_1KI': 0.7308546537653662, 'J_D_1KI': 0.00838876822154157}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
index 3b1a4d8..c76a143 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 334616, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.390317678451538, "TIME_S_1KI": 0.03105146699037565, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1009.4583482408524, "W": 73.59, "J_1KI": 3.0167665271261757, "W_1KI": 0.21992373347359362, "W_D": 57.37650000000001, "J_D": 787.052410896063, "W_D_1KI": 0.17146968465345352, "J_D_1KI": 0.0005124371956315703}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 334626, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109118700027466, "TIME_S_1KI": 0.030210200940833844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1025.1804789018631, "W": 73.29, "J_1KI": 3.063660561049838, "W_1KI": 0.219020637965968, "W_D": 56.847750000000005, "J_D": 795.1862951220274, "W_D_1KI": 0.16988443814885876, "J_D_1KI": 0.0005076845139016656}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
index ab60bf4..81b5928 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0494227409362793}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.020055532455444336}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([2191, 1647, 4069,  ..., 3482,  688, 2162]),
-       values=tensor([0.7127, 0.2553, 0.3133,  ..., 0.9149, 0.5638, 0.5628]),
+       col_indices=tensor([2122, 3396, 4900,  ..., 3006, 1251, 2017]),
+       values=tensor([0.1868, 0.0259, 0.3673,  ..., 0.0909, 0.5358, 0.3608]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.0865, 0.0532, 0.7203,  ..., 0.4777, 0.7863, 0.0162])
+tensor([0.5194, 0.1996, 0.4802,  ..., 0.7962, 0.4168, 0.5561])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,37 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.0494227409362793 seconds
+Time: 0.020055532455444336 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '212452', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.666574239730835}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52354', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.6427783966064453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2498, 2499, 2500]),
+       col_indices=tensor([2755, 4785,  642,  ...,  761, 1671, 4009]),
+       values=tensor([0.6216, 0.3711, 0.5927,  ..., 0.4412, 0.8122, 0.2675]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.4766, 0.5997, 0.2696,  ..., 0.3490, 0.2681, 0.0383])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 1.6427783966064453 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '334626', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109118700027466}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([2208, 2123, 4174,  ..., 2091,   42, 2382]),
-       values=tensor([0.8755, 0.2371, 0.7047,  ..., 0.2373, 0.9261, 0.2864]),
+       col_indices=tensor([3166, 2984, 4242,  ..., 1801,  191, 2968]),
+       values=tensor([0.8061, 0.6429, 0.9344,  ..., 0.0259, 0.3545, 0.7535]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3651, 0.6415, 0.7426,  ..., 0.3371, 0.9910, 0.6174])
+tensor([0.9083, 0.2521, 0.7196,  ..., 0.0784, 0.7825, 0.2390])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 6.666574239730835 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '334616', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.390317678451538}
+Time: 10.109118700027466 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2498, 2498, 2500]),
-       col_indices=tensor([1385, 3626, 3706,  ...,  891, 2896, 4403]),
-       values=tensor([0.8264, 0.4439, 0.4297,  ..., 0.4171, 0.8922, 0.6160]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3166, 2984, 4242,  ..., 1801,  191, 2968]),
+       values=tensor([0.8061, 0.6429, 0.9344,  ..., 0.0259, 0.3545, 0.7535]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2966, 0.7201, 0.1357,  ..., 0.1499, 0.6981, 0.8153])
+tensor([0.9083, 0.2521, 0.7196,  ..., 0.0784, 0.7825, 0.2390])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,29 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.390317678451538 seconds
+Time: 10.109118700027466 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2498, 2498, 2500]),
-       col_indices=tensor([1385, 3626, 3706,  ...,  891, 2896, 4403]),
-       values=tensor([0.8264, 0.4439, 0.4297,  ..., 0.4171, 0.8922, 0.6160]),
-       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2966, 0.7201, 0.1357,  ..., 0.1499, 0.6981, 0.8153])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 2500
-Density: 0.0001
-Time: 10.390317678451538 seconds
-
-[18.39, 18.14, 17.96, 18.23, 18.13, 17.78, 18.17, 18.05, 18.11, 18.01]
-[73.59]
-13.71733045578003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334616, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.390317678451538, 'TIME_S_1KI': 0.03105146699037565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.4583482408524, 'W': 73.59}
-[18.39, 18.14, 17.96, 18.23, 18.13, 17.78, 18.17, 18.05, 18.11, 18.01, 18.29, 17.99, 18.0, 17.86, 17.97, 18.01, 17.96, 17.81, 17.79, 17.93]
-324.27
-16.2135
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334616, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.390317678451538, 'TIME_S_1KI': 0.03105146699037565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.4583482408524, 'W': 73.59, 'J_1KI': 3.0167665271261757, 'W_1KI': 0.21992373347359362, 'W_D': 57.37650000000001, 'J_D': 787.052410896063, 'W_D_1KI': 0.17146968465345352, 'J_D_1KI': 0.0005124371956315703}
+[20.29, 18.13, 18.23, 18.27, 18.36, 17.99, 18.37, 18.01, 18.07, 17.87]
+[73.29]
+13.987999439239502
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109118700027466, 'TIME_S_1KI': 0.030210200940833844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1025.1804789018631, 'W': 73.29}
+[20.29, 18.13, 18.23, 18.27, 18.36, 17.99, 18.37, 18.01, 18.07, 17.87, 18.97, 18.06, 18.34, 18.0, 18.27, 18.07, 18.01, 18.54, 18.55, 18.02]
+328.845
+16.44225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109118700027466, 'TIME_S_1KI': 0.030210200940833844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1025.1804789018631, 'W': 73.29, 'J_1KI': 3.063660561049838, 'W_1KI': 0.219020637965968, 'W_D': 56.847750000000005, 'J_D': 795.1862951220274, 'W_D_1KI': 0.16988443814885876, 'J_D_1KI': 0.0005076845139016656}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
index e6fec4b..417052f 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 248893, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.503267288208008, "TIME_S_1KI": 0.04219993044484179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1056.0920481681824, "W": 74.8, "J_1KI": 4.243156891387795, "W_1KI": 0.3005307501617161, "W_D": 58.488749999999996, "J_D": 825.7955051109194, "W_D_1KI": 0.2349955603411908, "J_D_1KI": 0.0009441629951070973}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 248882, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432960033416748, "TIME_S_1KI": 0.041919303257836035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.6097160863876, "W": 75.02, "J_1KI": 4.2172986237911445, "W_1KI": 0.3014279859531826, "W_D": 58.4725, "J_D": 818.0925636345148, "W_D_1KI": 0.2349406546074043, "J_D_1KI": 0.0009439841153936576}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
index d474848..121e30b 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0580594539642334}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.018964052200317383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     7,  ..., 24994, 24998, 25000]),
-       col_indices=tensor([ 985, 1057,  218,  ..., 4882, 1671, 4380]),
-       values=tensor([0.5160, 0.3498, 0.0303,  ..., 0.2263, 0.8538, 0.6441]),
+tensor(crow_indices=tensor([    0,     4,    14,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 484, 2538, 3016,  ..., 2694, 4380, 4909]),
+       values=tensor([0.3483, 0.1743, 0.1939,  ..., 0.2265, 0.8602, 0.7977]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8672, 0.0025, 0.6942,  ..., 0.2074, 0.2932, 0.8728])
+tensor([0.6164, 0.4905, 0.7241,  ..., 0.3672, 0.4239, 0.9077])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.0580594539642334 seconds
+Time: 0.018964052200317383 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '180849', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.629418849945068}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '55367', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.335857391357422}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     7,    12,  ..., 24984, 24994, 25000]),
-       col_indices=tensor([ 206,  438, 1117,  ..., 3589, 4561, 4654]),
-       values=tensor([0.7806, 0.0093, 0.9775,  ..., 0.2394, 0.5986, 0.1036]),
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([1163, 1240, 1422,  ..., 4522, 4571, 4830]),
+       values=tensor([0.2729, 0.7937, 0.6768,  ..., 0.2019, 0.8649, 0.0759]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9079, 0.6440, 0.7990,  ..., 0.4243, 0.2944, 0.4838])
+tensor([0.9266, 0.1779, 0.1408,  ..., 0.8566, 0.7762, 0.0695])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 7.629418849945068 seconds
+Time: 2.335857391357422 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '248893', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.503267288208008}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '248882', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432960033416748}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    11,  ..., 24987, 24992, 25000]),
-       col_indices=tensor([ 263, 1234, 1436,  ..., 3199, 3400, 4091]),
-       values=tensor([0.7110, 0.3838, 0.4652,  ..., 0.0537, 0.9297, 0.5811]),
+tensor(crow_indices=tensor([    0,    10,    15,  ..., 24986, 24991, 25000]),
+       col_indices=tensor([ 131,  267,  600,  ..., 3068, 3643, 3839]),
+       values=tensor([0.7605, 0.7816, 0.2401,  ..., 0.7557, 0.2099, 0.5290]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4941, 0.0109, 0.4935,  ..., 0.1517, 0.7151, 0.3544])
+tensor([0.8976, 0.4401, 0.6879,  ..., 0.0741, 0.3573, 0.5052])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.503267288208008 seconds
+Time: 10.432960033416748 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    11,  ..., 24987, 24992, 25000]),
-       col_indices=tensor([ 263, 1234, 1436,  ..., 3199, 3400, 4091]),
-       values=tensor([0.7110, 0.3838, 0.4652,  ..., 0.0537, 0.9297, 0.5811]),
+tensor(crow_indices=tensor([    0,    10,    15,  ..., 24986, 24991, 25000]),
+       col_indices=tensor([ 131,  267,  600,  ..., 3068, 3643, 3839]),
+       values=tensor([0.7605, 0.7816, 0.2401,  ..., 0.7557, 0.2099, 0.5290]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.4941, 0.0109, 0.4935,  ..., 0.1517, 0.7151, 0.3544])
+tensor([0.8976, 0.4401, 0.6879,  ..., 0.0741, 0.3573, 0.5052])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.503267288208008 seconds
+Time: 10.432960033416748 seconds
 
-[18.2, 18.48, 18.33, 18.03, 18.1, 18.08, 18.14, 18.78, 18.15, 17.99]
-[74.8]
-14.118877649307251
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248893, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.503267288208008, 'TIME_S_1KI': 0.04219993044484179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1056.0920481681824, 'W': 74.8}
-[18.2, 18.48, 18.33, 18.03, 18.1, 18.08, 18.14, 18.78, 18.15, 17.99, 18.23, 17.98, 17.97, 18.01, 17.96, 18.02, 17.91, 18.17, 17.92, 17.97]
-326.225
-16.31125
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248893, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.503267288208008, 'TIME_S_1KI': 0.04219993044484179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1056.0920481681824, 'W': 74.8, 'J_1KI': 4.243156891387795, 'W_1KI': 0.3005307501617161, 'W_D': 58.488749999999996, 'J_D': 825.7955051109194, 'W_D_1KI': 0.2349955603411908, 'J_D_1KI': 0.0009441629951070973}
+[18.55, 18.1, 18.04, 18.77, 18.18, 18.14, 18.08, 18.33, 18.19, 18.07]
+[75.02]
+13.991065263748169
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248882, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432960033416748, 'TIME_S_1KI': 0.041919303257836035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.6097160863876, 'W': 75.02}
+[18.55, 18.1, 18.04, 18.77, 18.18, 18.14, 18.08, 18.33, 18.19, 18.07, 18.74, 17.95, 18.05, 18.34, 18.14, 19.14, 18.15, 18.14, 20.44, 18.18]
+330.95
+16.5475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248882, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432960033416748, 'TIME_S_1KI': 0.041919303257836035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.6097160863876, 'W': 75.02, 'J_1KI': 4.2172986237911445, 'W_1KI': 0.3014279859531826, 'W_D': 58.4725, 'J_D': 818.0925636345148, 'W_D_1KI': 0.2349406546074043, 'J_D_1KI': 0.0009439841153936576}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
index 6c317ff..e80d2db 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 167260, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.636158227920532, "TIME_S_1KI": 0.0635905669491841, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1183.6357300853729, "W": 83.74, "J_1KI": 7.076621607589219, "W_1KI": 0.5006576587349038, "W_D": 67.26599999999999, "J_D": 950.7814786233901, "W_D_1KI": 0.40216429510941043, "J_D_1KI": 0.002404426014046457}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 163647, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.69955587387085, "TIME_S_1KI": 0.06538192495964393, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1158.7786533069611, "W": 84.04, "J_1KI": 7.080964840827887, "W_1KI": 0.5135443973919473, "W_D": 67.71475000000001, "J_D": 933.6792814614178, "W_D_1KI": 0.41378546505588254, "J_D_1KI": 0.002528524599020346}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
index 72ef990..483c400 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.0799260139465332}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.021794557571411133}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     35,     85,  ..., 249913, 249959,
+tensor(crow_indices=tensor([     0,     52,    109,  ..., 249903, 249954,
                             250000]),
-       col_indices=tensor([  50,   52,  142,  ..., 3906, 4174, 4757]),
-       values=tensor([0.0913, 0.8215, 0.1970,  ..., 0.8521, 0.9478, 0.8405]),
+       col_indices=tensor([  37,   94,  126,  ..., 4726, 4735, 4938]),
+       values=tensor([0.6014, 0.9404, 0.8499,  ..., 0.7854, 0.8553, 0.6608]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5132, 0.5547, 0.3014,  ..., 0.6656, 0.4241, 0.0798])
+tensor([0.8748, 0.2409, 0.9137,  ..., 0.2396, 0.5569, 0.4924])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.0799260139465332 seconds
+Time: 0.021794557571411133 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '131371', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.24699854850769}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '48177', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.0911431312561035}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     34,     94,  ..., 249884, 249936,
+tensor(crow_indices=tensor([     0,     42,    103,  ..., 249895, 249947,
                             250000]),
-       col_indices=tensor([   2,  398,  450,  ..., 4930, 4969, 4985]),
-       values=tensor([0.5923, 0.5022, 0.7915,  ..., 0.6018, 0.8801, 0.8622]),
+       col_indices=tensor([ 128,  146,  225,  ..., 4883, 4900, 4933]),
+       values=tensor([0.9316, 0.8049, 0.8923,  ..., 0.5892, 0.9266, 0.8295]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1968, 0.0295, 0.9143,  ..., 0.4064, 0.2286, 0.1114])
+tensor([0.8118, 0.4201, 0.8184,  ..., 0.0833, 0.7283, 0.6165])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 8.24699854850769 seconds
+Time: 3.0911431312561035 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '167260', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.636158227920532}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '163647', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.69955587387085}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     41,     97,  ..., 249903, 249957,
+tensor(crow_indices=tensor([     0,     65,    110,  ..., 249906, 249948,
                             250000]),
-       col_indices=tensor([   6,   32,   62,  ..., 4630, 4959, 4982]),
-       values=tensor([0.7649, 0.1722, 0.7795,  ..., 0.2616, 0.2192, 0.2761]),
+       col_indices=tensor([  32,  165,  212,  ..., 4365, 4391, 4539]),
+       values=tensor([0.5399, 0.4522, 0.1183,  ..., 0.3103, 0.6929, 0.7632]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3363, 0.3219, 0.7361,  ..., 0.7182, 0.1290, 0.5403])
+tensor([0.3241, 0.6966, 0.4101,  ..., 0.4425, 0.6108, 0.0322])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.636158227920532 seconds
+Time: 10.69955587387085 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     41,     97,  ..., 249903, 249957,
+tensor(crow_indices=tensor([     0,     65,    110,  ..., 249906, 249948,
                             250000]),
-       col_indices=tensor([   6,   32,   62,  ..., 4630, 4959, 4982]),
-       values=tensor([0.7649, 0.1722, 0.7795,  ..., 0.2616, 0.2192, 0.2761]),
+       col_indices=tensor([  32,  165,  212,  ..., 4365, 4391, 4539]),
+       values=tensor([0.5399, 0.4522, 0.1183,  ..., 0.3103, 0.6929, 0.7632]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3363, 0.3219, 0.7361,  ..., 0.7182, 0.1290, 0.5403])
+tensor([0.3241, 0.6966, 0.4101,  ..., 0.4425, 0.6108, 0.0322])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.636158227920532 seconds
+Time: 10.69955587387085 seconds
 
-[18.39, 18.16, 18.03, 17.97, 18.05, 19.85, 18.01, 18.29, 18.12, 18.31]
-[83.74]
-14.13465166091919
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 167260, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.636158227920532, 'TIME_S_1KI': 0.0635905669491841, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.6357300853729, 'W': 83.74}
-[18.39, 18.16, 18.03, 17.97, 18.05, 19.85, 18.01, 18.29, 18.12, 18.31, 17.95, 19.62, 17.88, 18.37, 18.2, 18.02, 18.09, 18.08, 18.42, 17.99]
-329.48
-16.474
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 167260, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.636158227920532, 'TIME_S_1KI': 0.0635905669491841, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.6357300853729, 'W': 83.74, 'J_1KI': 7.076621607589219, 'W_1KI': 0.5006576587349038, 'W_D': 67.26599999999999, 'J_D': 950.7814786233901, 'W_D_1KI': 0.40216429510941043, 'J_D_1KI': 0.002404426014046457}
+[18.19, 18.02, 18.18, 18.28, 17.94, 18.08, 18.33, 18.44, 17.82, 18.2]
+[84.04]
+13.788418054580688
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 163647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.69955587387085, 'TIME_S_1KI': 0.06538192495964393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.7786533069611, 'W': 84.04}
+[18.19, 18.02, 18.18, 18.28, 17.94, 18.08, 18.33, 18.44, 17.82, 18.2, 18.17, 18.08, 18.2, 17.87, 17.92, 18.23, 18.75, 17.8, 18.09, 18.39]
+326.505
+16.32525
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 163647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.69955587387085, 'TIME_S_1KI': 0.06538192495964393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.7786533069611, 'W': 84.04, 'J_1KI': 7.080964840827887, 'W_1KI': 0.5135443973919473, 'W_D': 67.71475000000001, 'J_D': 933.6792814614178, 'W_D_1KI': 0.41378546505588254, 'J_D_1KI': 0.002528524599020346}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
index cee5ebd..868cce2 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46485, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.648370742797852, "TIME_S_1KI": 0.2290711141830236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1255.2527691650391, "W": 87.87, "J_1KI": 27.003393980101947, "W_1KI": 1.8902871894159408, "W_D": 71.29950000000001, "J_D": 1018.5375533752442, "W_D_1KI": 1.5338173604388514, "J_D_1KI": 0.03299596343850385}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46354, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460933923721313, "TIME_S_1KI": 0.22567489156753062, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1249.411584854126, "W": 87.62, "J_1KI": 26.95369514721763, "W_1KI": 1.8902360098373387, "W_D": 71.2095, "J_D": 1015.4071473598481, "W_D_1KI": 1.5362104672735903, "J_D_1KI": 0.03314083935094254}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
index cd79c3c..056b89d 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.24121379852294922}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03854489326477051}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     267,     541,  ..., 1249516,
-                            1249748, 1250000]),
-       col_indices=tensor([  43,   75,  121,  ..., 4958, 4960, 4986]),
-       values=tensor([0.9222, 0.1508, 0.6151,  ..., 0.6191, 0.5090, 0.9494]),
+tensor(crow_indices=tensor([      0,     268,     547,  ..., 1249522,
+                            1249752, 1250000]),
+       col_indices=tensor([  55,   88,   92,  ..., 4943, 4993, 4995]),
+       values=tensor([0.0205, 0.9162, 0.9855,  ..., 0.3784, 0.4892, 0.4396]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.9528, 0.4494, 0.6520,  ..., 0.1607, 0.1619, 0.1321])
+tensor([0.2348, 0.0514, 0.2847,  ..., 0.2791, 0.0660, 0.1214])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.24121379852294922 seconds
+Time: 0.03854489326477051 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43529', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.83215594291687}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27240', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.170231342315674}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     232,     495,  ..., 1249518,
-                            1249779, 1250000]),
-       col_indices=tensor([  48,   77,  155,  ..., 4840, 4912, 4927]),
-       values=tensor([0.7412, 0.4704, 0.5361,  ..., 0.0050, 0.3320, 0.0792]),
+tensor(crow_indices=tensor([      0,     248,     482,  ..., 1249549,
+                            1249769, 1250000]),
+       col_indices=tensor([  15,   21,   37,  ..., 4863, 4919, 4974]),
+       values=tensor([0.5137, 0.1549, 0.4159,  ..., 0.2935, 0.1856, 0.8128]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.0891, 0.9943, 0.3145,  ..., 0.1784, 0.0363, 0.2532])
+tensor([0.7661, 0.7592, 0.8422,  ..., 0.1260, 0.8520, 0.6179])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 9.83215594291687 seconds
+Time: 6.170231342315674 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46485', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.648370742797852}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46354', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460933923721313}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     231,     510,  ..., 1249526,
-                            1249780, 1250000]),
-       col_indices=tensor([  32,   71,  112,  ..., 4895, 4929, 4940]),
-       values=tensor([0.5396, 0.2475, 0.0729,  ..., 0.2451, 0.2187, 0.9449]),
+tensor(crow_indices=tensor([      0,     262,     517,  ..., 1249525,
+                            1249760, 1250000]),
+       col_indices=tensor([   1,   93,  112,  ..., 4957, 4968, 4975]),
+       values=tensor([0.1837, 0.8744, 0.5620,  ..., 0.5820, 0.1645, 0.1343]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6746, 0.7318, 0.7509,  ..., 0.9415, 0.3905, 0.0197])
+tensor([0.3700, 0.8319, 0.5287,  ..., 0.8208, 0.8249, 0.9051])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.648370742797852 seconds
+Time: 10.460933923721313 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     231,     510,  ..., 1249526,
-                            1249780, 1250000]),
-       col_indices=tensor([  32,   71,  112,  ..., 4895, 4929, 4940]),
-       values=tensor([0.5396, 0.2475, 0.0729,  ..., 0.2451, 0.2187, 0.9449]),
+tensor(crow_indices=tensor([      0,     262,     517,  ..., 1249525,
+                            1249760, 1250000]),
+       col_indices=tensor([   1,   93,  112,  ..., 4957, 4968, 4975]),
+       values=tensor([0.1837, 0.8744, 0.5620,  ..., 0.5820, 0.1645, 0.1343]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6746, 0.7318, 0.7509,  ..., 0.9415, 0.3905, 0.0197])
+tensor([0.3700, 0.8319, 0.5287,  ..., 0.8208, 0.8249, 0.9051])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.648370742797852 seconds
+Time: 10.460933923721313 seconds
 
-[18.37, 17.89, 18.14, 18.27, 18.28, 18.81, 17.93, 18.0, 17.89, 19.89]
-[87.87]
-14.28533935546875
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46485, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.648370742797852, 'TIME_S_1KI': 0.2290711141830236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1255.2527691650391, 'W': 87.87}
-[18.37, 17.89, 18.14, 18.27, 18.28, 18.81, 17.93, 18.0, 17.89, 19.89, 18.31, 18.14, 18.17, 17.98, 21.39, 18.55, 17.96, 18.39, 18.34, 17.99]
-331.40999999999997
-16.5705
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46485, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.648370742797852, 'TIME_S_1KI': 0.2290711141830236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1255.2527691650391, 'W': 87.87, 'J_1KI': 27.003393980101947, 'W_1KI': 1.8902871894159408, 'W_D': 71.29950000000001, 'J_D': 1018.5375533752442, 'W_D_1KI': 1.5338173604388514, 'J_D_1KI': 0.03299596343850385}
+[18.2, 17.97, 18.75, 20.89, 18.05, 18.04, 17.92, 18.15, 18.09, 18.27]
+[87.62]
+14.25943374633789
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460933923721313, 'TIME_S_1KI': 0.22567489156753062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.411584854126, 'W': 87.62}
+[18.2, 17.97, 18.75, 20.89, 18.05, 18.04, 17.92, 18.15, 18.09, 18.27, 18.35, 17.98, 17.96, 18.31, 17.83, 17.89, 18.02, 18.18, 17.85, 17.84]
+328.21000000000004
+16.410500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460933923721313, 'TIME_S_1KI': 0.22567489156753062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.411584854126, 'W': 87.62, 'J_1KI': 26.95369514721763, 'W_1KI': 1.8902360098373387, 'W_D': 71.2095, 'J_D': 1015.4071473598481, 'W_D_1KI': 1.5362104672735903, 'J_D_1KI': 0.03314083935094254}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
index 0fd50b0..30ee0bd 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19767, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.566095352172852, "TIME_S_1KI": 0.5345320661796353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1281.5856591796876, "W": 87.51, "J_1KI": 64.8346061202857, "W_1KI": 4.427075428744878, "W_D": 71.21225000000001, "J_D": 1042.904792114258, "W_D_1KI": 3.6025825871401835, "J_D_1KI": 0.18225236946123255}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19580, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.499455213546753, "TIME_S_1KI": 0.5362336676990169, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1272.879629366398, "W": 87.53, "J_1KI": 65.00917412494371, "W_1KI": 4.470377936670071, "W_D": 70.8715, "J_D": 1030.6282263525725, "W_D_1KI": 3.6195863125638406, "J_D_1KI": 0.18486140513604907}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
index df7022e..e7e4d14 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5311744213104248}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06696105003356934}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     483,     993,  ..., 2498991,
-                            2499491, 2500000]),
-       col_indices=tensor([  15,   20,   28,  ..., 4987, 4988, 4995]),
-       values=tensor([0.8912, 0.6515, 0.2376,  ..., 0.2173, 0.7300, 0.9523]),
+tensor(crow_indices=tensor([      0,     497,     967,  ..., 2499018,
+                            2499482, 2500000]),
+       col_indices=tensor([   1,    5,   13,  ..., 4954, 4967, 4978]),
+       values=tensor([0.9405, 0.9423, 0.1495,  ..., 0.5665, 0.9976, 0.7425]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3817, 0.2295, 0.0793,  ..., 0.5917, 0.1851, 0.3088])
+tensor([0.7464, 0.9379, 0.0503,  ..., 0.7608, 0.2384, 0.8928])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.5311744213104248 seconds
+Time: 0.06696105003356934 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19767', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.566095352172852}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15680', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.408252954483032}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     521,    1020,  ..., 2499029,
-                            2499506, 2500000]),
-       col_indices=tensor([   2,   18,   32,  ..., 4991, 4992, 4995]),
-       values=tensor([0.1206, 0.3118, 0.4014,  ..., 0.4488, 0.4763, 0.9896]),
+tensor(crow_indices=tensor([      0,     497,     993,  ..., 2499009,
+                            2499527, 2500000]),
+       col_indices=tensor([  20,   30,   45,  ..., 4969, 4978, 4985]),
+       values=tensor([0.7997, 0.6173, 0.7759,  ..., 0.4558, 0.1544, 0.5880]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0140, 0.3283, 0.7098,  ..., 0.4613, 0.1962, 0.1627])
+tensor([0.0537, 0.7107, 0.3297,  ..., 0.5168, 0.3739, 0.3386])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.566095352172852 seconds
+Time: 8.408252954483032 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19580', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.499455213546753}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     521,    1020,  ..., 2499029,
-                            2499506, 2500000]),
-       col_indices=tensor([   2,   18,   32,  ..., 4991, 4992, 4995]),
-       values=tensor([0.1206, 0.3118, 0.4014,  ..., 0.4488, 0.4763, 0.9896]),
+tensor(crow_indices=tensor([      0,     479,    1002,  ..., 2498942,
+                            2499462, 2500000]),
+       col_indices=tensor([   2,    9,   12,  ..., 4986, 4993, 4996]),
+       values=tensor([0.9703, 0.1385, 0.8259,  ..., 0.8503, 0.3399, 0.6161]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0140, 0.3283, 0.7098,  ..., 0.4613, 0.1962, 0.1627])
+tensor([0.4198, 0.0425, 0.0591,  ..., 0.3064, 0.5860, 0.9864])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.566095352172852 seconds
+Time: 10.499455213546753 seconds
 
-[18.51, 17.88, 18.11, 18.97, 18.57, 17.75, 18.11, 17.81, 18.02, 17.72]
-[87.51]
-14.64501953125
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19767, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.566095352172852, 'TIME_S_1KI': 0.5345320661796353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.5856591796876, 'W': 87.51}
-[18.51, 17.88, 18.11, 18.97, 18.57, 17.75, 18.11, 17.81, 18.02, 17.72, 18.1, 18.14, 18.04, 18.33, 18.06, 17.95, 18.01, 18.09, 18.02, 17.86]
-325.95500000000004
-16.29775
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19767, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.566095352172852, 'TIME_S_1KI': 0.5345320661796353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.5856591796876, 'W': 87.51, 'J_1KI': 64.8346061202857, 'W_1KI': 4.427075428744878, 'W_D': 71.21225000000001, 'J_D': 1042.904792114258, 'W_D_1KI': 3.6025825871401835, 'J_D_1KI': 0.18225236946123255}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     479,    1002,  ..., 2498942,
+                            2499462, 2500000]),
+       col_indices=tensor([   2,    9,   12,  ..., 4986, 4993, 4996]),
+       values=tensor([0.9703, 0.1385, 0.8259,  ..., 0.8503, 0.3399, 0.6161]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4198, 0.0425, 0.0591,  ..., 0.3064, 0.5860, 0.9864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.499455213546753 seconds
+
+[18.41, 17.85, 18.5, 18.03, 18.16, 17.87, 17.95, 21.49, 18.34, 18.02]
+[87.53]
+14.54220986366272
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.499455213546753, 'TIME_S_1KI': 0.5362336676990169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.879629366398, 'W': 87.53}
+[18.41, 17.85, 18.5, 18.03, 18.16, 17.87, 17.95, 21.49, 18.34, 18.02, 18.22, 18.11, 19.31, 20.46, 18.5, 18.28, 17.98, 18.24, 17.73, 18.09]
+333.17
+16.6585
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.499455213546753, 'TIME_S_1KI': 0.5362336676990169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.879629366398, 'W': 87.53, 'J_1KI': 65.00917412494371, 'W_1KI': 4.470377936670071, 'W_D': 70.8715, 'J_D': 1030.6282263525725, 'W_D_1KI': 3.6195863125638406, 'J_D_1KI': 0.18486140513604907}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..e35fdc8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.682870388031006, "TIME_S_1KI": 1.1649804130895316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1294.0732465410233, "W": 85.83, "J_1KI": 141.1203104188684, "W_1KI": 9.359869138495092, "W_D": 69.25649999999999, "J_D": 1044.1918187005517, "W_D_1KI": 7.552508178844056, "J_D_1KI": 0.8236104884235611}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..5537faf
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.13022398948669434}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1019,    1995,  ..., 4997994,
+                            4998986, 5000000]),
+       col_indices=tensor([   3,    6,   16,  ..., 4985, 4988, 4992]),
+       values=tensor([0.7011, 0.4191, 0.0262,  ..., 0.8760, 0.6600, 0.8030]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6407, 0.2177, 0.8923,  ..., 0.2240, 0.7772, 0.6410])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.13022398948669434 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8063', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.23195219039917}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     967,    1963,  ..., 4997972,
+                            4999023, 5000000]),
+       col_indices=tensor([   1,    8,   12,  ..., 4992, 4997, 4998]),
+       values=tensor([0.9256, 0.0587, 0.0398,  ..., 0.7563, 0.3156, 0.8023]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2454, 0.6913, 0.0165,  ..., 0.7228, 0.0396, 0.9796])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 9.23195219039917 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9170', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.682870388031006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1039,    2045,  ..., 4997951,
+                            4999001, 5000000]),
+       col_indices=tensor([   1,    3,   21,  ..., 4981, 4993, 4997]),
+       values=tensor([0.7269, 0.5102, 0.5857,  ..., 0.9126, 0.4820, 0.1281]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7504, 0.3556, 0.6036,  ..., 0.9776, 0.8116, 0.9002])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.682870388031006 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1039,    2045,  ..., 4997951,
+                            4999001, 5000000]),
+       col_indices=tensor([   1,    3,   21,  ..., 4981, 4993, 4997]),
+       values=tensor([0.7269, 0.5102, 0.5857,  ..., 0.9126, 0.4820, 0.1281]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7504, 0.3556, 0.6036,  ..., 0.9776, 0.8116, 0.9002])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.682870388031006 seconds
+
+[18.71, 17.74, 17.83, 18.37, 18.26, 18.61, 17.98, 17.99, 18.09, 22.02]
+[85.83]
+15.07716703414917
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.682870388031006, 'TIME_S_1KI': 1.1649804130895316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.0732465410233, 'W': 85.83}
+[18.71, 17.74, 17.83, 18.37, 18.26, 18.61, 17.98, 17.99, 18.09, 22.02, 18.37, 17.98, 18.13, 19.94, 19.96, 18.01, 18.17, 17.86, 18.08, 17.84]
+331.47
+16.573500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.682870388031006, 'TIME_S_1KI': 1.1649804130895316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.0732465410233, 'W': 85.83, 'J_1KI': 141.1203104188684, 'W_1KI': 9.359869138495092, 'W_D': 69.25649999999999, 'J_D': 1044.1918187005517, 'W_D_1KI': 7.552508178844056, 'J_D_1KI': 0.8236104884235611}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..b09a9ac
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5638, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.486673831939697, "TIME_S_1KI": 1.859998905984338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1324.2176235437394, "W": 84.45, "J_1KI": 234.87364731176646, "W_1KI": 14.978715856686769, "W_D": 67.81275, "J_D": 1063.3373434098958, "W_D_1KI": 12.027802412202908, "J_D_1KI": 2.1333455857046664}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..933e0af
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.19828367233276367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1476,    3005,  ..., 7497107,
+                            7498540, 7500000]),
+       col_indices=tensor([   1,    2,    5,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6696, 0.7406, 0.6295,  ..., 0.1006, 0.8443, 0.3530]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.2303, 0.2102, 0.7986,  ..., 0.7152, 0.6703, 0.2250])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.19828367233276367 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5295', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.860368490219116}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1522,    3011,  ..., 7496960,
+                            7498495, 7500000]),
+       col_indices=tensor([   0,    6,    7,  ..., 4993, 4995, 4996]),
+       values=tensor([0.9810, 0.8874, 0.6033,  ..., 0.0373, 0.4053, 0.1135]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3541, 0.8542, 0.0162,  ..., 0.1880, 0.1632, 0.2816])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 9.860368490219116 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5638', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.486673831939697}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1590,    3134,  ..., 7496893,
+                            7498424, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4990, 4995, 4996]),
+       values=tensor([0.2379, 0.8284, 0.2265,  ..., 0.1431, 0.4495, 0.9988]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.8528, 0.9292, 0.5816,  ..., 0.4785, 0.5785, 0.0165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.486673831939697 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1590,    3134,  ..., 7496893,
+                            7498424, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4990, 4995, 4996]),
+       values=tensor([0.2379, 0.8284, 0.2265,  ..., 0.1431, 0.4495, 0.9988]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.8528, 0.9292, 0.5816,  ..., 0.4785, 0.5785, 0.0165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.486673831939697 seconds
+
+[18.49, 17.91, 18.44, 17.9, 18.58, 17.81, 17.92, 21.43, 18.39, 17.95]
+[84.45]
+15.680492877960205
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.486673831939697, 'TIME_S_1KI': 1.859998905984338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1324.2176235437394, 'W': 84.45}
+[18.49, 17.91, 18.44, 17.9, 18.58, 17.81, 17.92, 21.43, 18.39, 17.95, 18.43, 20.84, 19.1, 18.06, 18.27, 17.75, 18.15, 17.72, 18.16, 17.76]
+332.745
+16.63725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.486673831939697, 'TIME_S_1KI': 1.859998905984338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1324.2176235437394, 'W': 84.45, 'J_1KI': 234.87364731176646, 'W_1KI': 14.978715856686769, 'W_D': 67.81275, 'J_D': 1063.3373434098958, 'W_D_1KI': 12.027802412202908, 'J_D_1KI': 2.1333455857046664}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..6bb4210
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2997, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.852682113647461, "TIME_S_1KI": 3.6211818864355894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1423.3116033363342, "W": 83.38, "J_1KI": 474.9121132253367, "W_1KI": 27.82115448782115, "W_D": 66.859, "J_D": 1141.295160559654, "W_D_1KI": 22.30864197530864, "J_D_1KI": 7.443657649418965}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..7229c6c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.37653517723083496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2029,     4093,  ...,  9995991,
+                             9998004, 10000000]),
+       col_indices=tensor([   2,    3,    6,  ..., 4991, 4993, 4995]),
+       values=tensor([0.7344, 0.2463, 0.3008,  ..., 0.7679, 0.9534, 0.0176]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9829, 0.9205, 0.8754,  ..., 0.7887, 0.3539, 0.3362])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.37653517723083496 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2788', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.764836072921753}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2035,     4051,  ...,  9995975,
+                             9998000, 10000000]),
+       col_indices=tensor([   1,    7,   13,  ..., 4993, 4995, 4999]),
+       values=tensor([0.5646, 0.0016, 0.0540,  ..., 0.1360, 0.4218, 0.1397]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2261, 0.1429, 0.2182,  ..., 0.9969, 0.6483, 0.4023])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 9.764836072921753 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2997', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.852682113647461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2037,     4021,  ...,  9995904,
+                             9997943, 10000000]),
+       col_indices=tensor([   4,    9,   10,  ..., 4993, 4994, 4995]),
+       values=tensor([0.7234, 0.7434, 0.9107,  ..., 0.4914, 0.1939, 0.0446]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2935, 0.8834, 0.9590,  ..., 0.1952, 0.7236, 0.3428])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.852682113647461 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2037,     4021,  ...,  9995904,
+                             9997943, 10000000]),
+       col_indices=tensor([   4,    9,   10,  ..., 4993, 4994, 4995]),
+       values=tensor([0.7234, 0.7434, 0.9107,  ..., 0.4914, 0.1939, 0.0446]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2935, 0.8834, 0.9590,  ..., 0.1952, 0.7236, 0.3428])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.852682113647461 seconds
+
+[18.3, 18.02, 18.13, 22.02, 18.21, 18.26, 17.95, 17.94, 17.94, 18.03]
+[83.38]
+17.07017993927002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.852682113647461, 'TIME_S_1KI': 3.6211818864355894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1423.3116033363342, 'W': 83.38}
+[18.3, 18.02, 18.13, 22.02, 18.21, 18.26, 17.95, 17.94, 17.94, 18.03, 18.77, 18.01, 18.52, 18.59, 18.17, 17.81, 18.04, 18.12, 18.22, 17.84]
+330.41999999999996
+16.520999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.852682113647461, 'TIME_S_1KI': 3.6211818864355894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1423.3116033363342, 'W': 83.38, 'J_1KI': 474.9121132253367, 'W_1KI': 27.82115448782115, 'W_D': 66.859, 'J_D': 1141.295160559654, 'W_D_1KI': 22.30864197530864, 'J_D_1KI': 7.443657649418965}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..65b89f9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2368, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.176312685012817, "TIME_S_1KI": 4.297429343333116, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1437.3391367673873, "W": 78.7, "J_1KI": 606.984432756498, "W_1KI": 33.2347972972973, "W_D": 62.306000000000004, "J_D": 1137.9269663968087, "W_D_1KI": 26.311655405405407, "J_D_1KI": 11.111340965120526}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..4c78e30
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.48082637786865234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2493,     4988,  ..., 12494973,
+                            12497489, 12500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4995, 4996, 4997]),
+       values=tensor([0.7019, 0.9323, 0.5533,  ..., 0.8475, 0.3948, 0.0670]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2037, 0.7210, 0.2412,  ..., 0.0549, 0.3207, 0.0757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.48082637786865234 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2183', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.679495573043823}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2494,     4974,  ..., 12494974,
+                            12497479, 12500000]),
+       col_indices=tensor([   2,    3,    5,  ..., 4990, 4992, 4993]),
+       values=tensor([0.3550, 0.2209, 0.9878,  ..., 0.1100, 0.7010, 0.8735]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0897, 0.5505, 0.7451,  ..., 0.8823, 0.0649, 0.5912])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.679495573043823 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2368', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.176312685012817}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2500,     5025,  ..., 12495060,
+                            12497549, 12500000]),
+       col_indices=tensor([   3,    7,    9,  ..., 4994, 4997, 4998]),
+       values=tensor([0.3762, 0.0915, 0.3071,  ..., 0.1948, 0.2052, 0.2572]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2717, 0.6125, 0.0994,  ..., 0.4315, 0.1911, 0.5863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.176312685012817 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2500,     5025,  ..., 12495060,
+                            12497549, 12500000]),
+       col_indices=tensor([   3,    7,    9,  ..., 4994, 4997, 4998]),
+       values=tensor([0.3762, 0.0915, 0.3071,  ..., 0.1948, 0.2052, 0.2572]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2717, 0.6125, 0.0994,  ..., 0.4315, 0.1911, 0.5863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.176312685012817 seconds
+
+[18.38, 18.14, 18.13, 18.09, 18.53, 18.05, 18.22, 17.96, 18.23, 17.96]
+[78.7]
+18.263521432876587
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.176312685012817, 'TIME_S_1KI': 4.297429343333116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.3391367673873, 'W': 78.7}
+[18.38, 18.14, 18.13, 18.09, 18.53, 18.05, 18.22, 17.96, 18.23, 17.96, 18.81, 17.96, 18.41, 17.98, 18.95, 18.19, 18.23, 17.96, 18.21, 18.13]
+327.88
+16.394
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.176312685012817, 'TIME_S_1KI': 4.297429343333116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.3391367673873, 'W': 78.7, 'J_1KI': 606.984432756498, 'W_1KI': 33.2347972972973, 'W_D': 62.306000000000004, 'J_D': 1137.9269663968087, 'W_D_1KI': 26.311655405405407, 'J_D_1KI': 11.111340965120526}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
index 4be18ec..2ac6398 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 355144, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.430037498474121, "TIME_S_1KI": 0.02936847447366173, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.3673194527627, "W": 73.03, "J_1KI": 2.808346246741498, "W_1KI": 0.20563489739373325, "W_D": 56.462250000000004, "J_D": 771.1023268899322, "W_D_1KI": 0.15898410222332351, "J_D_1KI": 0.0004476609550585777}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 357325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.499107599258423, "TIME_S_1KI": 0.029382516194664303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1011.0901748657226, "W": 72.8, "J_1KI": 2.829609388835717, "W_1KI": 0.20373609459175818, "W_D": 46.4985, "J_D": 645.7991276922226, "W_D_1KI": 0.1301294339886658, "J_D_1KI": 0.0003641766850588842}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
index 1e00532..109e8d2 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07530069351196289}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.020415544509887695}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
-       col_indices=tensor([1366, 2183,  387, 4785,  591, 3875, 1782, 3853, 3491,
-                           1111, 4311, 1391, 2949, 4195, 1174,   98, 1356,  809,
-                           1785,  447, 2538, 4572, 2460, 1800,  303, 1931, 4013,
-                           4968, 4004, 1588, 1643, 1967, 3906, 4748, 1447, 2599,
-                            629, 3538, 4520, 4776, 4758, 2464, 1751, 3806,   96,
-                            198,  731, 3443, 3712, 4600, 4270, 2744, 4125,  400,
-                            468,  107, 2682, 4704,  252, 1804, 2511, 1911,  162,
-                           2509,  972, 3478,  980, 1895, 2935, 3965, 2890, 3988,
-                           2804, 3654, 1037, 4790, 2965,  394, 3461, 2942, 2671,
-                           4602,  851, 2319, 1925, 2531, 2262, 2466,  138, 3192,
-                           4165, 2776, 2205, 2786, 1112, 4160, 4088, 4917, 1466,
-                             32, 4695, 2757, 3360, 3218,  455,  480, 4012, 3928,
-                           3689, 1276, 1963, 1058, 3861, 2863, 4421, 4459, 4424,
-                           4964, 4366, 2158, 3511,  768, 3822, 1025, 3276, 1349,
-                           1095, 2928, 2660, 1067, 2626,  893, 4611, 4619, 1553,
-                           2755, 3328, 4431, 1950, 4722, 1972, 4066, 2996, 4851,
-                           2711, 2693, 4611, 1116, 4304, 1246, 2511, 2934, 4826,
-                           2926, 3416, 3468, 2846, 4286, 3701, 3015, 2373, 3319,
-                           2586, 1704, 3671, 1535, 4335, 3487, 2710, 3432, 1408,
-                           2336, 4517, 3976, 4761, 1747,  150, 3884, 4390, 3319,
-                           3373, 3574, 3662, 1429, 4058, 1144, 1909, 4439, 1862,
-                            343, 1833, 2363, 3001, 1926, 4696,  409, 4669, 2313,
-                           1538, 3220, 3305,  493, 2975, 4619, 1565, 4245, 1991,
-                            380, 1379, 2494, 2025,  851, 1740,  171, 2270, 2261,
-                           2794, 4072, 4453, 4823,  695,  669, 3117, 1730, 3920,
-                           4849, 3714, 1313, 3918, 1033, 1224, 3117, 2450, 3021,
-                           3892, 3817, 1313, 2580, 4367, 3947, 3099, 4651, 3006,
-                           4264,  712, 4793, 3855, 4618,  272, 4548]),
-       values=tensor([0.5356, 0.5172, 0.5088, 0.7213, 0.3478, 0.1053, 0.9439,
-                      0.9314, 0.4347, 0.5009, 0.9214, 0.0299, 0.2703, 0.5553,
-                      0.3016, 0.4455, 0.2361, 0.8920, 0.7432, 0.6139, 0.7733,
-                      0.3556, 0.1748, 0.8314, 0.8776, 0.8348, 0.1485, 0.4702,
-                      0.4810, 0.8748, 0.6149, 0.8907, 0.9641, 0.0939, 0.1055,
-                      0.6954, 0.2399, 0.1624, 0.3696, 0.9614, 0.3594, 0.5972,
-                      0.9819, 0.0645, 0.3543, 0.1275, 0.6800, 0.3878, 0.7605,
-                      0.6525, 0.7013, 0.5154, 0.4064, 0.1554, 0.5527, 0.2023,
-                      0.3691, 0.5797, 0.9886, 0.9941, 0.9352, 0.7550, 0.0819,
-                      0.3616, 0.7623, 0.6193, 0.3361, 0.9681, 0.4246, 0.6029,
-                      0.5772, 0.0561, 0.2661, 0.5456, 0.2304, 0.3887, 0.2381,
-                      0.3730, 0.7517, 0.6162, 0.2738, 0.4697, 0.7504, 0.9515,
-                      0.7210, 0.4160, 0.4959, 0.5300, 0.2485, 0.7381, 0.3695,
-                      0.4257, 0.1829, 0.0551, 0.7619, 0.8081, 0.4964, 0.4779,
-                      0.0357, 0.2681, 0.0521, 0.0389, 0.0434, 0.3566, 0.7098,
-                      0.1066, 0.0800, 0.4058, 0.5388, 0.9446, 0.2771, 0.5488,
-                      0.8493, 0.4334, 0.8666, 0.8039, 0.2616, 0.8733, 0.8412,
-                      0.6075, 0.0051, 0.7165, 0.9628, 0.7661, 0.4765, 0.6812,
-                      0.1095, 0.7697, 0.6192, 0.6769, 0.9349, 0.0052, 0.1322,
-                      0.1324, 0.9038, 0.2020, 0.6337, 0.8080, 0.2834, 0.0511,
-                      0.6009, 0.2042, 0.5100, 0.6688, 0.2408, 0.9657, 0.8116,
-                      0.8985, 0.0972, 0.8199, 0.3158, 0.7270, 0.0200, 0.2146,
-                      0.9137, 0.0484, 0.2512, 0.2305, 0.1410, 0.9701, 0.3767,
-                      0.1641, 0.2509, 0.4147, 0.6141, 0.4403, 0.2333, 0.3371,
-                      0.6103, 0.2630, 0.2671, 0.0768, 0.8063, 0.8867, 0.9092,
-                      0.7796, 0.9853, 0.4951, 0.2086, 0.4307, 0.0119, 0.1662,
-                      0.8220, 0.7333, 0.1521, 0.6924, 0.6584, 0.6936, 0.1717,
-                      0.0561, 0.9517, 0.6184, 0.4753, 0.7656, 0.9019, 0.5502,
-                      0.9529, 0.5922, 0.4037, 0.0988, 0.7843, 0.0649, 0.2485,
-                      0.3469, 0.9377, 0.6160, 0.3297, 0.1479, 0.3514, 0.4560,
-                      0.6809, 0.0681, 0.5510, 0.6925, 0.2032, 0.7181, 0.5101,
-                      0.1339, 0.8347, 0.2363, 0.9076, 0.1946, 0.5622, 0.8947,
-                      0.8049, 0.7599, 0.8724, 0.5959, 0.8922, 0.7182, 0.4477,
-                      0.5685, 0.4980, 0.5565, 0.2995, 0.7747, 0.8395, 0.0020,
-                      0.6022, 0.0279, 0.4498, 0.0752, 0.1893, 0.3529, 0.6947,
-                      0.9277, 0.8241, 0.1856, 0.0213, 0.6132]),
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1333, 4323,  980, 4513,  212, 4800,  425, 2650, 4181,
+                            969,  725, 4708, 2978, 4829, 2835, 2475, 4298,  490,
+                            871, 3121, 4762, 4479,  218, 2461, 4825, 2919,  512,
+                           1363, 1944, 2772,  493, 2448, 4663, 4533, 3227, 2251,
+                           3206, 1036, 4160, 3108, 4112,  520, 4884, 1695, 3846,
+                           3433, 4341,  314, 4963, 4557, 1460, 2013, 3131, 1087,
+                           1051, 4820, 1214, 2275,   33, 4231,  755, 1677, 4993,
+                              6,  698, 1317, 4059, 4178, 3988, 3341, 1529, 1327,
+                           4807, 1459, 3985, 2360, 1563, 1141, 2987, 4820, 3117,
+                           3988,  665, 4532, 3163, 3252, 4621, 4938, 2411, 4883,
+                           2036, 4582,  143, 3182,  722, 2609, 1076, 3554,  197,
+                             34, 2097, 1008, 2860, 1655, 2265, 3241, 2350, 1493,
+                           3432,  135, 3780, 2490, 3232, 1376, 4188, 3874, 3039,
+                            211, 1650, 4645, 4026, 3400, 1898,  591, 1094, 2347,
+                           3740, 1237,  925, 2787, 2157, 2054, 2757, 4014, 2493,
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+                           3373,  401, 3150, 4902,  982, 3824, 3035,  683, 1843,
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+                             94, 1127,  616,   21, 1697,  646,  658, 1404, 4972,
+                           1106, 2493,  822, 2442, 3750, 3893,  297, 3607, 4990,
+                           1137, 2122, 3346,   36,  515, 2447, 1921, 2437, 4525,
+                            918, 1225, 2348, 1665, 3413,  132, 2821,  111,  225,
+                           4735, 1677, 1746, 1399, 3492, 1994, 2191]),
+       values=tensor([0.3602, 0.2302, 0.1896, 0.7717, 0.8956, 0.2078, 0.9487,
+                      0.0390, 0.3051, 0.4281, 0.9325, 0.8375, 0.6844, 0.7644,
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+                      0.1715, 0.2483, 0.6015, 0.5276, 0.6350]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4529, 0.0478, 0.6057,  ..., 0.4541, 0.9032, 0.3518])
+tensor([0.0146, 0.8199, 0.1846,  ..., 0.5715, 0.8048, 0.4041])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.07530069351196289 seconds
+Time: 0.020415544509887695 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '139440', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.12260627746582}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51431', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5112977027893066}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4955, 3285, 1092, 4534, 4976,  442, 2522, 4514, 4006,
-                           1710, 2609,  275, 2553,  192,   68, 4509,  517, 1487,
-                           4557, 2975, 2588, 4021, 2076, 3240, 3988,  435, 2254,
-                           2223, 4880, 3865, 3818, 4642, 3945, 4353,  601, 3917,
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-                            751, 3677, 3364, 2676, 4496, 2911, 2638,  552, 4753,
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-                           2397, 4058, 1153, 4577, 3965, 4609, 1999, 4032,   95,
-                           1807, 3734, 3107, 2958, 2169, 4822, 1527, 3639,  620,
-                           4908, 4406,  564, 2813, 4923, 3870, 2382, 1337, 4050,
-                           4071, 2788, 1336, 4894, 4067, 1978, 1895,  498, 3798,
-                           1258,  549,  714, 3988, 3759, 3303, 1452, 1683, 4641,
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-                           1793, 3735,  793, 3175, 4362, 3857, 3311, 3724,  615,
-                           3226, 2202, 4290, 2384,  657, 2313, 1172,  518, 1645,
-                            899, 4853, 1109, 2856, 2859,  137, 3910,  650, 1455,
-                           3154, 3652, 1672, 4613, 1991,  246, 2555,    4, 2614,
-                           2633, 1294, 2903, 1660, 4703, 2866, 3053, 1012, 3045,
-                           4172, 3476,  296, 4197, 2675, 2071, 2677, 1326, 2255,
-                            468, 4989, 2355, 4824,  996,   43, 2583]),
-       values=tensor([0.3090, 0.2901, 0.9593, 0.2041, 0.3894, 0.4919, 0.4096,
-                      0.8215, 0.1866, 0.7740, 0.2336, 0.6944, 0.1434, 0.9450,
-                      0.5954, 0.3044, 0.5006, 0.3429, 0.4467, 0.0518, 0.6871,
-                      0.3725, 0.7034, 0.7486, 0.8746, 0.3907, 0.1517, 0.4997,
-                      0.1845, 0.7706, 0.6244, 0.6342, 0.6033, 0.6938, 0.2438,
-                      0.1144, 0.3513, 0.6893, 0.7703, 0.3523, 0.2076, 0.7465,
-                      0.4913, 0.9688, 0.0028, 0.1578, 0.0568, 0.7822, 0.7028,
-                      0.3600, 0.2439, 0.4360, 0.7037, 0.4050, 0.8531, 0.5414,
-                      0.4773, 0.3671, 0.4547, 0.2754, 0.4488, 0.0085, 0.3071,
-                      0.4601, 0.4770, 0.5158, 0.4421, 0.5651, 0.5805, 0.4433,
-                      0.3995, 0.5205, 0.7157, 0.7315, 0.6363, 0.9589, 0.7223,
-                      0.9785, 0.4132, 0.5851, 0.7482, 0.0942, 0.2741, 0.5798,
-                      0.8967, 0.4132, 0.5974, 0.3338, 0.4602, 0.6811, 0.5641,
-                      0.0144, 0.5238, 0.0767, 0.8325, 0.0088, 0.0767, 0.2907,
-                      0.8996, 0.8420, 0.5348, 0.2313, 0.0781, 0.9045, 0.3083,
-                      0.9636, 0.2543, 0.6828, 0.1620, 0.2858, 0.1124, 0.3208,
-                      0.6389, 0.9267, 0.6353, 0.0688, 0.9267, 0.9566, 0.7499,
-                      0.7412, 0.4162, 0.5378, 0.6296, 0.9489, 0.6620, 0.4205,
-                      0.9920, 0.8509, 0.1746, 0.9154, 0.0320, 0.1367, 0.7287,
-                      0.4725, 0.2424, 0.3738, 0.1897, 0.9348, 0.6165, 0.7516,
-                      0.3874, 0.0970, 0.8851, 0.3148, 0.3850, 0.4337, 0.7076,
-                      0.4992, 0.1955, 0.2344, 0.3528, 0.9558, 0.2944, 0.6120,
-                      0.9024, 0.3017, 0.3837, 0.0724, 0.3520, 0.1259, 0.2545,
-                      0.1286, 0.8847, 0.1428, 0.4622, 0.0540, 0.3001, 0.6109,
-                      0.7042, 0.7070, 0.7848, 0.3801, 0.3847, 0.7723, 0.6446,
-                      0.9716, 0.3773, 0.8839, 0.4889, 0.3169, 0.6431, 0.7083,
-                      0.1827, 0.5140, 0.9487, 0.5911, 0.8204, 0.6180, 0.4421,
-                      0.3128, 0.9545, 0.2240, 0.5569, 0.0329, 0.3919, 0.3248,
-                      0.2245, 0.3333, 0.9672, 0.9062, 0.0547, 0.3239, 0.2321,
-                      0.0070, 0.4820, 0.4051, 0.2674, 0.7057, 0.7544, 0.3960,
-                      0.7548, 0.0492, 0.5769, 0.2071, 0.4627, 0.2573, 0.4606,
-                      0.6077, 0.9484, 0.5943, 0.5295, 0.3192, 0.6949, 0.6336,
-                      0.2976, 0.4421, 0.9484, 0.4080, 0.0752, 0.8220, 0.3509,
-                      0.7514, 0.8530, 0.4354, 0.9063, 0.8031, 0.3178, 0.2957,
-                      0.6220, 0.2051, 0.4848, 0.8340, 0.8353, 0.5340, 0.0238,
-                      0.3897, 0.4510, 0.4716, 0.8420, 0.2532]),
+       col_indices=tensor([4110, 4882, 2528,  560, 2850, 1135, 2189,  267,  591,
+                           4248,  392, 4389,  952, 4426,  675,  791, 4134, 2003,
+                            330, 4266, 1795, 3038, 2537, 1414, 4513, 1875, 1420,
+                           3040, 4172, 4236, 4029,   37, 2565, 1038, 1280,  832,
+                           4901, 4729,   13,   26,  702, 4420, 4734, 1556,  899,
+                           2280, 4293, 3965, 1974, 4811, 4611, 1009,  692, 1950,
+                            707, 1172, 1767, 2120, 1996,  634, 3311, 4113, 1903,
+                           2733, 3685, 3363, 3449, 2814, 1438, 4520,  218,  740,
+                            498,  655, 1313, 1745, 4116,   95,  357, 4580, 2628,
+                           4483, 1655, 4732,  165, 2596,  205, 1356, 2811, 2330,
+                           4604, 2547, 3455, 1624, 3457, 1604, 4751, 1859, 1095,
+                           4469, 2397, 2809, 2539, 2371, 2342, 4452, 4342, 4591,
+                           2097, 1276, 1250, 2060, 1578, 4677, 4803, 4831, 2724,
+                           2018, 2284, 2775, 4691,  199, 4210, 2309,  943, 2881,
+                           1649, 3410, 3567, 3333, 2952, 4866, 2146, 2388, 3555,
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+                           3586, 4367, 3371, 2520, 3654,   86,  810, 3504, 2051,
+                           4871, 4860,  841,  544, 3974, 2005,  981, 3244,  488,
+                           3372, 1091, 2635, 1589, 2299,  733, 4674, 2817, 2103,
+                           1209, 3577, 3623, 4300, 2443, 1295, 4756, 2336, 3550,
+                           2095, 4292, 2842, 1971, 4691, 4042, 3354,  592, 2063,
+                           1832, 1378, 3596, 4385,  745, 2532, 1883, 1739, 1247,
+                           2943, 1826, 3219, 4178, 3486, 2469, 1068, 4370, 1539,
+                            326, 2628, 3947, 4717, 3828, 4255, 4155, 4188, 3551,
+                           3390, 3231, 2307, 2529, 2288,  367, 4198, 4896,  743,
+                           1579, 1013,  645, 2200, 4435, 4882, 1640,  288, 3098,
+                           3761, 2384,  847,  509, 4067, 1126, 4356, 2690, 2684,
+                           3064,  181,  819,  409, 3928, 4195, 3862]),
+       values=tensor([0.6256, 0.4971, 0.1060, 0.7139, 0.1823, 0.2509, 0.2673,
+                      0.8663, 0.0835, 0.0808, 0.2049, 0.4420, 0.4395, 0.8635,
+                      0.0169, 0.4133, 0.6820, 0.9890, 0.7762, 0.6220, 0.1534,
+                      0.5506, 0.6723, 0.8443, 0.3974, 0.8265, 0.0110, 0.1617,
+                      0.9872, 0.3380, 0.0737, 0.5011, 0.8812, 0.5991, 0.0522,
+                      0.5730, 0.8120, 0.7506, 0.2397, 0.8742, 0.8477, 0.3557,
+                      0.0116, 0.0076, 0.2125, 0.8492, 0.7414, 0.8939, 0.9161,
+                      0.6369, 0.7960, 0.8359, 0.4748, 0.7205, 0.4515, 0.6595,
+                      0.2492, 0.2025, 0.4674, 0.4437, 0.5543, 0.2380, 0.4617,
+                      0.5720, 0.4895, 0.6114, 0.1214, 0.9049, 0.4914, 0.2397,
+                      0.6242, 0.3103, 0.8200, 0.7931, 0.9387, 0.1219, 0.7863,
+                      0.3172, 0.8665, 0.4111, 0.3328, 0.3737, 0.2933, 0.4526,
+                      0.3424, 0.8586, 0.4153, 0.8629, 0.3744, 0.5686, 0.0746,
+                      0.0221, 0.4632, 0.2182, 0.7406, 0.2698, 0.1778, 0.5649,
+                      0.4080, 0.6883, 0.2942, 0.9953, 0.6518, 0.2163, 0.7535,
+                      0.6975, 0.2221, 0.2275, 0.5405, 0.0798, 0.5573, 0.6302,
+                      0.4985, 0.1104, 0.5174, 0.9401, 0.1389, 0.1581, 0.0424,
+                      0.6895, 0.3613, 0.0928, 0.0065, 0.9081, 0.0044, 0.4960,
+                      0.9823, 0.5854, 0.2610, 0.0038, 0.7425, 0.4802, 0.1516,
+                      0.8368, 0.1838, 0.8782, 0.0715, 0.4223, 0.7005, 0.4430,
+                      0.9462, 0.7502, 0.6645, 0.2891, 0.9383, 0.3987, 0.8215,
+                      0.5606, 0.2261, 0.8011, 0.4375, 0.3961, 0.7279, 0.2806,
+                      0.0713, 0.4871, 0.3038, 0.5371, 0.2216, 0.6841, 0.2878,
+                      0.3646, 0.4532, 0.8491, 0.7578, 0.6510, 0.7523, 0.4340,
+                      0.7007, 0.4708, 0.9965, 0.8203, 0.2246, 0.8996, 0.3303,
+                      0.0148, 0.3760, 0.7093, 0.5329, 0.2574, 0.3450, 0.1580,
+                      0.7186, 0.2033, 0.7798, 0.4593, 0.7075, 0.5720, 0.1499,
+                      0.1156, 0.4260, 0.0288, 0.5145, 0.3668, 0.5589, 0.8589,
+                      0.3216, 0.4607, 0.7258, 0.8841, 0.3750, 0.0423, 0.5771,
+                      0.4191, 0.6488, 0.7104, 0.5144, 0.6011, 0.7757, 0.0524,
+                      0.4180, 0.1533, 0.5397, 0.3671, 0.1765, 0.9044, 0.7096,
+                      0.5951, 0.8654, 0.2226, 0.3541, 0.2302, 0.8815, 0.7479,
+                      0.9847, 0.5335, 0.2697, 0.6788, 0.5998, 0.9345, 0.5600,
+                      0.8733, 0.1996, 0.0591, 0.2713, 0.9857, 0.2291, 0.3974,
+                      0.8274, 0.9419, 0.8414, 0.0360, 0.8374, 0.9999, 0.3537,
+                      0.3299, 0.5865, 0.7469, 0.2686, 0.3364]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.2666, 0.0606, 0.0325,  ..., 0.3347, 0.5904, 0.3218])
+tensor([0.4917, 0.3630, 0.9281,  ..., 0.8752, 0.5291, 0.1405])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,107 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 4.12260627746582 seconds
+Time: 1.5112977027893066 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355144', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.430037498474121}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '357325', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.499107599258423}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 576,  858,  411, 4414, 2665,  376, 3054, 3322, 4372,
-                           4279, 4109, 1090, 4955, 1792, 2761, 3831, 3980,  529,
-                           2775,  617, 4117, 1022, 2357, 2558, 3577, 3578, 3958,
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-                            694, 4893, 4079, 3781, 2587, 2843, 2494, 2488,  824,
-                           1995, 2151,  656,  824, 1220, 2366, 1835]),
-       values=tensor([3.0955e-01, 7.8676e-01, 4.1266e-01, 3.7203e-01,
-                      7.1730e-01, 3.7179e-01, 6.0963e-01, 1.0902e-02,
-                      1.1230e-01, 2.1823e-01, 1.9100e-01, 8.5284e-01,
-                      3.9664e-01, 7.2699e-01, 1.5904e-01, 3.5501e-01,
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-                      7.7463e-01, 6.6460e-01, 4.6171e-01, 5.2632e-01,
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+tensor([0.5944, 0.7034, 0.9760,  ..., 0.8304, 0.5842, 0.4500])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -266,104 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.430037498474121 seconds
+Time: 10.499107599258423 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      0.8222, 0.8265, 0.6019, 0.5462, 0.6062, 0.4916, 0.3704,
+                      0.9894, 0.1946, 0.9052, 0.1356, 0.8292, 0.4460, 0.3066,
+                      0.5424, 0.9972, 0.4829, 0.2518, 0.5680, 0.4922, 0.3950,
+                      0.5510, 0.1881, 0.6119, 0.3857, 0.5521, 0.8319, 0.6787,
+                      0.8327, 0.0041, 0.6533, 0.9035, 0.3529, 0.2045, 0.8781,
+                      0.7715, 0.7148, 0.2012, 0.4429, 0.6908, 0.0217, 0.9409,
+                      0.2398, 0.9282, 0.8675, 0.2060, 0.8887, 0.5702, 0.9940,
+                      0.9652, 0.4202, 0.5062, 0.1515, 0.6839, 0.1289, 0.7773,
+                      0.8039, 0.1706, 0.5382, 0.6842, 0.4665, 0.8705, 0.9740,
+                      0.3731, 0.3796, 0.0945, 0.8938, 0.9546, 0.7375, 0.4347,
+                      0.0317, 0.0560, 0.1482, 0.2781, 0.1532, 0.0170, 0.3948,
+                      0.1619, 0.3596, 0.1826, 0.6650, 0.4684, 0.2191, 0.9123,
+                      0.0088, 0.2608, 0.7519, 0.3594, 0.8499, 0.1793, 0.6810,
+                      0.4007, 0.7171, 0.6382, 0.5741, 0.6774, 0.9314, 0.4111,
+                      0.3073, 0.3304, 0.1048, 0.8381, 0.8387, 0.6626, 0.9350,
+                      0.3559, 0.0898, 0.9007, 0.8371, 0.0933, 0.7505, 0.1760,
+                      0.8200, 0.6000, 0.8535, 0.8461, 0.8576, 0.1788, 0.4860,
+                      0.8224, 0.8204, 0.6502, 0.3745, 0.9926, 0.3104, 0.0651,
+                      0.2962, 0.9752, 0.3565, 0.2747, 0.0907, 0.8216, 0.2197,
+                      0.9709, 0.0139, 0.1557, 0.9115, 0.1512, 0.0977, 0.5834,
+                      0.2007, 0.9483, 0.8228, 0.2172, 0.6709, 0.2237, 0.7140,
+                      0.3427, 0.4328, 0.7857, 0.0871, 0.8851]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5944, 0.7034, 0.9760,  ..., 0.8304, 0.5842, 0.4500])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -371,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.430037498474121 seconds
+Time: 10.499107599258423 seconds
 
-[18.28, 21.02, 18.58, 17.81, 18.18, 17.91, 18.02, 17.91, 18.05, 17.86]
-[73.03]
-13.656953573226929
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355144, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.430037498474121, 'TIME_S_1KI': 0.02936847447366173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3673194527627, 'W': 73.03}
-[18.28, 21.02, 18.58, 17.81, 18.18, 17.91, 18.02, 17.91, 18.05, 17.86, 18.18, 17.95, 18.17, 18.08, 18.7, 17.95, 18.01, 17.92, 21.06, 17.75]
-331.35499999999996
-16.567749999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355144, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.430037498474121, 'TIME_S_1KI': 0.02936847447366173, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.3673194527627, 'W': 73.03, 'J_1KI': 2.808346246741498, 'W_1KI': 0.20563489739373325, 'W_D': 56.462250000000004, 'J_D': 771.1023268899322, 'W_D_1KI': 0.15898410222332351, 'J_D_1KI': 0.0004476609550585777}
+[18.49, 21.77, 18.79, 18.12, 18.06, 18.03, 18.09, 17.74, 18.25, 17.89]
+[72.8]
+13.888601303100586
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 357325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.499107599258423, 'TIME_S_1KI': 0.029382516194664303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1011.0901748657226, 'W': 72.8}
+[18.49, 21.77, 18.79, 18.12, 18.06, 18.03, 18.09, 17.74, 18.25, 17.89, 37.95, 41.51, 40.96, 43.46, 47.23, 47.25, 47.57, 33.36, 24.72, 27.91]
+526.03
+26.301499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 357325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.499107599258423, 'TIME_S_1KI': 0.029382516194664303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1011.0901748657226, 'W': 72.8, 'J_1KI': 2.829609388835717, 'W_1KI': 0.20373609459175818, 'W_D': 46.4985, 'J_D': 645.7991276922226, 'W_D_1KI': 0.1301294339886658, 'J_D_1KI': 0.0003641766850588842}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..e1d6586
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 344337, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.209988117218018, "TIME_S_1KI": 0.029651150231366417, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 996.8449161434174, "W": 73.42, "J_1KI": 2.894968929111357, "W_1KI": 0.21322135001466586, "W_D": 47.158, "J_D": 640.2780244550705, "W_D_1KI": 0.13695304309441042, "J_D_1KI": 0.0003977296749823877}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..395a17f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019530296325683594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2184, 3902, 2442,  ..., 1965, 3430, 1316]),
+       values=tensor([0.1298, 0.8618, 0.5661,  ..., 0.6807, 0.5041, 0.2985]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7563, 0.3154, 0.3739,  ..., 0.6493, 0.1597, 0.7226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.019530296325683594 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53762', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6393845081329346}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([ 543, 3603,  176,  ..., 1860, 3976,  394]),
+       values=tensor([0.3999, 0.3952, 0.5467,  ..., 0.7650, 0.4806, 0.1331]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.3582, 0.6649, 0.9739,  ..., 0.1792, 0.8232, 0.7194])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.6393845081329346 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '344337', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.209988117218018}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2469, 1957, 4805,  ...,  986, 4084, 3397]),
+       values=tensor([0.1203, 0.9340, 0.5005,  ..., 0.1600, 0.9840, 0.6585]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7772, 0.5114, 0.7654,  ..., 0.7246, 0.7942, 0.6121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.209988117218018 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2469, 1957, 4805,  ...,  986, 4084, 3397]),
+       values=tensor([0.1203, 0.9340, 0.5005,  ..., 0.1600, 0.9840, 0.6585]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7772, 0.5114, 0.7654,  ..., 0.7246, 0.7942, 0.6121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.209988117218018 seconds
+
+[40.04, 39.64, 39.87, 39.84, 41.87, 42.36, 39.49, 37.87, 39.91, 39.97]
+[73.42]
+13.577293872833252
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 344337, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.209988117218018, 'TIME_S_1KI': 0.029651150231366417, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 996.8449161434174, 'W': 73.42}
+[40.04, 39.64, 39.87, 39.84, 41.87, 42.36, 39.49, 37.87, 39.91, 39.97, 18.46, 17.91, 18.96, 18.18, 18.6, 18.04, 18.09, 18.2, 18.22, 17.91]
+525.24
+26.262
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 344337, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.209988117218018, 'TIME_S_1KI': 0.029651150231366417, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 996.8449161434174, 'W': 73.42, 'J_1KI': 2.894968929111357, 'W_1KI': 0.21322135001466586, 'W_D': 47.158, 'J_D': 640.2780244550705, 'W_D_1KI': 0.13695304309441042, 'J_D_1KI': 0.0003977296749823877}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..41d6bb0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 17.146100997924805, "TIME_S_1KI": 17.146100997924805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1342.6130161285403, "W": 65.43610945887401, "J_1KI": 1342.6130161285403, "W_1KI": 65.43610945887401, "W_D": 46.466109458874016, "J_D": 953.388028173447, "W_D_1KI": 46.466109458874016, "J_D_1KI": 46.466109458874016}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..451f542
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 17.146100997924805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      17,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([13952, 31113, 48803,  ..., 72766, 82982, 86351]),
+       values=tensor([0.4430, 0.0507, 0.5237,  ..., 0.5341, 0.7602, 0.3481]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1669, 0.1860, 0.1675,  ..., 0.5137, 0.0308, 0.0638])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 17.146100997924805 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      17,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([13952, 31113, 48803,  ..., 72766, 82982, 86351]),
+       values=tensor([0.4430, 0.0507, 0.5237,  ..., 0.5341, 0.7602, 0.3481]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1669, 0.1860, 0.1675,  ..., 0.5137, 0.0308, 0.0638])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 17.146100997924805 seconds
+
+[21.44, 21.28, 21.16, 21.16, 20.96, 20.96, 20.96, 21.0, 21.24, 21.4]
+[21.44, 21.28, 21.92, 23.96, 24.84, 38.64, 55.84, 70.04, 84.52, 91.04, 91.04, 91.56, 90.84, 89.12, 88.88, 89.24, 89.64, 88.8, 89.08, 90.0]
+20.517922401428223
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 17.146100997924805, 'TIME_S_1KI': 17.146100997924805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1342.6130161285403, 'W': 65.43610945887401}
+[21.44, 21.28, 21.16, 21.16, 20.96, 20.96, 20.96, 21.0, 21.24, 21.4, 21.16, 21.04, 21.08, 20.92, 20.88, 20.88, 21.04, 20.96, 21.2, 21.36]
+379.4
+18.97
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 17.146100997924805, 'TIME_S_1KI': 17.146100997924805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1342.6130161285403, 'W': 65.43610945887401, 'J_1KI': 1342.6130161285403, 'W_1KI': 65.43610945887401, 'W_D': 46.466109458874016, 'J_D': 953.388028173447, 'W_D_1KI': 46.466109458874016, 'J_D_1KI': 46.466109458874016}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..c78bc8a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 159.18061113357544, "TIME_S_1KI": 159.18061113357544, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 13305.551260681154, "W": 81.94491850885544, "J_1KI": 13305.551260681154, "W_1KI": 81.94491850885544, "W_D": 61.991918508855434, "J_D": 10065.7449476676, "W_D_1KI": 61.991918508855434, "J_D_1KI": 61.991918508855434}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..a21bcbb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 159.18061113357544}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       95,      182,  ...,  9999785,
+                             9999891, 10000000]),
+       col_indices=tensor([ 2375,  2397,  2562,  ..., 95994, 97725, 99229]),
+       values=tensor([3.2988e-01, 7.8520e-04, 8.6482e-01,  ...,
+                      9.5198e-01, 4.5600e-01, 9.5863e-01]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4135, 0.2091, 0.0976,  ..., 0.1293, 0.9759, 0.9614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 159.18061113357544 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       95,      182,  ...,  9999785,
+                             9999891, 10000000]),
+       col_indices=tensor([ 2375,  2397,  2562,  ..., 95994, 97725, 99229]),
+       values=tensor([3.2988e-01, 7.8520e-04, 8.6482e-01,  ...,
+                      9.5198e-01, 4.5600e-01, 9.5863e-01]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4135, 0.2091, 0.0976,  ..., 0.1293, 0.9759, 0.9614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 159.18061113357544 seconds
+
+[22.04, 21.88, 21.96, 22.12, 21.96, 21.92, 22.08, 22.08, 22.24, 22.08]
+[22.12, 22.0, 22.4, 24.16, 25.0, 26.76, 35.48, 36.76, 45.64, 60.64, 68.76, 79.68, 90.72, 91.28, 91.28, 90.96, 89.36, 89.36, 87.68, 88.24, 87.52, 88.44, 91.68, 90.56, 91.8, 92.44, 92.04, 93.12, 92.52, 92.52, 92.88, 91.88, 91.24, 91.12, 90.44, 89.64, 89.48, 89.16, 87.68, 85.84, 85.0, 86.96, 88.04, 88.04, 88.0, 89.08, 87.48, 87.52, 86.2, 84.8, 84.64, 85.48, 85.76, 86.32, 87.44, 87.44, 88.64, 89.72, 89.72, 89.4, 87.64, 88.72, 89.0, 89.12, 89.76, 91.04, 88.36, 87.88, 86.68, 87.88, 86.48, 86.8, 86.68, 87.68, 87.68, 86.8, 87.56, 86.76, 84.72, 85.2, 85.08, 85.44, 86.48, 85.92, 86.4, 86.84, 84.56, 83.28, 84.6, 84.6, 85.76, 88.64, 88.68, 89.48, 90.88, 87.96, 88.04, 89.64, 89.6, 88.16, 88.6, 87.04, 86.96, 86.24, 86.24, 87.56, 87.32, 88.48, 89.36, 88.68, 89.56, 88.2, 85.8, 85.8, 86.36, 86.36, 88.2, 88.52, 91.48, 91.48, 91.08, 90.04, 89.24, 88.08, 88.36, 89.92, 89.76, 90.6, 89.4, 87.04, 84.72, 85.04, 83.32, 84.92, 84.92, 84.2, 85.16, 84.0, 84.2, 83.92, 84.84, 84.84, 87.04, 88.68, 91.04, 91.52, 89.88, 89.96, 89.44, 89.44, 88.36, 88.12, 90.24, 89.76, 88.8, 88.0, 86.72, 86.4]
+162.37188959121704
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 159.18061113357544, 'TIME_S_1KI': 159.18061113357544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13305.551260681154, 'W': 81.94491850885544}
+[22.04, 21.88, 21.96, 22.12, 21.96, 21.92, 22.08, 22.08, 22.24, 22.08, 21.88, 22.04, 22.48, 22.64, 22.36, 22.6, 22.6, 22.44, 21.76, 21.8]
+399.06000000000006
+19.953000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 159.18061113357544, 'TIME_S_1KI': 159.18061113357544, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 13305.551260681154, 'W': 81.94491850885544, 'J_1KI': 13305.551260681154, 'W_1KI': 81.94491850885544, 'W_D': 61.991918508855434, 'J_D': 10065.7449476676, 'W_D_1KI': 61.991918508855434, 'J_D_1KI': 61.991918508855434}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..dc43877
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3301, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.605360507965088, "TIME_S_1KI": 3.8186490481566455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 881.8972729587555, "W": 57.01281240597429, "J_1KI": 267.1606400965633, "W_1KI": 17.27137606966807, "W_D": 37.77781240597429, "J_D": 584.3625026237966, "W_D_1KI": 11.444353955157311, "J_D_1KI": 3.466935460514181}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..842e972
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['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": 3.180464267730713}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99997,  99997,
+                            100000]),
+       col_indices=tensor([ 3926, 50379, 15277,  ..., 29136, 40772, 68436]),
+       values=tensor([0.5699, 0.5366, 0.1661,  ..., 0.2141, 0.3018, 0.3946]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8865, 0.6102, 0.2945,  ..., 0.5701, 0.8700, 0.6634])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 3.180464267730713 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3301 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.605360507965088}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99998,
+                            100000]),
+       col_indices=tensor([33916, 32242, 16140,  ..., 45457, 58350, 84955]),
+       values=tensor([0.9718, 0.7827, 0.4187,  ..., 0.1750, 0.8602, 0.7313]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7754, 0.6786, 0.3605,  ..., 0.9739, 0.1301, 0.4075])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 12.605360507965088 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99998,
+                            100000]),
+       col_indices=tensor([33916, 32242, 16140,  ..., 45457, 58350, 84955]),
+       values=tensor([0.9718, 0.7827, 0.4187,  ..., 0.1750, 0.8602, 0.7313]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7754, 0.6786, 0.3605,  ..., 0.9739, 0.1301, 0.4075])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 12.605360507965088 seconds
+
+[21.24, 21.24, 21.36, 21.4, 21.52, 21.72, 21.56, 21.64, 21.48, 21.48]
+[21.4, 21.4, 21.8, 22.88, 23.96, 35.92, 52.84, 66.08, 81.2, 91.72, 90.6, 91.36, 91.84, 91.24, 91.24]
+15.46840500831604
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.605360507965088, 'TIME_S_1KI': 3.8186490481566455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 881.8972729587555, 'W': 57.01281240597429}
+[21.24, 21.24, 21.36, 21.4, 21.52, 21.72, 21.56, 21.64, 21.48, 21.48, 21.28, 21.28, 21.32, 21.48, 21.6, 21.48, 21.28, 21.12, 20.84, 20.76]
+384.69999999999993
+19.234999999999996
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.605360507965088, 'TIME_S_1KI': 3.8186490481566455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 881.8972729587555, 'W': 57.01281240597429, 'J_1KI': 267.1606400965633, 'W_1KI': 17.27137606966807, 'W_D': 37.77781240597429, 'J_D': 584.3625026237966, 'W_D_1KI': 11.444353955157311, 'J_D_1KI': 3.466935460514181}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..b901590
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 32089, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109246492385864, "TIME_S_1KI": 0.3150377541333748, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.60778367996215, "W": 22.396548812340328, "J_1KI": 9.92887854654125, "W_1KI": 0.6979509742385342, "W_D": 4.033548812340328, "J_D": 57.38027131915093, "W_D_1KI": 0.1256988005964763, "J_D_1KI": 0.003917192826092315}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..8b8a1a7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.3272056579589844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([ 654, 4587, 9013,  ..., 1787, 1854, 8773]),
+       values=tensor([0.1124, 0.2109, 0.1818,  ..., 0.9520, 0.5472, 0.0091]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1189, 0.4488, 0.9345,  ..., 0.0324, 0.3464, 0.4030])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.3272056579589844 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32089 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109246492385864}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([6261, 1350, 3983,  ..., 9586, 2579, 6781]),
+       values=tensor([0.3771, 0.7405, 0.3284,  ..., 0.1626, 0.7239, 0.9996]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1818, 0.1444, 0.2139,  ..., 0.0964, 0.7255, 0.0411])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.109246492385864 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([6261, 1350, 3983,  ..., 9586, 2579, 6781]),
+       values=tensor([0.3771, 0.7405, 0.3284,  ..., 0.1626, 0.7239, 0.9996]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1818, 0.1444, 0.2139,  ..., 0.0964, 0.7255, 0.0411])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.109246492385864 seconds
+
+[20.52, 20.4, 20.52, 20.52, 20.52, 20.32, 20.32, 20.2, 20.36, 20.24]
+[20.32, 20.48, 20.88, 25.52, 26.52, 27.24, 27.6, 24.68, 23.88, 23.88, 23.36, 23.6, 23.68, 23.6]
+14.225753545761108
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32089, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109246492385864, 'TIME_S_1KI': 0.3150377541333748, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.60778367996215, 'W': 22.396548812340328}
+[20.52, 20.4, 20.52, 20.52, 20.52, 20.32, 20.32, 20.2, 20.36, 20.24, 20.72, 20.52, 20.6, 20.4, 20.32, 20.52, 20.44, 20.32, 20.16, 20.16]
+367.26
+18.363
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32089, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109246492385864, 'TIME_S_1KI': 0.3150377541333748, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.60778367996215, 'W': 22.396548812340328, 'J_1KI': 9.92887854654125, 'W_1KI': 0.6979509742385342, 'W_D': 4.033548812340328, 'J_D': 57.38027131915093, 'W_D_1KI': 0.1256988005964763, 'J_D_1KI': 0.003917192826092315}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..28a5b99
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.211250305175781, "TIME_S_1KI": 2.236366689701222, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.4135251998902, "W": 22.432139051903395, "J_1KI": 64.91754822599435, "W_1KI": 4.912864444131274, "W_D": 4.068139051903394, "J_D": 53.75552614879615, "W_D_1KI": 0.8909634366849307, "J_D_1KI": 0.19512996861255602}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..f081110
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 2.2992472648620605}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99980,  99989,
+                            100000]),
+       col_indices=tensor([ 655, 1592, 1705,  ..., 9238, 9783, 9811]),
+       values=tensor([0.0624, 0.8226, 0.1738,  ..., 0.6448, 0.8074, 0.7220]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5841, 0.1855, 0.2176,  ..., 0.5967, 0.9561, 0.0240])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.2992472648620605 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4566 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.211250305175781}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99989,  99992,
+                            100000]),
+       col_indices=tensor([ 778, 1147, 3454,  ..., 4854, 5919, 8867]),
+       values=tensor([0.6002, 0.4939, 0.0259,  ..., 0.9282, 0.0584, 0.5342]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8806, 0.9663, 0.5124,  ..., 0.2617, 0.2277, 0.6355])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.211250305175781 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99989,  99992,
+                            100000]),
+       col_indices=tensor([ 778, 1147, 3454,  ..., 4854, 5919, 8867]),
+       values=tensor([0.6002, 0.4939, 0.0259,  ..., 0.9282, 0.0584, 0.5342]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8806, 0.9663, 0.5124,  ..., 0.2617, 0.2277, 0.6355])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.211250305175781 seconds
+
+[20.52, 20.28, 20.28, 20.24, 20.2, 20.2, 20.16, 20.44, 20.56, 20.64]
+[20.68, 20.52, 20.84, 21.8, 22.68, 26.2, 26.72, 26.88, 26.88, 26.92, 24.56, 24.6, 24.48]
+13.21378779411316
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.211250305175781, 'TIME_S_1KI': 2.236366689701222, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.4135251998902, 'W': 22.432139051903395}
+[20.52, 20.28, 20.28, 20.24, 20.2, 20.2, 20.16, 20.44, 20.56, 20.64, 20.56, 20.52, 20.32, 20.28, 20.24, 20.4, 20.6, 20.68, 20.68, 20.68]
+367.28000000000003
+18.364
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.211250305175781, 'TIME_S_1KI': 2.236366689701222, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.4135251998902, 'W': 22.432139051903395, 'J_1KI': 64.91754822599435, 'W_1KI': 4.912864444131274, 'W_D': 4.068139051903394, 'J_D': 53.75552614879615, 'W_D_1KI': 0.8909634366849307, 'J_D_1KI': 0.19512996861255602}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..123a4ab
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 21.205244779586792, "TIME_S_1KI": 21.205244779586792, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 620.3200452423096, "W": 24.48389957916532, "J_1KI": 620.3200452423096, "W_1KI": 24.48389957916532, "W_D": 6.114899579165321, "J_D": 154.92608811497686, "W_D_1KI": 6.114899579165321, "J_D_1KI": 6.114899579165321}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..9e8858d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 21.205244779586792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      87,     190,  ...,  999787,
+                             999893, 1000000]),
+       col_indices=tensor([  40,  232,  261,  ..., 9741, 9779, 9904]),
+       values=tensor([0.6083, 0.3635, 0.2569,  ..., 0.1971, 0.1171, 0.3174]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7546, 0.0325, 0.8716,  ..., 0.3834, 0.9539, 0.7452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.205244779586792 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      87,     190,  ...,  999787,
+                             999893, 1000000]),
+       col_indices=tensor([  40,  232,  261,  ..., 9741, 9779, 9904]),
+       values=tensor([0.6083, 0.3635, 0.2569,  ..., 0.1971, 0.1171, 0.3174]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7546, 0.0325, 0.8716,  ..., 0.3834, 0.9539, 0.7452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 21.205244779586792 seconds
+
+[20.12, 20.4, 20.4, 20.36, 20.16, 20.24, 20.32, 20.32, 20.32, 20.52]
+[20.52, 20.76, 21.12, 22.84, 24.76, 33.76, 34.6, 34.52, 33.84, 26.88, 24.24, 24.24, 24.12, 24.12, 24.04, 24.0, 24.0, 24.04, 24.12, 23.96, 24.0, 24.12, 23.96, 23.96, 24.0]
+25.335835218429565
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.205244779586792, 'TIME_S_1KI': 21.205244779586792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 620.3200452423096, 'W': 24.48389957916532}
+[20.12, 20.4, 20.4, 20.36, 20.16, 20.24, 20.32, 20.32, 20.32, 20.52, 20.52, 20.56, 20.6, 20.44, 20.44, 20.32, 20.28, 20.64, 20.68, 20.64]
+367.38
+18.369
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 21.205244779586792, 'TIME_S_1KI': 21.205244779586792, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 620.3200452423096, 'W': 24.48389957916532, 'J_1KI': 620.3200452423096, 'W_1KI': 24.48389957916532, 'W_D': 6.114899579165321, 'J_D': 154.92608811497686, 'W_D_1KI': 6.114899579165321, 'J_D_1KI': 6.114899579165321}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..054792b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 106.56615614891052, "TIME_S_1KI": 106.56615614891052, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2781.6071025085453, "W": 24.542113905929394, "J_1KI": 2781.6071025085453, "W_1KI": 24.542113905929394, "W_D": 6.099113905929396, "J_D": 691.2745423955923, "W_D_1KI": 6.099113905929396, "J_D_1KI": 6.099113905929396}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..1064fac
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 106.56615614891052}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     509,    1020,  ..., 4998992,
+                            4999488, 5000000]),
+       col_indices=tensor([   3,   11,   31,  ..., 9971, 9976, 9990]),
+       values=tensor([0.8435, 0.0304, 0.5451,  ..., 0.3255, 0.3710, 0.6386]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1386, 0.0671, 0.1165,  ..., 0.0400, 0.5375, 0.5366])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.56615614891052 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     509,    1020,  ..., 4998992,
+                            4999488, 5000000]),
+       col_indices=tensor([   3,   11,   31,  ..., 9971, 9976, 9990]),
+       values=tensor([0.8435, 0.0304, 0.5451,  ..., 0.3255, 0.3710, 0.6386]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1386, 0.0671, 0.1165,  ..., 0.0400, 0.5375, 0.5366])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 106.56615614891052 seconds
+
+[20.4, 20.64, 20.52, 20.56, 20.6, 20.52, 20.56, 20.52, 20.2, 20.56]
+[20.56, 20.68, 20.68, 24.36, 26.16, 32.64, 39.96, 40.52, 36.88, 36.0, 27.96, 24.36, 24.12, 24.28, 24.28, 24.44, 24.64, 24.72, 24.6, 24.36, 24.2, 23.96, 24.12, 24.08, 24.12, 24.24, 24.24, 24.28, 24.68, 24.56, 24.72, 24.84, 24.88, 24.56, 24.48, 24.32, 24.44, 24.52, 24.52, 24.72, 24.68, 24.68, 24.28, 24.0, 23.92, 23.84, 24.0, 24.24, 24.44, 24.48, 24.44, 24.44, 24.64, 24.68, 24.76, 24.72, 24.68, 24.52, 24.56, 24.64, 24.52, 24.68, 24.44, 24.44, 24.4, 24.64, 24.8, 24.68, 24.76, 24.64, 24.4, 24.12, 24.48, 24.56, 24.72, 24.72, 24.8, 24.96, 24.52, 24.32, 24.36, 24.24, 24.08, 24.04, 23.96, 24.08, 24.4, 24.48, 24.48, 24.64, 24.76, 24.4, 24.36, 24.4, 24.56, 24.68, 24.68, 24.48, 24.36, 24.2, 24.2, 24.56, 24.48, 24.4, 24.36, 24.44, 24.36, 24.44, 24.36, 24.64, 24.52, 24.48]
+113.34015941619873
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56615614891052, 'TIME_S_1KI': 106.56615614891052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2781.6071025085453, 'W': 24.542113905929394}
+[20.4, 20.64, 20.52, 20.56, 20.6, 20.52, 20.56, 20.52, 20.2, 20.56, 20.52, 20.48, 20.48, 20.56, 20.48, 20.36, 20.48, 20.52, 20.44, 20.4]
+368.85999999999996
+18.442999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 106.56615614891052, 'TIME_S_1KI': 106.56615614891052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2781.6071025085453, 'W': 24.542113905929394, 'J_1KI': 2781.6071025085453, 'W_1KI': 24.542113905929394, 'W_D': 6.099113905929396, 'J_D': 691.2745423955923, 'W_D_1KI': 6.099113905929396, 'J_D_1KI': 6.099113905929396}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..175168f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.99842429161072, "TIME_S_1KI": 210.99842429161072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 5320.685762977602, "W": 24.45864835325204, "J_1KI": 5320.685762977602, "W_1KI": 24.45864835325204, "W_D": 6.0876483532520425, "J_D": 1324.2949264960312, "W_D_1KI": 6.0876483532520425, "J_D_1KI": 6.0876483532520425}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..1928ba2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 210.99842429161072}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1065,     2071,  ...,  9998045,
+                             9999047, 10000000]),
+       col_indices=tensor([   6,   19,   22,  ..., 9974, 9992, 9993]),
+       values=tensor([0.1921, 0.6014, 0.9806,  ..., 0.7679, 0.7737, 0.6028]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1676, 0.2617, 0.2303,  ..., 0.3636, 0.4445, 0.4181])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.99842429161072 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1065,     2071,  ...,  9998045,
+                             9999047, 10000000]),
+       col_indices=tensor([   6,   19,   22,  ..., 9974, 9992, 9993]),
+       values=tensor([0.1921, 0.6014, 0.9806,  ..., 0.7679, 0.7737, 0.6028]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1676, 0.2617, 0.2303,  ..., 0.3636, 0.4445, 0.4181])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 210.99842429161072 seconds
+
+[20.48, 20.52, 20.52, 20.68, 20.64, 20.64, 20.6, 20.56, 20.36, 20.48]
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+217.5380129814148
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.99842429161072, 'TIME_S_1KI': 210.99842429161072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5320.685762977602, 'W': 24.45864835325204}
+[20.48, 20.52, 20.52, 20.68, 20.64, 20.64, 20.6, 20.56, 20.36, 20.48, 20.4, 20.36, 20.32, 20.32, 20.2, 20.2, 20.28, 20.16, 20.24, 20.28]
+367.41999999999996
+18.371
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 210.99842429161072, 'TIME_S_1KI': 210.99842429161072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 5320.685762977602, 'W': 24.45864835325204, 'J_1KI': 5320.685762977602, 'W_1KI': 24.45864835325204, 'W_D': 6.0876483532520425, 'J_D': 1324.2949264960312, 'W_D_1KI': 6.0876483532520425, 'J_D_1KI': 6.0876483532520425}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..40d44d2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 142926, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.514646053314209, "TIME_S_1KI": 0.07356706304880994, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 341.9344939994813, "W": 24.04792007204229, "J_1KI": 2.3923883268228403, "W_1KI": 0.1682543419114947, "W_D": 4.089920072042293, "J_D": 58.15408343601235, "W_D_1KI": 0.02861564776207473, "J_D_1KI": 0.00020021303165326625}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..e3d4653
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1414 @@
+['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": 0.08297038078308105}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
+       col_indices=tensor([9541, 4744, 3707, 3759, 5602,  648, 5760, 9547, 5348,
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+                      0.1200, 0.6279, 0.3021, 0.5054, 0.7498, 0.9300]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3956, 0.7164, 0.0973,  ..., 0.3827, 0.2591, 0.9120])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.08297038078308105 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126551 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.296976089477539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([2203, 8070, 6104, 2428, 4613, 3691, 9216, 6131,  986,
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+                      6.3070e-01, 8.9669e-01, 3.3070e-01, 5.7151e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.6841, 0.8830, 0.8336,  ..., 0.0293, 0.2899, 0.6914])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.296976089477539 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 142926 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.514646053314209}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      6.9758e-01, 5.9161e-01, 8.3040e-01, 9.4507e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7781, 0.3926, 0.9305,  ..., 0.7246, 0.2621, 0.2068])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.514646053314209 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      6.9758e-01, 5.9161e-01, 8.3040e-01, 9.4507e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7781, 0.3926, 0.9305,  ..., 0.7246, 0.2621, 0.2068])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.514646053314209 seconds
+
+[21.04, 21.72, 22.28, 22.76, 23.44, 23.84, 24.4, 25.56, 26.08, 25.56]
+[25.56, 25.8, 25.36, 25.76, 26.12, 26.96, 27.2, 26.52, 26.28, 25.72, 25.56, 25.04, 24.92, 24.92]
+14.21888017654419
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 142926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.514646053314209, 'TIME_S_1KI': 0.07356706304880994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.9344939994813, 'W': 24.04792007204229}
+[21.04, 21.72, 22.28, 22.76, 23.44, 23.84, 24.4, 25.56, 26.08, 25.56, 20.64, 20.76, 20.84, 20.68, 20.84, 20.92, 20.48, 20.28, 20.36, 20.6]
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diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..510a9ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 37.050822496414185, "TIME_S_1KI": 37.050822496414185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3160.1361892318723, "W": 76.88509213042873, "J_1KI": 3160.1361892318723, "W_1KI": 76.88509213042873, "W_D": 56.91509213042873, "J_D": 2339.3279161286355, "W_D_1KI": 56.91509213042873, "J_D_1KI": 56.91509213042873}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..259a25f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 37.050822496414185}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499993,
+                            2499998, 2500000]),
+       col_indices=tensor([159663, 166958, 205263,  ..., 483859,  36662,
+                           138241]),
+       values=tensor([0.8479, 0.2779, 0.6227,  ..., 0.0012, 0.5209, 0.2466]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9479, 0.6643, 0.5077,  ..., 0.6908, 0.0479, 0.6658])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 37.050822496414185 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499993,
+                            2499998, 2500000]),
+       col_indices=tensor([159663, 166958, 205263,  ..., 483859,  36662,
+                           138241]),
+       values=tensor([0.8479, 0.2779, 0.6227,  ..., 0.0012, 0.5209, 0.2466]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9479, 0.6643, 0.5077,  ..., 0.6908, 0.0479, 0.6658])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 37.050822496414185 seconds
+
+[22.44, 22.28, 22.0, 22.08, 22.44, 22.44, 22.24, 22.28, 22.28, 21.92]
+[21.84, 21.84, 22.08, 23.32, 24.04, 37.0, 51.0, 68.72, 84.04, 93.2, 93.2, 96.84, 97.16, 96.0, 93.56, 93.56, 93.4, 92.48, 94.2, 94.4, 93.76, 94.28, 92.52, 92.4, 93.48, 93.48, 95.4, 93.6, 93.04, 91.68, 87.68, 87.08, 87.96, 88.4, 87.72, 87.2, 88.56, 88.0, 89.28, 89.12]
+41.1020667552948
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 37.050822496414185, 'TIME_S_1KI': 37.050822496414185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3160.1361892318723, 'W': 76.88509213042873}
+[22.44, 22.28, 22.0, 22.08, 22.44, 22.44, 22.24, 22.28, 22.28, 21.92, 21.72, 21.72, 21.8, 22.04, 22.2, 22.32, 22.48, 22.44, 22.24, 22.16]
+399.4
+19.97
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 37.050822496414185, 'TIME_S_1KI': 37.050822496414185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3160.1361892318723, 'W': 76.88509213042873, 'J_1KI': 3160.1361892318723, 'W_1KI': 76.88509213042873, 'W_D': 56.91509213042873, 'J_D': 2339.3279161286355, 'W_D_1KI': 56.91509213042873, 'J_D_1KI': 56.91509213042873}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..372bd49
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.0001.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": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.036840677261353, "TIME_S_1KI": 10.036840677261353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.6639336013795, "W": 51.991207713568286, "J_1KI": 692.6639336013795, "W_1KI": 51.991207713568286, "W_D": 33.03220771356828, "J_D": 440.0786197633745, "W_D_1KI": 33.03220771356828, "J_D_1KI": 33.03220771356828}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..338ad01
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_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 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": 10.036840677261353}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     16,  ..., 249987, 249992,
+                            250000]),
+       col_indices=tensor([ 4880, 10510, 11344,  ..., 23863, 34979, 45750]),
+       values=tensor([0.1743, 0.6413, 0.1893,  ..., 0.8376, 0.7119, 0.1905]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5173, 0.4884, 0.4084,  ..., 0.9473, 0.2501, 0.4146])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.036840677261353 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     16,  ..., 249987, 249992,
+                            250000]),
+       col_indices=tensor([ 4880, 10510, 11344,  ..., 23863, 34979, 45750]),
+       values=tensor([0.1743, 0.6413, 0.1893,  ..., 0.8376, 0.7119, 0.1905]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5173, 0.4884, 0.4084,  ..., 0.9473, 0.2501, 0.4146])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.036840677261353 seconds
+
+[21.04, 21.04, 21.36, 21.48, 21.32, 21.52, 21.4, 21.16, 21.08, 21.12]
+[21.08, 21.08, 21.08, 22.4, 23.64, 33.72, 51.68, 65.48, 81.88, 95.88, 94.44, 94.2, 93.16]
+13.322712898254395
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.036840677261353, 'TIME_S_1KI': 10.036840677261353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6639336013795, 'W': 51.991207713568286}
+[21.04, 21.04, 21.36, 21.48, 21.32, 21.52, 21.4, 21.16, 21.08, 21.12, 21.08, 21.04, 20.96, 20.96, 20.76, 20.68, 20.64, 20.84, 20.92, 20.8]
+379.18
+18.959
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.036840677261353, 'TIME_S_1KI': 10.036840677261353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6639336013795, 'W': 51.991207713568286, 'J_1KI': 692.6639336013795, 'W_1KI': 51.991207713568286, 'W_D': 33.03220771356828, 'J_D': 440.0786197633745, 'W_D_1KI': 33.03220771356828, 'J_D_1KI': 33.03220771356828}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..aeffedd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 64.77457070350647, "TIME_S_1KI": 64.77457070350647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4994.562112493517, "W": 73.50821663676467, "J_1KI": 4994.562112493517, "W_1KI": 73.50821663676467, "W_D": 54.114216636764674, "J_D": 3676.8245582232494, "W_D_1KI": 54.114216636764674, "J_D_1KI": 54.114216636764674}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..56ca1f5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 64.77457070350647}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499908,
+                            2499960, 2500000]),
+       col_indices=tensor([  291,  1039,  1041,  ..., 49096, 49434, 49928]),
+       values=tensor([0.5586, 0.2987, 0.2608,  ..., 0.4587, 0.5222, 0.8471]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1693, 0.3191, 0.9556,  ..., 0.1736, 0.4599, 0.2505])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 64.77457070350647 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,      96,  ..., 2499908,
+                            2499960, 2500000]),
+       col_indices=tensor([  291,  1039,  1041,  ..., 49096, 49434, 49928]),
+       values=tensor([0.5586, 0.2987, 0.2608,  ..., 0.4587, 0.5222, 0.8471]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1693, 0.3191, 0.9556,  ..., 0.1736, 0.4599, 0.2505])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 64.77457070350647 seconds
+
+[21.48, 21.56, 21.32, 21.36, 21.52, 21.32, 21.32, 21.4, 21.44, 21.56]
+[21.64, 21.6, 22.08, 23.76, 24.8, 35.36, 47.08, 60.56, 71.04, 80.24, 82.48, 82.48, 82.88, 83.68, 82.36, 82.16, 82.04, 80.88, 80.72, 80.88, 80.44, 80.6, 80.44, 80.8, 80.56, 79.4, 78.92, 78.92, 78.4, 78.64, 80.36, 81.28, 82.52, 82.68, 81.64, 81.52, 81.32, 81.2, 80.48, 80.32, 82.04, 81.64, 81.64, 82.44, 82.48, 81.2, 81.08, 81.04, 81.44, 81.2, 81.68, 81.32, 81.0, 81.28, 81.36, 81.24, 81.36, 81.36, 81.88, 82.48, 83.6, 83.4, 83.64, 82.8, 82.56, 82.36]
+67.94563031196594
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 64.77457070350647, 'TIME_S_1KI': 64.77457070350647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4994.562112493517, 'W': 73.50821663676467}
+[21.48, 21.56, 21.32, 21.36, 21.52, 21.32, 21.32, 21.4, 21.44, 21.56, 21.88, 21.92, 21.8, 21.52, 21.68, 21.52, 21.68, 21.68, 21.6, 21.56]
+387.88
+19.394
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 64.77457070350647, 'TIME_S_1KI': 64.77457070350647, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4994.562112493517, 'W': 73.50821663676467, 'J_1KI': 4994.562112493517, 'W_1KI': 73.50821663676467, 'W_D': 54.114216636764674, 'J_D': 3676.8245582232494, 'W_D_1KI': 54.114216636764674, 'J_D_1KI': 54.114216636764674}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..2428814
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 6367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.819957971572876, "TIME_S_1KI": 2.3276202248426068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 977.0843494701387, "W": 58.720628093786544, "J_1KI": 153.46071139785437, "W_1KI": 9.222652441304625, "W_D": 39.857628093786545, "J_D": 663.2126712820532, "W_D_1KI": 6.260032683176778, "J_D_1KI": 0.9831997303560197}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..c94062e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 2.0054540634155273}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([29300, 37118, 28917,  ..., 16725, 28059, 47397]),
+       values=tensor([0.1773, 0.7310, 0.0095,  ..., 0.4568, 0.7722, 0.2574]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0745, 0.0507, 0.1628,  ..., 0.0663, 0.8219, 0.2626])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 2.0054540634155273 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5235 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.632020473480225}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 6005,  4214, 13465,  ..., 35902,  7875,  2053]),
+       values=tensor([0.3591, 0.3792, 0.0771,  ..., 0.2893, 0.2529, 0.4673]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1098, 0.6338, 0.4539,  ..., 0.7586, 0.0998, 0.7821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 8.632020473480225 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6367 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.819957971572876}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([30839, 39998,  2326,  ..., 30652, 20576,  5061]),
+       values=tensor([0.3250, 0.6882, 0.6966,  ..., 0.0105, 0.7219, 0.0367]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0417, 0.5559, 0.6322,  ..., 0.5652, 0.2111, 0.1243])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 14.819957971572876 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([30839, 39998,  2326,  ..., 30652, 20576,  5061]),
+       values=tensor([0.3250, 0.6882, 0.6966,  ..., 0.0105, 0.7219, 0.0367]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0417, 0.5559, 0.6322,  ..., 0.5652, 0.2111, 0.1243])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 14.819957971572876 seconds
+
+[20.72, 20.6, 20.92, 21.12, 21.0, 21.04, 21.08, 20.8, 20.88, 21.08]
+[20.96, 21.0, 21.0, 24.32, 26.04, 35.64, 50.52, 67.6, 77.24, 93.48, 91.28, 89.6, 88.96, 88.4, 88.92, 90.04]
+16.63954186439514
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 6367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.819957971572876, 'TIME_S_1KI': 2.3276202248426068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 977.0843494701387, 'W': 58.720628093786544}
+[20.72, 20.6, 20.92, 21.12, 21.0, 21.04, 21.08, 20.8, 20.88, 21.08, 20.88, 20.92, 20.68, 20.84, 21.0, 21.0, 20.92, 21.32, 21.28, 21.04]
+377.26
+18.863
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 6367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.819957971572876, 'TIME_S_1KI': 2.3276202248426068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 977.0843494701387, 'W': 58.720628093786544, 'J_1KI': 153.46071139785437, 'W_1KI': 9.222652441304625, 'W_D': 39.857628093786545, 'J_D': 663.2126712820532, 'W_D_1KI': 6.260032683176778, 'J_D_1KI': 0.9831997303560197}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..fef2205
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 97519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570974826812744, "TIME_S_1KI": 0.10839913070081465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.3015551567077, "W": 22.190891521159134, "J_1KI": 3.233232038440793, "W_1KI": 0.22755454343419368, "W_D": 3.682891521159135, "J_D": 52.32874141120907, "W_D_1KI": 0.037765886864704674, "J_D_1KI": 0.00038726696197361203}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..c6cbfe2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.11602067947387695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 613, 2610, 3896,  ..., 2268, 1349, 1721]),
+       values=tensor([0.3594, 0.2050, 0.8766,  ..., 0.2511, 0.4340, 0.6606]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7862, 0.0116, 0.6512,  ..., 0.0192, 0.3599, 0.4463])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.11602067947387695 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 90501 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.744299173355103}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([3869,  881, 2923,  ..., 3064, 1070, 3092]),
+       values=tensor([0.3867, 0.1123, 0.7736,  ..., 0.1665, 0.3688, 0.6121]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7562, 0.4892, 0.9144,  ..., 0.6968, 0.8474, 0.7157])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 9.744299173355103 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97519 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.570974826812744}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2497, 2499, 2500]),
+       col_indices=tensor([4211, 3231, 4340,  ..., 2446, 2540,  154]),
+       values=tensor([0.2167, 0.9555, 0.6550,  ..., 0.2361, 0.5850, 0.4084]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3692, 0.1411, 0.4138,  ..., 0.1913, 0.1315, 0.0581])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.570974826812744 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2497, 2499, 2500]),
+       col_indices=tensor([4211, 3231, 4340,  ..., 2446, 2540,  154]),
+       values=tensor([0.2167, 0.9555, 0.6550,  ..., 0.2361, 0.5850, 0.4084]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3692, 0.1411, 0.4138,  ..., 0.1913, 0.1315, 0.0581])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.570974826812744 seconds
+
+[20.44, 20.44, 20.48, 20.4, 20.48, 20.68, 20.76, 20.76, 20.72, 20.88]
+[20.64, 20.56, 21.92, 23.68, 23.68, 25.04, 25.48, 26.12, 24.52, 24.44, 23.92, 24.04, 24.32, 24.36]
+14.20860242843628
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570974826812744, 'TIME_S_1KI': 0.10839913070081465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.3015551567077, 'W': 22.190891521159134}
+[20.44, 20.44, 20.48, 20.4, 20.48, 20.68, 20.76, 20.76, 20.72, 20.88, 20.4, 20.52, 20.56, 20.56, 20.56, 20.6, 20.52, 20.4, 20.6, 20.52]
+370.15999999999997
+18.508
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.570974826812744, 'TIME_S_1KI': 0.10839913070081465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.3015551567077, 'W': 22.190891521159134, 'J_1KI': 3.233232038440793, 'W_1KI': 0.22755454343419368, 'W_D': 3.682891521159135, 'J_D': 52.32874141120907, 'W_D_1KI': 0.037765886864704674, 'J_D_1KI': 0.00038726696197361203}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..6041c9a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17764, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.691047191619873, "TIME_S_1KI": 0.6018378288459735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.0331690597534, "W": 23.15708952749249, "J_1KI": 18.522470674383776, "W_1KI": 1.3035965732657337, "W_D": 4.568089527492493, "J_D": 64.90681706762312, "W_D_1KI": 0.2571543305276116, "J_D_1KI": 0.01447615010851225}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..626c252
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6273210048675537}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     7,  ..., 24988, 24996, 25000]),
+       col_indices=tensor([1892, 2918, 4655,  ..., 2029, 2603, 3010]),
+       values=tensor([0.8283, 0.5273, 0.2909,  ..., 0.5828, 0.6477, 0.7502]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8412, 0.7891, 0.2404,  ..., 0.8503, 0.9914, 0.6212])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.6273210048675537 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 16737 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.892534494400024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,    10,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([4752,  479, 2068,  ..., 1338, 4478, 4539]),
+       values=tensor([0.3996, 0.8763, 0.4834,  ..., 0.3300, 0.4860, 0.9993]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9991, 0.1904, 0.1090,  ..., 0.8295, 0.4248, 0.2043])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.892534494400024 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17764 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.691047191619873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    15,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([1098, 1490, 1639,  ..., 3549, 3645, 4602]),
+       values=tensor([0.2763, 0.7775, 0.9451,  ..., 0.5590, 0.3508, 0.3085]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1047, 0.7118, 0.3308,  ..., 0.3344, 0.9893, 0.0200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.691047191619873 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    15,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([1098, 1490, 1639,  ..., 3549, 3645, 4602]),
+       values=tensor([0.2763, 0.7775, 0.9451,  ..., 0.5590, 0.3508, 0.3085]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1047, 0.7118, 0.3308,  ..., 0.3344, 0.9893, 0.0200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.691047191619873 seconds
+
+[20.44, 20.2, 20.44, 20.64, 20.6, 20.72, 21.04, 21.04, 21.04, 20.88]
+[20.72, 20.68, 20.52, 21.28, 23.28, 29.52, 30.36, 30.6, 30.4, 23.8, 23.72, 23.72, 23.84, 23.92]
+14.208744525909424
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.691047191619873, 'TIME_S_1KI': 0.6018378288459735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.0331690597534, 'W': 23.15708952749249}
+[20.44, 20.2, 20.44, 20.64, 20.6, 20.72, 21.04, 21.04, 21.04, 20.88, 20.68, 20.8, 20.72, 20.4, 20.32, 20.52, 20.64, 20.6, 20.72, 20.68]
+371.78
+18.589
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17764, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.691047191619873, 'TIME_S_1KI': 0.6018378288459735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.0331690597534, 'W': 23.15708952749249, 'J_1KI': 18.522470674383776, 'W_1KI': 1.3035965732657337, 'W_D': 4.568089527492493, 'J_D': 64.90681706762312, 'W_D_1KI': 0.2571543305276116, 'J_D_1KI': 0.01447615010851225}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..5558ea5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1959, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.120546340942383, "TIME_S_1KI": 5.676644380266659, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.91848049163815, "W": 23.94849862540507, "J_1KI": 173.51632490640029, "W_1KI": 12.224858920574308, "W_D": 5.3214986254050665, "J_D": 75.53190515112868, "W_D_1KI": 2.716436255949498, "J_D_1KI": 1.3866443368808055}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..c4f4108
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.658592224121094}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     40,     84,  ..., 249908, 249949,
+                            250000]),
+       col_indices=tensor([ 330,  398,  412,  ..., 4758, 4825, 4990]),
+       values=tensor([0.1241, 0.3411, 0.2552,  ..., 0.9324, 0.8443, 0.4144]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9270, 0.0262, 0.1807,  ..., 0.7250, 0.9803, 0.9114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 5.658592224121094 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1855 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.939346075057983}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     42,     99,  ..., 249900, 249944,
+                            250000]),
+       col_indices=tensor([  71,   83,  134,  ..., 4502, 4510, 4544]),
+       values=tensor([0.1222, 0.9313, 0.0593,  ..., 0.6337, 0.4012, 0.6808]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6318, 0.1040, 0.0347,  ..., 0.9714, 0.1743, 0.3337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 9.939346075057983 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1959 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.120546340942383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     52,     94,  ..., 249896, 249940,
+                            250000]),
+       col_indices=tensor([  62,  114,  171,  ..., 4675, 4821, 4860]),
+       values=tensor([0.5686, 0.1100, 0.2304,  ..., 0.6863, 0.4817, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8036, 0.6564, 0.9943,  ..., 0.3026, 0.0525, 0.3398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 11.120546340942383 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     52,     94,  ..., 249896, 249940,
+                            250000]),
+       col_indices=tensor([  62,  114,  171,  ..., 4675, 4821, 4860]),
+       values=tensor([0.5686, 0.1100, 0.2304,  ..., 0.6863, 0.4817, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8036, 0.6564, 0.9943,  ..., 0.3026, 0.0525, 0.3398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 11.120546340942383 seconds
+
+[20.4, 20.56, 20.84, 20.88, 21.08, 21.16, 21.16, 20.92, 20.72, 20.76]
+[20.2, 20.2, 20.96, 22.4, 23.64, 30.44, 31.56, 31.08, 30.2, 30.2, 24.44, 24.12, 24.04, 23.84]
+14.19372820854187
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.120546340942383, 'TIME_S_1KI': 5.676644380266659, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.91848049163815, 'W': 23.94849862540507}
+[20.4, 20.56, 20.84, 20.88, 21.08, 21.16, 21.16, 20.92, 20.72, 20.76, 20.32, 20.52, 20.64, 20.6, 20.44, 20.28, 20.44, 20.6, 20.64, 20.64]
+372.54
+18.627000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.120546340942383, 'TIME_S_1KI': 5.676644380266659, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.91848049163815, 'W': 23.94849862540507, 'J_1KI': 173.51632490640029, 'W_1KI': 12.224858920574308, 'W_D': 5.3214986254050665, 'J_D': 75.53190515112868, 'W_D_1KI': 2.716436255949498, 'J_D_1KI': 1.3866443368808055}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..88b3edd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.64292335510254, "TIME_S_1KI": 26.64292335510254, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.6764411926268, "W": 24.280702381341985, "J_1KI": 738.6764411926268, "W_1KI": 24.280702381341985, "W_D": 5.882702381341982, "J_D": 178.96573136138895, "W_D_1KI": 5.882702381341982, "J_D_1KI": 5.882702381341982}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..d050d33
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 26.64292335510254}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     258,     500,  ..., 1249494,
+                            1249753, 1250000]),
+       col_indices=tensor([  51,   83,   92,  ..., 4940, 4981, 4997]),
+       values=tensor([0.9970, 0.1345, 0.9294,  ..., 0.5035, 0.6973, 0.4629]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6657, 0.7944, 0.8404,  ..., 0.5502, 0.2324, 0.9138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.64292335510254 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     258,     500,  ..., 1249494,
+                            1249753, 1250000]),
+       col_indices=tensor([  51,   83,   92,  ..., 4940, 4981, 4997]),
+       values=tensor([0.9970, 0.1345, 0.9294,  ..., 0.5035, 0.6973, 0.4629]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6657, 0.7944, 0.8404,  ..., 0.5502, 0.2324, 0.9138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 26.64292335510254 seconds
+
+[20.76, 20.56, 20.76, 20.24, 20.0, 20.0, 19.96, 19.88, 20.0, 20.0]
+[20.2, 20.56, 20.84, 23.48, 25.48, 31.96, 32.48, 32.96, 29.96, 29.16, 23.84, 23.8, 23.88, 23.88, 24.28, 24.2, 24.36, 24.32, 23.8, 24.12, 24.2, 24.36, 24.28, 24.24, 24.24, 24.24, 24.32, 24.24, 24.48, 24.52]
+30.422367095947266
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.64292335510254, 'TIME_S_1KI': 26.64292335510254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.6764411926268, 'W': 24.280702381341985}
+[20.76, 20.56, 20.76, 20.24, 20.0, 20.0, 19.96, 19.88, 20.0, 20.0, 20.52, 20.44, 20.56, 20.64, 20.52, 20.76, 20.64, 20.92, 20.96, 20.96]
+367.96000000000004
+18.398000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 26.64292335510254, 'TIME_S_1KI': 26.64292335510254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 738.6764411926268, 'W': 24.280702381341985, 'J_1KI': 738.6764411926268, 'W_1KI': 24.280702381341985, 'W_D': 5.882702381341982, 'J_D': 178.96573136138895, 'W_D_1KI': 5.882702381341982, 'J_D_1KI': 5.882702381341982}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..70fec2c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.09800863265991, "TIME_S_1KI": 53.09800863265991, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1383.3292028236385, "W": 24.387974690726086, "J_1KI": 1383.3292028236385, "W_1KI": 24.387974690726086, "W_D": 5.857974690726085, "J_D": 332.2747198915477, "W_D_1KI": 5.857974690726085, "J_D_1KI": 5.857974690726085}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..20994df
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 53.09800863265991}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,     997,  ..., 2498998,
+                            2499500, 2500000]),
+       col_indices=tensor([   3,   13,   35,  ..., 4966, 4993, 4997]),
+       values=tensor([0.0178, 0.5574, 0.8921,  ..., 0.2131, 0.1882, 0.3495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5571, 0.9138, 0.4400,  ..., 0.1682, 0.6225, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.09800863265991 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,     997,  ..., 2498998,
+                            2499500, 2500000]),
+       col_indices=tensor([   3,   13,   35,  ..., 4966, 4993, 4997]),
+       values=tensor([0.0178, 0.5574, 0.8921,  ..., 0.2131, 0.1882, 0.3495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5571, 0.9138, 0.4400,  ..., 0.1682, 0.6225, 0.2202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 53.09800863265991 seconds
+
+[20.6, 20.6, 20.72, 20.68, 20.72, 20.8, 20.84, 20.8, 20.92, 20.52]
+[20.6, 20.4, 23.56, 24.32, 24.32, 28.84, 34.48, 35.48, 33.04, 32.72, 26.72, 24.2, 24.24, 24.24, 24.0, 24.0, 23.96, 23.96, 23.88, 23.96, 23.96, 23.92, 23.92, 23.68, 23.64, 23.76, 24.08, 24.12, 24.08, 24.08, 24.32, 24.08, 23.92, 24.04, 24.0, 23.96, 24.12, 24.24, 24.28, 24.24, 24.2, 23.92, 23.92, 24.0, 24.2, 24.24, 24.4, 24.2, 24.16, 24.0, 24.16, 24.32, 24.36, 24.36, 24.36, 24.12]
+56.72177457809448
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.09800863265991, 'TIME_S_1KI': 53.09800863265991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.3292028236385, 'W': 24.387974690726086}
+[20.6, 20.6, 20.72, 20.68, 20.72, 20.8, 20.84, 20.8, 20.92, 20.52, 20.32, 20.36, 20.4, 20.48, 20.44, 20.64, 20.48, 20.36, 20.48, 20.32]
+370.6
+18.53
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 53.09800863265991, 'TIME_S_1KI': 53.09800863265991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.3292028236385, 'W': 24.387974690726086, 'J_1KI': 1383.3292028236385, 'W_1KI': 24.387974690726086, 'W_D': 5.857974690726085, 'J_D': 332.2747198915477, 'W_D_1KI': 5.857974690726085, 'J_D_1KI': 5.857974690726085}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..7d0783d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 275920, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.428882360458374, "TIME_S_1KI": 0.037796761236801875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.0905113220215, "W": 22.127187461407587, "J_1KI": 1.141963291251165, "W_1KI": 0.08019421376271234, "W_D": 3.664187461407586, "J_D": 52.177923778533895, "W_D_1KI": 0.013279890770540686, "J_D_1KI": 4.812949684887172e-05}
diff --git a/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..a254447
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,383 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04542350769042969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1381, 3398, 2478, 1052,  529,  491, 2775, 3229, 1279,
+                           3454,  296, 3084, 4650, 2467,  784,  568,  918,  741,
+                           4819, 1730,  837,  408, 1523,  948, 4825, 1342,  952,
+                           2524, 3378, 2774,  370, 2319, 3980, 4108,  276, 4067,
+                           3823, 3153, 3158,  540, 2360, 1999, 1044, 1298, 4540,
+                            533, 3507, 1489, 2361, 1008,  555, 3416,  305, 3290,
+                           1136, 3809, 4448, 2408, 3611, 2892, 2540, 3779, 2041,
+                           4793, 4839,  534, 3664, 2180, 4711, 4601, 1136, 3681,
+                            165, 2858, 2937, 1364, 4737, 4916, 4412, 4772, 4253,
+                            200, 1254, 2702, 3949, 1138, 3253, 4523, 3563, 4932,
+                            724, 1152, 3157, 1713, 4323, 2340,  951, 3022, 1343,
+                           2260, 3881, 2605,  161, 4434, 3331, 1742, 2563, 4238,
+                            127, 3937,  396, 2283, 1557, 1554, 3292, 4855, 4197,
+                           4720,  716,   85, 4379, 3823, 2263, 2186, 2869, 1787,
+                           1168, 2429, 3045, 2919, 2350, 3479, 2094, 1065,  340,
+                           1288, 1877, 3764, 3457,  509, 1055, 3089,  605, 1110,
+                           3765, 3334, 3358,  602, 1278, 2312, 2279, 3749, 3299,
+                           4530,  804, 4261,  418, 4624,  585, 3050, 3236,  596,
+                           2133,  933, 4209, 3895,  174,  765, 3980,  381, 2181,
+                           2969,   46, 3997, 2920, 1083, 1216, 4056,  126,  248,
+                           1696,  352, 2821,  625, 3058, 4954, 4557,  865, 2010,
+                           2268, 2460, 1542,  329, 4649, 4740, 2546, 1491, 1783,
+                           2436, 2269, 2383,  734, 4372, 4876, 3373,  210, 4004,
+                           4560, 1501, 3320, 2378, 1630,  757, 3013, 4961, 4950,
+                           3415, 2145, 1401, 3711, 4355,  611, 1420, 3710, 4405,
+                           2508, 3816,    3, 3115, 4093, 2712, 1642, 4784, 2945,
+                           3902, 1255, 2147, 1010, 3088, 1205, 4589,  714, 2492,
+                           1954, 4006, 3877,  588,  962,   61, 4470]),
+       values=tensor([6.2379e-01, 5.1445e-01, 5.2888e-01, 6.4643e-01,
+                      3.6807e-01, 4.6260e-01, 2.5238e-01, 5.8157e-01,
+                      8.8267e-01, 2.6474e-01, 2.8446e-01, 9.5475e-01,
+                      4.8999e-01, 6.6621e-01, 3.2615e-02, 2.5044e-01,
+                      4.5496e-01, 3.7415e-01, 2.9199e-01, 2.8386e-01,
+                      7.1383e-01, 3.1109e-01, 1.1332e-01, 2.2089e-01,
+                      2.1912e-01, 5.6452e-01, 4.7190e-01, 5.8604e-01,
+                      7.8763e-01, 9.5122e-01, 1.1018e-01, 1.3969e-01,
+                      7.2800e-01, 6.6977e-01, 2.9413e-01, 6.1351e-01,
+                      4.9889e-01, 3.4691e-01, 3.9756e-01, 7.5031e-01,
+                      1.4612e-01, 6.6037e-01, 2.5630e-01, 9.1057e-02,
+                      8.2140e-01, 9.9620e-01, 5.5939e-01, 1.0762e-01,
+                      7.8811e-01, 5.4825e-01, 1.0084e-01, 8.9423e-01,
+                      7.7729e-01, 2.7164e-01, 7.0220e-01, 1.6836e-01,
+                      5.3765e-01, 2.0228e-01, 1.5568e-02, 8.3985e-01,
+                      2.3206e-01, 6.7022e-01, 4.7791e-01, 6.4798e-01,
+                      6.7036e-01, 1.6005e-01, 7.3101e-01, 9.4913e-01,
+                      2.2292e-01, 4.6540e-01, 7.6590e-01, 2.9344e-01,
+                      5.6223e-01, 8.4355e-01, 8.4945e-01, 1.4869e-01,
+                      2.8265e-01, 3.2754e-01, 5.8549e-01, 9.8812e-01,
+                      5.4427e-01, 9.3814e-01, 8.4516e-01, 1.7512e-01,
+                      1.2307e-02, 2.2939e-01, 7.7071e-01, 1.9977e-01,
+                      6.3831e-01, 1.4402e-01, 3.9596e-02, 8.3780e-01,
+                      6.9744e-01, 5.2304e-02, 1.7853e-01, 2.9282e-01,
+                      5.7428e-01, 3.6008e-01, 1.5117e-01, 8.0683e-01,
+                      6.9041e-02, 5.8242e-01, 9.0514e-01, 7.4588e-01,
+                      7.5412e-01, 9.1699e-01, 3.8286e-01, 9.6918e-01,
+                      7.4727e-01, 1.2312e-01, 4.8375e-01, 3.6856e-01,
+                      5.6299e-01, 5.3561e-01, 1.4061e-01, 8.9669e-01,
+                      2.2440e-02, 2.9850e-01, 1.9549e-01, 5.4525e-01,
+                      3.6535e-01, 4.3468e-01, 5.3884e-01, 4.1129e-01,
+                      3.4185e-02, 4.7048e-01, 7.9007e-01, 7.4755e-01,
+                      6.5822e-01, 7.8901e-01, 5.2911e-01, 9.3945e-02,
+                      3.8728e-01, 2.4384e-01, 9.0271e-01, 7.7139e-01,
+                      4.5138e-01, 3.9539e-01, 2.1438e-01, 3.5791e-01,
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+                      7.6600e-01, 4.0656e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.9879, 0.6356, 0.7019,  ..., 0.7112, 0.3671, 0.2365])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04542350769042969 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 231157 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.796557664871216}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3111, 1505, 3032,  732, 1363, 1458, 3691, 3479, 1828,
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+                      2.3487e-01, 5.2578e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.6897, 0.5913, 0.2174,  ..., 0.0385, 0.9360, 0.0281])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 8.796557664871216 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 275920 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.428882360458374}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1877, 1947, 3773, 1993, 3968, 2885, 3290, 1001, 3173,
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+                      0.2829, 0.0867, 0.8730, 0.1781, 0.6966]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7582, 0.3275, 0.7400,  ..., 0.8955, 0.3174, 0.3280])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.428882360458374 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1877, 1947, 3773, 1993, 3968, 2885, 3290, 1001, 3173,
+                           4910, 3638, 1123,  363, 1623, 2284, 1294, 3337, 1282,
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+                           3508, 4583, 1928, 3999, 2112,  430, 3549, 2224, 4453,
+                            292,  788, 4633,  434, 1519, 2797, 4314, 3456, 1463,
+                           1133, 1520, 2779,  195,  566, 4705, 4339,   87, 3759,
+                           1171,  632, 4702, 4443, 3675, 4063, 3423, 1515, 3264,
+                           3975, 3586,  907, 4416,  890, 2296, 2089, 4867, 4932,
+                           4241, 1398,  950, 4682, 2581, 4604, 1861, 1492, 4359,
+                           3001,  171, 3190, 4056, 2779, 2102, 2341, 2228,  666,
+                           4124, 3282, 4080, 1125, 1782, 4068, 4582, 1989, 1861,
+                           2397, 1906, 3592, 4009, 2809, 3893, 4602, 4885, 4329,
+                           1546, 3221, 1533, 1812,  711,  832, 3637, 2430,  702,
+                           1951, 2527, 1663, 4378, 3187, 1848, 1976, 4944, 1611,
+                           3986, 4768, 1832,  171,  533,  127, 3370, 4616, 3556,
+                           3675, 2756, 3820, 3848, 2775, 4085, 1946]),
+       values=tensor([0.3630, 0.4957, 0.7258, 0.9637, 0.5431, 0.7370, 0.5194,
+                      0.1412, 0.9194, 0.8806, 0.2809, 0.4495, 0.3054, 0.7229,
+                      0.6894, 0.5378, 0.4829, 0.7917, 0.1077, 0.9396, 0.0834,
+                      0.8145, 0.2291, 0.0220, 0.8667, 0.8206, 0.7176, 0.1748,
+                      0.5433, 0.5398, 0.6732, 0.5495, 0.1751, 0.1751, 0.5534,
+                      0.4533, 0.5127, 0.9043, 0.7276, 0.3139, 0.4018, 0.6593,
+                      0.5712, 0.8906, 0.5321, 0.0490, 0.8603, 0.3211, 0.9292,
+                      0.2516, 0.5976, 0.6960, 0.6822, 0.0183, 0.1419, 0.0510,
+                      0.5915, 0.9381, 0.7663, 0.9175, 0.1026, 0.1428, 0.3603,
+                      0.1690, 0.2574, 0.9703, 0.3816, 0.3120, 0.6138, 0.6402,
+                      0.0171, 0.1702, 0.0571, 0.1251, 0.4789, 0.2100, 0.4597,
+                      0.8236, 0.2093, 0.3392, 0.8809, 0.8206, 0.6653, 0.7105,
+                      0.9427, 0.4744, 0.2605, 0.1657, 0.1195, 0.1792, 0.5307,
+                      0.1174, 0.6758, 0.8184, 0.0607, 0.0558, 0.3782, 0.8926,
+                      0.6897, 0.9924, 0.7956, 0.0060, 0.2666, 0.9269, 0.6602,
+                      0.5276, 0.2277, 0.4849, 0.8321, 0.2135, 0.2296, 0.7282,
+                      0.5446, 0.1493, 0.5845, 0.2697, 0.2635, 0.0055, 0.3342,
+                      0.6531, 0.8835, 0.6970, 0.3925, 0.6332, 0.2833, 0.7464,
+                      0.9403, 0.9564, 0.8529, 0.8534, 0.4902, 0.3672, 0.4884,
+                      0.3826, 0.8277, 0.2524, 0.5006, 0.8262, 0.8556, 0.5518,
+                      0.9345, 0.1818, 0.7419, 0.5510, 0.7359, 0.2338, 0.5242,
+                      0.8847, 0.7894, 0.5148, 0.5220, 0.3152, 0.5588, 0.6758,
+                      0.0222, 0.8094, 0.8800, 0.5482, 0.7029, 0.4511, 0.5521,
+                      0.1426, 0.5819, 0.4684, 0.3203, 0.4558, 0.0605, 0.4645,
+                      0.6967, 0.5420, 0.5383, 0.3399, 0.6017, 0.2217, 0.2779,
+                      0.6034, 0.6186, 0.5877, 0.7226, 0.4771, 0.2736, 0.9442,
+                      0.4016, 0.5813, 0.3926, 0.6636, 0.2000, 0.5234, 0.8594,
+                      0.4283, 0.8253, 0.1300, 0.3810, 0.0496, 0.8722, 0.5976,
+                      0.0028, 0.5374, 0.0379, 0.0610, 0.9205, 0.9022, 0.6780,
+                      0.7337, 0.3928, 0.7007, 0.0730, 0.0899, 0.4352, 0.2480,
+                      0.7721, 0.6286, 0.0462, 0.5434, 0.2214, 0.2005, 0.5352,
+                      0.2866, 0.1634, 0.3716, 0.1574, 0.2559, 0.6104, 0.9417,
+                      0.5436, 0.9351, 0.6446, 0.8506, 0.6360, 0.5124, 0.9341,
+                      0.9751, 0.4728, 0.6908, 0.5778, 0.2603, 0.9571, 0.5985,
+                      0.0453, 0.2921, 0.4748, 0.9573, 0.6189, 0.2369, 0.4918,
+                      0.2829, 0.0867, 0.8730, 0.1781, 0.6966]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7582, 0.3275, 0.7400,  ..., 0.8955, 0.3174, 0.3280])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.428882360458374 seconds
+
+[20.56, 20.68, 20.64, 20.44, 20.64, 20.48, 20.48, 20.64, 20.68, 20.72]
+[20.84, 20.84, 21.12, 24.24, 25.12, 25.84, 26.16, 24.96, 23.68, 23.68, 23.56, 23.8, 24.0, 23.88]
+14.239971160888672
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 275920, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.428882360458374, 'TIME_S_1KI': 0.037796761236801875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.0905113220215, 'W': 22.127187461407587}
+[20.56, 20.68, 20.64, 20.44, 20.64, 20.48, 20.48, 20.64, 20.68, 20.72, 20.56, 20.72, 20.56, 20.64, 20.56, 20.24, 20.2, 20.44, 20.2, 20.2]
+369.26
+18.463
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 275920, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.428882360458374, 'TIME_S_1KI': 0.037796761236801875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.0905113220215, 'W': 22.127187461407587, 'J_1KI': 1.141963291251165, 'W_1KI': 0.08019421376271234, 'W_D': 3.664187461407586, 'J_D': 52.177923778533895, 'W_D_1KI': 0.013279890770540686, 'J_D_1KI': 4.812949684887172e-05}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..4a28d6c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 70787, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.243863582611084, "TIME_S_1KI": 0.15884079820604183, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.8814449405672, "W": 142.52, "J_1KI": 29.184475185282146, "W_1KI": 2.01336403576928, "W_D": 106.74100000000001, "J_D": 1547.2512722032072, "W_D_1KI": 1.5079181205588599, "J_D_1KI": 0.02130218995802704}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..320d8b0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.22568225860595703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      21,  ...,  999976,
+                             999987, 1000000]),
+       col_indices=tensor([66167, 77335, 80388,  ..., 91843, 96961, 99110]),
+       values=tensor([0.4269, 0.3181, 0.3880,  ..., 0.8858, 0.0510, 0.2541]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0143, 0.7097, 0.7299,  ..., 0.1191, 0.1743, 0.7741])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.22568225860595703 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46525', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.901124477386475}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      22,  ...,  999980,
+                             999991, 1000000]),
+       col_indices=tensor([ 6899, 15825, 20330,  ..., 53773, 69034, 81991]),
+       values=tensor([0.2590, 0.4256, 0.8626,  ..., 0.0809, 0.7182, 0.1540]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5717, 0.8218, 0.0250,  ..., 0.8733, 0.0737, 0.0088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 6.901124477386475 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70787', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.243863582611084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999983,
+                             999990, 1000000]),
+       col_indices=tensor([  361,  1115,  8788,  ..., 71181, 76543, 91304]),
+       values=tensor([0.4904, 0.2440, 0.4094,  ..., 0.6184, 0.1804, 0.3924]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2770, 0.4028, 0.6616,  ..., 0.6682, 0.3245, 0.3679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 11.243863582611084 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      22,  ...,  999983,
+                             999990, 1000000]),
+       col_indices=tensor([  361,  1115,  8788,  ..., 71181, 76543, 91304]),
+       values=tensor([0.4904, 0.2440, 0.4094,  ..., 0.6184, 0.1804, 0.3924]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2770, 0.4028, 0.6616,  ..., 0.6682, 0.3245, 0.3679])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 11.243863582611084 seconds
+
+[40.71, 39.67, 39.62, 40.0, 39.39, 40.7, 39.22, 40.0, 39.3, 39.88]
+[142.52]
+14.495379209518433
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70787, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.243863582611084, 'TIME_S_1KI': 0.15884079820604183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.8814449405672, 'W': 142.52}
+[40.71, 39.67, 39.62, 40.0, 39.39, 40.7, 39.22, 40.0, 39.3, 39.88, 39.86, 40.15, 39.35, 40.1, 39.35, 40.22, 39.27, 39.7, 39.16, 40.31]
+715.5799999999999
+35.778999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70787, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.243863582611084, 'TIME_S_1KI': 0.15884079820604183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.8814449405672, 'W': 142.52, 'J_1KI': 29.184475185282146, 'W_1KI': 2.01336403576928, 'W_D': 106.74100000000001, 'J_D': 1547.2512722032072, 'W_D_1KI': 1.5079181205588599, 'J_D_1KI': 0.02130218995802704}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..0f55c41
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4257, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.433454513549805, "TIME_S_1KI": 2.685800919321072, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1901.6043726658822, "W": 126.17, "J_1KI": 446.7005808470477, "W_1KI": 29.638242894056848, "W_D": 90.424, "J_D": 1362.8491225643158, "W_D_1KI": 21.241249706365988, "J_D_1KI": 4.989722740513504}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..dc2dadc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.4663901329040527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      118,      225,  ...,  9999805,
+                             9999897, 10000000]),
+       col_indices=tensor([ 1682,  1744,  2076,  ..., 96929, 97254, 99780]),
+       values=tensor([0.4019, 0.5057, 0.8739,  ..., 0.0479, 0.2913, 0.6813]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2475, 0.7795, 0.3565,  ..., 0.8481, 0.6371, 0.4321])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 2.4663901329040527 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4257', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.433454513549805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       96,      213,  ...,  9999811,
+                             9999904, 10000000]),
+       col_indices=tensor([  561,   663,  1931,  ..., 97741, 99513, 99851]),
+       values=tensor([0.7974, 0.7905, 0.8203,  ..., 0.5966, 0.6231, 0.2009]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4967, 0.7208, 0.9275,  ..., 0.8267, 0.3582, 0.8531])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.433454513549805 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       96,      213,  ...,  9999811,
+                             9999904, 10000000]),
+       col_indices=tensor([  561,   663,  1931,  ..., 97741, 99513, 99851]),
+       values=tensor([0.7974, 0.7905, 0.8203,  ..., 0.5966, 0.6231, 0.2009]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4967, 0.7208, 0.9275,  ..., 0.8267, 0.3582, 0.8531])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.433454513549805 seconds
+
+[41.19, 39.18, 40.43, 39.7, 39.84, 39.19, 40.16, 39.17, 40.24, 39.08]
+[126.17]
+15.071763277053833
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.433454513549805, 'TIME_S_1KI': 2.685800919321072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.6043726658822, 'W': 126.17}
+[41.19, 39.18, 40.43, 39.7, 39.84, 39.19, 40.16, 39.17, 40.24, 39.08, 41.18, 39.54, 39.33, 39.89, 39.75, 39.57, 39.78, 39.56, 39.24, 39.25]
+714.9200000000001
+35.746
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.433454513549805, 'TIME_S_1KI': 2.685800919321072, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1901.6043726658822, 'W': 126.17, 'J_1KI': 446.7005808470477, 'W_1KI': 29.638242894056848, 'W_D': 90.424, 'J_D': 1362.8491225643158, 'W_D_1KI': 21.241249706365988, 'J_D_1KI': 4.989722740513504}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..382c4a4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 103292, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.412234544754028, "TIME_S_1KI": 0.10080388166318813, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1494.9555576324462, "W": 114.72, "J_1KI": 14.473101088491328, "W_1KI": 1.1106378035085003, "W_D": 77.68374999999999, "J_D": 1012.3235163897275, "W_D_1KI": 0.7520790574294233, "J_D_1KI": 0.0072810968654825475}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..a8441fc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.13699960708618164}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 8916, 68486, 49297,  ..., 83214, 51117, 46502]),
+       values=tensor([0.0565, 0.4187, 0.1663,  ..., 0.8089, 0.3832, 0.9501]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6605, 0.5566, 0.3055,  ..., 0.1791, 0.1309, 0.6380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.13699960708618164 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '76642', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.790924310684204}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99997,  99998,
+                            100000]),
+       col_indices=tensor([17249, 94297, 21433,  ..., 88389, 79911, 81112]),
+       values=tensor([0.0934, 0.2541, 0.4263,  ..., 0.3405, 0.2702, 0.1947]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1521, 0.7703, 0.8999,  ..., 0.0235, 0.4756, 0.0049])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 7.790924310684204 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '103292', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.412234544754028}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([40816, 84426, 84611,  ..., 44515, 10095, 58427]),
+       values=tensor([0.3036, 0.7331, 0.5691,  ..., 0.0050, 0.0920, 0.5982]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8398, 0.7355, 0.2034,  ..., 0.0172, 0.0859, 0.7739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.412234544754028 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([40816, 84426, 84611,  ..., 44515, 10095, 58427]),
+       values=tensor([0.3036, 0.7331, 0.5691,  ..., 0.0050, 0.0920, 0.5982]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8398, 0.7355, 0.2034,  ..., 0.0172, 0.0859, 0.7739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.412234544754028 seconds
+
+[39.92, 39.29, 39.24, 40.21, 39.35, 45.54, 40.38, 40.14, 39.43, 39.27]
+[114.72]
+13.031342029571533
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103292, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.412234544754028, 'TIME_S_1KI': 0.10080388166318813, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.9555576324462, 'W': 114.72}
+[39.92, 39.29, 39.24, 40.21, 39.35, 45.54, 40.38, 40.14, 39.43, 39.27, 39.96, 39.14, 45.44, 43.38, 51.52, 39.13, 40.45, 39.57, 39.41, 39.06]
+740.7250000000001
+37.03625000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103292, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.412234544754028, 'TIME_S_1KI': 0.10080388166318813, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1494.9555576324462, 'W': 114.72, 'J_1KI': 14.473101088491328, 'W_1KI': 1.1106378035085003, 'W_D': 77.68374999999999, 'J_D': 1012.3235163897275, 'W_D_1KI': 0.7520790574294233, 'J_D_1KI': 0.0072810968654825475}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..2ba417a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 289765, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.56649661064148, "TIME_S_1KI": 0.03646574503698334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1246.5325114130974, "W": 97.77, "J_1KI": 4.3018739717118955, "W_1KI": 0.33741135057719185, "W_D": 62.23799999999999, "J_D": 793.5122271180152, "W_D_1KI": 0.21478784532293407, "J_D_1KI": 0.0007412484093073148}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..4b8f527
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.053604841232299805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([4181, 1858, 2276,  ..., 2485, 7240, 8510]),
+       values=tensor([0.9106, 0.2407, 0.2677,  ..., 0.1883, 0.5204, 0.9919]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4673, 0.8867, 0.2183,  ..., 0.9392, 0.5032, 0.8250])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.053604841232299805 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '195877', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.097846031188965}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([6113, 1564,  232,  ..., 3255, 2043, 9640]),
+       values=tensor([0.7859, 0.4083, 0.3727,  ..., 0.9664, 0.2618, 0.1646]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1848, 0.2081, 0.2382,  ..., 0.7788, 0.6054, 0.6678])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.097846031188965 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289765', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.56649661064148}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([4848, 1770,   22,  ..., 2903,  374, 1123]),
+       values=tensor([0.4832, 0.4922, 0.1673,  ..., 0.2881, 0.3225, 0.2417]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.7933, 0.2380, 0.0639,  ..., 0.5554, 0.1913, 0.9685])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.56649661064148 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([4848, 1770,   22,  ..., 2903,  374, 1123]),
+       values=tensor([0.4832, 0.4922, 0.1673,  ..., 0.2881, 0.3225, 0.2417]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.7933, 0.2380, 0.0639,  ..., 0.5554, 0.1913, 0.9685])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.56649661064148 seconds
+
+[39.58, 39.81, 39.03, 39.86, 38.88, 39.9, 39.07, 38.97, 41.59, 39.84]
+[97.77]
+12.749642133712769
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289765, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.56649661064148, 'TIME_S_1KI': 0.03646574503698334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5325114130974, 'W': 97.77}
+[39.58, 39.81, 39.03, 39.86, 38.88, 39.9, 39.07, 38.97, 41.59, 39.84, 40.25, 39.66, 38.91, 39.84, 38.8, 39.81, 38.94, 39.0, 38.89, 39.69]
+710.6400000000001
+35.532000000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289765, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.56649661064148, 'TIME_S_1KI': 0.03646574503698334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5325114130974, 'W': 97.77, 'J_1KI': 4.3018739717118955, 'W_1KI': 0.33741135057719185, 'W_D': 62.23799999999999, 'J_D': 793.5122271180152, 'W_D_1KI': 0.21478784532293407, 'J_D_1KI': 0.0007412484093073148}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..463b3c7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 132694, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.224570512771606, "TIME_S_1KI": 0.07705375158463537, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1067.35663690567, "W": 103.59999999999998, "J_1KI": 8.043744531822615, "W_1KI": 0.7807436658778842, "W_D": 68.22474999999997, "J_D": 702.8971014838812, "W_D_1KI": 0.5141509789440364, "J_D_1KI": 0.003874711584126158}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..f18d35d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,65 @@
+['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": 0.07912898063659668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     15,  ...,  99979,  99988,
+                            100000]),
+       col_indices=tensor([ 430,  646,  878,  ..., 7983, 8028, 8773]),
+       values=tensor([0.1249, 0.1009, 0.6404,  ..., 0.8347, 0.6604, 0.7086]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6668, 0.6238, 0.5068,  ..., 0.0173, 0.0134, 0.2844])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.07912898063659668 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '132694', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.224570512771606}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ...,  99972,  99988,
+                            100000]),
+       col_indices=tensor([ 681, 2736, 3433,  ..., 9108, 9366, 9692]),
+       values=tensor([0.5511, 0.6516, 0.1231,  ..., 0.0939, 0.8699, 0.6381]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2594, 0.0089, 0.5427,  ..., 0.9106, 0.5838, 0.6290])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.224570512771606 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ...,  99972,  99988,
+                            100000]),
+       col_indices=tensor([ 681, 2736, 3433,  ..., 9108, 9366, 9692]),
+       values=tensor([0.5511, 0.6516, 0.1231,  ..., 0.0939, 0.8699, 0.6381]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2594, 0.0089, 0.5427,  ..., 0.9106, 0.5838, 0.6290])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.224570512771606 seconds
+
+[39.52, 39.72, 39.2, 38.94, 38.87, 40.37, 39.0, 39.66, 38.95, 39.86]
+[103.6]
+10.302670240402222
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132694, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.224570512771606, 'TIME_S_1KI': 0.07705375158463537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.35663690567, 'W': 103.59999999999998}
+[39.52, 39.72, 39.2, 38.94, 38.87, 40.37, 39.0, 39.66, 38.95, 39.86, 39.41, 39.62, 39.01, 39.67, 38.86, 38.84, 38.84, 39.8, 38.92, 39.68]
+707.505
+35.37525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132694, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.224570512771606, 'TIME_S_1KI': 0.07705375158463537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1067.35663690567, 'W': 103.59999999999998, 'J_1KI': 8.043744531822615, 'W_1KI': 0.7807436658778842, 'W_D': 68.22474999999997, 'J_D': 702.8971014838812, 'W_D_1KI': 0.5141509789440364, 'J_D_1KI': 0.003874711584126158}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..995b9fb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 107069, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.18355131149292, "TIME_S_1KI": 0.10445181435796468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1763.49504935503, "W": 132.69, "J_1KI": 16.470640889099833, "W_1KI": 1.2392942868617434, "W_D": 96.9815, "J_D": 1288.9169879344702, "W_D_1KI": 0.905785054497567, "J_D_1KI": 0.008459825481675993}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..afaa491
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,105 @@
+['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": 0.1338520050048828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      91,     190,  ...,  999794,
+                             999887, 1000000]),
+       col_indices=tensor([  40,  344,  548,  ..., 9830, 9841, 9960]),
+       values=tensor([0.4008, 0.1162, 0.8586,  ..., 0.0804, 0.9517, 0.8982]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6204, 0.8036, 0.5749,  ..., 0.0150, 0.4782, 0.5342])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.1338520050048828 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78444', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.085982084274292}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     187,  ...,  999812,
+                             999901, 1000000]),
+       col_indices=tensor([ 276,  302,  470,  ..., 9539, 9540, 9930]),
+       values=tensor([0.4664, 0.1616, 0.7456,  ..., 0.5929, 0.0487, 0.3579]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7338, 0.4039, 0.6812,  ..., 0.4093, 0.7174, 0.1386])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 8.085982084274292 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '101862', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.98931097984314}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      97,     208,  ...,  999782,
+                             999887, 1000000]),
+       col_indices=tensor([ 113,  292,  413,  ..., 9756, 9814, 9863]),
+       values=tensor([0.7037, 0.4902, 0.2249,  ..., 0.1343, 0.1681, 0.3653]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0898, 0.3365, 0.9954,  ..., 0.9623, 0.9055, 0.9870])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.98931097984314 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '107069', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.18355131149292}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     101,     199,  ...,  999771,
+                             999895, 1000000]),
+       col_indices=tensor([  30,   45,   94,  ..., 9508, 9668, 9839]),
+       values=tensor([0.9351, 0.0667, 0.7279,  ..., 0.8651, 0.3266, 0.8240]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5009, 0.1141, 0.1222,  ..., 0.6365, 0.9492, 0.1421])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 11.18355131149292 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     101,     199,  ...,  999771,
+                             999895, 1000000]),
+       col_indices=tensor([  30,   45,   94,  ..., 9508, 9668, 9839]),
+       values=tensor([0.9351, 0.0667, 0.7279,  ..., 0.8651, 0.3266, 0.8240]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5009, 0.1141, 0.1222,  ..., 0.6365, 0.9492, 0.1421])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 11.18355131149292 seconds
+
+[39.9, 40.21, 39.53, 40.02, 39.22, 40.03, 39.19, 40.06, 39.89, 39.6]
+[132.69]
+13.29033875465393
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107069, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.18355131149292, 'TIME_S_1KI': 0.10445181435796468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1763.49504935503, 'W': 132.69}
+[39.9, 40.21, 39.53, 40.02, 39.22, 40.03, 39.19, 40.06, 39.89, 39.6, 40.49, 39.14, 39.91, 39.61, 39.91, 39.69, 39.58, 39.1, 39.07, 40.03]
+714.1700000000001
+35.7085
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107069, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.18355131149292, 'TIME_S_1KI': 0.10445181435796468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1763.49504935503, 'W': 132.69, 'J_1KI': 16.470640889099833, 'W_1KI': 1.2392942868617434, 'W_D': 96.9815, 'J_D': 1288.9169879344702, 'W_D_1KI': 0.905785054497567, 'J_D_1KI': 0.008459825481675993}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..05bc7cf
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28163, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459697484970093, "TIME_S_1KI": 0.371398554307783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2122.055966448784, "W": 150.9, "J_1KI": 75.34907383619587, "W_1KI": 5.358093953058979, "W_D": 115.1085, "J_D": 1618.7321352814438, "W_D_1KI": 4.087224372403509, "J_D_1KI": 0.1451274499308848}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..e823d57
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.4528634548187256}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     492,     984,  ..., 4999007,
+                            4999498, 5000000]),
+       col_indices=tensor([  17,   26,   49,  ..., 9943, 9965, 9968]),
+       values=tensor([0.3785, 0.7951, 0.2972,  ..., 0.3720, 0.7853, 0.1204]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5665, 0.1637, 0.5801,  ..., 0.5211, 0.8646, 0.6970])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.4528634548187256 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23185', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.643981695175171}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     462,     943,  ..., 4999021,
+                            4999500, 5000000]),
+       col_indices=tensor([   4,   33,   72,  ..., 9956, 9968, 9998]),
+       values=tensor([0.9717, 0.2077, 0.4481,  ..., 0.1268, 0.5535, 0.1753]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9761, 0.2557, 0.3900,  ..., 0.3250, 0.2223, 0.7021])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 8.643981695175171 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28163', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459697484970093}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     510,    1022,  ..., 4999000,
+                            4999485, 5000000]),
+       col_indices=tensor([  31,   34,   40,  ..., 9926, 9941, 9984]),
+       values=tensor([0.9067, 0.8635, 0.5661,  ..., 0.0254, 0.7052, 0.7869]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2490, 0.1590, 0.1294,  ..., 0.2235, 0.7822, 0.7952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.459697484970093 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     510,    1022,  ..., 4999000,
+                            4999485, 5000000]),
+       col_indices=tensor([  31,   34,   40,  ..., 9926, 9941, 9984]),
+       values=tensor([0.9067, 0.8635, 0.5661,  ..., 0.0254, 0.7052, 0.7869]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2490, 0.1590, 0.1294,  ..., 0.2235, 0.7822, 0.7952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.459697484970093 seconds
+
+[40.87, 39.6, 40.38, 39.19, 40.47, 39.26, 39.48, 39.2, 40.2, 39.44]
+[150.9]
+14.062663793563843
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28163, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459697484970093, 'TIME_S_1KI': 0.371398554307783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2122.055966448784, 'W': 150.9}
+[40.87, 39.6, 40.38, 39.19, 40.47, 39.26, 39.48, 39.2, 40.2, 39.44, 41.45, 39.66, 39.99, 39.31, 39.56, 39.24, 40.15, 39.52, 39.84, 39.8]
+715.8299999999999
+35.7915
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28163, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459697484970093, 'TIME_S_1KI': 0.371398554307783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2122.055966448784, 'W': 150.9, 'J_1KI': 75.34907383619587, 'W_1KI': 5.358093953058979, 'W_D': 115.1085, 'J_D': 1618.7321352814438, 'W_D_1KI': 4.087224372403509, 'J_D_1KI': 0.1451274499308848}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..8638631
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 5238, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.192334175109863, "TIME_S_1KI": 2.1367571926517495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2038.619791316986, "W": 124.02, "J_1KI": 389.1981273991955, "W_1KI": 23.676975945017183, "W_D": 88.21424999999999, "J_D": 1450.0509266746044, "W_D_1KI": 16.841208476517753, "J_D_1KI": 3.2151982582126295}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..6292d32
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.209188461303711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      961,     2007,  ...,  9997952,
+                             9998968, 10000000]),
+       col_indices=tensor([  14,   18,   26,  ..., 9968, 9972, 9997]),
+       values=tensor([0.9669, 0.3653, 0.3089,  ..., 0.5289, 0.5202, 0.9028]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8016, 0.0222, 0.4456,  ..., 0.4115, 0.6943, 0.5313])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 2.209188461303711 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4752', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.52530813217163}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      954,     1940,  ...,  9998038,
+                             9998994, 10000000]),
+       col_indices=tensor([   0,    3,    4,  ..., 9964, 9979, 9998]),
+       values=tensor([0.5875, 0.0019, 0.5119,  ..., 0.4152, 0.5002, 0.2921]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8144, 0.0248, 0.0526,  ..., 0.0067, 0.4287, 0.2758])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 9.52530813217163 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5238', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.192334175109863}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1001,     2002,  ...,  9997918,
+                             9998966, 10000000]),
+       col_indices=tensor([   9,   21,   97,  ..., 9973, 9981, 9990]),
+       values=tensor([0.6111, 0.6801, 0.6895,  ..., 0.1092, 0.3002, 0.2815]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0277, 0.0823, 0.3111,  ..., 0.6513, 0.2238, 0.0558])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.192334175109863 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1001,     2002,  ...,  9997918,
+                             9998966, 10000000]),
+       col_indices=tensor([   9,   21,   97,  ..., 9973, 9981, 9990]),
+       values=tensor([0.6111, 0.6801, 0.6895,  ..., 0.1092, 0.3002, 0.2815]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0277, 0.0823, 0.3111,  ..., 0.6513, 0.2238, 0.0558])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.192334175109863 seconds
+
+[41.1, 40.26, 39.38, 39.47, 39.45, 40.18, 39.58, 40.31, 39.46, 40.14]
+[124.02]
+16.437830924987793
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.192334175109863, 'TIME_S_1KI': 2.1367571926517495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2038.619791316986, 'W': 124.02}
+[41.1, 40.26, 39.38, 39.47, 39.45, 40.18, 39.58, 40.31, 39.46, 40.14, 40.09, 40.24, 39.54, 40.08, 39.32, 40.12, 39.35, 39.35, 39.19, 40.34]
+716.115
+35.80575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.192334175109863, 'TIME_S_1KI': 2.1367571926517495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2038.619791316986, 'W': 124.02, 'J_1KI': 389.1981273991955, 'W_1KI': 23.676975945017183, 'W_D': 88.21424999999999, 'J_D': 1450.0509266746044, 'W_D_1KI': 16.841208476517753, 'J_D_1KI': 3.2151982582126295}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..4449f80
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 362169, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.681929588317871, "TIME_S_1KI": 0.029494323336116207, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.685609395504, "W": 96.13, "J_1KI": 3.4864541399056903, "W_1KI": 0.2654285706396737, "W_D": 60.50875, "J_D": 794.7937986841798, "W_D_1KI": 0.1670732448111241, "J_D_1KI": 0.0004613129362566208}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..f681b74
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.044791460037231445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([9942, 6806, 8769, 3673, 2619, 2553, 2772, 6991, 9638,
+                           9629, 9158, 6212, 5182, 5529, 2344, 2346,  122, 7028,
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+                      9.2290e-01, 3.5271e-01, 4.6772e-01, 3.3679e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4545, 0.4841, 0.5462,  ..., 0.7193, 0.8873, 0.8657])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.044791460037231445 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '234419', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.79626727104187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8987, 0.7248, 0.0383,  ..., 0.6918, 0.0447, 0.2254])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 6.79626727104187 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '362169', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.681929588317871}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4226, 0.0556, 0.1398,  ..., 0.5751, 0.9814, 0.4838])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.681929588317871 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      8.6251e-01, 4.5483e-01, 7.5964e-01, 2.8131e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4226, 0.0556, 0.1398,  ..., 0.5751, 0.9814, 0.4838])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.681929588317871 seconds
+
+[41.15, 38.98, 39.75, 38.85, 39.88, 39.03, 39.18, 38.82, 39.8, 38.85]
+[96.13]
+13.135187864303589
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 362169, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.681929588317871, 'TIME_S_1KI': 0.029494323336116207, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.685609395504, 'W': 96.13}
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diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..c41b46b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21272, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.296250820159912, "TIME_S_1KI": 0.4840283386686683, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2004.567332353592, "W": 151.77, "J_1KI": 94.23501938480594, "W_1KI": 7.134731101918015, "W_D": 115.36950000000002, "J_D": 1523.7921252551082, "W_D_1KI": 5.423537984204589, "J_D_1KI": 0.2549613569107084}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..ff821c9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.5370402336120605}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([   667,  84326, 231414,  ..., 445492, 452435,
+                           478533]),
+       values=tensor([0.3723, 0.9059, 0.5582,  ..., 0.5128, 0.0660, 0.1881]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0315, 0.2189, 0.8055,  ..., 0.9902, 0.0196, 0.5860])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.5370402336120605 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19551', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.650388717651367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([ 11262,  76750, 152870,  ..., 221537, 283064,
+                           452441]),
+       values=tensor([0.8111, 0.5495, 0.0260,  ..., 0.8118, 0.4893, 0.3789]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5436, 0.8281, 0.7063,  ..., 0.1699, 0.2640, 0.5110])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.650388717651367 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21272', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.296250820159912}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499993,
+                            2499998, 2500000]),
+       col_indices=tensor([ 13054, 157067, 258216,  ..., 445117, 194165,
+                           431781]),
+       values=tensor([0.8472, 0.4724, 0.5562,  ..., 0.8941, 0.8667, 0.3682]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2043, 0.9144, 0.3718,  ..., 0.9024, 0.4544, 0.2083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.296250820159912 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499993,
+                            2499998, 2500000]),
+       col_indices=tensor([ 13054, 157067, 258216,  ..., 445117, 194165,
+                           431781]),
+       values=tensor([0.8472, 0.4724, 0.5562,  ..., 0.8941, 0.8667, 0.3682]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2043, 0.9144, 0.3718,  ..., 0.9024, 0.4544, 0.2083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.296250820159912 seconds
+
+[40.04, 40.36, 39.54, 40.31, 47.07, 40.26, 39.56, 39.95, 39.57, 41.2]
+[151.77]
+13.207928657531738
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.296250820159912, 'TIME_S_1KI': 0.4840283386686683, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2004.567332353592, 'W': 151.77}
+[40.04, 40.36, 39.54, 40.31, 47.07, 40.26, 39.56, 39.95, 39.57, 41.2, 46.29, 39.48, 40.21, 39.42, 40.34, 39.68, 39.47, 39.18, 40.2, 39.29]
+728.01
+36.4005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21272, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.296250820159912, 'TIME_S_1KI': 0.4840283386686683, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2004.567332353592, 'W': 151.77, 'J_1KI': 94.23501938480594, 'W_1KI': 7.134731101918015, 'W_D': 115.36950000000002, 'J_D': 1523.7921252551082, 'W_D_1KI': 5.423537984204589, 'J_D_1KI': 0.2549613569107084}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..8fb8b3c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91738, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.729677200317383, "TIME_S_1KI": 0.1169600078518976, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1421.8676947784425, "W": 116.68, "J_1KI": 15.49922272971334, "W_1KI": 1.2718829710697859, "W_D": 81.037, "J_D": 987.5205037860871, "W_D_1KI": 0.883352591074582, "J_D_1KI": 0.009629080545407377}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..e8b4fe3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,105 @@
+['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": 0.13608026504516602}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     12,  ..., 249989, 249998,
+                            250000]),
+       col_indices=tensor([17323, 35611, 42973,  ..., 47252,  2994, 12259]),
+       values=tensor([0.7287, 0.3464, 0.0193,  ..., 0.7636, 0.2298, 0.3699]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4030, 0.5063, 0.1399,  ..., 0.2219, 0.6631, 0.1030])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.13608026504516602 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77160', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.564647197723389}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 249992, 249997,
+                            250000]),
+       col_indices=tensor([ 7731,  9587, 38710,  ..., 32177, 32664, 36235]),
+       values=tensor([0.0671, 0.3654, 0.2011,  ..., 0.4377, 0.9797, 0.5456]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4354, 0.6450, 0.5949,  ..., 0.4585, 0.1162, 0.0017])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.564647197723389 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84705', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.694962739944458}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     11,  ..., 249991, 249993,
+                            250000]),
+       col_indices=tensor([19445, 22750, 27321,  ..., 31731, 39710, 46259]),
+       values=tensor([0.4009, 0.2006, 0.6920,  ..., 0.2884, 0.6470, 0.2171]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3109, 0.8999, 0.0558,  ..., 0.1822, 0.8563, 0.0744])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.694962739944458 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91738', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.729677200317383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([20378, 29361, 44885,  ..., 25194, 39048, 45113]),
+       values=tensor([0.6839, 0.7204, 0.3118,  ..., 0.2854, 0.8671, 0.0496]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2159, 0.7026, 0.3184,  ..., 0.1135, 0.4559, 0.6374])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.729677200317383 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([20378, 29361, 44885,  ..., 25194, 39048, 45113]),
+       values=tensor([0.6839, 0.7204, 0.3118,  ..., 0.2854, 0.8671, 0.0496]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2159, 0.7026, 0.3184,  ..., 0.1135, 0.4559, 0.6374])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.729677200317383 seconds
+
+[40.36, 39.52, 40.11, 39.22, 40.19, 39.14, 40.18, 39.47, 39.42, 39.16]
+[116.68]
+12.186044692993164
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.729677200317383, 'TIME_S_1KI': 0.1169600078518976, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.8676947784425, 'W': 116.68}
+[40.36, 39.52, 40.11, 39.22, 40.19, 39.14, 40.18, 39.47, 39.42, 39.16, 39.82, 39.18, 40.07, 39.1, 40.14, 39.1, 40.11, 39.11, 39.63, 39.0]
+712.86
+35.643
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91738, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.729677200317383, 'TIME_S_1KI': 0.1169600078518976, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.8676947784425, 'W': 116.68, 'J_1KI': 15.49922272971334, 'W_1KI': 1.2718829710697859, 'W_D': 81.037, 'J_D': 987.5205037860871, 'W_D_1KI': 0.883352591074582, 'J_D_1KI': 0.009629080545407377}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..18b313c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46932, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.4467294216156, "TIME_S_1KI": 0.22259288804260632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1943.0940554380418, "W": 146.55, "J_1KI": 41.40232795188873, "W_1KI": 3.122602914855536, "W_D": 110.75150000000002, "J_D": 1468.4447716195587, "W_D_1KI": 2.3598291144634795, "J_D_1KI": 0.05028187834448734}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..c54c9fb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.2965991497039795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      49,     105,  ..., 2499896,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1888,  3456,  5299,  ..., 45108, 48153, 49689]),
+       values=tensor([0.2133, 0.4832, 0.5162,  ..., 0.1550, 0.2104, 0.0398]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8558, 0.3690, 0.3196,  ..., 0.7609, 0.2901, 0.1393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.2965991497039795 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35401', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.9200310707092285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      50,      98,  ..., 2499887,
+                            2499942, 2500000]),
+       col_indices=tensor([ 1341,  6881,  6901,  ..., 49243, 49539, 49603]),
+       values=tensor([0.6621, 0.7599, 0.1509,  ..., 0.9636, 0.0388, 0.7851]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4875, 0.8207, 0.8190,  ..., 0.4243, 0.1238, 0.4257])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 7.9200310707092285 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46932', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.4467294216156}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      37,      82,  ..., 2499888,
+                            2499942, 2500000]),
+       col_indices=tensor([ 2117,  2189,  2263,  ..., 47568, 48115, 49415]),
+       values=tensor([0.8006, 0.3321, 0.7026,  ..., 0.2322, 0.3552, 0.1894]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9529, 0.8532, 0.0899,  ..., 0.0711, 0.7399, 0.8898])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.4467294216156 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      37,      82,  ..., 2499888,
+                            2499942, 2500000]),
+       col_indices=tensor([ 2117,  2189,  2263,  ..., 47568, 48115, 49415]),
+       values=tensor([0.8006, 0.3321, 0.7026,  ..., 0.2322, 0.3552, 0.1894]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9529, 0.8532, 0.0899,  ..., 0.0711, 0.7399, 0.8898])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.4467294216156 seconds
+
+[40.61, 39.27, 40.33, 39.32, 40.41, 39.18, 40.21, 39.47, 39.59, 40.71]
+[146.55]
+13.258915424346924
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46932, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.4467294216156, 'TIME_S_1KI': 0.22259288804260632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.0940554380418, 'W': 146.55}
+[40.61, 39.27, 40.33, 39.32, 40.41, 39.18, 40.21, 39.47, 39.59, 40.71, 40.22, 40.14, 39.29, 40.17, 39.21, 40.37, 39.38, 39.54, 39.32, 40.0]
+715.9699999999999
+35.7985
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46932, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.4467294216156, 'TIME_S_1KI': 0.22259288804260632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1943.0940554380418, 'W': 146.55, 'J_1KI': 41.40232795188873, 'W_1KI': 3.122602914855536, 'W_D': 110.75150000000002, 'J_D': 1468.4447716195587, 'W_D_1KI': 2.3598291144634795, 'J_D_1KI': 0.05028187834448734}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..a53c562
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 132622, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.695917844772339, "TIME_S_1KI": 0.08064964971703291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1381.936604347229, "W": 102.52, "J_1KI": 10.420115850667528, "W_1KI": 0.7730240834853945, "W_D": 66.90350000000001, "J_D": 901.8376473755837, "W_D_1KI": 0.5044675845636472, "J_D_1KI": 0.0038038001580706607}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..89e9f79
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,100 @@
+['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": 0.13474559783935547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([43476,  3093, 41733,  ..., 42921, 16006, 37299]),
+       values=tensor([0.8834, 0.6775, 0.5620,  ..., 0.7889, 0.3307, 0.4663]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1655, 0.9515, 0.3152,  ..., 0.5133, 0.8067, 0.9282])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.13474559783935547 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '77924', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.767163991928101}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([35071, 44060, 31911,  ..., 37021, 35082, 17458]),
+       values=tensor([0.6370, 0.7388, 0.5924,  ..., 0.3636, 0.5677, 0.2522]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8033, 0.0482, 0.8958,  ..., 0.4016, 0.2560, 0.2344])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 6.767163991928101 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '120907', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.572461605072021}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 3082, 46101, 46713,  ..., 40768, 36655, 17054]),
+       values=tensor([0.2693, 0.1416, 0.6603,  ..., 0.5561, 0.2474, 0.5454]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5277, 0.5906, 0.6144,  ..., 0.6636, 0.4334, 0.5688])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.572461605072021 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '132622', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.695917844772339}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([ 1978, 29423,  7022,  ..., 46456, 14629, 46564]),
+       values=tensor([0.3729, 0.4306, 0.6677,  ..., 0.7805, 0.6392, 0.2909]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9195, 0.7845, 0.1112,  ..., 0.9886, 0.0043, 0.8706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.695917844772339 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([ 1978, 29423,  7022,  ..., 46456, 14629, 46564]),
+       values=tensor([0.3729, 0.4306, 0.6677,  ..., 0.7805, 0.6392, 0.2909]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9195, 0.7845, 0.1112,  ..., 0.9886, 0.0043, 0.8706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.695917844772339 seconds
+
+[40.91, 39.21, 40.28, 39.06, 40.18, 39.24, 39.39, 39.11, 40.12, 39.03]
+[102.52]
+13.4796781539917
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.695917844772339, 'TIME_S_1KI': 0.08064964971703291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.936604347229, 'W': 102.52}
+[40.91, 39.21, 40.28, 39.06, 40.18, 39.24, 39.39, 39.11, 40.12, 39.03, 40.67, 39.4, 40.19, 38.91, 39.61, 38.93, 39.87, 39.08, 40.01, 38.87]
+712.3299999999999
+35.616499999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 132622, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.695917844772339, 'TIME_S_1KI': 0.08064964971703291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.936604347229, 'W': 102.52, 'J_1KI': 10.420115850667528, 'W_1KI': 0.7730240834853945, 'W_D': 66.90350000000001, 'J_D': 901.8376473755837, 'W_D_1KI': 0.5044675845636472, 'J_D_1KI': 0.0038038001580706607}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..c740a6d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 450692, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661210536956787, "TIME_S_1KI": 0.023655202526241394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.7642656326295, "W": 94.48, "J_1KI": 2.82845993634817, "W_1KI": 0.2096331863001784, "W_D": 59.36250000000001, "J_D": 800.9440486729146, "W_D_1KI": 0.13171411962049473, "J_D_1KI": 0.00029224863015206556}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..eed9694
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05054283142089844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 483, 2169,  757,  ...,  173, 4439, 4656]),
+       values=tensor([0.9876, 0.6258, 0.5982,  ..., 0.3562, 0.6626, 0.2988]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5486, 0.1022, 0.5660,  ..., 0.0025, 0.4692, 0.8005])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.05054283142089844 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '207744', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.839913845062256}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1064,  259,  704,  ..., 2037, 4830,  899]),
+       values=tensor([0.7873, 0.2357, 0.4656,  ..., 0.3402, 0.5396, 0.7236]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3390, 0.6218, 0.4185,  ..., 0.9245, 0.2892, 0.5586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 4.839913845062256 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '450692', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661210536956787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2769, 4978, 3269,  ..., 2907, 4470, 1850]),
+       values=tensor([0.1814, 0.5969, 0.2629,  ..., 0.3883, 0.1478, 0.5451]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9019, 0.2172, 0.0888,  ..., 0.3698, 0.8940, 0.4050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.661210536956787 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2769, 4978, 3269,  ..., 2907, 4470, 1850]),
+       values=tensor([0.1814, 0.5969, 0.2629,  ..., 0.3883, 0.1478, 0.5451]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9019, 0.2172, 0.0888,  ..., 0.3698, 0.8940, 0.4050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.661210536956787 seconds
+
+[39.19, 39.19, 38.63, 39.33, 38.89, 39.39, 38.57, 39.43, 38.51, 39.19]
+[94.48]
+13.492424488067627
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 450692, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661210536956787, 'TIME_S_1KI': 0.023655202526241394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.7642656326295, 'W': 94.48}
+[39.19, 39.19, 38.63, 39.33, 38.89, 39.39, 38.57, 39.43, 38.51, 39.19, 39.32, 38.88, 39.47, 38.52, 39.48, 38.52, 39.58, 38.58, 39.31, 38.44]
+702.3499999999999
+35.11749999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 450692, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661210536956787, 'TIME_S_1KI': 0.023655202526241394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.7642656326295, 'W': 94.48, 'J_1KI': 2.82845993634817, 'W_1KI': 0.2096331863001784, 'W_D': 59.36250000000001, 'J_D': 800.9440486729146, 'W_D_1KI': 0.13171411962049473, 'J_D_1KI': 0.00029224863015206556}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..5a45f3d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 249519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.236942052841187, "TIME_S_1KI": 0.04102670358907012, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1222.1109838342666, "W": 97.13999999999999, "J_1KI": 4.897867432276767, "W_1KI": 0.3893090305748259, "W_D": 61.76424999999999, "J_D": 777.0513520000576, "W_D_1KI": 0.24753325398065876, "J_D_1KI": 0.0009920417041614415}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..bf3e9ad
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05724024772644043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    15,  ..., 24983, 24992, 25000]),
+       col_indices=tensor([ 471, 1370, 1845,  ..., 3191, 3518, 3659]),
+       values=tensor([0.0299, 0.9557, 0.6054,  ..., 0.0635, 0.2604, 0.4528]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0205, 0.7752, 0.1498,  ..., 0.2089, 0.1619, 0.7193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.05724024772644043 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '183437', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.719191074371338}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,     8,  ..., 24996, 24997, 25000]),
+       col_indices=tensor([1493, 2121, 2213,  ...,  623, 2347, 4713]),
+       values=tensor([0.6456, 0.4495, 0.4360,  ..., 0.5144, 0.5794, 0.1984]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2703, 0.0672, 0.3072,  ..., 0.2566, 0.5122, 0.5785])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.719191074371338 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '249519', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.236942052841187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    11,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([ 752,  886,  972,  ...,  802, 1974, 3630]),
+       values=tensor([0.4437, 0.0647, 0.4607,  ..., 0.1209, 0.0125, 0.5794]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9393, 0.0560, 0.5479,  ..., 0.4533, 0.0776, 0.5900])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.236942052841187 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    11,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([ 752,  886,  972,  ...,  802, 1974, 3630]),
+       values=tensor([0.4437, 0.0647, 0.4607,  ..., 0.1209, 0.0125, 0.5794]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9393, 0.0560, 0.5479,  ..., 0.4533, 0.0776, 0.5900])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.236942052841187 seconds
+
+[40.22, 39.73, 39.42, 38.78, 39.93, 38.71, 40.68, 38.8, 39.45, 38.55]
+[97.14]
+12.580924272537231
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 249519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.236942052841187, 'TIME_S_1KI': 0.04102670358907012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1222.1109838342666, 'W': 97.13999999999999}
+[40.22, 39.73, 39.42, 38.78, 39.93, 38.71, 40.68, 38.8, 39.45, 38.55, 40.16, 39.21, 38.74, 39.25, 39.41, 39.27, 38.96, 39.33, 38.96, 38.84]
+707.515
+35.37575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 249519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.236942052841187, 'TIME_S_1KI': 0.04102670358907012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1222.1109838342666, 'W': 97.13999999999999, 'J_1KI': 4.897867432276767, 'W_1KI': 0.3893090305748259, 'W_D': 61.76424999999999, 'J_D': 777.0513520000576, 'W_D_1KI': 0.24753325398065876, 'J_D_1KI': 0.0009920417041614415}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..e6c8fdb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 146173, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.55378007888794, "TIME_S_1KI": 0.07220061214374707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1340.7953616142272, "W": 116.1, "J_1KI": 9.17266089916898, "W_1KI": 0.7942643306219342, "W_D": 80.21499999999999, "J_D": 926.372953763008, "W_D_1KI": 0.5487675562518385, "J_D_1KI": 0.0037542333827166336}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..021f115
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.09166121482849121}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     47,     94,  ..., 249894, 249942,
+                            250000]),
+       col_indices=tensor([ 119,  293,  345,  ..., 4744, 4847, 4998]),
+       values=tensor([0.2600, 0.0492, 0.0782,  ..., 0.6942, 0.7814, 0.7527]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8315, 0.0983, 0.7447,  ..., 0.4668, 0.9945, 0.1855])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.09166121482849121 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '114552', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.2285475730896}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     57,    113,  ..., 249897, 249950,
+                            250000]),
+       col_indices=tensor([  60,   61,   88,  ..., 4754, 4809, 4933]),
+       values=tensor([0.8655, 0.3309, 0.5749,  ..., 0.8443, 0.2705, 0.0665]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6570, 0.9775, 0.7976,  ..., 0.2365, 0.6987, 0.3821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.2285475730896 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '146173', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.55378007888794}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     53,    109,  ..., 249914, 249951,
+                            250000]),
+       col_indices=tensor([  51,   99,  229,  ..., 4435, 4821, 4904]),
+       values=tensor([0.7585, 0.4725, 0.0422,  ..., 0.0029, 0.1086, 0.7072]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3786, 0.1898, 0.9439,  ..., 0.4562, 0.4771, 0.6918])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.55378007888794 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     53,    109,  ..., 249914, 249951,
+                            250000]),
+       col_indices=tensor([  51,   99,  229,  ..., 4435, 4821, 4904]),
+       values=tensor([0.7585, 0.4725, 0.0422,  ..., 0.0029, 0.1086, 0.7072]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3786, 0.1898, 0.9439,  ..., 0.4562, 0.4771, 0.6918])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.55378007888794 seconds
+
+[39.38, 39.69, 39.36, 39.25, 38.8, 39.79, 38.95, 39.94, 38.86, 45.03]
+[116.1]
+11.548624992370605
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 146173, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.55378007888794, 'TIME_S_1KI': 0.07220061214374707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.7953616142272, 'W': 116.1}
+[39.38, 39.69, 39.36, 39.25, 38.8, 39.79, 38.95, 39.94, 38.86, 45.03, 39.59, 39.72, 39.05, 38.89, 38.89, 39.88, 38.77, 40.8, 45.19, 39.74]
+717.7
+35.885000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 146173, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.55378007888794, 'TIME_S_1KI': 0.07220061214374707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.7953616142272, 'W': 116.1, 'J_1KI': 9.17266089916898, 'W_1KI': 0.7942643306219342, 'W_D': 80.21499999999999, 'J_D': 926.372953763008, 'W_D_1KI': 0.5487675562518385, 'J_D_1KI': 0.0037542333827166336}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..4f9e011
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 92778, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.476318120956421, "TIME_S_1KI": 0.11291812844592922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1732.4749909281732, "W": 131.59, "J_1KI": 18.6733384091937, "W_1KI": 1.4183319321390848, "W_D": 96.0545, "J_D": 1264.6251160126926, "W_D_1KI": 1.0353154842742893, "J_D_1KI": 0.011159062323765217}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..85eae8d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.1557161808013916}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     231,     495,  ..., 1249487,
+                            1249744, 1250000]),
+       col_indices=tensor([   9,   30,   58,  ..., 4828, 4865, 4971]),
+       values=tensor([0.7438, 0.5258, 0.4698,  ..., 0.4344, 0.2594, 0.0033]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.1880, 0.8169, 0.5226,  ..., 0.2752, 0.9006, 0.0611])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.1557161808013916 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67430', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 7.631251096725464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     248,     518,  ..., 1249509,
+                            1249753, 1250000]),
+       col_indices=tensor([  31,   45,  102,  ..., 4944, 4977, 4981]),
+       values=tensor([0.8150, 0.4433, 0.0676,  ..., 0.5361, 0.0056, 0.9882]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0156, 0.0219, 0.6064,  ..., 0.7934, 0.6259, 0.0204])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 7.631251096725464 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '92778', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.476318120956421}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     269,     520,  ..., 1249470,
+                            1249738, 1250000]),
+       col_indices=tensor([  32,   37,   46,  ..., 4950, 4963, 4989]),
+       values=tensor([0.4206, 0.9091, 0.7478,  ..., 0.6711, 0.2779, 0.9141]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6953, 0.1111, 0.6307,  ..., 0.1029, 0.6511, 0.8226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.476318120956421 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     269,     520,  ..., 1249470,
+                            1249738, 1250000]),
+       col_indices=tensor([  32,   37,   46,  ..., 4950, 4963, 4989]),
+       values=tensor([0.4206, 0.9091, 0.7478,  ..., 0.6711, 0.2779, 0.9141]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6953, 0.1111, 0.6307,  ..., 0.1029, 0.6511, 0.8226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.476318120956421 seconds
+
+[40.71, 39.95, 38.97, 39.83, 39.79, 39.14, 38.93, 39.78, 39.42, 39.71]
+[131.59]
+13.165704011917114
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92778, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.476318120956421, 'TIME_S_1KI': 0.11291812844592922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1732.4749909281732, 'W': 131.59}
+[40.71, 39.95, 38.97, 39.83, 39.79, 39.14, 38.93, 39.78, 39.42, 39.71, 40.83, 39.04, 39.95, 38.93, 39.14, 39.24, 39.86, 38.92, 39.75, 38.89]
+710.71
+35.5355
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 92778, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.476318120956421, 'TIME_S_1KI': 0.11291812844592922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1732.4749909281732, 'W': 131.59, 'J_1KI': 18.6733384091937, 'W_1KI': 1.4183319321390848, 'W_D': 96.0545, 'J_D': 1264.6251160126926, 'W_D_1KI': 1.0353154842742893, 'J_D_1KI': 0.011159062323765217}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..d3bf1fe
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52513, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.26560354232788, "TIME_S_1KI": 0.19548689928832635, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1794.3579230117798, "W": 136.24, "J_1KI": 34.16978506297069, "W_1KI": 2.594405194904119, "W_D": 100.32050000000001, "J_D": 1321.2777746293546, "W_D_1KI": 1.9103936168186928, "J_D_1KI": 0.036379441601483306}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..ca278ce
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.2491617202758789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     513,    1030,  ..., 2499018,
+                            2499503, 2500000]),
+       col_indices=tensor([   5,    7,    9,  ..., 4974, 4988, 4992]),
+       values=tensor([0.9314, 0.8722, 0.2786,  ..., 0.3461, 0.5001, 0.4531]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5860, 0.7303, 0.0322,  ..., 0.3067, 0.0639, 0.6907])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.2491617202758789 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42141', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.425995349884033}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     480,     969,  ..., 2498991,
+                            2499495, 2500000]),
+       col_indices=tensor([   1,    8,   15,  ..., 4990, 4995, 4997]),
+       values=tensor([0.6450, 0.7913, 0.7669,  ..., 0.2675, 0.7315, 0.7922]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8872, 0.3458, 0.7222,  ..., 0.3185, 0.9459, 0.1327])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 8.425995349884033 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52513', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.26560354232788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     496,    1000,  ..., 2499050,
+                            2499547, 2500000]),
+       col_indices=tensor([   1,    8,   12,  ..., 4944, 4951, 4977]),
+       values=tensor([0.2566, 0.4868, 0.9344,  ..., 0.5912, 0.8684, 0.6618]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5960, 0.0213, 0.1088,  ..., 0.8621, 0.3601, 0.4544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.26560354232788 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     496,    1000,  ..., 2499050,
+                            2499547, 2500000]),
+       col_indices=tensor([   1,    8,   12,  ..., 4944, 4951, 4977]),
+       values=tensor([0.2566, 0.4868, 0.9344,  ..., 0.5912, 0.8684, 0.6618]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5960, 0.0213, 0.1088,  ..., 0.8621, 0.3601, 0.4544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.26560354232788 seconds
+
+[47.36, 40.59, 40.08, 39.29, 39.8, 40.2, 40.01, 39.07, 40.08, 38.96]
+[136.24]
+13.170566082000732
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.26560354232788, 'TIME_S_1KI': 0.19548689928832635, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1794.3579230117798, 'W': 136.24}
+[47.36, 40.59, 40.08, 39.29, 39.8, 40.2, 40.01, 39.07, 40.08, 38.96, 40.34, 40.06, 39.51, 39.55, 39.19, 39.83, 39.16, 39.75, 38.95, 39.88]
+718.3900000000001
+35.919500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.26560354232788, 'TIME_S_1KI': 0.19548689928832635, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1794.3579230117798, 'W': 136.24, 'J_1KI': 34.16978506297069, 'W_1KI': 2.594405194904119, 'W_D': 100.32050000000001, 'J_D': 1321.2777746293546, 'W_D_1KI': 1.9103936168186928, 'J_D_1KI': 0.036379441601483306}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..b172c0c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 470922, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.104466915130615, "TIME_S_1KI": 0.021456773977708867, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1145.4606072449685, "W": 92.79, "J_1KI": 2.4323786258551703, "W_1KI": 0.19703900008918676, "W_D": 57.138000000000005, "J_D": 705.3489403681756, "W_D_1KI": 0.12133219514059655, "J_D_1KI": 0.0002576481777037313}
diff --git a/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..263466b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,356 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06183266639709473}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2604,  880,   70, 3579, 4688, 1415, 4052, 2136, 2789,
+                           1920, 1039, 1558, 2117, 2959,  828,  201, 2786, 2764,
+                           2257,  277, 2288,  309, 1119, 4553,  992, 4344, 1852,
+                           1654, 3440, 2337, 4465, 3747,  865, 1053,  722, 4388,
+                           1118, 2434, 2479, 2179, 2623, 1327, 1850, 4354, 1080,
+                            294, 3733, 2629, 4844, 2052,  338, 3690, 2779, 4781,
+                            442,  500, 2501, 2111, 2134, 4050, 4965, 2490, 1539,
+                           1728, 3791, 2480,  429,   85, 2238, 4139, 1911, 2702,
+                           1667,  623,  834,  958, 2640,  639, 3527, 4275, 2167,
+                           2457,  991,  806, 4483,  513, 3720, 1136, 1176, 1064,
+                            771,  912, 1234, 1122, 4461, 4277, 1464,  345, 1997,
+                           2256, 2917,   38, 2975,  472, 2189, 2640,  491,  245,
+                            718, 3839, 2523,  240, 4832, 1434, 3727, 2402, 3795,
+                            977, 2914, 3289, 1194, 1229, 3616, 4441, 1900, 4483,
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+                           2932, 2437, 4078, 1005, 2493, 4749, 4500, 4776, 2110,
+                           3771, 1500, 4456, 4652, 2281, 3889, 3267, 2338, 1779,
+                           1663, 1964,  223, 2535, 4215, 2012,  431, 2610, 2606,
+                           1802, 4804, 2967,  365, 3887, 1133, 2945,   28,  647,
+                            466, 4656, 1939, 1716, 1723, 1159, 2034, 3057, 1288,
+                            284,  673, 4283,  506, 1331,  614,  631, 4195, 2134,
+                           2612, 1089, 4012, 2128,  736, 1710, 4895, 1258, 2802,
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+                           1556,  110, 4249,  222, 1573, 3450, 1707, 4825, 3455,
+                            279, 1371, 3150,  620,  486,  544, 4512, 3097, 2958,
+                           3135,   21, 1955,  802, 3984, 2259, 2773, 1786, 4464,
+                           4164, 2686, 4882, 4392, 2240, 1975, 2258]),
+       values=tensor([0.5027, 0.7084, 0.3487, 0.0753, 0.4164, 0.9980, 0.6580,
+                      0.4935, 0.3902, 0.5664, 0.2658, 0.3783, 0.8206, 0.5243,
+                      0.7985, 0.9823, 0.7694, 0.1060, 0.0192, 0.9550, 0.7866,
+                      0.3204, 0.1228, 0.4101, 0.8052, 0.9732, 0.1676, 0.7257,
+                      0.3426, 0.4203, 0.8249, 0.6182, 0.8414, 0.1007, 0.5404,
+                      0.5322, 0.6815, 0.5471, 0.5528, 0.9304, 0.5952, 0.6825,
+                      0.1470, 0.9592, 0.1633, 0.8148, 0.7106, 0.4684, 0.6378,
+                      0.2787, 0.1559, 0.9606, 0.6114, 0.8631, 0.8476, 0.0374,
+                      0.0974, 0.1508, 0.6160, 0.2538, 0.9193, 0.3221, 0.6792,
+                      0.1039, 0.5088, 0.3858, 0.8567, 0.5930, 0.1245, 0.9954,
+                      0.1659, 0.1382, 0.3631, 0.0415, 0.2608, 0.5523, 0.3431,
+                      0.5922, 0.9276, 0.2417, 0.9820, 0.0941, 0.0465, 0.6122,
+                      0.3473, 0.8672, 0.7451, 0.4632, 0.6761, 0.3844, 0.6143,
+                      0.9600, 0.7204, 0.0168, 0.7425, 0.2772, 0.4866, 0.2756,
+                      0.3148, 0.2142, 0.2884, 0.7150, 0.6972, 0.0578, 0.3403,
+                      0.6794, 0.7790, 0.6966, 0.8236, 0.6083, 0.5211, 0.6301,
+                      0.9543, 0.5553, 0.9115, 0.9237, 0.2270, 0.6441, 0.7009,
+                      0.1070, 0.9702, 0.2577, 0.6283, 0.2972, 0.6911, 0.1725,
+                      0.0282, 0.9157, 0.7996, 0.8026, 0.3516, 0.8308, 0.1003,
+                      0.0248, 0.7281, 0.0565, 0.4669, 0.2079, 0.4864, 0.2943,
+                      0.0681, 0.8545, 0.6221, 0.1251, 0.9854, 0.1397, 0.1128,
+                      0.9416, 0.0256, 0.6346, 0.9861, 0.8618, 0.7250, 0.4296,
+                      0.7583, 0.0529, 0.9738, 0.1783, 0.4879, 0.4079, 0.1074,
+                      0.5057, 0.9961, 0.1328, 0.5920, 0.7290, 0.7943, 0.2699,
+                      0.4245, 0.8340, 0.8310, 0.7824, 0.7435, 0.8129, 0.8814,
+                      0.7889, 0.8688, 0.4636, 0.6432, 0.6209, 0.5976, 0.7619,
+                      0.1123, 0.6496, 0.0741, 0.4224, 0.7444, 0.0204, 0.2397,
+                      0.8878, 0.9369, 0.8874, 0.3159, 0.4066, 0.7965, 0.9182,
+                      0.6430, 0.4446, 0.9224, 0.9817, 0.9823, 0.2288, 0.4574,
+                      0.8650, 0.3584, 0.5672, 0.6737, 0.6909, 0.8267, 0.7004,
+                      0.1349, 0.9181, 0.4535, 0.2086, 0.7357, 0.4116, 0.8581,
+                      0.4745, 0.8694, 0.4770, 0.7691, 0.7362, 0.3193, 0.0221,
+                      0.8677, 0.6112, 0.7624, 0.0925, 0.5125, 0.8534, 0.7050,
+                      0.0262, 0.5351, 0.3163, 0.2383, 0.0599, 0.2394, 0.4205,
+                      0.6550, 0.0849, 0.3824, 0.5505, 0.5900, 0.6050, 0.9085,
+                      0.2972, 0.8380, 0.5688, 0.8007, 0.1354]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8800, 0.9246, 0.8175,  ..., 0.7580, 0.5437, 0.3847])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.06183266639709473 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '169813', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.7862648963928223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4929, 3000, 2082, 1973, 3068,  607, 2961,   29,  351,
+                           4460, 1744, 1352, 1928,  620, 2963, 2161, 3031, 1297,
+                           2919,  205, 4433, 3348, 1763,  856, 1768, 4451, 4553,
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+                           2961, 4938, 4706, 2892,  918, 4326, 4938, 1016, 1946,
+                           3193, 4622, 2689, 1925, 1828, 3491, 4755]),
+       values=tensor([6.5732e-01, 5.5709e-01, 9.0255e-01, 3.7373e-01,
+                      9.2539e-01, 5.3507e-01, 6.8389e-01, 8.5026e-01,
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+                      2.7927e-01, 5.3053e-01, 3.0237e-01, 7.6440e-02,
+                      4.1133e-01, 1.4339e-01, 4.0853e-01, 4.2458e-01,
+                      5.2413e-01, 1.0859e-03, 2.4440e-01, 2.9440e-02,
+                      5.4994e-01, 7.3144e-01, 9.1113e-01, 3.6059e-03,
+                      9.4994e-01, 3.3446e-01, 5.3742e-01, 4.4632e-01,
+                      7.2486e-02, 6.4910e-01, 1.3537e-01, 8.5198e-01,
+                      1.0295e-01, 9.4804e-01, 7.3070e-01, 6.7511e-01,
+                      9.8159e-01, 8.2450e-01, 9.4960e-03, 8.6690e-01,
+                      4.2671e-02, 1.4742e-01, 8.7106e-01, 3.5370e-01,
+                      2.7525e-01, 5.1878e-01, 4.3630e-01, 6.5541e-01,
+                      2.5515e-01, 4.3745e-01, 1.7148e-01, 1.7999e-01,
+                      9.8168e-02, 4.2671e-01, 8.0177e-01, 6.3035e-01,
+                      5.4076e-01, 7.7599e-01, 6.2263e-01, 2.3030e-01,
+                      6.9773e-01, 8.4732e-01, 8.0053e-01, 8.6019e-01,
+                      2.2649e-01, 6.7521e-01, 8.5825e-01, 6.0515e-01,
+                      9.8639e-01, 1.4857e-01, 2.9126e-01, 6.5170e-01,
+                      4.0089e-01, 1.9759e-01, 4.6747e-03, 6.9883e-02,
+                      3.7716e-01, 6.0957e-01, 3.6578e-01, 4.8538e-04,
+                      4.0192e-01, 4.0856e-01, 2.3977e-01, 8.9289e-01,
+                      4.4473e-01, 1.9347e-01, 4.3197e-01, 4.7259e-01,
+                      3.6158e-01, 6.2329e-01, 7.8778e-01, 2.0247e-01,
+                      5.4445e-02, 9.9327e-01, 1.4720e-01, 6.7916e-01,
+                      8.7100e-01, 3.3540e-01, 9.5084e-01, 3.4452e-02,
+                      2.6256e-01, 1.8338e-01, 9.7536e-01, 3.5124e-01,
+                      2.8707e-01, 7.8855e-01, 6.7111e-01, 5.7173e-01,
+                      9.5579e-01, 6.0574e-01, 6.8834e-01, 1.3845e-01,
+                      6.9447e-01, 7.9333e-02, 6.1603e-01, 6.4107e-03,
+                      3.1443e-02, 2.2338e-01, 7.6880e-01, 4.8996e-01,
+                      7.2451e-01, 2.5495e-01, 1.1564e-01, 6.2903e-01,
+                      6.6600e-01, 9.4852e-01, 4.0126e-01, 4.9942e-01,
+                      3.5796e-01, 8.0719e-01, 6.5464e-01, 2.6782e-01,
+                      9.4003e-01, 6.5438e-01, 3.6967e-01, 1.8464e-01,
+                      4.7524e-01, 7.2208e-01, 1.2031e-01, 5.8708e-01,
+                      2.0250e-01, 6.5919e-01, 4.4919e-01, 5.7088e-01,
+                      6.2858e-01, 1.8170e-01, 2.2030e-01, 3.1361e-01,
+                      3.8840e-01, 1.4761e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.8065, 0.5790, 0.9005,  ..., 0.0135, 0.6788, 0.2076])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.7862648963928223 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '470922', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.104466915130615}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 286, 2098, 2957,   31, 4770, 3649, 4063, 3564, 3211,
+                            519,  372, 1653, 2583, 2464, 2987, 1744, 4556, 3480,
+                           4025,  965, 3221, 3480, 1237,  638, 2731, 2204, 3903,
+                            337, 4640, 3708, 4928,  846, 4069, 3241, 2342, 3155,
+                           3904, 4645, 1110, 3262,  206, 2707,  291, 1906, 3653,
+                           4410, 4931, 1727, 4173, 1383, 3385, 3838, 1305, 3168,
+                           1375, 4057, 2761, 2787, 2307,    6, 2503, 1872, 3680,
+                           2234, 1597, 3084, 1758,  491, 3779, 4890, 3184,  831,
+                            331, 2968, 3525, 1971,  454,  168, 2971, 2622, 1099,
+                           3321, 3822, 4888, 2660, 4331, 3839,  847,  453, 3854,
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+                           2678, 3408,  277, 1706, 3353, 4272,  200, 4495, 1971,
+                           1057, 2080, 4776, 2636, 1840, 1457, 1455, 3267,  879,
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+                           2250,  357, 4188,  146, 2230, 1761, 1304, 1785,  442,
+                           2853, 3699,   79, 4930, 2598, 3595, 2987,  205,  247,
+                           2873, 2237, 1134, 2086, 3420, 2896, 4246, 2080, 1618,
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+                            765, 3494, 3345,  738, 3535, 3354, 3147, 4390,  602,
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+                           2781, 3899, 4448, 4692, 1277,  259, 2144, 2798, 4087,
+                           2596, 4771, 4479,  733, 3005, 1161, 3811, 3147, 4464,
+                           4683,  773, 3834, 3088, 1039, 3766, 2820, 3923, 3718,
+                           3049, 1976,  990, 3587, 2696, 4263, 2139, 3191, 1101,
+                           4701, 4465,  551, 3012, 2514, 2260, 1927, 3611, 4115,
+                           4664,  772, 3814, 2744, 2328,  560, 3629, 3666, 4110,
+                           1272,  515, 3230, 2775, 3191, 4516, 1702]),
+       values=tensor([0.4950, 0.4387, 0.7062, 0.8184, 0.9685, 0.9491, 0.6387,
+                      0.3930, 0.4627, 0.2264, 0.4673, 0.2803, 0.8352, 0.7116,
+                      0.3144, 0.9721, 0.1277, 0.9601, 0.0123, 0.3968, 0.9183,
+                      0.0517, 0.5676, 0.9009, 0.4901, 0.3378, 0.4750, 0.6307,
+                      0.7160, 0.7754, 0.8317, 0.5508, 0.6443, 0.1719, 0.1190,
+                      0.2292, 0.9505, 0.2302, 0.5965, 0.4343, 0.9706, 0.9472,
+                      0.7071, 0.4120, 0.5080, 0.6133, 0.5804, 0.7848, 0.1131,
+                      0.7398, 0.2113, 0.5136, 0.9362, 0.4868, 0.7307, 0.9542,
+                      0.1907, 0.7842, 0.0075, 0.1654, 0.1604, 0.5554, 0.9265,
+                      0.9594, 0.1847, 0.0412, 0.1458, 0.3185, 0.9474, 0.7262,
+                      0.9867, 0.9175, 0.8563, 0.0555, 0.5865, 0.1402, 0.0777,
+                      0.1693, 0.3284, 0.8041, 0.3119, 0.6054, 0.1208, 0.1474,
+                      0.6411, 0.6397, 0.9233, 0.0205, 0.1838, 0.9985, 0.4716,
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+                      0.7001, 0.1542, 0.3964, 0.0402, 0.9298, 0.1070, 0.4902,
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+                      0.1563, 0.2571, 0.2028, 0.2313, 0.3287, 0.3989, 0.4172,
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+                      0.4976, 0.7177, 0.7966, 0.8166, 0.9702, 0.2066, 0.9091,
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+                      0.8346, 0.3511, 0.4625, 0.1176, 0.9732, 0.6568, 0.0814,
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+                      0.4955, 0.2041, 0.9406, 0.8471, 0.5173, 0.1622, 0.0921,
+                      0.5950, 0.5479, 0.1406, 0.5404, 0.7323]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4539, 0.8865, 0.6514,  ..., 0.0864, 0.1789, 0.3670])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.104466915130615 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 286, 2098, 2957,   31, 4770, 3649, 4063, 3564, 3211,
+                            519,  372, 1653, 2583, 2464, 2987, 1744, 4556, 3480,
+                           4025,  965, 3221, 3480, 1237,  638, 2731, 2204, 3903,
+                            337, 4640, 3708, 4928,  846, 4069, 3241, 2342, 3155,
+                           3904, 4645, 1110, 3262,  206, 2707,  291, 1906, 3653,
+                           4410, 4931, 1727, 4173, 1383, 3385, 3838, 1305, 3168,
+                           1375, 4057, 2761, 2787, 2307,    6, 2503, 1872, 3680,
+                           2234, 1597, 3084, 1758,  491, 3779, 4890, 3184,  831,
+                            331, 2968, 3525, 1971,  454,  168, 2971, 2622, 1099,
+                           3321, 3822, 4888, 2660, 4331, 3839,  847,  453, 3854,
+                            958, 4865, 2336,  403, 4990,  684,  801, 4446,  671,
+                           4256, 4579, 3616,  522, 3560, 4436, 4875, 4839, 4252,
+                           2678, 3408,  277, 1706, 3353, 4272,  200, 4495, 1971,
+                           1057, 2080, 4776, 2636, 1840, 1457, 1455, 3267,  879,
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+                           2873, 2237, 1134, 2086, 3420, 2896, 4246, 2080, 1618,
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+                           4701, 4465,  551, 3012, 2514, 2260, 1927, 3611, 4115,
+                           4664,  772, 3814, 2744, 2328,  560, 3629, 3666, 4110,
+                           1272,  515, 3230, 2775, 3191, 4516, 1702]),
+       values=tensor([0.4950, 0.4387, 0.7062, 0.8184, 0.9685, 0.9491, 0.6387,
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+                      0.8346, 0.3511, 0.4625, 0.1176, 0.9732, 0.6568, 0.0814,
+                      0.1466, 0.9735, 0.9996, 0.5023, 0.0806, 0.6393, 0.9851,
+                      0.9968, 0.7168, 0.8555, 0.4797, 0.5400, 0.6489, 0.3087,
+                      0.4955, 0.2041, 0.9406, 0.8471, 0.5173, 0.1622, 0.0921,
+                      0.5950, 0.5479, 0.1406, 0.5404, 0.7323]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4539, 0.8865, 0.6514,  ..., 0.0864, 0.1789, 0.3670])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.104466915130615 seconds
+
+[39.77, 39.06, 39.0, 43.22, 38.95, 38.87, 39.0, 38.96, 40.09, 38.55]
+[92.79]
+12.344655752182007
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 470922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.104466915130615, 'TIME_S_1KI': 0.021456773977708867, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1145.4606072449685, 'W': 92.79}
+[39.77, 39.06, 39.0, 43.22, 38.95, 38.87, 39.0, 38.96, 40.09, 38.55, 44.05, 41.96, 39.49, 38.5, 38.79, 39.12, 39.88, 38.31, 39.38, 38.55]
+713.04
+35.652
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 470922, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.104466915130615, 'TIME_S_1KI': 0.021456773977708867, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1145.4606072449685, 'W': 92.79, 'J_1KI': 2.4323786258551703, 'W_1KI': 0.19703900008918676, 'W_D': 57.138000000000005, 'J_D': 705.3489403681756, 'W_D_1KI': 0.12133219514059655, 'J_D_1KI': 0.0002576481777037313}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..648e77d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33926, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.632460117340088, "TIME_S_1KI": 0.31340152441608465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1251.6098043680192, "W": 88.42000000000002, "J_1KI": 36.892348180393185, "W_1KI": 2.606260685020339, "W_D": 71.92675000000001, "J_D": 1018.1432424375416, "W_D_1KI": 2.120106997582975, "J_D_1KI": 0.062492100382685115}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..aff18b0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.309490442276001}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      15,  ...,  999979,
+                             999989, 1000000]),
+       col_indices=tensor([ 8594, 29009, 41843,  ..., 77886, 78317, 95347]),
+       values=tensor([0.9328, 0.5746, 0.1196,  ..., 0.5058, 0.9583, 0.4434]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8206, 0.6612, 0.6620,  ..., 0.9270, 0.4872, 0.3406])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.309490442276001 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33926', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.632460117340088}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      18,  ...,  999986,
+                             999991, 1000000]),
+       col_indices=tensor([ 9555, 32072, 52846,  ..., 78086, 80072, 96075]),
+       values=tensor([0.9751, 0.3269, 0.5720,  ..., 0.0320, 0.6071, 0.6982]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5445, 0.0121, 0.5604,  ..., 0.3280, 0.5430, 0.6322])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.632460117340088 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      18,  ...,  999986,
+                             999991, 1000000]),
+       col_indices=tensor([ 9555, 32072, 52846,  ..., 78086, 80072, 96075]),
+       values=tensor([0.9751, 0.3269, 0.5720,  ..., 0.0320, 0.6071, 0.6982]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5445, 0.0121, 0.5604,  ..., 0.3280, 0.5430, 0.6322])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.632460117340088 seconds
+
+[18.53, 17.97, 18.07, 18.07, 17.99, 18.09, 21.33, 17.98, 18.39, 17.84]
+[88.42]
+14.155279397964478
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.632460117340088, 'TIME_S_1KI': 0.31340152441608465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.6098043680192, 'W': 88.42000000000002}
+[18.53, 17.97, 18.07, 18.07, 17.99, 18.09, 21.33, 17.98, 18.39, 17.84, 18.71, 17.91, 17.99, 18.02, 18.68, 18.08, 17.92, 18.59, 18.24, 18.01]
+329.865
+16.49325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33926, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.632460117340088, 'TIME_S_1KI': 0.31340152441608465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.6098043680192, 'W': 88.42000000000002, 'J_1KI': 36.892348180393185, 'W_1KI': 2.606260685020339, 'W_D': 71.92675000000001, 'J_D': 1018.1432424375416, 'W_D_1KI': 2.120106997582975, 'J_D_1KI': 0.062492100382685115}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..b1b6585
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2890, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.78333592414856, "TIME_S_1KI": 3.731258105241716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1463.5743112421035, "W": 81.86, "J_1KI": 506.4270973156068, "W_1KI": 28.325259515570934, "W_D": 65.62225000000001, "J_D": 1173.259703712523, "W_D_1KI": 22.706660899653983, "J_D_1KI": 7.856976089845669}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..1352df1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.6327288150787354}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       95,      182,  ...,  9999803,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1164,  1511,  2606,  ..., 97059, 99366, 99637]),
+       values=tensor([0.1789, 0.4314, 0.0466,  ..., 0.4339, 0.7049, 0.9540]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5756, 0.3189, 0.9065,  ..., 0.6359, 0.4482, 0.1651])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 3.6327288150787354 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2890', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.78333592414856}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      118,      207,  ...,  9999808,
+                             9999910, 10000000]),
+       col_indices=tensor([  712,   968,  1059,  ..., 96997, 98856, 99104]),
+       values=tensor([0.5177, 0.6712, 0.5343,  ..., 0.8226, 0.3425, 0.6939]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8858, 0.8376, 0.8837,  ..., 0.6861, 0.2657, 0.8920])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.78333592414856 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      118,      207,  ...,  9999808,
+                             9999910, 10000000]),
+       col_indices=tensor([  712,   968,  1059,  ..., 96997, 98856, 99104]),
+       values=tensor([0.5177, 0.6712, 0.5343,  ..., 0.8226, 0.3425, 0.6939]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8858, 0.8376, 0.8837,  ..., 0.6861, 0.2657, 0.8920])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.78333592414856 seconds
+
+[18.4, 18.1, 18.05, 18.1, 18.29, 18.11, 18.1, 17.9, 17.96, 17.98]
+[81.86]
+17.878992319107056
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2890, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.78333592414856, 'TIME_S_1KI': 3.731258105241716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1463.5743112421035, 'W': 81.86}
+[18.4, 18.1, 18.05, 18.1, 18.29, 18.11, 18.1, 17.9, 17.96, 17.98, 18.26, 17.88, 17.92, 17.96, 18.1, 17.9, 17.98, 18.14, 18.06, 17.77]
+324.755
+16.23775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2890, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.78333592414856, 'TIME_S_1KI': 3.731258105241716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1463.5743112421035, 'W': 81.86, 'J_1KI': 506.4270973156068, 'W_1KI': 28.325259515570934, 'W_D': 65.62225000000001, 'J_D': 1173.259703712523, 'W_D_1KI': 22.706660899653983, 'J_D_1KI': 7.856976089845669}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..c182694
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64311, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.418502807617188, "TIME_S_1KI": 0.16200187849072767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1158.3267517518998, "W": 82.98, "J_1KI": 18.011331681234932, "W_1KI": 1.2902924849559174, "W_D": 66.6565, "J_D": 930.4652582327127, "W_D_1KI": 1.036471210212872, "J_D_1KI": 0.01611654631731542}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..055aab1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.17745423316955566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      3,  ...,  99998, 100000,
+                            100000]),
+       col_indices=tensor([42546, 58983, 86183,  ..., 98460, 14991, 73616]),
+       values=tensor([0.4174, 0.2060, 0.0899,  ..., 0.6212, 0.4971, 0.7481]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8074, 0.4851, 0.0283,  ..., 0.2070, 0.7576, 0.4733])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.17745423316955566 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '59170', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.660528182983398}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([96712,  9860, 17593,  ..., 59712, 70511, 99970]),
+       values=tensor([0.7958, 0.9740, 0.0109,  ..., 0.7243, 0.7214, 0.8821]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4741, 0.0741, 0.4151,  ..., 0.2722, 0.2577, 0.9729])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 9.660528182983398 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64311', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.418502807617188}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([60832, 83948,   658,  ..., 83631, 80017, 34658]),
+       values=tensor([0.5224, 0.7895, 0.2144,  ..., 0.4897, 0.2214, 0.9534]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6371, 0.8407, 0.9472,  ..., 0.9476, 0.5347, 0.4303])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.418502807617188 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([60832, 83948,   658,  ..., 83631, 80017, 34658]),
+       values=tensor([0.5224, 0.7895, 0.2144,  ..., 0.4897, 0.2214, 0.9534]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6371, 0.8407, 0.9472,  ..., 0.9476, 0.5347, 0.4303])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.418502807617188 seconds
+
+[18.9, 18.01, 18.86, 18.3, 17.96, 18.02, 18.19, 17.91, 18.92, 17.86]
+[82.98]
+13.959107637405396
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64311, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.418502807617188, 'TIME_S_1KI': 0.16200187849072767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.3267517518998, 'W': 82.98}
+[18.9, 18.01, 18.86, 18.3, 17.96, 18.02, 18.19, 17.91, 18.92, 17.86, 18.32, 17.96, 18.01, 17.83, 18.19, 17.85, 17.88, 18.01, 18.1, 17.86]
+326.47
+16.323500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64311, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.418502807617188, 'TIME_S_1KI': 0.16200187849072767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.3267517518998, 'W': 82.98, 'J_1KI': 18.011331681234932, 'W_1KI': 1.2902924849559174, 'W_D': 66.6565, 'J_D': 930.4652582327127, 'W_D_1KI': 1.036471210212872, 'J_D_1KI': 0.01611654631731542}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..c677488
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253635, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.510948419570923, "TIME_S_1KI": 0.04144123807664921, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.5495847654342, "W": 74.66, "J_1KI": 4.1380313630430905, "W_1KI": 0.29436000551974295, "W_D": 58.32449999999999, "J_D": 819.9096538528203, "W_D_1KI": 0.22995446212076406, "J_D_1KI": 0.0009066353702003433}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..d7cd9dc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.057019948959350586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     5,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([5511, 5632, 9392,  ..., 1424, 5807, 9708]),
+       values=tensor([0.8862, 0.8794, 0.5579,  ..., 0.8535, 0.8536, 0.3017]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8843, 0.1620, 0.1106,  ..., 0.3314, 0.8529, 0.5084])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.057019948959350586 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '184146', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.623284816741943}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([5228, 7612, 8334,  ..., 8947, 2750, 8241]),
+       values=tensor([0.5331, 0.8440, 0.9594,  ..., 0.6439, 0.5967, 0.7449]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9017, 0.2905, 0.1618,  ..., 0.3745, 0.4560, 0.4176])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 7.623284816741943 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253635', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.510948419570923}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([ 773, 7277, 5799,  ..., 6666, 7394, 1954]),
+       values=tensor([0.1024, 0.0437, 0.8987,  ..., 0.7237, 0.2930, 0.3597]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1275, 0.0118, 0.1480,  ..., 0.4560, 0.1036, 0.8618])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.510948419570923 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([ 773, 7277, 5799,  ..., 6666, 7394, 1954]),
+       values=tensor([0.1024, 0.0437, 0.8987,  ..., 0.7237, 0.2930, 0.3597]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1275, 0.0118, 0.1480,  ..., 0.4560, 0.1036, 0.8618])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.510948419570923 seconds
+
+[18.3, 17.87, 18.03, 17.87, 18.05, 17.86, 19.1, 17.97, 18.04, 17.74]
+[74.66]
+14.057722806930542
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.510948419570923, 'TIME_S_1KI': 0.04144123807664921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.5495847654342, 'W': 74.66}
+[18.3, 17.87, 18.03, 17.87, 18.05, 17.86, 19.1, 17.97, 18.04, 17.74, 18.05, 17.87, 18.1, 17.95, 17.96, 18.0, 19.85, 17.84, 18.3, 18.01]
+326.71000000000004
+16.335500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.510948419570923, 'TIME_S_1KI': 0.04144123807664921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.5495847654342, 'W': 74.66, 'J_1KI': 4.1380313630430905, 'W_1KI': 0.29436000551974295, 'W_D': 58.32449999999999, 'J_D': 819.9096538528203, 'W_D_1KI': 0.22995446212076406, 'J_D_1KI': 0.0009066353702003433}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..8f37619
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 197679, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.670233726501465, "TIME_S_1KI": 0.053977578430189674, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1114.4275546693802, "W": 79.73, "J_1KI": 5.637561676603889, "W_1KI": 0.40333065221900155, "W_D": 63.12950000000001, "J_D": 882.3937578389646, "W_D_1KI": 0.31935359851071693, "J_D_1KI": 0.001615516056387967}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..4a4c171
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.06928658485412598}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     25,  ...,  99976,  99987,
+                            100000]),
+       col_indices=tensor([ 333,  360, 7030,  ..., 7825, 8274, 9549]),
+       values=tensor([0.8393, 0.7372, 0.2908,  ..., 0.1152, 0.3448, 0.5520]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6596, 0.1551, 0.2351,  ..., 0.2147, 0.9669, 0.0099])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.06928658485412598 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '151544', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.049443006515503}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     20,  ...,  99982,  99989,
+                            100000]),
+       col_indices=tensor([ 534,  848, 1028,  ..., 7528, 7587, 7919]),
+       values=tensor([0.8744, 0.7231, 0.5055,  ..., 0.6485, 0.2326, 0.7897]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6730, 0.3279, 0.8164,  ..., 0.2443, 0.5036, 0.1429])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.049443006515503 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '197679', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.670233726501465}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     18,  ...,  99979,  99990,
+                            100000]),
+       col_indices=tensor([ 654,  920, 2120,  ..., 5173, 5860, 7868]),
+       values=tensor([0.9786, 0.8942, 0.8907,  ..., 0.0590, 0.7963, 0.5333]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8342, 0.1347, 0.2067,  ..., 0.1241, 0.4408, 0.8118])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.670233726501465 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     18,  ...,  99979,  99990,
+                            100000]),
+       col_indices=tensor([ 654,  920, 2120,  ..., 5173, 5860, 7868]),
+       values=tensor([0.9786, 0.8942, 0.8907,  ..., 0.0590, 0.7963, 0.5333]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8342, 0.1347, 0.2067,  ..., 0.1241, 0.4408, 0.8118])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.670233726501465 seconds
+
+[18.34, 17.98, 18.08, 17.94, 18.13, 18.09, 21.2, 18.15, 17.85, 18.53]
+[79.73]
+13.977518558502197
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 197679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.670233726501465, 'TIME_S_1KI': 0.053977578430189674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.4275546693802, 'W': 79.73}
+[18.34, 17.98, 18.08, 17.94, 18.13, 18.09, 21.2, 18.15, 17.85, 18.53, 18.16, 18.1, 18.61, 18.87, 18.17, 17.77, 20.46, 17.84, 18.3, 17.91]
+332.01
+16.6005
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 197679, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.670233726501465, 'TIME_S_1KI': 0.053977578430189674, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.4275546693802, 'W': 79.73, 'J_1KI': 5.637561676603889, 'W_1KI': 0.40333065221900155, 'W_D': 63.12950000000001, 'J_D': 882.3937578389646, 'W_D_1KI': 0.31935359851071693, 'J_D_1KI': 0.001615516056387967}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..23a3327
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58160, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.467525959014893, "TIME_S_1KI": 0.17997809420589567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.377723479271, "W": 87.15, "J_1KI": 21.10346842295858, "W_1KI": 1.4984525447042643, "W_D": 70.98275000000001, "J_D": 999.6861285289527, "W_D_1KI": 1.2204736932599725, "J_D_1KI": 0.020984760888238866}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..35e5d90
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.19839882850646973}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      97,     186,  ...,  999796,
+                             999897, 1000000]),
+       col_indices=tensor([ 169,  359,  528,  ..., 9765, 9789, 9792]),
+       values=tensor([0.6521, 0.9085, 0.4727,  ..., 0.8814, 0.1698, 0.8627]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7127, 0.9881, 0.6892,  ..., 0.7113, 0.3734, 0.9813])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.19839882850646973 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52923', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.554424524307251}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     112,     189,  ...,  999798,
+                             999899, 1000000]),
+       col_indices=tensor([ 113,  156,  184,  ..., 9769, 9838, 9941]),
+       values=tensor([0.0187, 0.7839, 0.6319,  ..., 0.9818, 0.7594, 0.0765]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4252, 0.8416, 0.9146,  ..., 0.0970, 0.6595, 0.8304])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.554424524307251 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58160', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.467525959014893}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     191,  ...,  999802,
+                             999899, 1000000]),
+       col_indices=tensor([  46,   78,  103,  ..., 9585, 9899, 9954]),
+       values=tensor([0.1947, 0.9409, 0.0413,  ..., 0.0261, 0.0318, 0.5135]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1045, 0.5937, 0.6366,  ..., 0.8712, 0.6092, 0.3132])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.467525959014893 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      93,     191,  ...,  999802,
+                             999899, 1000000]),
+       col_indices=tensor([  46,   78,  103,  ..., 9585, 9899, 9954]),
+       values=tensor([0.1947, 0.9409, 0.0413,  ..., 0.0261, 0.0318, 0.5135]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1045, 0.5937, 0.6366,  ..., 0.8712, 0.6092, 0.3132])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.467525959014893 seconds
+
+[18.42, 18.1, 18.09, 17.94, 17.96, 18.13, 17.89, 17.87, 18.11, 18.12]
+[87.15]
+14.083508014678955
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.467525959014893, 'TIME_S_1KI': 0.17997809420589567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.377723479271, 'W': 87.15}
+[18.42, 18.1, 18.09, 17.94, 17.96, 18.13, 17.89, 17.87, 18.11, 18.12, 18.39, 17.75, 17.9, 17.92, 17.94, 17.89, 17.89, 17.89, 17.75, 17.72]
+323.34499999999997
+16.16725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.467525959014893, 'TIME_S_1KI': 0.17997809420589567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.377723479271, 'W': 87.15, 'J_1KI': 21.10346842295858, 'W_1KI': 1.4984525447042643, 'W_D': 70.98275000000001, 'J_D': 999.6861285289527, 'W_D_1KI': 1.2204736932599725, 'J_D_1KI': 0.020984760888238866}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..4e517f7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8810, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.41358470916748, "TIME_S_1KI": 1.18201869570573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1329.5588334417344, "W": 83.19, "J_1KI": 150.91473705354534, "W_1KI": 9.442678774120317, "W_D": 66.8505, "J_D": 1068.4177520735263, "W_D_1KI": 7.588024971623155, "J_D_1KI": 0.8612968185724353}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..8074157
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 1.1917307376861572}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     493,     986,  ..., 4999011,
+                            4999486, 5000000]),
+       col_indices=tensor([   9,   19,   72,  ..., 9981, 9987, 9993]),
+       values=tensor([0.5847, 0.5648, 0.9368,  ..., 0.4963, 0.0551, 0.2254]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1357, 0.6996, 0.1280,  ..., 0.8014, 0.9186, 0.9128])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 1.1917307376861572 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8810', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.41358470916748}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     520,    1021,  ..., 4999015,
+                            4999518, 5000000]),
+       col_indices=tensor([   2,   21,   23,  ..., 9856, 9947, 9960]),
+       values=tensor([0.9436, 0.1483, 0.1830,  ..., 0.0068, 0.4770, 0.7006]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0991, 0.7135, 0.2277,  ..., 0.9430, 0.0011, 0.3680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.41358470916748 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     520,    1021,  ..., 4999015,
+                            4999518, 5000000]),
+       col_indices=tensor([   2,   21,   23,  ..., 9856, 9947, 9960]),
+       values=tensor([0.9436, 0.1483, 0.1830,  ..., 0.0068, 0.4770, 0.7006]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0991, 0.7135, 0.2277,  ..., 0.9430, 0.0011, 0.3680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.41358470916748 seconds
+
+[18.42, 18.1, 18.34, 18.2, 17.91, 17.99, 18.73, 17.98, 18.05, 17.95]
+[83.19]
+15.982195377349854
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8810, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.41358470916748, 'TIME_S_1KI': 1.18201869570573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1329.5588334417344, 'W': 83.19}
+[18.42, 18.1, 18.34, 18.2, 17.91, 17.99, 18.73, 17.98, 18.05, 17.95, 18.53, 18.25, 18.01, 18.36, 18.13, 18.03, 18.01, 18.04, 18.22, 17.98]
+326.79
+16.3395
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8810, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.41358470916748, 'TIME_S_1KI': 1.18201869570573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1329.5588334417344, 'W': 83.19, 'J_1KI': 150.91473705354534, 'W_1KI': 9.442678774120317, 'W_D': 66.8505, 'J_D': 1068.4177520735263, 'W_D_1KI': 7.588024971623155, 'J_D_1KI': 0.8612968185724353}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..3f0bb83
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2918, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.42000699043274, "TIME_S_1KI": 3.5709413949392523, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1397.7840516352655, "W": 79.43, "J_1KI": 479.0212651251767, "W_1KI": 27.220699108978753, "W_D": 63.01475000000001, "J_D": 1108.913666974485, "W_D_1KI": 21.595185058259084, "J_D_1KI": 7.400680280417781}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..099bdad
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.597771644592285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      977,     1956,  ...,  9997922,
+                             9998976, 10000000]),
+       col_indices=tensor([   2,    3,    9,  ..., 9970, 9977, 9979]),
+       values=tensor([0.1332, 0.2138, 0.7669,  ..., 0.0474, 0.1604, 0.1097]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2601, 0.5133, 0.4344,  ..., 0.1772, 0.3859, 0.7315])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 3.597771644592285 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2918', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.42000699043274}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1029,     2018,  ...,  9998096,
+                             9999045, 10000000]),
+       col_indices=tensor([   7,   14,   18,  ..., 9941, 9949, 9980]),
+       values=tensor([0.9805, 0.4931, 0.0315,  ..., 0.9071, 0.5605, 0.7269]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0123, 0.2996, 0.0215,  ..., 0.5909, 0.6219, 0.0073])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.42000699043274 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1029,     2018,  ...,  9998096,
+                             9999045, 10000000]),
+       col_indices=tensor([   7,   14,   18,  ..., 9941, 9949, 9980]),
+       values=tensor([0.9805, 0.4931, 0.0315,  ..., 0.9071, 0.5605, 0.7269]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0123, 0.2996, 0.0215,  ..., 0.5909, 0.6219, 0.0073])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.42000699043274 seconds
+
+[21.55, 18.44, 17.92, 18.49, 18.01, 17.89, 18.03, 17.94, 18.06, 18.03]
+[79.43]
+17.597684144973755
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2918, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.42000699043274, 'TIME_S_1KI': 3.5709413949392523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.7840516352655, 'W': 79.43}
+[21.55, 18.44, 17.92, 18.49, 18.01, 17.89, 18.03, 17.94, 18.06, 18.03, 19.32, 18.84, 17.98, 18.09, 18.0, 17.91, 18.09, 18.07, 18.13, 17.93]
+328.30499999999995
+16.415249999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2918, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.42000699043274, 'TIME_S_1KI': 3.5709413949392523, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1397.7840516352655, 'W': 79.43, 'J_1KI': 479.0212651251767, 'W_1KI': 27.220699108978753, 'W_D': 63.01475000000001, 'J_D': 1108.913666974485, 'W_D_1KI': 21.595185058259084, 'J_D_1KI': 7.400680280417781}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..114ef88
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 286411, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.634347915649414, "TIME_S_1KI": 0.03712967698743908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1001.8400530457496, "W": 73.11, "J_1KI": 3.4979105308306933, "W_1KI": 0.25526254229062434, "W_D": 56.78875, "J_D": 778.186900730431, "W_D_1KI": 0.19827712622769378, "J_D_1KI": 0.0006922818125969107}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..b127831
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['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": 0.05389976501464844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
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+                      6.9318e-01, 9.8979e-01, 3.2336e-01, 9.6873e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7118, 0.5445, 0.7766,  ..., 0.3375, 0.1794, 0.0442])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.05389976501464844 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '194806', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.141697645187378}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      6.7460e-01, 3.0153e-01, 5.3121e-01, 2.6773e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1721, 0.8059, 0.1299,  ..., 0.7732, 0.0077, 0.2449])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 7.141697645187378 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '286411', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.634347915649414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      4.6688e-01, 8.6118e-01, 7.9898e-01, 6.4956e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7123, 0.9016, 0.3604,  ..., 0.7264, 0.3786, 0.3585])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.634347915649414 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5653,  663, 2356, 6335, 1601, 9179, 5758, 4032, 1184,
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+                      4.6688e-01, 8.6118e-01, 7.9898e-01, 6.4956e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7123, 0.9016, 0.3604,  ..., 0.7264, 0.3786, 0.3585])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.634347915649414 seconds
+
+[18.41, 17.8, 19.45, 17.82, 18.17, 17.94, 18.34, 18.13, 18.06, 17.93]
+[73.11]
+13.703187704086304
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.634347915649414, 'TIME_S_1KI': 0.03712967698743908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1001.8400530457496, 'W': 73.11}
+[18.41, 17.8, 19.45, 17.82, 18.17, 17.94, 18.34, 18.13, 18.06, 17.93, 18.35, 18.13, 18.05, 17.73, 17.88, 18.02, 18.72, 17.91, 17.93, 18.0]
+326.42499999999995
+16.32125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 286411, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.634347915649414, 'TIME_S_1KI': 0.03712967698743908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1001.8400530457496, 'W': 73.11, 'J_1KI': 3.4979105308306933, 'W_1KI': 0.25526254229062434, 'W_D': 56.78875, 'J_D': 778.186900730431, 'W_D_1KI': 0.19827712622769378, 'J_D_1KI': 0.0006922818125969107}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..df20cd2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8417, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.80616807937622, "TIME_S_1KI": 1.2838503123887632, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1304.2690537071228, "W": 87.74, "J_1KI": 154.95652295439263, "W_1KI": 10.424141618153737, "W_D": 71.23675, "J_D": 1058.9456178672315, "W_D_1KI": 8.463437091600332, "J_D_1KI": 1.005517059712526}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..2e9f271
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 1.2474722862243652}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      15,  ..., 2499985,
+                            2499994, 2500000]),
+       col_indices=tensor([131168, 178693, 230148,  ..., 341937, 350836,
+                           404119]),
+       values=tensor([0.5017, 0.1065, 0.8260,  ..., 0.9970, 0.9497, 0.3007]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0502, 0.1581, 0.5974,  ..., 0.5502, 0.6695, 0.7013])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 1.2474722862243652 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8417', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.80616807937622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      12,  ..., 2499995,
+                            2499999, 2500000]),
+       col_indices=tensor([108465, 113027, 118372,  ..., 354925, 391668,
+                            96483]),
+       values=tensor([0.8038, 0.4194, 0.3623,  ..., 0.9532, 0.5964, 0.0297]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4181, 0.0420, 0.6704,  ..., 0.4969, 0.1289, 0.9173])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.80616807937622 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      12,  ..., 2499995,
+                            2499999, 2500000]),
+       col_indices=tensor([108465, 113027, 118372,  ..., 354925, 391668,
+                            96483]),
+       values=tensor([0.8038, 0.4194, 0.3623,  ..., 0.9532, 0.5964, 0.0297]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4181, 0.0420, 0.6704,  ..., 0.4969, 0.1289, 0.9173])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.80616807937622 seconds
+
+[18.27, 18.7, 18.19, 18.06, 20.48, 17.92, 18.42, 18.1, 18.5, 17.88]
+[87.74]
+14.865159034729004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8417, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.80616807937622, 'TIME_S_1KI': 1.2838503123887632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1304.2690537071228, 'W': 87.74}
+[18.27, 18.7, 18.19, 18.06, 20.48, 17.92, 18.42, 18.1, 18.5, 17.88, 18.52, 18.21, 18.12, 18.29, 18.48, 18.28, 17.96, 17.98, 18.11, 17.86]
+330.06500000000005
+16.50325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8417, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.80616807937622, 'TIME_S_1KI': 1.2838503123887632, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1304.2690537071228, 'W': 87.74, 'J_1KI': 154.95652295439263, 'W_1KI': 10.424141618153737, 'W_D': 71.23675, 'J_D': 1058.9456178672315, 'W_D_1KI': 8.463437091600332, 'J_D_1KI': 1.005517059712526}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..3576579
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 79200, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.557747602462769, "TIME_S_1KI": 0.1333048939704895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1194.9571797966958, "W": 76.09, "J_1KI": 15.087843179251209, "W_1KI": 0.9607323232323233, "W_D": 59.703, "J_D": 937.6071560704709, "W_D_1KI": 0.7538257575757576, "J_D_1KI": 0.009518001989592899}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..9da61ce
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.14916634559631348}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 249992, 249995,
+                            250000]),
+       col_indices=tensor([14210, 18192, 24309,  ..., 18863, 37423, 45495]),
+       values=tensor([0.9647, 0.6185, 0.9345,  ..., 0.6478, 0.4104, 0.2751]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7636, 0.2305, 0.9236,  ..., 0.5850, 0.9097, 0.3088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.14916634559631348 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70391', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.332123041152954}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     13,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([ 8823, 10157, 22008,  ..., 15217, 25723, 27383]),
+       values=tensor([0.1165, 0.9082, 0.4420,  ..., 0.1019, 0.9218, 0.7818]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6996, 0.2341, 0.0689,  ..., 0.7606, 0.0770, 0.0289])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.332123041152954 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '79200', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.557747602462769}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249995, 249997,
+                            250000]),
+       col_indices=tensor([ 4540,  7121,  8304,  ...,  4489, 19051, 41158]),
+       values=tensor([0.2192, 0.6581, 0.9045,  ..., 0.0804, 0.2632, 0.1591]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8844, 0.7148, 0.4526,  ..., 0.9882, 0.2475, 0.5582])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.557747602462769 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249995, 249997,
+                            250000]),
+       col_indices=tensor([ 4540,  7121,  8304,  ...,  4489, 19051, 41158]),
+       values=tensor([0.2192, 0.6581, 0.9045,  ..., 0.0804, 0.2632, 0.1591]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8844, 0.7148, 0.4526,  ..., 0.9882, 0.2475, 0.5582])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.557747602462769 seconds
+
+[18.33, 17.85, 17.91, 18.15, 17.99, 17.93, 17.79, 17.99, 18.26, 17.87]
+[76.09]
+15.70452332496643
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 79200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.557747602462769, 'TIME_S_1KI': 0.1333048939704895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1194.9571797966958, 'W': 76.09}
+[18.33, 17.85, 17.91, 18.15, 17.99, 17.93, 17.79, 17.99, 18.26, 17.87, 18.24, 18.68, 17.78, 17.91, 20.54, 18.31, 17.97, 18.31, 18.26, 17.78]
+327.74
+16.387
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 79200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.557747602462769, 'TIME_S_1KI': 0.1333048939704895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1194.9571797966958, 'W': 76.09, 'J_1KI': 15.087843179251209, 'W_1KI': 0.9607323232323233, 'W_D': 59.703, 'J_D': 937.6071560704709, 'W_D_1KI': 0.7538257575757576, 'J_D_1KI': 0.009518001989592899}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..fcc4d9e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17543, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.699259996414185, "TIME_S_1KI": 0.6098877042931189, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.7915307188034, "W": 87.71, "J_1KI": 74.14875053974825, "W_1KI": 4.9997149860343155, "W_D": 71.40625, "J_D": 1058.997209444642, "W_D_1KI": 4.070355697429174, "J_D_1KI": 0.2320216438140098}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..4d40449
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.5985264778137207}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      56,     105,  ..., 2499904,
+                            2499950, 2500000]),
+       col_indices=tensor([  106,  3863,  5117,  ..., 48831, 49457, 49843]),
+       values=tensor([0.6065, 0.7453, 0.1054,  ..., 0.0788, 0.7875, 0.5947]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1569, 0.4932, 0.6676,  ..., 0.2477, 0.5860, 0.5432])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.5985264778137207 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17543', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.699259996414185}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      54,      99,  ..., 2499881,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1025,  3202,  3517,  ..., 49482, 49487, 49789]),
+       values=tensor([0.3859, 0.1414, 0.1100,  ..., 0.9363, 0.6699, 0.1002]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4003, 0.0598, 0.2302,  ..., 0.6994, 0.7206, 0.2744])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.699259996414185 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      54,      99,  ..., 2499881,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1025,  3202,  3517,  ..., 49482, 49487, 49789]),
+       values=tensor([0.3859, 0.1414, 0.1100,  ..., 0.9363, 0.6699, 0.1002]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4003, 0.0598, 0.2302,  ..., 0.6994, 0.7206, 0.2744])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.699259996414185 seconds
+
+[18.49, 17.83, 18.02, 18.17, 18.01, 17.87, 18.35, 18.02, 18.01, 17.87]
+[87.71]
+14.83059549331665
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17543, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.699259996414185, 'TIME_S_1KI': 0.6098877042931189, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.7915307188034, 'W': 87.71}
+[18.49, 17.83, 18.02, 18.17, 18.01, 17.87, 18.35, 18.02, 18.01, 17.87, 18.45, 18.41, 18.09, 17.88, 18.15, 18.34, 17.89, 18.68, 17.87, 18.16]
+326.075
+16.30375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17543, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.699259996414185, 'TIME_S_1KI': 0.6098877042931189, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.7915307188034, 'W': 87.71, 'J_1KI': 74.14875053974825, 'W_1KI': 4.9997149860343155, 'W_D': 71.40625, 'J_D': 1058.997209444642, 'W_D_1KI': 4.070355697429174, 'J_D_1KI': 0.2320216438140098}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..8166250
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 109532, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.03889274597168, "TIME_S_1KI": 0.09165260148606508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1026.1644780921936, "W": 75.74, "J_1KI": 9.368627233066078, "W_1KI": 0.6914874192016944, "W_D": 59.13049999999999, "J_D": 801.1304287276266, "W_D_1KI": 0.539846802760837, "J_D_1KI": 0.004928667446598592}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..709a214
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/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": 0.12936139106750488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([44477, 18295, 41758,  ..., 46506, 28720, 46164]),
+       values=tensor([0.4132, 0.4608, 0.2599,  ..., 0.0448, 0.1303, 0.6544]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0039, 0.4422, 0.0639,  ..., 0.1130, 0.9521, 0.1334])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.12936139106750488 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '81167', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.780844211578369}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([38361, 15493, 29627,  ..., 27733, 22368, 35508]),
+       values=tensor([0.9149, 0.3524, 0.3637,  ..., 0.0393, 0.5821, 0.3741]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5874, 0.0444, 0.7896,  ..., 0.3503, 0.3177, 0.2388])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 7.780844211578369 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '109532', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.03889274597168}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24997, 24997, 25000]),
+       col_indices=tensor([17005,  6306, 21289,  ...,  8288,  8622, 19411]),
+       values=tensor([0.2779, 0.9469, 0.2610,  ..., 0.9922, 0.2668, 0.6005]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1194, 0.4917, 0.3228,  ..., 0.2258, 0.0044, 0.3600])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.03889274597168 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24997, 24997, 25000]),
+       col_indices=tensor([17005,  6306, 21289,  ...,  8288,  8622, 19411]),
+       values=tensor([0.2779, 0.9469, 0.2610,  ..., 0.9922, 0.2668, 0.6005]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1194, 0.4917, 0.3228,  ..., 0.2258, 0.0044, 0.3600])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.03889274597168 seconds
+
+[19.15, 18.96, 18.11, 18.03, 19.81, 17.96, 18.06, 18.12, 18.35, 17.94]
+[75.74]
+13.548514366149902
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109532, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.03889274597168, 'TIME_S_1KI': 0.09165260148606508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1026.1644780921936, 'W': 75.74}
+[19.15, 18.96, 18.11, 18.03, 19.81, 17.96, 18.06, 18.12, 18.35, 17.94, 18.12, 21.35, 17.92, 18.62, 18.1, 18.19, 17.98, 17.91, 18.07, 18.09]
+332.19000000000005
+16.609500000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109532, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.03889274597168, 'TIME_S_1KI': 0.09165260148606508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1026.1644780921936, 'W': 75.74, 'J_1KI': 9.368627233066078, 'W_1KI': 0.6914874192016944, 'W_D': 59.13049999999999, 'J_D': 801.1304287276266, 'W_D_1KI': 0.539846802760837, 'J_D_1KI': 0.004928667446598592}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..3b1a4d8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 334616, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.390317678451538, "TIME_S_1KI": 0.03105146699037565, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1009.4583482408524, "W": 73.59, "J_1KI": 3.0167665271261757, "W_1KI": 0.21992373347359362, "W_D": 57.37650000000001, "J_D": 787.052410896063, "W_D_1KI": 0.17146968465345352, "J_D_1KI": 0.0005124371956315703}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..ab60bf4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0494227409362793}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2191, 1647, 4069,  ..., 3482,  688, 2162]),
+       values=tensor([0.7127, 0.2553, 0.3133,  ..., 0.9149, 0.5638, 0.5628]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.0865, 0.0532, 0.7203,  ..., 0.4777, 0.7863, 0.0162])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.0494227409362793 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '212452', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.666574239730835}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([2208, 2123, 4174,  ..., 2091,   42, 2382]),
+       values=tensor([0.8755, 0.2371, 0.7047,  ..., 0.2373, 0.9261, 0.2864]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3651, 0.6415, 0.7426,  ..., 0.3371, 0.9910, 0.6174])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.666574239730835 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '334616', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.390317678451538}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1385, 3626, 3706,  ...,  891, 2896, 4403]),
+       values=tensor([0.8264, 0.4439, 0.4297,  ..., 0.4171, 0.8922, 0.6160]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2966, 0.7201, 0.1357,  ..., 0.1499, 0.6981, 0.8153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.390317678451538 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1385, 3626, 3706,  ...,  891, 2896, 4403]),
+       values=tensor([0.8264, 0.4439, 0.4297,  ..., 0.4171, 0.8922, 0.6160]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2966, 0.7201, 0.1357,  ..., 0.1499, 0.6981, 0.8153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.390317678451538 seconds
+
+[18.39, 18.14, 17.96, 18.23, 18.13, 17.78, 18.17, 18.05, 18.11, 18.01]
+[73.59]
+13.71733045578003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334616, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.390317678451538, 'TIME_S_1KI': 0.03105146699037565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.4583482408524, 'W': 73.59}
+[18.39, 18.14, 17.96, 18.23, 18.13, 17.78, 18.17, 18.05, 18.11, 18.01, 18.29, 17.99, 18.0, 17.86, 17.97, 18.01, 17.96, 17.81, 17.79, 17.93]
+324.27
+16.2135
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334616, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.390317678451538, 'TIME_S_1KI': 0.03105146699037565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.4583482408524, 'W': 73.59, 'J_1KI': 3.0167665271261757, 'W_1KI': 0.21992373347359362, 'W_D': 57.37650000000001, 'J_D': 787.052410896063, 'W_D_1KI': 0.17146968465345352, 'J_D_1KI': 0.0005124371956315703}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..e6fec4b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 248893, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.503267288208008, "TIME_S_1KI": 0.04219993044484179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1056.0920481681824, "W": 74.8, "J_1KI": 4.243156891387795, "W_1KI": 0.3005307501617161, "W_D": 58.488749999999996, "J_D": 825.7955051109194, "W_D_1KI": 0.2349955603411908, "J_D_1KI": 0.0009441629951070973}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..d474848
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0580594539642334}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     7,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 985, 1057,  218,  ..., 4882, 1671, 4380]),
+       values=tensor([0.5160, 0.3498, 0.0303,  ..., 0.2263, 0.8538, 0.6441]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8672, 0.0025, 0.6942,  ..., 0.2074, 0.2932, 0.8728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.0580594539642334 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '180849', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.629418849945068}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    12,  ..., 24984, 24994, 25000]),
+       col_indices=tensor([ 206,  438, 1117,  ..., 3589, 4561, 4654]),
+       values=tensor([0.7806, 0.0093, 0.9775,  ..., 0.2394, 0.5986, 0.1036]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9079, 0.6440, 0.7990,  ..., 0.4243, 0.2944, 0.4838])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 7.629418849945068 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '248893', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.503267288208008}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([ 263, 1234, 1436,  ..., 3199, 3400, 4091]),
+       values=tensor([0.7110, 0.3838, 0.4652,  ..., 0.0537, 0.9297, 0.5811]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4941, 0.0109, 0.4935,  ..., 0.1517, 0.7151, 0.3544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.503267288208008 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([ 263, 1234, 1436,  ..., 3199, 3400, 4091]),
+       values=tensor([0.7110, 0.3838, 0.4652,  ..., 0.0537, 0.9297, 0.5811]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4941, 0.0109, 0.4935,  ..., 0.1517, 0.7151, 0.3544])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.503267288208008 seconds
+
+[18.2, 18.48, 18.33, 18.03, 18.1, 18.08, 18.14, 18.78, 18.15, 17.99]
+[74.8]
+14.118877649307251
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248893, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.503267288208008, 'TIME_S_1KI': 0.04219993044484179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1056.0920481681824, 'W': 74.8}
+[18.2, 18.48, 18.33, 18.03, 18.1, 18.08, 18.14, 18.78, 18.15, 17.99, 18.23, 17.98, 17.97, 18.01, 17.96, 18.02, 17.91, 18.17, 17.92, 17.97]
+326.225
+16.31125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248893, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.503267288208008, 'TIME_S_1KI': 0.04219993044484179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1056.0920481681824, 'W': 74.8, 'J_1KI': 4.243156891387795, 'W_1KI': 0.3005307501617161, 'W_D': 58.488749999999996, 'J_D': 825.7955051109194, 'W_D_1KI': 0.2349955603411908, 'J_D_1KI': 0.0009441629951070973}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..6c317ff
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 167260, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.636158227920532, "TIME_S_1KI": 0.0635905669491841, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1183.6357300853729, "W": 83.74, "J_1KI": 7.076621607589219, "W_1KI": 0.5006576587349038, "W_D": 67.26599999999999, "J_D": 950.7814786233901, "W_D_1KI": 0.40216429510941043, "J_D_1KI": 0.002404426014046457}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..72ef990
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.0799260139465332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     35,     85,  ..., 249913, 249959,
+                            250000]),
+       col_indices=tensor([  50,   52,  142,  ..., 3906, 4174, 4757]),
+       values=tensor([0.0913, 0.8215, 0.1970,  ..., 0.8521, 0.9478, 0.8405]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5132, 0.5547, 0.3014,  ..., 0.6656, 0.4241, 0.0798])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.0799260139465332 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '131371', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.24699854850769}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     34,     94,  ..., 249884, 249936,
+                            250000]),
+       col_indices=tensor([   2,  398,  450,  ..., 4930, 4969, 4985]),
+       values=tensor([0.5923, 0.5022, 0.7915,  ..., 0.6018, 0.8801, 0.8622]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1968, 0.0295, 0.9143,  ..., 0.4064, 0.2286, 0.1114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.24699854850769 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '167260', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.636158227920532}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     41,     97,  ..., 249903, 249957,
+                            250000]),
+       col_indices=tensor([   6,   32,   62,  ..., 4630, 4959, 4982]),
+       values=tensor([0.7649, 0.1722, 0.7795,  ..., 0.2616, 0.2192, 0.2761]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3363, 0.3219, 0.7361,  ..., 0.7182, 0.1290, 0.5403])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.636158227920532 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     41,     97,  ..., 249903, 249957,
+                            250000]),
+       col_indices=tensor([   6,   32,   62,  ..., 4630, 4959, 4982]),
+       values=tensor([0.7649, 0.1722, 0.7795,  ..., 0.2616, 0.2192, 0.2761]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3363, 0.3219, 0.7361,  ..., 0.7182, 0.1290, 0.5403])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.636158227920532 seconds
+
+[18.39, 18.16, 18.03, 17.97, 18.05, 19.85, 18.01, 18.29, 18.12, 18.31]
+[83.74]
+14.13465166091919
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 167260, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.636158227920532, 'TIME_S_1KI': 0.0635905669491841, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.6357300853729, 'W': 83.74}
+[18.39, 18.16, 18.03, 17.97, 18.05, 19.85, 18.01, 18.29, 18.12, 18.31, 17.95, 19.62, 17.88, 18.37, 18.2, 18.02, 18.09, 18.08, 18.42, 17.99]
+329.48
+16.474
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 167260, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.636158227920532, 'TIME_S_1KI': 0.0635905669491841, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1183.6357300853729, 'W': 83.74, 'J_1KI': 7.076621607589219, 'W_1KI': 0.5006576587349038, 'W_D': 67.26599999999999, 'J_D': 950.7814786233901, 'W_D_1KI': 0.40216429510941043, 'J_D_1KI': 0.002404426014046457}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..cee5ebd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46485, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.648370742797852, "TIME_S_1KI": 0.2290711141830236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1255.2527691650391, "W": 87.87, "J_1KI": 27.003393980101947, "W_1KI": 1.8902871894159408, "W_D": 71.29950000000001, "J_D": 1018.5375533752442, "W_D_1KI": 1.5338173604388514, "J_D_1KI": 0.03299596343850385}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..cd79c3c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.24121379852294922}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     267,     541,  ..., 1249516,
+                            1249748, 1250000]),
+       col_indices=tensor([  43,   75,  121,  ..., 4958, 4960, 4986]),
+       values=tensor([0.9222, 0.1508, 0.6151,  ..., 0.6191, 0.5090, 0.9494]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9528, 0.4494, 0.6520,  ..., 0.1607, 0.1619, 0.1321])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.24121379852294922 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43529', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.83215594291687}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     232,     495,  ..., 1249518,
+                            1249779, 1250000]),
+       col_indices=tensor([  48,   77,  155,  ..., 4840, 4912, 4927]),
+       values=tensor([0.7412, 0.4704, 0.5361,  ..., 0.0050, 0.3320, 0.0792]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0891, 0.9943, 0.3145,  ..., 0.1784, 0.0363, 0.2532])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.83215594291687 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46485', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.648370742797852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     231,     510,  ..., 1249526,
+                            1249780, 1250000]),
+       col_indices=tensor([  32,   71,  112,  ..., 4895, 4929, 4940]),
+       values=tensor([0.5396, 0.2475, 0.0729,  ..., 0.2451, 0.2187, 0.9449]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6746, 0.7318, 0.7509,  ..., 0.9415, 0.3905, 0.0197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.648370742797852 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     231,     510,  ..., 1249526,
+                            1249780, 1250000]),
+       col_indices=tensor([  32,   71,  112,  ..., 4895, 4929, 4940]),
+       values=tensor([0.5396, 0.2475, 0.0729,  ..., 0.2451, 0.2187, 0.9449]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6746, 0.7318, 0.7509,  ..., 0.9415, 0.3905, 0.0197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.648370742797852 seconds
+
+[18.37, 17.89, 18.14, 18.27, 18.28, 18.81, 17.93, 18.0, 17.89, 19.89]
+[87.87]
+14.28533935546875
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46485, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.648370742797852, 'TIME_S_1KI': 0.2290711141830236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1255.2527691650391, 'W': 87.87}
+[18.37, 17.89, 18.14, 18.27, 18.28, 18.81, 17.93, 18.0, 17.89, 19.89, 18.31, 18.14, 18.17, 17.98, 21.39, 18.55, 17.96, 18.39, 18.34, 17.99]
+331.40999999999997
+16.5705
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46485, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.648370742797852, 'TIME_S_1KI': 0.2290711141830236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1255.2527691650391, 'W': 87.87, 'J_1KI': 27.003393980101947, 'W_1KI': 1.8902871894159408, 'W_D': 71.29950000000001, 'J_D': 1018.5375533752442, 'W_D_1KI': 1.5338173604388514, 'J_D_1KI': 0.03299596343850385}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..0fd50b0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19767, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.566095352172852, "TIME_S_1KI": 0.5345320661796353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1281.5856591796876, "W": 87.51, "J_1KI": 64.8346061202857, "W_1KI": 4.427075428744878, "W_D": 71.21225000000001, "J_D": 1042.904792114258, "W_D_1KI": 3.6025825871401835, "J_D_1KI": 0.18225236946123255}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..df7022e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5311744213104248}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     483,     993,  ..., 2498991,
+                            2499491, 2500000]),
+       col_indices=tensor([  15,   20,   28,  ..., 4987, 4988, 4995]),
+       values=tensor([0.8912, 0.6515, 0.2376,  ..., 0.2173, 0.7300, 0.9523]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3817, 0.2295, 0.0793,  ..., 0.5917, 0.1851, 0.3088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.5311744213104248 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19767', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.566095352172852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     521,    1020,  ..., 2499029,
+                            2499506, 2500000]),
+       col_indices=tensor([   2,   18,   32,  ..., 4991, 4992, 4995]),
+       values=tensor([0.1206, 0.3118, 0.4014,  ..., 0.4488, 0.4763, 0.9896]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0140, 0.3283, 0.7098,  ..., 0.4613, 0.1962, 0.1627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.566095352172852 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     521,    1020,  ..., 2499029,
+                            2499506, 2500000]),
+       col_indices=tensor([   2,   18,   32,  ..., 4991, 4992, 4995]),
+       values=tensor([0.1206, 0.3118, 0.4014,  ..., 0.4488, 0.4763, 0.9896]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0140, 0.3283, 0.7098,  ..., 0.4613, 0.1962, 0.1627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.566095352172852 seconds
+
+[18.51, 17.88, 18.11, 18.97, 18.57, 17.75, 18.11, 17.81, 18.02, 17.72]
+[87.51]
+14.64501953125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19767, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.566095352172852, 'TIME_S_1KI': 0.5345320661796353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.5856591796876, 'W': 87.51}
+[18.51, 17.88, 18.11, 18.97, 18.57, 17.75, 18.11, 17.81, 18.02, 17.72, 18.1, 18.14, 18.04, 18.33, 18.06, 17.95, 18.01, 18.09, 18.02, 17.86]
+325.95500000000004
+16.29775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19767, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.566095352172852, 'TIME_S_1KI': 0.5345320661796353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1281.5856591796876, 'W': 87.51, 'J_1KI': 64.8346061202857, 'W_1KI': 4.427075428744878, 'W_D': 71.21225000000001, 'J_D': 1042.904792114258, 'W_D_1KI': 3.6025825871401835, 'J_D_1KI': 0.18225236946123255}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..4be18ec
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 355144, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.430037498474121, "TIME_S_1KI": 0.02936847447366173, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.3673194527627, "W": 73.03, "J_1KI": 2.808346246741498, "W_1KI": 0.20563489739373325, "W_D": 56.462250000000004, "J_D": 771.1023268899322, "W_D_1KI": 0.15898410222332351, "J_D_1KI": 0.0004476609550585777}
diff --git a/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..1e00532
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,383 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07530069351196289}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([1366, 2183,  387, 4785,  591, 3875, 1782, 3853, 3491,
+                           1111, 4311, 1391, 2949, 4195, 1174,   98, 1356,  809,
+                           1785,  447, 2538, 4572, 2460, 1800,  303, 1931, 4013,
+                           4968, 4004, 1588, 1643, 1967, 3906, 4748, 1447, 2599,
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+                            198,  731, 3443, 3712, 4600, 4270, 2744, 4125,  400,
+                            468,  107, 2682, 4704,  252, 1804, 2511, 1911,  162,
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+                           2804, 3654, 1037, 4790, 2965,  394, 3461, 2942, 2671,
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+                           3892, 3817, 1313, 2580, 4367, 3947, 3099, 4651, 3006,
+                           4264,  712, 4793, 3855, 4618,  272, 4548]),
+       values=tensor([0.5356, 0.5172, 0.5088, 0.7213, 0.3478, 0.1053, 0.9439,
+                      0.9314, 0.4347, 0.5009, 0.9214, 0.0299, 0.2703, 0.5553,
+                      0.3016, 0.4455, 0.2361, 0.8920, 0.7432, 0.6139, 0.7733,
+                      0.3556, 0.1748, 0.8314, 0.8776, 0.8348, 0.1485, 0.4702,
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+                      0.8220, 0.7333, 0.1521, 0.6924, 0.6584, 0.6936, 0.1717,
+                      0.0561, 0.9517, 0.6184, 0.4753, 0.7656, 0.9019, 0.5502,
+                      0.9529, 0.5922, 0.4037, 0.0988, 0.7843, 0.0649, 0.2485,
+                      0.3469, 0.9377, 0.6160, 0.3297, 0.1479, 0.3514, 0.4560,
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+                      0.1339, 0.8347, 0.2363, 0.9076, 0.1946, 0.5622, 0.8947,
+                      0.8049, 0.7599, 0.8724, 0.5959, 0.8922, 0.7182, 0.4477,
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+                      0.6022, 0.0279, 0.4498, 0.0752, 0.1893, 0.3529, 0.6947,
+                      0.9277, 0.8241, 0.1856, 0.0213, 0.6132]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4529, 0.0478, 0.6057,  ..., 0.4541, 0.9032, 0.3518])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.07530069351196289 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '139440', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.12260627746582}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4955, 3285, 1092, 4534, 4976,  442, 2522, 4514, 4006,
+                           1710, 2609,  275, 2553,  192,   68, 4509,  517, 1487,
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+                           4172, 3476,  296, 4197, 2675, 2071, 2677, 1326, 2255,
+                            468, 4989, 2355, 4824,  996,   43, 2583]),
+       values=tensor([0.3090, 0.2901, 0.9593, 0.2041, 0.3894, 0.4919, 0.4096,
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+                      0.3897, 0.4510, 0.4716, 0.8420, 0.2532]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.2666, 0.0606, 0.0325,  ..., 0.3347, 0.5904, 0.3218])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 4.12260627746582 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355144', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.430037498474121}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 576,  858,  411, 4414, 2665,  376, 3054, 3322, 4372,
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+                           1995, 2151,  656,  824, 1220, 2366, 1835]),
+       values=tensor([3.0955e-01, 7.8676e-01, 4.1266e-01, 3.7203e-01,
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+Time: 10.430037498474121 seconds
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+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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